diff --git a/llama/LlamaRunner.swift b/Sources/llama/LlamaRunner.swift similarity index 100% rename from llama/LlamaRunner.swift rename to Sources/llama/LlamaRunner.swift diff --git a/Sources/llama/llama-Bridging-Header.h b/Sources/llama/llama-Bridging-Header.h new file mode 100644 index 0000000..a89a092 --- /dev/null +++ b/Sources/llama/llama-Bridging-Header.h @@ -0,0 +1,10 @@ +// +// Llama-Bridging-Header.h +// llama +// +// Created by Alex Rozanski on 15/03/2023. +// + +#import +#import +#import diff --git a/llama/LlamaError.m b/Sources/llamaObjCxx/LlamaError.m similarity index 100% rename from llama/LlamaError.m rename to Sources/llamaObjCxx/LlamaError.m diff --git a/llama/bridge/LlamaEvent.mm b/Sources/llamaObjCxx/bridge/LlamaEvent.mm similarity index 100% rename from llama/bridge/LlamaEvent.mm rename to Sources/llamaObjCxx/bridge/LlamaEvent.mm diff --git a/llama/bridge/LlamaPredictOperation.hh b/Sources/llamaObjCxx/bridge/LlamaPredictOperation.hh similarity index 100% rename from llama/bridge/LlamaPredictOperation.hh rename to Sources/llamaObjCxx/bridge/LlamaPredictOperation.hh diff --git a/llama/bridge/LlamaPredictOperation.mm b/Sources/llamaObjCxx/bridge/LlamaPredictOperation.mm similarity index 100% rename from llama/bridge/LlamaPredictOperation.mm rename to Sources/llamaObjCxx/bridge/LlamaPredictOperation.mm diff --git a/llama/bridge/LlamaRunnerBridge.mm b/Sources/llamaObjCxx/bridge/LlamaRunnerBridge.mm similarity index 100% rename from llama/bridge/LlamaRunnerBridge.mm rename to Sources/llamaObjCxx/bridge/LlamaRunnerBridge.mm diff --git a/llama/bridge/LlamaRunnerBridgeConfig.m b/Sources/llamaObjCxx/bridge/LlamaRunnerBridgeConfig.m similarity index 100% rename from llama/bridge/LlamaRunnerBridgeConfig.m rename to Sources/llamaObjCxx/bridge/LlamaRunnerBridgeConfig.m diff --git a/cpp/ggml.c b/Sources/llamaObjCxx/cpp/ggml.c similarity index 100% rename from cpp/ggml.c rename to Sources/llamaObjCxx/cpp/ggml.c diff --git a/cpp/quantize.cpp b/Sources/llamaObjCxx/cpp/quantize.cpp similarity index 100% rename from cpp/quantize.cpp rename to Sources/llamaObjCxx/cpp/quantize.cpp diff --git a/cpp/utils.cpp b/Sources/llamaObjCxx/cpp/utils.cpp similarity index 100% rename from cpp/utils.cpp rename to Sources/llamaObjCxx/cpp/utils.cpp diff --git a/cpp/ggml.h b/Sources/llamaObjCxx/include/private/ggml.h similarity index 100% rename from cpp/ggml.h rename to Sources/llamaObjCxx/include/private/ggml.h diff --git a/cpp/utils.h b/Sources/llamaObjCxx/include/private/utils.h similarity index 100% rename from cpp/utils.h rename to Sources/llamaObjCxx/include/private/utils.h diff --git a/llama/LlamaError.h b/Sources/llamaObjCxx/include/public/LlamaError.h similarity index 100% rename from llama/LlamaError.h rename to Sources/llamaObjCxx/include/public/LlamaError.h diff --git a/llama/bridge/LlamaEvent.h b/Sources/llamaObjCxx/include/public/LlamaEvent.h similarity index 100% rename from llama/bridge/LlamaEvent.h rename to Sources/llamaObjCxx/include/public/LlamaEvent.h diff --git a/llama/bridge/LlamaRunnerBridge.h b/Sources/llamaObjCxx/include/public/LlamaRunnerBridge.h similarity index 100% rename from llama/bridge/LlamaRunnerBridge.h rename to Sources/llamaObjCxx/include/public/LlamaRunnerBridge.h diff --git a/llama/bridge/LlamaRunnerBridgeConfig.h b/Sources/llamaObjCxx/include/public/LlamaRunnerBridgeConfig.h similarity index 100% rename from llama/bridge/LlamaRunnerBridgeConfig.h rename to Sources/llamaObjCxx/include/public/LlamaRunnerBridgeConfig.h diff --git a/cpp/main.cpp b/cpp/main.cpp deleted file mode 100644 index 6dc9ae9..0000000 --- a/cpp/main.cpp +++ /dev/null @@ -1,1056 +0,0 @@ -#include "ggml.h" - -#include "utils.h" - -#include -#include -#include -#include -#include -#include -#include -#include - -#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) -#include -#include -#endif - -#define ANSI_COLOR_RED "\x1b[31m" -#define ANSI_COLOR_GREEN "\x1b[32m" -#define ANSI_COLOR_YELLOW "\x1b[33m" -#define ANSI_COLOR_BLUE "\x1b[34m" -#define ANSI_COLOR_MAGENTA "\x1b[35m" -#define ANSI_COLOR_CYAN "\x1b[36m" -#define ANSI_COLOR_RESET "\x1b[0m" -#define ANSI_BOLD "\x1b[1m" - -// determine number of model parts based on the dimension -static const std::map LLAMA_N_PARTS = { - { 4096, 1 }, - { 5120, 2 }, - { 6656, 4 }, - { 8192, 8 }, -}; - -// default hparams (LLaMA 7B) -struct llama_hparams { - int32_t n_vocab = 32000; - int32_t n_ctx = 512; // this is provided as user input? - int32_t n_embd = 4096; - int32_t n_mult = 256; - int32_t n_head = 32; - int32_t n_layer = 32; - int32_t n_rot = 64; - int32_t f16 = 1; -}; - -struct llama_layer { - // normalization - struct ggml_tensor * attention_norm; - - // attention - struct ggml_tensor * wq; - struct ggml_tensor * wk; - struct ggml_tensor * wv; - struct ggml_tensor * wo; - - // normalization - struct ggml_tensor * ffn_norm; - - // ff - struct ggml_tensor * w1; - struct ggml_tensor * w2; - struct ggml_tensor * w3; -}; - -struct llama_model { - llama_hparams hparams; - - struct ggml_tensor * tok_embeddings; - - struct ggml_tensor * norm; - struct ggml_tensor * output; - - std::vector layers; - - // key + value memory - struct ggml_tensor * memory_k; - struct ggml_tensor * memory_v; - - // - struct ggml_context * ctx; - std::map tensors; -}; - -// load the model's weights from a file -bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx) { - fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str()); - - std::vector f_buf(1024*1024); - - auto fin = std::ifstream(fname, std::ios::binary); - fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size()); - if (!fin) { - fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str()); - return false; - } - - // verify magic - { - uint32_t magic; - fin.read((char *) &magic, sizeof(magic)); - if (magic != 0x67676d6c) { - fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str()); - return false; - } - } - - int n_ff = 0; - int n_parts = 0; - - // load hparams - { - auto & hparams = model.hparams; - - fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab)); - //fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx)); - fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd)); - fin.read((char *) &hparams.n_mult, sizeof(hparams.n_mult)); - fin.read((char *) &hparams.n_head, sizeof(hparams.n_head)); - fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer)); - fin.read((char *) &hparams.n_rot, sizeof(hparams.n_rot)); - fin.read((char *) &hparams.f16, sizeof(hparams.f16)); - - hparams.n_ctx = n_ctx; - - n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult; - n_parts = LLAMA_N_PARTS.at(hparams.n_embd); - - fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab); - fprintf(stderr, "%s: n_ctx = %d\n", __func__, hparams.n_ctx); - fprintf(stderr, "%s: n_embd = %d\n", __func__, hparams.n_embd); - fprintf(stderr, "%s: n_mult = %d\n", __func__, hparams.n_mult); - fprintf(stderr, "%s: n_head = %d\n", __func__, hparams.n_head); - fprintf(stderr, "%s: n_layer = %d\n", __func__, hparams.n_layer); - fprintf(stderr, "%s: n_rot = %d\n", __func__, hparams.n_rot); - fprintf(stderr, "%s: f16 = %d\n", __func__, hparams.f16); - fprintf(stderr, "%s: n_ff = %d\n", __func__, n_ff); - fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts); - } - - // load vocab - { - const int32_t n_vocab = model.hparams.n_vocab; - - if (n_vocab != model.hparams.n_vocab) { - fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n", - __func__, fname.c_str(), n_vocab, model.hparams.n_vocab); - return false; - } - - std::string word; - for (int i = 0; i < n_vocab; i++) { - uint32_t len; - fin.read((char *) &len, sizeof(len)); - - word.resize(len); - fin.read((char *) word.data(), len); - - vocab.token_to_id[word] = i; - vocab.id_to_token[i] = word; - - //if (i < 30000) { - // fprintf(stderr, "%s: vocab[%d] = '%s'\n", __func__, i, word.c_str()); - //} - } - } - - // for the big tensors, we have the option to store the data in 16-bit floats or quantized - // in order to save memory and also to speed up the computation - ggml_type wtype = GGML_TYPE_COUNT; - switch (model.hparams.f16) { - case 0: wtype = GGML_TYPE_F32; break; - case 1: wtype = GGML_TYPE_F16; break; - case 2: wtype = GGML_TYPE_Q4_0; break; - case 3: wtype = GGML_TYPE_Q4_1; break; - default: - { - fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n", - __func__, fname.c_str(), model.hparams.f16); - return false; - } - } - - const ggml_type wtype2 = GGML_TYPE_F32; - - auto & ctx = model.ctx; - - size_t ctx_size = 0; - - { - const auto & hparams = model.hparams; - - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_ctx = hparams.n_ctx; - const int n_vocab = hparams.n_vocab; - - ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // tok_embeddings - - ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // norm - - ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // output - - ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attention_norm - - ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wq - ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wk - ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wv - ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wo - - ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ffn_norm - - ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w1 - ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w2 - ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w3 - - ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k - ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v - - ctx_size += (5 + 10*n_layer)*256; // object overhead - - fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0)); - } - - // create the ggml context - { - struct ggml_init_params params = { - /*.mem_size =*/ ctx_size, - /*.mem_buffer =*/ NULL, - }; - - model.ctx = ggml_init(params); - if (!model.ctx) { - fprintf(stderr, "%s: ggml_init() failed\n", __func__); - return false; - } - } - - // prepare memory for the weights - { - const auto & hparams = model.hparams; - - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_ctx = hparams.n_ctx; - const int n_vocab = hparams.n_vocab; - - model.layers.resize(n_layer); - - model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab); - - model.norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - model.output = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab); - - // map by name - model.tensors["tok_embeddings.weight"] = model.tok_embeddings; - - model.tensors["norm.weight"] = model.norm; - model.tensors["output.weight"] = model.output; - - for (int i = 0; i < n_layer; ++i) { - auto & layer = model.layers[i]; - - layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - - layer.wq = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd); - layer.wk = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd); - layer.wv = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd); - layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd); - - layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - - layer.w1 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff); - layer.w2 = ggml_new_tensor_2d(ctx, wtype, n_ff, n_embd); - layer.w3 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff); - - // map by name - model.tensors["layers." + std::to_string(i) + ".attention_norm.weight"] = layer.attention_norm; - - model.tensors["layers." + std::to_string(i) + ".attention.wq.weight"] = layer.wq; - model.tensors["layers." + std::to_string(i) + ".attention.wk.weight"] = layer.wk; - model.tensors["layers." + std::to_string(i) + ".attention.wv.weight"] = layer.wv; - model.tensors["layers." + std::to_string(i) + ".attention.wo.weight"] = layer.wo; - - model.tensors["layers." + std::to_string(i) + ".ffn_norm.weight"] = layer.ffn_norm; - - model.tensors["layers." + std::to_string(i) + ".feed_forward.w1.weight"] = layer.w1; - model.tensors["layers." + std::to_string(i) + ".feed_forward.w2.weight"] = layer.w2; - model.tensors["layers." + std::to_string(i) + ".feed_forward.w3.weight"] = layer.w3; - } - } - - // key + value memory - { - const auto & hparams = model.hparams; - - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_ctx = hparams.n_ctx; - - const int n_mem = n_layer*n_ctx; - const int n_elements = n_embd*n_mem; - - model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements); - model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements); - - const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v); - - fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem); - } - - const