mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 10:54:36 +00:00
llama : add support for control vectors (#5970)
* control vector api and implementation * control-vectors : minor code style updates * disable control vector when data == nullptr use -1 for disabled range (also on init) in case we ever support controlling layer 0 (embeddings) --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Theia Vogel
GitHub
parent
12247f4c69
commit
877b4d0c62
@ -568,6 +568,34 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
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break;
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}
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params.lora_base = argv[i];
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} else if (arg == "--control-vector") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.control_vectors.push_back({ 1.0f, argv[i], });
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} else if (arg == "--control-vector-scaled") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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const char * fname = argv[i];
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.control_vectors.push_back({ std::stof(argv[i]), fname, });
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} else if (arg == "--control-vector-layer-range") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.control_vector_layer_start = std::stoi(argv[i]);
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.control_vector_layer_end = std::stoi(argv[i]);
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} else if (arg == "--mmproj") {
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if (++i >= argc) {
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invalid_param = true;
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@ -1095,6 +1123,12 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
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printf(" --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n");
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printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
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printf(" --control-vector FNAME\n");
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printf(" add a control vector\n");
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printf(" --control-vector-scaled FNAME S\n");
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printf(" add a control vector with user defined scaling S\n");
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printf(" --control-vector-layer-range START END\n");
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printf(" layer range to apply the control vector(s) to, start and end inclusive\n");
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printf(" -m FNAME, --model FNAME\n");
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printf(" model path (default: %s)\n", params.model.c_str());
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printf(" -md FNAME, --model-draft FNAME\n");
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@ -1360,6 +1394,30 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
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return std::make_tuple(nullptr, nullptr);
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}
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if (!params.control_vectors.empty()) {
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if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
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if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model);
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const auto cvec = llama_control_vector_load(params.control_vectors);
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if (cvec.n_embd == -1) {
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llama_free(lctx);
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llama_free_model(model);
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return std::make_tuple(nullptr, nullptr);
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}
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int err = llama_control_vector_apply(lctx,
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cvec.data.data(),
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cvec.data.size(),
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cvec.n_embd,
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params.control_vector_layer_start,
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params.control_vector_layer_end);
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if (err) {
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llama_free(lctx);
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llama_free_model(model);
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return std::make_tuple(nullptr, nullptr);
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}
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}
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for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
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const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]);
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float lora_scale = std::get<1>(params.lora_adapter[i]);
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@ -1890,3 +1948,160 @@ float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n)
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return sum / (sqrt(sum1) * sqrt(sum2));
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}
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//
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// Control vector utils
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//
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static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
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int32_t n_tensors;
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size_t n_bytes = 0;
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uint32_t max_direction_layer = 0;
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llama_control_vector_data result = { -1, {} };
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// calculate size of ctx needed for tensors, ensure tensors are f32, and find max layer
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{
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struct ggml_init_params meta_params = {
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/* .mem_size = */ ggml_tensor_overhead() * 128 + ggml_graph_overhead(),
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/* .mem_buffer = */ nullptr,
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/* .no_alloc = */ true,
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};
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ggml_context * meta_ctx = ggml_init(meta_params);
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struct gguf_init_params meta_gguf_params = {
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/* .no_alloc = */ true,
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/* .ctx = */ &meta_ctx,
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};
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struct gguf_context * meta_ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
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if (!meta_ctx_gguf) {
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fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str());
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ggml_free(meta_ctx);
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return result;
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}
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n_tensors = gguf_get_n_tensors(meta_ctx_gguf);
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for (int i = 0; i < n_tensors; i++) {
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std::string name = gguf_get_tensor_name(meta_ctx_gguf, i);
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// split on '.'
