From f5a77a629bd0f37ae1696747633ab42a5530ec15 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 22 Mar 2023 07:32:36 +0200 Subject: Introduce C-style API (#370) * Major refactoring - introduce C-style API * Clean up * Add * Add * Add .... * Fix timing reporting and accumulation * Measure eval time only for single-token calls * Change llama_tokenize return meaning --- llama.cpp | 1565 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1565 insertions(+) create mode 100644 llama.cpp (limited to 'llama.cpp') diff --git a/llama.cpp b/llama.cpp new file mode 100644 index 00000000..08dfcb31 --- /dev/null +++ b/llama.cpp @@ -0,0 +1,1565 @@ +#include "llama.h" + +#include "ggml.h" + +#include +#include +#include +#include +#include +#include +#include + +// determine number of model parts based on the dimension +static const std::unordered_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::unordered_map tensors; +}; + +struct llama_vocab { + using id = int32_t; + using token = std::string; + + struct token_score { + token tok; + float score; + }; + + std::unordered_map token_to_id; + std::vector id_to_token; +}; + +struct llama_context { + std::mt19937 rng; + + int64_t t_load_us = 0; + int64_t t_start_us = 0; + + int64_t t_sample_us = 0; + int64_t t_eval_us = 0; + + int32_t n_sample = 0; // number of tokens sampled + int32_t n_eval = 0; // number of eval calls + + llama_model model; + llama_vocab vocab; + + size_t mem_per_token = 0; + + // decode output (2-dimensional array: [n_tokens][n_vocab]) + std::vector logits; + bool logits_all = false; +}; + +struct llama_context_params llama_context_default_params() { + struct llama_context_params result = { + /*.n_ctx =*/ 512, + /*.n_parts =*/ -1, + /*.seed =*/ 0, + /*.f16_kv =*/ false, + /*.logits_all =*/ false, + /*.vocab_only =*/ false, + }; + + return result; +} + +// +// model loading +// + +static bool llama_model_load( + const std::string & fname, + llama_context & lctx, + int n_ctx, + int n_parts, + ggml_type memory_type, + bool vocab_only) { + fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str()); + + const int64_t t_start_us = ggml_time_us(); + + lctx.t_start_us = t_start_us; + + std::vector f_buf(1024*1024); + + auto & model = lctx.model; + auto & vocab = lctx.vocab; + + 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 == LLAMA_FILE_MAGIC_UNVERSIONED) { + fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n", + __func__, fname.c_str()); + return false; + } + if (magic != LLAMA_FILE_MAGIC) { + fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str()); + return false; + } + + uint32_t format_version; + fin.read((char *) &format_version, sizeof(format_version)); + + if (format_version != LLAMA_FILE_VERSION) { + fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n", + __func__, fname.c_str(), format_version, LLAMA_FILE_VERSION); + return false; + } + } + + int n_ff = 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; + + if (n_parts < 1) { + n_parts = LLAMA_N_PARTS.at(hparams.n_embd); + } + + // temp warning to tell the user to use "--n_parts" + if (hparams.f16 == 4 && n_parts != 1) { + fprintf(stderr, "%s: GPTQ model detected - are you sure n_parts should be %d? we normally expect it to be 1\n", __func__, n_parts); + fprintf(stderr, "%s: use '--n_parts 1' if necessary\n", __func__); + } + + 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 + { + std::string word; + vocab.id_to_token.resize(model.hparams.n_vocab); + std::vector tmp(64); + + for (int i = 0; i < model.hparams.n_vocab; i++) { + uint32_t len; + fin.read((char *) &len, sizeof(len)); + + word.resize(len); + if (len > 0) { + tmp.resize(len); + fin.read(tmp.data(), len); + word.assign(tmp.data(), len); + } else { + word.clear(); + } + + float score; + fin.read((char *) &score, sizeof(score)); + + vocab.token_to_id[word] = i; + + auto &tok_score = vocab.id_to_token[i]; + tok_score.tok = word; + tok_score.score = score; + } + } + + if (vocab_only) { + return true; + } + + // 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 + // wtype is for per-layer weights, while vtype is for other weights + ggml_type wtype, vtype; + switch (model.hparams.f16) { + case 0: wtype = vtype = GGML_TYPE_F32; break; + case 1: wtype = vtype = GGML_TYPE_F16; break; + case 2: wtype = vtype = GGML_TYPE_Q4_0; break; + case 3: wtype = vtype = GGML_TYPE_Q4_1; break; + case 4: wtype = GGML_TYPE_Q4_1; vtype = GGML_TYPE_F16; break; + default: + { + fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n", + __func__, fname.c_str(), model.hparams.f16); + return false; + } + } + + 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(vtype); // tok_embeddings + + ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // norm + + ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // 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(memory_type); // memory_k + ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // 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_vocab = hparams.n_vocab; + + model.layers.resize(n_layer); + + model.tok_embeddings = ggml_new_tensor_2d(ctx, vtype, n_embd, n_vocab); + + model.norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + model.output = ggml_new_tensor_2d(ctx, vtype, 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, memory_type, n_elements); + model.memory_v = ggml_new_tensor_1d(ctx, memory_type, 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(); + } + + lctx.logits.reserve(lctx.model.hparams.n_ctx); + + lctx.t_load_us = ggml_time_us() - t_start_us; + + return true; +} + +// evaluate the transformer +// +// - lctx: llama context +// - tokens: new batch of tokens to process +// - n_past: the context size so far +// - n_threads: number of threads to use +// +static bool llama_eval_internal( + llama_context & lctx, + const llama_token * tokens, + const int n_tokens, + const int n_past, + const int n_threads) { + const int64_t t_start_us = ggml_time_us(); + + const int N = n_tokens; + + const auto & model = lctx.model; + 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; + + auto & mem_per_token = lctx.mem_per_token; + + // TODO: fix this hardcoded size + 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 = 1.3*(mem_per_token*N); // add 30% 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, tokens, 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_rms_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_rms_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_rms_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); + + auto & logits_out = lctx.logits; + + if (lctx.logits_all) { + logits_out.resize(n_vocab * N); + memcpy(logits_out.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N); + } else { + // return result for just the last token + logits_out.resize(n_vocab); + memcpy(logits_out.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); + + // measure the performance only for the single-token evals + if (N == 1) { + lctx.t_eval_us += ggml_time_us() - t_start_us; + lctx.n_eval++; + } + + return true; +} + +// +// tokenizer +// + +static size_t utf8_len(char src) { + const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 }; + uint8_t highbits = static_cast(src) >> 4; + return lookup[highbits]; +} + +struct llama_sp_symbol { + using index = int; + index prev; + index next; + const char * text; + size_t n; +}; + +struct llama_sp_bigram { + struct comparator { + bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) { + return (l.score < r.score) || (l.score == r.score && l.left > r.left); + } + }; + using queue_storage = std::vector; + using queue = std::priority_queue; + llama_sp_symbol::index left; + llama_sp_symbol::index right; + float score; + size_t size; +}; + +// original implementation: +// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4 +struct llama_tokenizer { + llama_tokenizer(const llama_vocab & vocab): vocab_(vocab) {} + + void tokenize(const std::string & text, std::vector & output) { + // split string into utf8 chars + int index = 0; + size_t offs = 0; + while (offs < text.size()) { + llama_sp_symbol sym; + size_t char_len = std::min(text.size() - offs, utf8_len(text[offs])); + sym.text = text.c_str() + offs; + sym.n = char_len; + offs += char_len; + sym.prev = index - 1; + sym.next = offs == text.size() ? -1 : index + 