size_t file_offset = fin.tellg(); - - fin.close(); - - std::vector tmp; - - for (int i = 0; i < n_parts; ++i) { - const int part_id = i; - //const int part_id = n_parts - i - 1; - - std::string fname_part = fname; - if (i > 0) { - fname_part += "." + std::to_string(i); - } - - fprintf(stderr, "%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str()); - - fin = std::ifstream(fname_part, std::ios::binary); - fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size()); - fin.seekg(file_offset); - - // load weights - { - int n_tensors = 0; - size_t total_size = 0; - - fprintf(stderr, "%s: ", __func__); - - while (true) { - int32_t n_dims; - int32_t length; - int32_t ftype; - - fin.read(reinterpret_cast(&n_dims), sizeof(n_dims)); - fin.read(reinterpret_cast(&length), sizeof(length)); - fin.read(reinterpret_cast(&ftype), sizeof(ftype)); - - if (fin.eof()) { - break; - } - - int32_t nelements = 1; - int32_t ne[2] = { 1, 1 }; - for (int i = 0; i < n_dims; ++i) { - fin.read(reinterpret_cast(&ne[i]), sizeof(ne[i])); - nelements *= ne[i]; - } - - std::string name(length, 0); - fin.read(&name[0], length); - - if (model.tensors.find(name.data()) == model.tensors.end()) { - fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data()); - return false; - } - - // split_type = 0: split by columns - // split_type = 1: split by rows - int split_type = 0; - - // split_type = 0: - // regex: - // - tok_embeddings.* - // - layers.*.attention.wo.weight - // - layers.*.feed_forward.w2.weight - - // split_type = 1: - // regex: - // - output.* - // - layers.*.attention.wq.weight - // - layers.*.attention.wk.weight - // - layers.*.attention.wv.weight - // - layers.*.feed_forward.w1.weight - // - layers.*.feed_forward.w3.weight - if (name.find("tok_embeddings") != std::string::npos) { - split_type = 0; - } else if (name.find("layers") != std::string::npos) { - if (name.find("attention.wo.weight") != std::string::npos) { - split_type = 0; - } else if (name.find("feed_forward.w2.weight") != std::string::npos) { - split_type = 0; - } else { - split_type = 1; - } - } else if (name.find("output") != std::string::npos) { - split_type = 1; - } - - auto tensor = model.tensors[name.data()]; - - if (n_dims == 1) { - if (ggml_nelements(tensor) != nelements) { - fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data()); - return false; - } - } else { - if (ggml_nelements(tensor)/n_parts != nelements) { - fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data()); - return false; - } - } - - if (n_dims == 1) { - if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) { - fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n", - __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]); - return false; - } - } else { - if (split_type == 0) { - if (tensor->ne[0]/n_parts != ne[0] || tensor->ne[1] != ne[1]) { - fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n", - __func__, name.data(), tensor->ne[0]/n_parts, tensor->ne[1], ne[0], ne[1]); - return false; - } - } else { - if (tensor->ne[0] != ne[0] || tensor->ne[1]/n_parts != ne[1]) { - fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n", - __func__, name.data(), tensor->ne[0], tensor->ne[1]/n_parts, ne[0], ne[1]); - return false; - } - } - } - - if (0) { - static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", }; - fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type); - } - - size_t bpe = 0; - - switch (ftype) { - case 0: bpe = ggml_type_size(GGML_TYPE_F32); break; - case 1: bpe = ggml_type_size(GGML_TYPE_F16); break; - case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break; - case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break; - default: - { - fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype); - return false; - } - }; - - if (n_dims == 1 || n_parts == 1) { - if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) { - fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n", - __func__, name.data(), ggml_nbytes(tensor), nelements*bpe); - return false; - } - - if (part_id == 0) { - fin.read(reinterpret_cast(tensor->data), ggml_nbytes(tensor)); - } else { - fin.seekg(ggml_nbytes(tensor), std::ios::cur); - } - - total_size += ggml_nbytes(tensor); - } else { - if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)/n_parts) { - fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n", - __func__, name.data(), ggml_nbytes(tensor)/n_parts, nelements*bpe); - return false; - } - - if (split_type == 0) { - const int np0 = ne[0]; - - const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type); - assert(row_size == tensor->nb[1]); - - for (int i1 = 0; i1 < ne[1]; ++i1) { - const size_t offset_row = i1*row_size; - const size_t offset = offset_row + ((part_id*np0)/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type); - fin.read(reinterpret_cast(tensor->data) + offset, row_size/n_parts); - } - } else { - const int np1 = ne[1]; - - const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type); - - for (int i1 = 0; i1 < ne[1]; ++i1) { - const size_t offset_row = (i1 + part_id*np1)*row_size; - fin.read(reinterpret_cast(tensor->data) + offset_row, row_size); - } - } - - total_size += ggml_nbytes(tensor)/n_parts; - } - - //fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0); - if (++n_tensors % 8 == 0) { - fprintf(stderr, "."); - fflush(stderr); - } - } - - fprintf(stderr, " done\n"); - - fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors); - } - - fin.close(); - } - - return true; -} - -// evaluate the transformer -// -// - model: the model -// - n_threads: number of threads to use -// - n_past: the context size so far -// - embd_inp: the embeddings of the tokens in the context -// - embd_w: the predicted logits for the next token -// -// The GPT-J model requires about 16MB of memory per input token. -// -bool llama_eval( - const llama_model & model, - const int n_threads, - const int n_past, - const std::vector & embd_inp, - std::vector & embd_w, - size_t & mem_per_token) { - const int N = embd_inp.size(); - - const auto & hparams = model.hparams; - - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_ctx = hparams.n_ctx; - const int n_head = hparams.n_head; - const int n_vocab = hparams.n_vocab; - const int