From 6277eac9cc2897d3ee0a1086cf417458c97bbe38 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Thu, 28 Sep 2023 12:02:20 -0400 Subject: [PATCH] backend: use llamamodel.cpp for StarCoder --- gpt4all-backend/CMakeLists.txt | 5 - gpt4all-backend/llamamodel.cpp | 3 +- gpt4all-backend/mpt.cpp | 1 - gpt4all-backend/starcoder.cpp | 1027 -------------------------------- 4 files changed, 2 insertions(+), 1034 deletions(-) delete mode 100644 gpt4all-backend/starcoder.cpp diff --git a/gpt4all-backend/CMakeLists.txt b/gpt4all-backend/CMakeLists.txt index b099ce7c..736cf47c 100644 --- a/gpt4all-backend/CMakeLists.txt +++ b/gpt4all-backend/CMakeLists.txt @@ -116,11 +116,6 @@ foreach(BUILD_VARIANT IN LISTS BUILD_VARIANTS) bert.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h) target_compile_definitions(bert-${BUILD_VARIANT} PRIVATE LLAMA_VERSIONS=>=3 LLAMA_DATE=999999) prepare_target(bert llama-mainline) - - add_library(starcoder-${BUILD_VARIANT} SHARED - starcoder.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h) - target_compile_definitions(starcoder-${BUILD_VARIANT} PRIVATE LLAMA_VERSIONS=>=3 LLAMA_DATE=999999) - prepare_target(starcoder llama-mainline) endif() endforeach() diff --git a/gpt4all-backend/llamamodel.cpp b/gpt4all-backend/llamamodel.cpp index 8af73f1e..34c93c7b 100644 --- a/gpt4all-backend/llamamodel.cpp +++ b/gpt4all-backend/llamamodel.cpp @@ -392,7 +392,8 @@ DLL_EXPORT bool magic_match(const char * fname) { return false; bool isValid = gguf_get_version(ctx_gguf) <= 2; - isValid = isValid && get_arch_name(ctx_gguf) == "llama"; + auto arch = get_arch_name(ctx_gguf); + isValid = isValid && (arch == "llama" || arch == "starcoder"); gguf_free(ctx_gguf); return isValid; diff --git a/gpt4all-backend/mpt.cpp b/gpt4all-backend/mpt.cpp index 4cc86b96..238704da 100644 --- a/gpt4all-backend/mpt.cpp +++ b/gpt4all-backend/mpt.cpp @@ -78,7 +78,6 @@ struct mpt_model { struct llm_kv_cache kv_self; struct ggml_context * ctx; - std::map tensors; llm_buffer eval_buf; diff --git a/gpt4all-backend/starcoder.cpp b/gpt4all-backend/starcoder.cpp deleted file mode 100644 index b1836231..00000000 --- a/gpt4all-backend/starcoder.cpp +++ /dev/null @@ -1,1027 +0,0 @@ -#define STARCODER_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE -#include "starcoder_impl.h" -#include "llama.h" -#include "utils.h" -#include "llmodel_shared.h" - -#include -#include -#include -#include -#include -#include - -namespace { -const char *modelType_ = "Starcoder"; -} - -#define STARCODER_MAGIC 0x67676d6c - -// default hparams (GPT-2 117M) -// https://huggingface.co/bigcode/gpt_bigcode-santacoder/blob/main/config.json -struct starcoder_hparams { - int32_t n_vocab = 49280; - int32_t n_ctx = 2048; - int32_t n_embd = 2048; - int32_t n_head = 16; - int32_t n_layer = 24; - int32_t ftype = 1; -}; - -struct starcoder_layer { - // normalization - struct ggml_tensor * ln_1_g; - struct ggml_tensor * ln_1_b; - - struct ggml_tensor * ln_2_g; - struct ggml_tensor * ln_2_b; - - // attention - struct ggml_tensor * c_attn_attn_w; - struct ggml_tensor * c_attn_attn_b; - - struct ggml_tensor * c_attn_proj_w; - struct ggml_tensor * c_attn_proj_b; - - // mlp - struct ggml_tensor * c_mlp_fc_w; - struct ggml_tensor * c_mlp_fc_b; - - struct ggml_tensor * c_mlp_proj_w; - struct ggml_tensor * c_mlp_proj_b; -}; - -struct starcoder_model { - starcoder_hparams hparams; - - // normalization - struct ggml_tensor * ln_f_g; - struct ggml_tensor * ln_f_b; - - struct ggml_tensor * wte; // position embedding - struct ggml_tensor * wpe; // token embedding - struct ggml_tensor * lm_head; // language model head - - std::vector layers; - - // key + value memory - llm_kv_cache kv_self; - - // - struct ggml_context * ctx; - std::map tensors; - - llm_buffer eval_buf; - llm_buffer scr0_buf; - llm_buffer scr1_buf; - llm_buffer work_buf; -}; - -static bool kv_cache_init( - const struct starcoder_hparams & hparams, - struct llm_kv_cache & cache, - ggml_type wtype, - int n_ctx) { - const int n_embd = hparams.n_embd; -#if 0 // FIXME: This code can be made in future to support much smaller KV cache - const int dim_head = n_embd / hparams.n_head; - const int dim_kv = dim_head; -#endif - const int n_layer = hparams.n_layer; - - const int64_t n_mem = (int64_t)n_layer*n_ctx; - const int64_t n_elements = n_embd * n_mem; - cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2_MiB); - struct ggml_init_params params; - params.mem_size = cache.buf.size; - params.mem_buffer = cache.buf.addr; - params.no_alloc = false; - - cache.ctx = ggml_init(params); - if (!cache.ctx) { - fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__); - return false; - } - - cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements); - cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements); - return true; -} - -// load the model's weights from a file -bool starcoder_model_load(const std::string & fname, starcoder_model & model, gpt_vocab & vocab, size_t *mem_req) { - printf("%s: loading model from '%s'\n", __func__, fname.c_str()); - if (mem_req) { - *mem_req = 0; - } - - auto fin = std::ifstream(fname, std::ios::binary); - 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; - } - } - - // 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_head, sizeof(hparams.n_head)); - fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer)); - fin.read((char *) &hparams.ftype, sizeof(hparams.ftype)); - - const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR; - - 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_head = %d\n", __func__, hparams.n_head); - printf("%s: n_layer = %d\n", __func__, hparams.n_layer); - printf("%s: ftype = %d\n", __func__, hparams.ftype); - printf("%s: qntvr = %d\n", __func__, qntvr); - - hparams.ftype %= GGML_QNT_VERSION_FACTOR; - } - - // load vocab - { - int32_t n_vocab = 0; - fin.read((char *) &n_vocab, sizeof(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; - std::vector buf(128); - - for (int i = 0; i < n_vocab; i++) { - uint32_t len; - fin.read((char *) &len, sizeof(len)); - - buf.resize(len); - fin.read((char *) buf.data(), len); - word.assign(buf.data(), len); - - vocab.token_to_id[word] = i; - vocab.id_to_token[i] = word; - - // if (i < 10) 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_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype)); - if (wtype == GGML_TYPE_COUNT) { - fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n", - __func__, fname.c_str(), model.hparams.ftype); - 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; - - const int head_dim = n_embd / hparams.n_head; - const int kv_heads = hparams.n_head; // 1 if MQA else hparams.n_head - const int kv_dim = kv_heads * head_dim; - - ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g - ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b - - ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype); // wte - ctx_size += n_ctx*n_embd*ggml_type_sizef(GGML_TYPE_F32); // wpe - ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype); // lm_head - - ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g - ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b - - ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_g - ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_b - - ctx_size += n_layer*((n_embd + 2*kv_dim)*n_embd*ggml_type_sizef(wtype)); // c_attn_attn_w // TODO: - ctx_size += n_layer*( (n_embd + 2*kv_dim)*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_attn_b - - ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w - ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_proj_b - - ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_fc_w - ctx_size += n_layer*( 4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b - - ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_proj_w - ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b - -// 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 += (6 + 12*n_layer)*512; // object overhead - - printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0)); - } - - if (mem_req) { - const int n_embd = model.hparams.n_embd; - const int dim_head = n_embd / model.hparams.n_head; - const int dim_kv = dim_head; - const int n_layer = model.hparams.n_layer; - - const int64_t n_mem = (int64_t)n_layer*model.hparams.n_ctx; - const