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size_t dotpos = name.find('.');
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if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
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try {
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uint32_t layer = std::stoi(name.substr(dotpos + 1));
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if (layer == 0) {
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fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
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ggml_free(meta_ctx);
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gguf_free(meta_ctx_gguf);
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return result;
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}
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if (layer > max_direction_layer) {
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max_direction_layer = layer;
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}
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} catch (...) {
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fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
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ggml_free(meta_ctx);
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gguf_free(meta_ctx_gguf);
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return result;
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}
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}
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struct ggml_tensor * tensor_meta = ggml_get_tensor(meta_ctx, name.c_str());
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if (tensor_meta->type != GGML_TYPE_F32 || ggml_n_dims(tensor_meta) != 1) {
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fprintf(stderr, "%s: direction tensor invalid in %s\n", __func__, load_info.fname.c_str());
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ggml_free(meta_ctx);
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gguf_free(meta_ctx_gguf);
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return result;
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}
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if (result.n_embd == -1) {
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result.n_embd = ggml_nelements(tensor_meta);
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} else if (ggml_nelements(tensor_meta) != result.n_embd) {
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fprintf(stderr, "%s: direction tensor sizes mismatched in %s\n", __func__, load_info.fname.c_str());
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ggml_free(meta_ctx);
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gguf_free(meta_ctx_gguf);
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return result;
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}
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n_bytes += ggml_nbytes(tensor_meta);
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}
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ggml_free(meta_ctx);
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gguf_free(meta_ctx_gguf);
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}
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if (n_tensors == 0) {
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fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
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return result;
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}
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// load and scale tensors into final control vector context
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struct ggml_init_params ggml_params = {
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/* .mem_size = */ ggml_tensor_overhead() * n_tensors + n_bytes,
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/* .mem_buffer = */ nullptr,
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/* .no_alloc = */ false,
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};
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struct ggml_context * ctx = ggml_init(ggml_params);
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struct gguf_init_params params = {
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/*.no_alloc = */ false,
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/*.ctx = */ &ctx,
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};
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struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), params);
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if (!ctx_gguf) {
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fprintf(stderr, "%s: failed to load control vector from %s\n", __func__, load_info.fname.c_str());
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ggml_free(ctx);
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return result;
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}
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// do not store data for layer 0 (it's not used)
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result.data.resize(result.n_embd * max_direction_layer);
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for (uint32_t il = 1; il <= max_direction_layer; il++) {
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const std::string name = "direction." + std::to_string(il);
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const ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
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float * dst = result.data.data() + result.n_embd * (il - 1);
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if (tensor) {
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const float * src = (const float *) tensor->data;
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for (int j = 0; j < result.n_embd; j++) {
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dst[j] = src[j] * load_info.strength;
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}
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} else {
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for (int j = 0; j < result.n_embd; j++) {
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dst[j] = 0.0f;
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}
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}
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}
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return result;
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}
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llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
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llama_control_vector_data result = { -1, {} };
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for (const auto & info : load_infos) {
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auto cur = llama_control_vector_load_one(info);
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if (cur.n_embd == -1) {
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return result;
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}
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if (result.n_embd != -1 && (result.n_embd != cur.n_embd || result.data.size() != cur.data.size())) {
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fprintf(stderr, "%s: control vector in %s does not match previous vector dimensions\n", __func__, info.fname.c_str());
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return result;
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}
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if (result.n_embd == -1) {
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result = std::move(cur);
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} else {
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for (size_t i = 0; i < cur.data.size(); i++) {
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result.data[i] += cur.data[i];
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}
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}
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}
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if (result.n_embd == -1) {
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fprintf(stderr, "%s: no vectors passed\n", __func__);
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}
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return result;
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}
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@ -37,10 +37,13 @@ extern char const *LLAMA_COMMIT;
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extern char const *LLAMA_COMPILER;
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extern char const *LLAMA_BUILD_TARGET;
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struct llama_control_vector_load_info;
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int32_t get_num_physical_cores();
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//
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// CLI argument parsing
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//
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int32_t get_num_physical_cores();
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struct gpt_params {
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uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed
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@ -103,6 +106,11 @@ struct gpt_params {
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std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
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std::string lora_base = ""; // base model path for the lora adapter
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std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale
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int32_t control_vector_layer_start = -1; // layer range for control vector
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int32_t control_vector_layer_end = -1; // layer range for control vector
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int ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
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int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
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// (which is more convenient to use for plotting)
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@ -269,3 +277,24 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40
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void llama_embd_normalize(const float * inp, float * out, int n);
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float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n);
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//
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// Control vector utils
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//
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struct llama_control_vector_data {
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int n_embd;
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// stores data for layers [1, n_layer] where n_layer = data.size() / n_embd
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std::vector<float> data;
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};
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struct llama_control_vector_load_info {
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float strength;
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std::string fname;
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};
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// Load control vectors, scale each by strength, and add them together.