n_rot = hparams.n_embd/hparams.n_head; - - const int d_key = n_embd/n_head; - - static size_t buf_size = 512u*1024*1024; - static void * buf = malloc(buf_size); - - if (mem_per_token > 0 && mem_per_token*N > buf_size) { - const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead - //fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new); - - // reallocate - buf_size = buf_size_new; - buf = realloc(buf, buf_size); - if (buf == nullptr) { - fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size); - return false; - } - } - - struct ggml_init_params params = { - /*.mem_size =*/ buf_size, - /*.mem_buffer =*/ buf, - }; - - struct ggml_context * ctx0 = ggml_init(params); - ggml_cgraph gf = {}; - gf.n_threads = n_threads; - - struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd)); - - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - struct ggml_tensor * cur; - - // norm - { - cur = ggml_norm(ctx0, inpL); - - // cur = attention_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model.layers[il].attention_norm, cur), - cur); - } - - // self-attention - { - struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); - struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); - struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); - - // store key and value to memory - if (N >= 1) { - struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past)); - struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past)); - - ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v)); - } - - // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3) - struct ggml_tensor * Q = - ggml_permute(ctx0, - ggml_rope(ctx0, - ggml_cpy(ctx0, - Qcur, - ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)), - n_past, n_rot, 0), - 0, 2, 1, 3); - - // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3) - struct ggml_tensor * K = - ggml_permute(ctx0, - ggml_rope(ctx0, - ggml_reshape_3d(ctx0, - ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd), - n_embd/n_head, n_head, n_past + N), - n_past, n_rot, 1), - 0, 2, 1, 3); - - // K * Q - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - struct ggml_tensor * KQ_scaled = - ggml_scale(ctx0, - KQ, - ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head)) - ); - - // KQ_masked = mask_past(KQ_scaled) - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); - - // KQ = soft_max(KQ_masked) - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - - // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous() - struct ggml_tensor * V_trans = - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd), - n_embd/n_head, n_head, n_past + N), - 1, 2, 0, 3); - - // KQV = transpose(V) * KQ_soft_max - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - - // cur = KQV_merged.contiguous().view(n_embd, N) - cur = ggml_cpy(ctx0, - KQV_merged, - ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - // projection (no bias) - cur = ggml_mul_mat(ctx0, - model.layers[il].wo, - cur); - } - - struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); - - // feed-forward network - { - // norm - { - cur = ggml_norm(ctx0, inpFF); - - // cur = ffn_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model.layers[il].ffn_norm, cur), - cur); - } - - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model.layers[il].w3, - cur); - - - cur = ggml_mul_mat(ctx0, - model.layers[il].w1, - cur); - - // SILU activation - cur = ggml_silu(ctx0, cur); - - cur = ggml_mul(ctx0, cur, tmp); - - cur = ggml_mul_mat(ctx0, - model.layers[il].w2, - cur); - } - - cur = ggml_add(ctx0, cur, inpFF); - - // input for next layer - inpL = cur; - } - - // norm - { - inpL = ggml_norm(ctx0, inpL); - - // inpL = norm*inpL - inpL = ggml_mul(ctx0, - ggml_repeat(ctx0, model.norm, inpL), - inpL); - } - - // lm_head - { - inpL = ggml_mul_mat(ctx0, model.output, inpL); - } - - // logits -> probs - //inpL = ggml_soft_max(ctx0, inpL); - - // run the computation - ggml_build_forward_expand(&gf, inpL); - ggml_graph_compute (ctx0, &gf); - - //if (n_past%100 == 0) { - // ggml_graph_print (&gf); - // ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot"); - //} - - //embd_w.resize(n_vocab*N); - //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N); - - // return result for just the last token - embd_w.resize(n_vocab); - memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab); - - if (mem_per_token == 0) { - mem_per_token = ggml_used_mem(ctx0)/N; - } - //fprintf(stderr, "used_mem = %zu\n", ggml_used_mem(ctx0)); - - ggml_free(ctx0); - - return true; -} - -static bool is_interacting = false; - -#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) -void sigint_handler(int signo) { - if (signo == SIGINT) { - if (!is_interacting) { - is_interacting=true; - } else { - _exit(130); - } - } -} -#endif - -const char * llama_print_system_info(void) { - static std::string s; - - s = ""; - s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | "; - s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | "; - s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | "; - s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | "; - s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | "; - s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | "; - s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " | "; - s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | "; - s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | "; - s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | "; - s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | "; - s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | "; - - return s.c_str(); -} - -int main(int argc, char ** argv) { - ggml_time_init(); - const int64_t t_main_start_us = ggml_time_us(); - - gpt_params params; - params.model = "models/llama-7B/ggml-model.bin"; - - if (gpt_params_parse(argc, argv, params) == false) { - return 1; - } - - if (params.seed < 0) { - params.seed = time(NULL); - } - - fprintf(stderr, "%s: seed = %d\n", __func__, params.seed); - - std::mt19937 rng(params.seed); - if (params.prompt.empty()) { - params.prompt = gpt_random_prompt(rng); - } - -// params.prompt = R"(// this function checks if the number n is prime -//bool is_prime(int n) {)"; - - int64_t t_load_us = 0; - - gpt_vocab