1; + index++; + symbols_.emplace_back(std::move(sym)); + } + + // seed the work queue with all possible 2-character tokens. + for (size_t i = 1; i < symbols_.size(); ++i) { + try_add_bigram(i - 1, i); + } + + // keep substituting the highest frequency pairs for as long as we can. + while (!work_queue_.empty()) { + auto bigram = work_queue_.top(); + work_queue_.pop(); + + auto & left_sym = symbols_[bigram.left]; + auto & right_sym = symbols_[bigram.right]; + + // if one of the symbols already got merged, skip it. + if (left_sym.n == 0 || right_sym.n == 0 || + left_sym.n + right_sym.n != bigram.size) { + continue; + } + + // merge the right sym into the left one + left_sym.n += right_sym.n; + right_sym.n = 0; + + //printf("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size); + + // remove the right sym from the chain + left_sym.next = right_sym.next; + if (right_sym.next >= 0) { + symbols_[right_sym.next].prev = bigram.left; + } + + // find more substitutions + try_add_bigram(left_sym.prev, bigram.left); + try_add_bigram(bigram.left, left_sym.next); + } + + for (int i = 0; i != -1; i = symbols_[i].next) { + auto & symbol = symbols_[i]; + auto token = vocab_.token_to_id.find(std::string(symbol.text, symbol.n)); + + if (token == vocab_.token_to_id.end()) { + // output any symbols that did not form tokens as bytes. + for (int j = 0; j < (int) symbol.n; ++j) { + llama_vocab::id token_id = static_cast(symbol.text[j]) + 3; + output.push_back(token_id); + } + } else { + output.push_back((*token).second); + } + } + } + +private: + void try_add_bigram(int left, int right) { + if (left == -1 || right == -1) { + return; + } + + const std::string text = std::string(symbols_[left].text, symbols_[left].n + symbols_[right].n); + auto token = vocab_.token_to_id.find(text); + + if (token == vocab_.token_to_id.end()) { + return; + } + + if (static_cast((*token).second) >= vocab_.id_to_token.size()) { + return; + } + + const auto &tok_score = vocab_.id_to_token[(*token).second]; + + llama_sp_bigram bigram; + bigram.left = left; + bigram.right = right; + bigram.score = tok_score.score; + bigram.size = text.size(); + work_queue_.push(bigram); + } + + const llama_vocab & vocab_; + std::vector symbols_; + llama_sp_bigram::queue work_queue_; +}; + +static std::vector llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos) { + llama_tokenizer tokenizer(vocab); + std::vector output; + + if (text.size() == 0) { + return output; + } + + if (bos) { + output.push_back(1); + } + + tokenizer.tokenize(text, output); + return output; +} + +// +// sampling +// + +static void sample_top_k(std::vector> & logits_id, int top_k) { + // find the top k tokens + std::partial_sort( + logits_id.begin(), + logits_id.begin() + top_k, logits_id.end(), + [](const std::pair & a, const std::pair & b) { + return a.first > b.first; + }); + + logits_id.resize(top_k); +} + +static llama_vocab::id llama_sample_top_p_top_k( + llama_context & lctx, + const std::vector & last_n_tokens, + int top_k, + double top_p, + double temp, + double repeat_penalty) { + auto & rng = lctx.rng; + + const auto & vocab = lctx.vocab; + const auto & logits = lctx.logits; + + int n_logits = vocab.id_to_token.size(); + + std::vector> logits_id; + logits_id.reserve(n_logits); + + { + const double scale = 1.0/temp; + for (int i = 0; i < n_logits; ++i) { + // repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858) + // credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main + if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) { + // if score < 0 then repetition penalty has to multiplied to reduce the previous token probability + if (logits[i] < 0.0) { + logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i)); + } else { + logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i)); + } + } else { + logits_id.push_back(std::make_pair(logits[i]*scale, i)); + } + } + } + + sample_top_k(logits_id, top_k); + + double maxl = -std::numeric_limits::infinity(); + for (const auto & kv : logits_id) { + maxl = std::max(maxl, kv.first); + } + + // compute probs for the top k tokens + std::vector probs; + probs.reserve(logits_id.size()); + + double