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// On error, returns {-1, empty}
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llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos);
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128
llama.cpp
128
llama.cpp
@ -1894,6 +1894,31 @@ struct llama_kv_cache {
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}
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};
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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<struct ggml_context *> ctxs;
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std::vector<ggml_backend_buffer_t> bufs;
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int32_t layer_start = -1;
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int32_t layer_end = -1;
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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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~llama_control_vector() {
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for (struct ggml_context * ctx : ctxs) {
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ggml_free(ctx);
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}
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for (ggml_backend_buffer_t buf : bufs) {
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ggml_backend_buffer_free(buf);
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}
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}
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};
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struct llama_vocab {
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using id = int32_t;
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using token = std::string;
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@ -2108,6 +2133,9 @@ struct llama_context {
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struct ggml_tensor * inp_s_mask; // F32 [1, kv_size]
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struct ggml_tensor * inp_s_seq; // I32 [kv_size, n_batch]
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// control vectors
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struct llama_control_vector cvec;
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#ifdef GGML_USE_MPI
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ggml_mpi_context * ctx_mpi = NULL;
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#endif
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@ -5931,6 +5959,12 @@ struct llm_build_context {
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "ffn_out", il);
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ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
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if (layer_dir != nullptr) {
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cur = ggml_add(ctx0, cur, layer_dir);
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}
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cb(cur, "l_out", il);
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// input for next layer
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@ -13366,6 +13400,10 @@ int32_t llama_n_embd(const struct llama_model * model) {
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return model->hparams.n_embd;
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}
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int32_t llama_n_layer(const struct llama_model * model) {
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return model->hparams.n_layer;
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}
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float llama_rope_freq_scale_train(const struct llama_model * model) {
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return model->hparams.rope_freq_scale_train;
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}
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@ -13465,6 +13503,96 @@ int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const
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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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// count layer buffer types
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std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
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for (int64_t i = 0; i < model.hparams.n_layer; i++) {
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buft_layer_count[model.buft_layer[i].buft]++;
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}
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// allocate contexts
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std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
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for (auto & it : buft_layer_count) {
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int n_layers = it.second;
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struct ggml_init_params params = {
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/*.mem_size =*/ n_layers * 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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LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
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return 1;
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}
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ctx_map[it.first] = ctx;
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}
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// make tensors
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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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struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft);
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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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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.ctxs.push_back(ctx);
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cvec.bufs.push_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(struct llama_context * lctx, const float * data, size_t len, int32_t n_embd, int32_t il_start, int32_t il_end) {
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||||
const llama_model & model = lctx->model;
|
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llama_control_vector & cvec = lctx->cvec;
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) {
|
||||
struct llama_kv_cache_view result = {
|
||||
/*.n_cells = */ 0,
|
||||
|
||||
23
llama.h
23
llama.h
@ -388,6 +388,7 @@ extern "C" {
|
||||
LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
|
||||
LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
|
||||
LLAMA_API int32_t llama_n_embd (const struct llama_model * model);
|
||||
LLAMA_API int32_t llama_n_layer (const struct llama_model * model);
|
||||
|
||||
// Get the model's RoPE frequency scaling factor
|
||||
LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
|
||||
@ -435,10 +436,24 @@ extern "C" {
|
||||
// Returns 0 on success
|
||||
LLAMA_API int32_t llama_model_apply_lora_from_file(
|
||||
const struct llama_model * model,
|
||||
const char * path_lora,
|
||||
float scale,
|
||||
const char * path_base_model,
|
||||
int32_t n_threads);
|
||||
const char * path_lora,
|
||||
float scale,
|
||||
const char * path_base_model,
|
||||
int32_t n_threads);
|
||||
|
||||
// Apply a loaded control vector to a llama_context, or if data is NULL, clear
|
||||
// the currently loaded vector.
|
||||
// n_embd should be the size of a single layer's control, and data should point
|
||||
// to an n_embd x n_layers buffer starting from layer 1.
|
||||
// il_start and il_end are the layer range the vector should apply to (both inclusive)
|
||||
// See llama_control_vector_load in common to load a control vector.
|
||||
LLAMA_API int32_t llama_control_vector_apply(
|
||||
struct llama_context * lctx,
|
||||
const float * data,
|
||||
size_t len,
|
||||
int32_t n_embd,
|
||||
int32_t il_start,
|
||||
int32_t il_end);
|
||||
|
||||
//
|
||||
// KV cache
|
||||
|
||||
Loading…
Reference in New Issue
Block a user