vocab; - llama_model model; - - // load the model - { - const int64_t t_start_us = ggml_time_us(); - - if (!llama_model_load(params.model, model, vocab, 512)) { // TODO: set context from user input ?? - fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str()); - return 1; - } - - t_load_us = ggml_time_us() - t_start_us; - } - - // print system information - { - fprintf(stderr, "\n"); - fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", - params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); - } - - int n_past = 0; - - int64_t t_sample_us = 0; - int64_t t_predict_us = 0; - - std::vector logits; - - // tokenize the prompt - std::vector embd_inp = ::llama_tokenize(vocab, params.prompt, true); - - params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size()); - - // tokenize the reverse prompt - std::vector antiprompt_inp = ::llama_tokenize(vocab, params.antiprompt, false); - - fprintf(stderr, "\n"); - fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str()); - fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); - for (int i = 0; i < (int) embd_inp.size(); i++) { - fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str()); - } - fprintf(stderr, "\n"); - if (params.interactive) { -#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) - struct sigaction sigint_action; - sigint_action.sa_handler = sigint_handler; - sigemptyset (&sigint_action.sa_mask); - sigint_action.sa_flags = 0; - sigaction(SIGINT, &sigint_action, NULL); -#endif - - fprintf(stderr, "%s: interactive mode on.\n", __func__); - - if(antiprompt_inp.size()) { - fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str()); - fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size()); - for (int i = 0; i < (int) antiprompt_inp.size(); i++) { - fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str()); - } - fprintf(stderr, "\n"); - } - } - fprintf(stderr, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty); - fprintf(stderr, "\n\n"); - - std::vector embd; - - // determine the required inference memory per token: - size_t mem_per_token = 0; - llama_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token); - - int last_n_size = params.repeat_last_n; - std::vector last_n_tokens(last_n_size); - std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); - - - if (params.interactive) { - fprintf(stderr, "== Running in interactive mode. ==\n" -#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) - " - Press Ctrl+C to interject at any time.\n" -#endif - " - Press Return to return control to LLaMa.\n" - " - If you want to submit another line, end your input in '\\'.\n"); - } - - int remaining_tokens = params.n_predict; - int input_consumed = 0; - bool input_noecho = false; - - // prompt user immediately after the starting prompt has been loaded - if (params.interactive_start) { - is_interacting = true; - } - - // set the color for the prompt which will be output initially - if (params.use_color) { - printf(ANSI_COLOR_YELLOW); - } - - while (remaining_tokens > 0) { - // predict - if (embd.size() > 0) { - const int64_t t_start_us = ggml_time_us(); - - if (!llama_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) { - fprintf(stderr, "Failed to predict\n"); - return 1; - } - - t_predict_us += ggml_time_us() - t_start_us; - } - - n_past += embd.size(); - embd.clear(); - - if (embd_inp.size() <= input_consumed) { - // out of user input, sample next token - const float top_k = params.top_k; - const float top_p = params.top_p; - const float temp = params.temp; - const float repeat_penalty = params.repeat_penalty; - - const int n_vocab = model.hparams.n_vocab; - - gpt_vocab::id id = 0; - - { - const int64_t t_start_sample_us = ggml_time_us(); - - id = llama_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_k, top_p, temp, rng); - - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(id); - - t_sample_us += ggml_time_us() - t_start_sample_us; - } - - // add it to the context - embd.push_back(id); - - // echo this to console - input_noecho = false; - - // decrement remaining sampling budget - --remaining_tokens; - } else { - // some user input remains from prompt or interaction, forward it to processing - while (embd_inp.size() > input_consumed) { - embd.push_back(embd_inp[input_consumed]); - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(embd_inp[input_consumed]); - ++input_consumed; - if (embd.size() > params.n_batch) { - break; - } - } - - // reset color to default if we there is no pending user input - if (!input_noecho && params.use_color && embd_inp.size() == input_consumed) { - printf(ANSI_COLOR_RESET); - } - } - - // display text - if (!input_noecho) { - for (auto id : embd) { - printf("%s", vocab.id_to_token[id].c_str()); - } - fflush(stdout); - } - - // in interactive mode, and not currently processing queued inputs; - // check if we should prompt the user for more - if (params.interactive && embd_inp.size() <= input_consumed) { - // check for reverse prompt - if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) { - // reverse prompt found - is_interacting = true; - } - if (is_interacting) { - // currently being interactive - bool another_line=true; - while (another_line) { - fflush(stdout); - char buf[256] = {0}; - int n_read; - if(params.use_color) printf(ANSI_BOLD ANSI_COLOR_GREEN); - if (scanf("%255[^\n]%n%*c", buf, &n_read) <= 0) { - // presumable empty line, consume the newline - scanf("%*c"); - n_read=0; - } - if(params.use_color) printf(ANSI_COLOR_RESET); - - if (n_read > 0 && buf[n_read-1]=='\\') { - another_line = true; - buf[n_read-1] = '\n'; - buf[n_read] = 0; - } else { - another_line = false; - buf[n_read] = '\n'; - buf[n_read+1] = 0; - } - - std::vector line_inp = ::llama_tokenize(vocab, buf, false); - embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); - - remaining_tokens -= line_inp.size(); - - input_noecho = true; // do not echo this again - } - - is_interacting = false; - } - } - - // end of text token - if (embd.back() == 2) { - fprintf(stderr, " [end of text]\n"); - break; - } - } - - - // report timing - { - const int64_t t_main_end_us = ggml_time_us(); - - fprintf(stderr, "\n\n"); - fprintf(stderr, "%s: mem per token = %8zu