sum = 0.0; + for (const auto & kv : logits_id) { + double p = exp(kv.first - maxl); + probs.push_back(p); + sum += p; + } + + // normalize the probs + for (auto & p : probs) { + p /= sum; + } + + if (top_p < 1.0f) { + double cumsum = 0.0f; + for (int i = 0; i < (int) probs.size(); i++) { + cumsum += probs[i]; + if (cumsum >= top_p) { + probs.resize(i + 1); + logits_id.resize(i + 1); + break; + } + } + + cumsum = 1.0/cumsum; + for (int i = 0; i < (int) probs.size(); i++) { + probs[i] *= cumsum; + } + } + + //printf("\n"); + //for (int i = 0; i < (int) 10; i++) { + // printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]); + //} + //printf("\n\n"); + //exit(0); + + std::discrete_distribution<> dist(probs.begin(), probs.end()); + int idx = dist(rng); + + return logits_id[idx].second; +} + +// +// quantization +// + +// TODO: reuse code from the llama_model_load() somehow +bool llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, int itype, int qk) { + ggml_type type = GGML_TYPE_Q4_1; + + switch (itype) { + case 2: type = GGML_TYPE_Q4_0; break; + case 3: type = GGML_TYPE_Q4_1; break; + default: fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype); return 1; + }; + + if (type != GGML_TYPE_Q4_0 && type != GGML_TYPE_Q4_1) { + fprintf(stderr, "%s: invalid quantization type %d\n", __func__, type); + return false; + } + + llama_vocab vocab; + + printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str()); + + auto finp = std::ifstream(fname_inp, std::ios::binary); + if (!finp) { + fprintf(stderr, "%s: failed to open '%s' for reading\n", __func__, fname_inp.c_str()); + return false; + } + + auto fout = std::ofstream(fname_out, std::ios::binary); + if (!fout) { + fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname_out.c_str()); + return false; + } + + // verify magic + { + uint32_t magic; + finp.read((char *) &magic, sizeof(magic)); + if (magic == LLAMA_FILE_MAGIC_UNVERSIONED) { + fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n", + __func__, fname_inp.c_str()); + return false; + } + if (magic != LLAMA_FILE_MAGIC) { + fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str()); + return false; + } + + fout.write((char *) &magic, sizeof(magic)); + + uint32_t format_version; + finp.read((char *) &format_version, sizeof(format_version)); + + if (format_version != LLAMA_FILE_VERSION) { + fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n", + __func__, fname_inp.c_str(), format_version, LLAMA_FILE_VERSION); + return false; + } + + fout.write((char *) &format_version, sizeof(format_version)); + } + + llama_hparams hparams; + + // load hparams + { + finp.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab)); + //finp.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx)); + finp.read((char *) &hparams.n_embd, sizeof(hparams.n_embd)); + finp.read((char *) &hparams.n_mult, sizeof(hparams.n_mult)); + finp.read((char *) &hparams.n_head, sizeof(hparams.n_head)); + finp.read((char *) &hparams.n_layer, sizeof(hparams.n_layer)); + finp.read((char *) &hparams.n_rot, sizeof(hparams.n_rot)); + finp.read((char *) &hparams.f16, sizeof(hparams.f16)); + + printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab); + printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx); + printf("%s: n_embd = %d\n", __func__, hparams.n_embd); + printf("%s: n_mult = %d\n", __func__, hparams.n_mult); + printf("%s: n_head = %d\n", __func__, hparams.n_head); + printf("%s: n_layer = %d\n", __func__, hparams.n_layer); + printf("%s: f16 = %d\n", __func__, hparams.f16); + + fout.write((char *) &hparams.n_vocab, sizeof(hparams.n_vocab)); + //fout.write((char *) &hparams.n_ctx, sizeof(hparams.n_ctx)); + fout.write((char *) &hparams.n_embd, sizeof(hparams.n_embd)); + fout.write((char *) &hparams.n_mult, sizeof(hparams.n_mult)); + fout.write((char *) &hparams.n_head, sizeof(hparams.n_head)); + fout.write((char *) &hparams.n_layer, sizeof(hparams.n_layer)); + fout.write((char *) &hparams.n_rot, sizeof(hparams.n_rot)); + fout.write((char *) &itype, sizeof(hparams.f16)); + } + + // load vocab + { + const int32_t n_vocab = hparams.n_vocab; + + if (n_vocab != hparams.n_vocab) { + fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n", + __func__, fname_inp.c_str(), n_vocab, hparams.n_vocab); + return false; + } + + std::string word; + vocab.id_to_token.resize(n_vocab); + for (int i = 0; i < n_vocab; i++) { + uint32_t len; + finp.read ((char *) &len, sizeof(len)); + fout.write((char *) &len, sizeof(len)); + + word.resize(len); + finp.read ((char *) word.data(), len); + fout.write((char *) word.data(), len); + + float score; + finp.read ((char *) &score, sizeof(score)); + fout.write((char *) &score, sizeof(score)); + + vocab.token_to_id[word] = i; + + auto &tok_score = vocab.id_to_token[i]; + tok_score.tok = word; + tok_score.score = score; + } + } + + // load weights + { + size_t total_size_org = 0; + size_t total_size_new = 0; + + std::vector work; + + std::vector data_u8; + std::vector data_f16; + std::vector data_f32; + + std::vector hist_all(1 << 4, 0); + + while (true) { + int32_t n_dims; + int32_t length; + int32_t ftype; + + finp.read(reinterpret_cast(&n_dims), sizeof(n_dims)); + finp.read(reinterpret_cast(&length), sizeof(length)); + finp.read(reinterpret_cast(&ftype), sizeof(ftype)); + + if (finp.eof()) { + break; + } + + int32_t nelements = 1; + int32_t ne[2] = { 1, 1 }; + for (int i = 0; i < n_dims; ++i) { + finp.read (reinterpret_cast(&ne[i]), sizeof(ne[i])); + nelements *= ne[i]; + } + + std::string name(length, 0); + finp.read (&name[0], length); + + { + static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", }; + printf("%48s - [%5d, %5d], type = %6s ", name.data(), ne[0], ne[1], ftype_str[ftype]); + } + + // regexes of tensor names to be quantized + const std::vector k_names = { + ".*weight", + }; + + bool quantize = false; + for (const auto & s : k_names) { + if (std::regex_match(name, std::regex(s))) { + quantize = true; + break; + } + } + + // quantize only 2D tensors + quantize &= (n_dims == 2); + + if (quantize) { + if (ftype != 0 && ftype != 1) { + fprintf(stderr, "%s: unsupported ftype %d for integer quantization\n", __func__, ftype); + return false; + } + + if (ftype == 1) { + data_f16.resize(nelements); + finp.read(reinterpret_cast(data_f16.data()), nelements * sizeof(ggml_fp16_t)); + data_f32.resize(nelements); + for (int i = 0; i < nelements; ++i) { + data_f32[i] = ggml_fp16_to_fp32(data_f16[i]); + } + } else { + data_f32.resize(nelements); + finp.read(reinterpret_cast(data_f32.data()), nelements * sizeof(float)); + } + + ftype = itype; + } else { + const int bpe = (ftype == 0) ? sizeof(float) : sizeof(uint16_t); + + data_u8.resize(nelements*bpe); + finp.read(reinterpret_cast(data_u8.data()), nelements * bpe); + } + + fout.write(reinterpret_cast(&n_dims), sizeof(n_dims)); + fout.write(reinterpret_cast(&length), sizeof(length)); + fout.write(reinterpret_cast(&ftype), sizeof(ftype)); + for (int i = 0; i < n_dims; ++i) { + fout.write(reinterpret_cast(&ne[i]), sizeof(ne[i])); + } + fout.write(&name[0], length); + + if (quantize) { + printf("quantizing .. "); + work.resize(nelements); // for quantization + + size_t cur_size = 0; + std::vector hist_cur(1 << 4, 0); + + switch (type) { + case GGML_TYPE_Q4_0: + { + cur_size = ggml_quantize_q4_0(data_f32.data(), work.data(), nelements, ne[0], qk, hist_cur.data()); + } break; + case GGML_TYPE_Q4_1: + { + cur_size = ggml_quantize_q4_1(data_f32.data(), work.data(), nelements, ne[0], qk, hist_cur.data()); + } break; + default: + { + fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, type); + return false; + } + } + + fout.write(reinterpret_cast(work.data()), cur_size); + total_size_new += cur_size; + + printf("size = %8.2f MB -> %8.2f MB | hist: ", nelements * sizeof(float)/1024.0/1024.0, cur_size/1024.0/1024.0); + for (int i = 0; i < (int) hist_cur.size(); ++i) { + hist_all[i] += hist_cur[i]; + } + + for (int i = 0; i < (int) hist_cur.size(); ++i) { + printf("%5.3f ", hist_cur[i] / (float)nelements); + } + printf("\n"); + } else { + printf("size = %8.3f MB\n", data_u8.size()/1024.0/1024.0); + fout.write(reinterpret_cast(data_u8.data()), data_u8.size()); + total_size_new += data_u8.size(); + } + + total_size_org += nelements * sizeof(float); + } + + printf("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); + printf("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0); + + { + int64_t sum_all = 0; + for (int i = 0; i < (int) hist_all.size(); ++i) { + sum_all += hist_all[i]; + } + + printf("%s: hist: ", __func__); + for (int i = 0; i < (int) hist_all.size(); ++i) { + printf("%5.3f ", hist_all[i] / (float)sum_all); + } + printf("\n"); + } + } + + finp.close(); + fout.close(); + + return true; +} + +// +// interface implementation +// + +struct llama_context * llama_init_from_file( + const char * path_model, + struct llama_context_params params) { + ggml_time_init(); + + llama_context * ctx = new llama_context; + + ctx->rng = std::mt19937(params.seed); + ctx->logits_all = params.logits_all; + + ggml_type type_memory = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32; + + if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_parts, type_memory, params.vocab_only)) { + fprintf(stderr, "%s: failed to load model\n", __func__); + delete ctx; + return nullptr; + } + + return ctx; +} + +void llama_free(struct llama_context * ctx) { + ggml_free(ctx->model.ctx); + + delete ctx; +} + +int llama_model_quantize( + const char * fname_inp, + const char * fname_out, + int itype, + int qk) { + if (!llama_model_quantize_internal(fname_inp, fname_out, itype, qk)) { + fprintf(stderr, "%s: failed to quantize\n", __func__); + return 1; + } + + return 0; +} + +int llama_eval( + struct llama_context * ctx, + const llama_token * tokens, + int n_tokens, + int n_past, + int n_threads) { + if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads)) { + fprintf(stderr, "%s: failed to eval\n", __func__); + return 1; + } + + return 0; +} + +int llama_tokenize( + struct llama_context * ctx, + const char * text, + llama_token * tokens, + int n_max_tokens, + bool add_bos) { + auto res = llama_tokenize(ctx->vocab, text, add_bos); + + if (n_max_tokens < (int) res.size()) { + fprintf(stderr, "%s: too many tokens\n", __func__); + return -((int) res.size()); + } + + for (size_t i = 0; i < res.size(); i++) { + tokens[i] = res[i]; + } + + return res.size(); +} + +int llama_n_vocab(struct llama_context * ctx) { + return ctx->vocab.id_to_token.size(); +} + +int llama_n_ctx(struct llama_context * ctx) { + return ctx->model.hparams.n_ctx; +} + +float * llama_get_logits(struct llama_context * ctx) { + return ctx->logits.data(); +} + +const char * llama_token_to_str(struct llama_context * ctx, llama_token token) { + if (token >= llama_n_vocab(ctx)) { + return nullptr; + } + + return ctx->vocab.id_to_token[token].tok.c_str(); +} + +llama_token llama_token_bos() { + return 1; +} + +llama_token llama_token_eos() { + return 2; +} + +llama_token llama_sample_top_p_top_k( + llama_context * ctx, + const llama_token * last_n_tokens_data, + int last_n_tokens_size, + int top_k, + double top_p, + double temp, + double repeat_penalty) { + const int64_t t_start_sample_us = ggml_time_us(); + + llama_token result = 0; + + // TODO: avoid this ... + const auto last_n_tokens = std::vector(last_n_tokens_data, last_n_tokens_data + last_n_tokens_size); + + result = llama_sample_top_p_top_k( + *ctx, + last_n_tokens, + top_k, + top_p, + temp, + repeat_penalty); + + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; + ctx->n_sample++; + + return result; +} + + +void llama_print_timings(struct llama_context * ctx) { + const int64_t t_end_us = ggml_time_us(); + + const int32_t n_sample = std::max(1, ctx->n_sample); + const int32_t n_eval = std::max(1, ctx->n_eval); + + fprintf(stderr, "\n"); + fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0f); + fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_sample_us, n_sample, 1e-3f * ctx->t_sample_us / n_sample); + fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_eval_us, n_eval, 1e-3f * ctx->t_eval_us / n_eval); + fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f); +} + +void llama_reset_timings(struct llama_context * ctx) { + ctx->t_start_us = ggml_time_us(); + + ctx->t_sample_us = ctx->n_sample = 0; + ctx->t_eval_us = ctx->n_eval = 0; +} + +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(); +} + -- cgit v1.2.3