mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 10:54:36 +00:00
Control vector loading fixes (#8137)
* Fixed leak in llama_control_vector_load_one() and allow llama_control_vector_load() to grow * refactored `llama_control_vector_load_one()` * allow multiple directions for same layer in same file * llama_control_vector_load_one() and llama_control_vector_load() now break on error * removed unnecessary ggml_free() call
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jukofyork
GitHub
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387952651a
commit
97877eb10b
@ -2804,125 +2804,87 @@ float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n)
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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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ggml_context * ctx = nullptr;
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struct gguf_init_params meta_gguf_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(), meta_gguf_params);
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if (!ctx_gguf) {
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fprintf(stderr, "%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
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return result;
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}
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int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
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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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for (int i = 0; i < n_tensors; i++) {
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std::string name = gguf_get_tensor_name(ctx_gguf, i);
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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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int layer_idx = -1;
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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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// 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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layer_idx = std::stoi(name.substr(dotpos + 1));
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} catch (...) {
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layer_idx = -1;
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}
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}
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if (layer_idx < 0) {
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fprintf(stderr, "%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
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result.n_embd = -1;
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break;
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} else if (layer_idx == 0) {
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fprintf(stderr, "%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
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result.n_embd = -1;
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break;
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}
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struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
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if (tensor->type != GGML_TYPE_F32) {
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fprintf(stderr, "%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
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result.n_embd = -1;
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break;
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}
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if (ggml_n_dims(tensor) != 1) {
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fprintf(stderr, "%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
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result.n_embd = -1;
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break;
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}
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if (result.n_embd == -1) {
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result.n_embd = ggml_nelements(tensor);
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} else if (ggml_nelements(tensor) != result.n_embd) {
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fprintf(stderr, "%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
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result.n_embd = -1;
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break;
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}
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// extend if necessary - do not store data for layer 0 (it's not used)
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result.data.resize(std::max(result.data.size(), static_cast<size_t>(result.n_embd * layer_idx)), 0.0f);
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const float * src = (const float *) tensor->data;
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float * dst = result.data.data() + result.n_embd * (layer_idx - 1); // layer 1 at [0]
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for (int j = 0; j < result.n_embd; j++) {
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dst[j] += src[j] * load_info.strength; // allows multiple directions for same layer in same file
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}
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}
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if (result.n_embd == -1) {
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fprintf(stderr, "%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
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result.data.clear();
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}
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gguf_free(ctx_gguf);
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ggml_free(ctx);
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return result;
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}
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@ -2933,16 +2895,19 @@ llama_control_vector_data llama_control_vector_load(const std::vector<llama_cont
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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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result.n_embd = -1;
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break;
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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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if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
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fprintf(stderr, "%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
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result.n_embd = -1;
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break;
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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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result.data.resize(std::max(result.data.size(), cur.data.size()), 0.0f); // extend if necessary
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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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@ -2950,7 +2915,8 @@ llama_control_vector_data llama_control_vector_load(const std::vector<llama_cont
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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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fprintf(stderr, "%s: no valid control vector files passed\n", __func__);
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result.data.clear();
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}
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return result;
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