bytes\n", __func__, mem_per_token); - fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f); - fprintf(stderr, "%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f); - fprintf(stderr, "%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past); - fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f); - } - - ggml_free(model.ctx); - - return 0; -} diff --git a/llama.xcodeproj/project.pbxproj b/llama.xcodeproj/project.pbxproj index 0247507..095c34e 100644 --- a/llama.xcodeproj/project.pbxproj +++ b/llama.xcodeproj/project.pbxproj @@ -7,25 +7,25 @@ objects = { /* Begin PBXBuildFile section */ - 82293E3D29BDC4ED00C67BD9 /* llama.h in Headers */ = {isa = PBXBuildFile; fileRef = 82293E3C29BDC4ED00C67BD9 /* llama.h */; settings = {ATTRIBUTES = (Public, ); }; }; - 82293E5229BDC5DE00C67BD9 /* LlamaRunnerBridge.h in Headers */ = {isa = PBXBuildFile; fileRef = 82293E5029BDC5DE00C67BD9 /* LlamaRunnerBridge.h */; settings = {ATTRIBUTES = (Public, ); }; }; - 82293E5329BDC5DE00C67BD9 /* LlamaRunnerBridge.mm in Sources */ = {isa = PBXBuildFile; fileRef = 82293E5129BDC5DE00C67BD9 /* LlamaRunnerBridge.mm */; }; 82293E5B29BDC71700C67BD9 /* main.swift in Sources */ = {isa = PBXBuildFile; fileRef = 82293E5A29BDC71700C67BD9 /* main.swift */; }; 82293E6229BDC73100C67BD9 /* llama.framework in Frameworks */ = {isa = PBXBuildFile; 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settings = {ATTRIBUTES = (Private, ); }; }; + 82819FB829C1DB5E00399B7E /* quantize.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 82819F7E29BF2BFC00399B7E /* quantize.cpp */; }; + 82819FB929C1DB5E00399B7E /* ggml.c in Sources */ = {isa = PBXBuildFile; fileRef = 82819F7D29BF2BFC00399B7E /* ggml.c */; }; + 82819FBA29C1DB5E00399B7E /* utils.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 82819F8129BF2BFC00399B7E /* utils.cpp */; }; + 82819FBB29C1DB6900399B7E /* LlamaError.h in Headers */ = {isa = PBXBuildFile; fileRef = 82819F9729C07BC900399B7E /* LlamaError.h */; settings = {ATTRIBUTES = (Public, ); }; }; + 82819FBC29C1DB6E00399B7E /* LlamaEvent.h in Headers */ = {isa = PBXBuildFile; fileRef = 82819F9429C0526100399B7E /* LlamaEvent.h */; settings = {ATTRIBUTES = (Public, ); }; }; + 82819FBD29C1DB7100399B7E /* LlamaRunnerBridge.h in Headers */ = {isa = PBXBuildFile; fileRef = 82293E5029BDC5DE00C67BD9 /* LlamaRunnerBridge.h */; settings = {ATTRIBUTES = (Public, ); }; }; + 82819FBE29C1DB7500399B7E /* LlamaRunnerBridgeConfig.h in Headers */ = {isa = PBXBuildFile; fileRef = 82819F8B29BF2F5800399B7E /* LlamaRunnerBridgeConfig.h */; settings = {ATTRIBUTES = (Public, ); }; }; + 82819FBF29C1DB7A00399B7E /* ggml.h in Headers */ = {isa = PBXBuildFile; fileRef = 82819FA029C1D72400399B7E /* ggml.h */; settings = {ATTRIBUTES = (Private, ); }; }; + 82819FC029C1DB7D00399B7E /* utils.h in Headers */ = {isa = PBXBuildFile; fileRef = 82819FA129C1D72400399B7E /* utils.h */; settings = {ATTRIBUTES = (Private, ); }; }; + 82819FC529C2585700399B7E /* libllamaObjCxx.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 82819FA929C1DB2900399B7E /* libllamaObjCxx.a */; }; /* End PBXBuildFile section */ /* Begin PBXContainerItemProxy section */ @@ -36,6 +36,13 @@ remoteGlobalIDString = 82293E3829BDC4ED00C67BD9; remoteInfo = llama; }; + 82819FC129C1DB8B00399B7E /* PBXContainerItemProxy */ = { + isa = PBXContainerItemProxy; + containerPortal = 82293E3029BDC4ED00C67BD9 /* Project object */; + proxyType = 1; + remoteGlobalIDString = 82819FA829C1DB2900399B7E; + remoteInfo = llamaObjCxx; + }; /* End PBXContainerItemProxy section */ /* Begin PBXCopyFilesBuildPhase section */ @@ -52,7 +59,6 @@ /* Begin PBXFileReference section */ 82293E3929BDC4ED00C67BD9 /* llama.framework */ = {isa = PBXFileReference; explicitFileType = wrapper.framework; includeInIndex = 0; path = llama.framework; sourceTree = BUILT_PRODUCTS_DIR; }; - 82293E3C29BDC4ED00C67BD9 /* llama.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = llama.h; sourceTree = ""; }; 82293E5029BDC5DE00C67BD9 /* LlamaRunnerBridge.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = LlamaRunnerBridge.h; sourceTree = ""; }; 82293E5129BDC5DE00C67BD9 /* LlamaRunnerBridge.mm */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.cpp.objcpp; path = LlamaRunnerBridge.mm; sourceTree = ""; }; 82293E5829BDC71700C67BD9 /* llamaTest */ = {isa = PBXFileReference; explicitFileType = "compiled.mach-o.executable"; includeInIndex = 0; path = llamaTest; sourceTree = BUILT_PRODUCTS_DIR; }; @@ -60,11 +66,9 @@ 82293E6429BDC7E200C67BD9 /* LlamaRunner.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LlamaRunner.swift; sourceTree = ""; }; 82819F7B29BDF61E00399B7E /* Info.plist */ = {isa = PBXFileReference; lastKnownFileType = text.plist.xml; path = Info.plist; sourceTree = ""; }; 82819F7C29BDF7CB00399B7E /* LlamaTest.xcconfig */ = {isa = PBXFileReference; lastKnownFileType = text.xcconfig; path = LlamaTest.xcconfig; sourceTree = ""; }; - 82819F7D29BF2BFC00399B7E /* ggml.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = ggml.c; path = cpp/ggml.c; sourceTree = SOURCE_ROOT; }; - 82819F7E29BF2BFC00399B7E /* quantize.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; name = quantize.cpp; path = cpp/quantize.cpp; sourceTree = SOURCE_ROOT; }; - 82819F8029BF2BFC00399B7E /* ggml.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = ggml.h; path = cpp/ggml.h; sourceTree = SOURCE_ROOT; }; - 82819F8129BF2BFC00399B7E /* utils.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; name = utils.cpp; path = cpp/utils.cpp; sourceTree = SOURCE_ROOT; }; - 82819F8229BF2BFC00399B7E /* utils.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = utils.h; path = cpp/utils.h; sourceTree = SOURCE_ROOT; }; + 82819F7D29BF2BFC00399B7E /* ggml.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = ggml.c; path = Sources/llamaObjCxx/cpp/ggml.c; sourceTree = SOURCE_ROOT; }; + 82819F7E29BF2BFC00399B7E /* quantize.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; name = quantize.cpp; path = Sources/llamaObjCxx/cpp/quantize.cpp; sourceTree = SOURCE_ROOT; }; + 82819F8129BF2BFC00399B7E /* utils.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; name = utils.cpp; path = Sources/llamaObjCxx/cpp/utils.cpp; sourceTree = SOURCE_ROOT; }; 82819F8B29BF2F5800399B7E /* LlamaRunnerBridgeConfig.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = LlamaRunnerBridgeConfig.h; sourceTree = ""; }; 82819F8C29BF2F5800399B7E /* LlamaRunnerBridgeConfig.m */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.objc; path = LlamaRunnerBridgeConfig.m; sourceTree = ""; }; 82819F8F29BF387400399B7E /* LlamaPredictOperation.hh */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.cpp.h; path = LlamaPredictOperation.hh; sourceTree = ""; }; @@ -75,6 +79,11 @@ 82819F9829C07BC900399B7E /* LlamaError.m */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.objc; path = LlamaError.m; sourceTree = ""; }; 82819F9B29C0881800399B7E /* README.md */ = {isa = PBXFileReference; lastKnownFileType = net.daringfireball.markdown; path = README.md; sourceTree = ""; }; 82819F9C29C0897900399B7E /* LICENSE */ = {isa = PBXFileReference; lastKnownFileType = text; path = LICENSE; sourceTree = ""; }; + 82819F9D29C1CCA300399B7E /* Package.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = Package.swift; sourceTree = ""; }; + 82819FA029C1D72400399B7E /* ggml.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = ggml.h; sourceTree = ""; }; + 82819FA129C1D72400399B7E /* utils.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = utils.h; sourceTree = ""; }; + 82819FA929C1DB2900399B7E /* libllamaObjCxx.a */ = {isa = PBXFileReference; explicitFileType = archive.ar; includeInIndex = 0; path = libllamaObjCxx.a; sourceTree = BUILT_PRODUCTS_DIR; }; + 82819FC429C1DEE700399B7E /* llama-Bridging-Header.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = "llama-Bridging-Header.h"; sourceTree = ""; }; /* End PBXFileReference section */ /* Begin PBXFrameworksBuildPhase section */ @@ -82,6 +91,7 @@ isa = PBXFrameworksBuildPhase; buildActionMask = 2147483647; files = ( + 82819FC529C2585700399B7E /* libllamaObjCxx.a in Frameworks */, ); runOnlyForDeploymentPostprocessing = 0; }; @@ -93,6 +103,13 @@ ); runOnlyForDeploymentPostprocessing = 0; }; + 82819FA729C1DB2900399B7E /* Frameworks */ = { + isa = PBXFrameworksBuildPhase; + buildActionMask = 2147483647; + files = ( + ); + runOnlyForDeploymentPostprocessing = 0; + }; /* End PBXFrameworksBuildPhase section */ /* Begin PBXGroup section */ @@ -101,7 +118,8 @@ children = ( 82819F9B29C0881800399B7E /* README.md */, 82819F9C29C0897900399B7E /* LICENSE */, - 82293E3B29BDC4ED00C67BD9 /* llama */, + 82819F9D29C1CCA300399B7E /* Package.swift */, + 82819FC629C289B400399B7E /* Sources */, 82293E5929BDC71700C67BD9 /* llamaTest */, 82293E3A29BDC4ED00C67BD9 /* Products */, 82293E6129BDC73100C67BD9 /* Frameworks */, @@ -113,6 +131,7 @@ children = ( 82293E3929BDC4ED00C67BD9 /* llama.framework */, 82293E5829BDC71700C67BD9 /* llamaTest */, + 82819FA929C1DB2900399B7E /* libllamaObjCxx.a */, ); name = Products; sourceTree = ""; @@ -120,12 +139,8 @@ 82293E3B29BDC4ED00C67BD9 /* llama */ = { isa = PBXGroup; children = ( - 82293E6329BDC75F00C67BD9 /* bridge */, - 82293E4329BDC51A00C67BD9 /* cpp */, - 82293E3C29BDC4ED00C67BD9 /* llama.h */, - 82819F9729C07BC900399B7E /* LlamaError.h */, - 82819F9829C07BC900399B7E /* LlamaError.m */, 82293E6429BDC7E200C67BD9 /* LlamaRunner.swift */, + 82819FC429C1DEE700399B7E /* llama-Bridging-Header.h */, ); path = llama; sourceTree = ""; @@ -134,10 +149,8 @@ isa = PBXGroup; children = ( 82819F7D29BF2BFC00399B7E /* ggml.c */, - 82819F8029BF2BFC00399B7E /* ggml.h */, 82819F7E29BF2BFC00399B7E /* quantize.cpp */, 82819F8129BF2BFC00399B7E /* utils.cpp */, - 82819F8229BF2BFC00399B7E /* utils.h */, ); path = cpp; sourceTree = ""; @@ -162,11 +175,8 @@ 82293E6329BDC75F00C67BD9 /* bridge */ = { isa = PBXGroup; children = ( - 82819F9429C0526100399B7E /* LlamaEvent.h */, 82819F9329C0526100399B7E /* LlamaEvent.mm */, - 82293E5029BDC5DE00C67BD9 /* LlamaRunnerBridge.h */, 82293E5129BDC5DE00C67BD9 /* LlamaRunnerBridge.mm */, - 82819F8B29BF2F5800399B7E /* LlamaRunnerBridgeConfig.h */, 82819F8C29BF2F5800399B7E /* LlamaRunnerBridgeConfig.m */, 82819F8F29BF387400399B7E /* LlamaPredictOperation.hh */, 82819F9029BF387400399B7E /* LlamaPredictOperation.mm */, @@ -174,6 +184,55 @@ path = bridge; sourceTree = ""; }; + 82819F9E29C1CE2000399B7E /* llamaObjCxx */ = { + isa = PBXGroup; + children = ( + 82819F9829C07BC900399B7E /* LlamaError.m */, + 82293E6329BDC75F00C67BD9 /* bridge */, + 82293E4329BDC51A00C67BD9 /* cpp */, + 82819FA429C1DAD800399B7E /* include */, + ); + path = llamaObjCxx; + sourceTree = ""; + }; + 82819F9F29C1CFAA00399B7E /* public */ = { + isa = PBXGroup; + children = ( + 82819F9729C07BC900399B7E /* LlamaError.h */, + 82819F9429C0526100399B7E /* LlamaEvent.h */, + 82293E5029BDC5DE00C67BD9 /* LlamaRunnerBridge.h */, + 82819F8B29BF2F5800399B7E /* LlamaRunnerBridgeConfig.h */, + ); + path = public; + sourceTree = ""; + }; + 82819FA329C1D9BF00399B7E /* private */ = { + isa = PBXGroup; + children = ( + 82819FA029C1D72400399B7E /* ggml.h */, + 82819FA129C1D72400399B7E /* utils.h */, + ); + path = private; + sourceTree = ""; + }; + 82819FA429C1DAD800399B7E /* include */ = { + isa = PBXGroup; + children = ( + 82819FA329C1D9BF00399B7E /* private */, + 82819F9F29C1CFAA00399B7E /* public */, + ); + path = include; + sourceTree = ""; + }; + 82819FC629C289B400399B7E /* Sources */ = { + isa = PBXGroup; + children = ( + 82293E3B29BDC4ED00C67BD9 /* llama */, + 82819F9E29C1CE2000399B7E /* llamaObjCxx */, + ); + path = Sources; + sourceTree = ""; + }; /* End PBXGroup section */ /* Begin PBXHeadersBuildPhase section */ @@ -181,14 +240,20 @@ isa = PBXHeadersBuildPhase; buildActionMask = 2147483647; files = ( - 82819F9629C0526100399B7E /* LlamaEvent.h in Headers */, - 82819F8629BF2BFC00399B7E /* ggml.h in Headers */, - 82293E5229BDC5DE00C67BD9 /* LlamaRunnerBridge.h in Headers */, - 82819F9929C07BC900399B7E /* LlamaError.h in Headers */, - 82819F8829BF2BFC00399B7E /* utils.h in Headers */, - 82819F8D29BF2F5800399B7E /* LlamaRunnerBridgeConfig.h in Headers */, - 82819F9129BF387400399B7E /* LlamaPredictOperation.hh in Headers */, - 82293E3D29BDC4ED00C67BD9 /* llama.h in Headers */, + ); + runOnlyForDeploymentPostprocessing = 0; + }; + 82819FA529C1DB2900399B7E /* Headers */ = { + isa = PBXHeadersBuildPhase; + buildActionMask = 2147483647; + files = ( + 82819FBC29C1DB6E00399B7E /* LlamaEvent.h in Headers */, + 82819FB729C1DB5800399B7E /* LlamaPredictOperation.hh in Headers */, + 82819FC029C1DB7D00399B7E /* utils.h in Headers */, + 82819FBB29C1DB6900399B7E /* LlamaError.h in Headers */, + 82819FBF29C1DB7A00399B7E /* ggml.h in Headers */, + 82819FBE29C1DB7500399B7E /* LlamaRunnerBridgeConfig.h in Headers */, + 82819FBD29C1DB7100399B7E /* LlamaRunnerBridge.h in Headers */, ); runOnlyForDeploymentPostprocessing = 0; }; @@ -207,6 +272,7 @@ buildRules = ( ); dependencies = ( + 82819FC229C1DB8B00399B7E /* PBXTargetDependency */, ); name = llama; productName = llama; @@ -231,6 +297,23 @@ productReference = 82293E5829BDC71700C67BD9 /* llamaTest */; productType = "com.apple.product-type.tool"; }; + 82819FA829C1DB2900399B7E /* llamaObjCxx */ = { + isa = PBXNativeTarget; + buildConfigurationList = 82819FAF29C1DB2900399B7E /* Build configuration list for PBXNativeTarget "llamaObjCxx" */; + buildPhases = ( + 82819FA529C1DB2900399B7E /* Headers */, + 82819FA629C1DB2900399B7E /* Sources */, + 82819FA729C1DB2900399B7E /* Frameworks */, + ); + buildRules = ( + ); + dependencies = ( + ); + name = llamaObjCxx; + productName = llamaObjCxx; + productReference = 82819FA929C1DB2900399B7E /* libllamaObjCxx.a */; + productType = "com.apple.product-type.library.static"; + }; /* End PBXNativeTarget section */ /* Begin PBXProject section */ @@ -249,6 +332,9 @@ CreatedOnToolsVersion = 14.1; LastSwiftMigration = 1410; }; + 82819FA829C1DB2900399B7E = { + CreatedOnToolsVersion = 14.1; + }; }; }; buildConfigurationList = 82293E3329BDC4ED00C67BD9 /* Build configuration list for PBXProject "llama" */; @@ -265,6 +351,7 @@ projectRoot = ""; targets = ( 82293E3829BDC4ED00C67BD9 /* llama */, + 82819FA829C1DB2900399B7E /* llamaObjCxx */, 82293E5729BDC71700C67BD9 /* llamaTest */, ); }; @@ -285,15 +372,7 @@ isa = PBXSourcesBuildPhase; buildActionMask = 2147483647; files = ( - 82819F9529C0526100399B7E /* LlamaEvent.mm in Sources */, - 82293E5329BDC5DE00C67BD9 /* LlamaRunnerBridge.mm in Sources */, 82293E6529BDC7E200C67BD9 /* LlamaRunner.swift in Sources */, - 82819F8E29BF2F5800399B7E /* LlamaRunnerBridgeConfig.m in Sources */, - 82819F9A29C07BC900399B7E /* LlamaError.m in Sources */, - 82819F8429BF2BFC00399B7E /* quantize.cpp in Sources */, - 82819F8729BF2BFC00399B7E /* utils.cpp in Sources */, - 82819F8329BF2BFC00399B7E /* ggml.c in Sources */, - 82819F9229BF387400399B7E /* LlamaPredictOperation.mm in Sources */, ); runOnlyForDeploymentPostprocessing = 0; }; @@ -305,6 +384,21 @@ ); runOnlyForDeploymentPostprocessing = 0; }; + 82819FA629C1DB2900399B7E /* Sources */ = { + isa = PBXSourcesBuildPhase; + buildActionMask = 2147483647; + files = ( + 82819FB829C1DB5E00399B7E /* quantize.cpp in Sources */, + 82819FB629C1DB5800399B7E /* LlamaPredictOperation.mm in Sources */, + 82819FB529C1DB5800399B7E /* LlamaRunnerBridgeConfig.m in Sources */, + 82819FBA29C1DB5E00399B7E /* utils.cpp in Sources */, + 82819FB429C1DB5800399B7E /* LlamaRunnerBridge.mm in Sources */, + 82819FB329C1DB5800399B7E /* LlamaEvent.mm in Sources */, + 82819FB929C1DB5E00399B7E /* ggml.c in Sources */, + 82819FB229C1DB5400399B7E /* LlamaError.m in Sources */, + ); + runOnlyForDeploymentPostprocessing = 0; + }; /* End PBXSourcesBuildPhase section */ /* Begin PBXTargetDependency section */ @@ -313,6 +407,11 @@ target = 82293E3829BDC4ED00C67BD9 /* llama */; targetProxy = 82293E5F29BDC72B00C67BD9 /* PBXContainerItemProxy */; }; + 82819FC229C1DB8B00399B7E /* PBXTargetDependency */ = { + isa = PBXTargetDependency; + target = 82819FA829C1DB2900399B7E /* llamaObjCxx */; + targetProxy = 82819FC129C1DB8B00399B7E /* PBXContainerItemProxy */; + }; /* End PBXTargetDependency section */ /* Begin XCBuildConfiguration section */ @@ -456,10 +555,16 @@ "@loader_path/Frameworks", ); MARKETING_VERSION = 1.0; + OTHER_LDFLAGS = ( + "-all_load", + "-lc++", + ); + PRESERVE_DEAD_CODE_INITS_AND_TERMS = NO; PRODUCT_BUNDLE_IDENTIFIER = com.alexrozanski.llama; PRODUCT_NAME = "$(TARGET_NAME:c99extidentifier)"; SKIP_INSTALL = YES; SWIFT_EMIT_LOC_STRINGS = YES; + SWIFT_OBJC_BRIDGING_HEADER = "Sources/llama/llama-Bridging-Header.h"; SWIFT_OPTIMIZATION_LEVEL = "-Onone"; SWIFT_VERSION = 5.0; }; @@ -486,10 +591,16 @@ "@loader_path/Frameworks", ); MARKETING_VERSION = 1.0; + OTHER_LDFLAGS = ( + "-all_load", + "-lc++", + ); + PRESERVE_DEAD_CODE_INITS_AND_TERMS = NO; PRODUCT_BUNDLE_IDENTIFIER = com.alexrozanski.llama; PRODUCT_NAME = "$(TARGET_NAME:c99extidentifier)"; SKIP_INSTALL = YES; SWIFT_EMIT_LOC_STRINGS = YES; + SWIFT_OBJC_BRIDGING_HEADER = "Sources/llama/llama-Bridging-Header.h"; SWIFT_VERSION = 5.0; }; name = Release; @@ -541,6 +652,28 @@ }; name = Release; }; + 82819FB029C1DB2900399B7E /* Debug */ = { + isa = XCBuildConfiguration; + buildSettings = { + CODE_SIGN_STYLE = Automatic; + DEVELOPMENT_TEAM = 44847G58BM; + EXECUTABLE_PREFIX = lib; + PRODUCT_NAME = "$(TARGET_NAME)"; + SKIP_INSTALL = YES; + }; + name = Debug; + }; + 82819FB129C1DB2900399B7E /* Release */ = { + isa = XCBuildConfiguration; + buildSettings = { + CODE_SIGN_STYLE = Automatic; + DEVELOPMENT_TEAM = 44847G58BM; + EXECUTABLE_PREFIX = lib; + PRODUCT_NAME = "$(TARGET_NAME)"; + SKIP_INSTALL = YES; + }; + name = Release; + }; /* End XCBuildConfiguration section */ /* Begin XCConfigurationList section */ @@ -571,6 +704,15 @@ defaultConfigurationIsVisible = 0; defaultConfigurationName = Release; }; + 82819FAF29C1DB2900399B7E /* Build configuration list for PBXNativeTarget "llamaObjCxx" */ = { + isa = XCConfigurationList; + buildConfigurations = ( + 82819FB029C1DB2900399B7E /* Debug */, + 82819FB129C1DB2900399B7E /* Release */, + ); + defaultConfigurationIsVisible = 0; + defaultConfigurationName = Release; + }; /* End XCConfigurationList section */ }; rootObject = 82293E3029BDC4ED00C67BD9 /* Project object */; diff --git a/llama/llama.h b/llama/llama.h deleted file mode 100644 index 645510c..0000000 --- a/llama/llama.h +++ /dev/null @@ -1,21 +0,0 @@ -// -// llama.h -// llama -// -// Created by Alex Rozanski on 12/03/2023. -// - -#import - -//! Project version number for llama. -FOUNDATION_EXPORT double llamaVersionNumber; - -//! Project version string for llama. -FOUNDATION_EXPORT const unsigned char llamaVersionString[]; - -// In this header, you should import all the public headers of your framework using statements like #import - -#import -#import -#import -