int64_t n_elements = dim_kv * n_mem; - size_t kv_cache_size = 2u*n_elements*ggml_type_size(wtype) + 2_MiB; - *mem_req = ctx_size + kv_cache_size; - return false; - } - - // create the ggml context - { - struct ggml_init_params params = { - .mem_size = ctx_size, - .mem_buffer = NULL, - .no_alloc = false, - }; - - 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; - - const int head_dim = n_embd / hparams.n_head; - const int kv_heads = hparams.n_head; // 1 if MQA else hparams.n_head - const int kv_dim = kv_heads * head_dim; - - model.layers.resize(n_layer); - - model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - - model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab); - model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx); - model.lm_head = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab); - - // map by name - model.tensors["model/ln_f/g"] = model.ln_f_g; - model.tensors["model/ln_f/b"] = model.ln_f_b; - - model.tensors["model/wte"] = model.wte; - model.tensors["model/wpe"] = model.wpe; - model.tensors["model/lm_head"] = model.lm_head; - - for (int i = 0; i < n_layer; ++i) { - auto & layer = model.layers[i]; - - layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - - layer.ln_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - layer.ln_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - - layer.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd + 2*kv_dim); - layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd + 2*kv_dim); - - layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd); - layer.c_attn_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - - layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd); //TODO: 4*n_embd = config.n_inner - layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd); - - layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd); - layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - - // map by name - model.tensors["model/h" + std::to_string(i) + "/ln_1/g"] = layer.ln_1_g; - model.tensors["model/h" + std::to_string(i) + "/ln_1/b"] = layer.ln_1_b; - - model.tensors["model/h" + std::to_string(i) + "/ln_2/g"] = layer.ln_2_g; - model.tensors["model/h" + std::to_string(i) + "/ln_2/b"] = layer.ln_2_b; - - model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w; - model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b; - - model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w; - model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b; - - model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"] = layer.c_mlp_fc_w; - model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"] = layer.c_mlp_fc_b; - - model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"] = layer.c_mlp_proj_w; - model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"] = layer.c_mlp_proj_b; - } - } - - // key + value memory - { - const auto & hparams = model.hparams; - - const int n_layer = hparams.n_layer; - const int n_ctx = hparams.n_ctx; - const int64_t n_mem = n_layer*n_ctx; - - if (!kv_cache_init(hparams, model.kv_self, GGML_TYPE_F32, model.hparams.n_ctx)) { - fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__); - ggml_free(ctx); - return false; - } - const size_t memory_size = ggml_nbytes(model.kv_self.k) + ggml_nbytes(model.kv_self.v); - - printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size/1024.0/1024.0, n_mem); - } - - // load weights - { - size_t total_size = 0; - - bool has_lm_head = false; - - while (true) { - int32_t n_dims; - int32_t length; - int32_t ttype; - - fin.read(reinterpret_cast(&n_dims), sizeof(n_dims)); - fin.read(reinterpret_cast(&length), sizeof(length)); - fin.read(reinterpret_cast(&ttype), sizeof(ttype)); - - 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; - } - - auto tensor = model.tensors[name.data()]; - 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(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]); - return false; - } - if (ggml_nelements(tensor) != nelements) { - fprintf(stderr, "%s: tensor '%s' has wrong size in model file. got %d, expected %d\n", - __func__, name.data(), (int) ggml_nelements(tensor), nelements); - return false; - } - - // for debugging - if (0) { - printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor)); - } - - const size_t bpe = ggml_type_size(ggml_type(ttype)); - - 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; - } - - fin.read(reinterpret_cast(tensor->data), ggml_nbytes(tensor)); - - // GPT-2 models share the WTE tensor as the LM head - if (name == "model/wte" && has_lm_head == false) { - memcpy(model.lm_head->data, tensor->data, ggml_nbytes(tensor)); - } - - if (name == "model/lm_head") { - has_lm_head = true; - } - - total_size += ggml_nbytes(tensor); - } - - printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0); - } - - fin.close(); - - model.eval_buf.resize(1280u * 1024 * 1024); - model.scr0_buf.resize(256u * 1024 * 1024); - model.scr1_buf.resize(256u * 1024 * 1024); - - 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 -// -bool starcoder_eval( - starcoder_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 size_t head_dim = n_embd / n_head; - - struct ggml_init_params eval_ctx_params = { - .mem_size = model.eval_buf.size, - .mem_buffer = model.eval_buf.addr, - .no_alloc = false, - }; - - struct ggml_context * ctx0 = ggml_init(eval_ctx_params); - struct ggml_cgraph gf = {}; - - 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 * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - for (int i = 0; i < N; ++i) { - ((int32_t *) position->data)[i] = n_past + i; - } - - // wte + wpe - struct ggml_tensor * inpL = - ggml_add(ctx0, - ggml_get_rows(ctx0, model.wte, embd), - ggml_get_rows(ctx0, model.wpe, position)); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * cur; - - ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, }); - - // norm - { - // [ 768, N] - cur = ggml_norm(ctx0, inpL, 1e-5f); - - // cur = ln_1_g*cur + ln_1_b - // [ 768, N] - cur = ggml_add(ctx0, - ggml_mul(ctx0, - ggml_repeat(ctx0, model.layers[il].ln_1_g, cur), - cur), - ggml_repeat(ctx0, model.layers[il].ln_1_b, cur)); - } - - // attn - // [2304, 768] - model.layers[il].c_attn_attn_w - // [2304, 1] - model.layers[il].c_attn_attn_b - // [ 768, N] - cur (in) - // [2304, N] - cur (out) - // - // cur = attn_w*cur + attn_b - // [2304, N] - { - cur = ggml_mul_mat(ctx0, - model.layers[il].c_attn_attn_w, - cur); - - cur = ggml_add(ctx0, - ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur), - cur); - } - - // self-attention - { - struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd); - struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd); - struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd); - - // store key and value to memory - if (N >= 1) { - struct ggml_tensor * k = ggml_view_1d(ctx0, model.kv_self.k, N*n_embd, (ggml_element_size(model.kv_self.k)*n_embd)*(il*n_ctx + n_past)); - struct ggml_tensor * v = ggml_view_1d(ctx0, model.kv_self.v, N*n_embd, (ggml_element_size(model.kv_self.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) - // [64, N, 12] - struct ggml_tensor * Q = - ggml_permute(ctx0, - ggml_cpy(ctx0, - Qcur, - ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)), - 0, 2, 1, 3); - - // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3) - // [64, n_past + N, 12] - struct ggml_tensor * K = - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_view_1d(ctx0, model.kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.k)*n_embd), - n_embd/n_head, n_head, n_past + N), - 0, 2, 1, 3); //TODO: need to be tiled - - // GG: flash attention - //struct ggml_tensor * V = - // ggml_cpy(ctx0, - // 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), - // ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head)); - - //struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true); - - // K * Q - // [n_past + N, N, 12] - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); //TODO: check if it broadcasts - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - // [n_past + N, N, 12] - struct ggml_tensor * KQ_scaled = - ggml_scale_inplace(ctx0, - KQ, - ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head)) - ); - - // KQ_masked = mask_past(KQ_scaled) - // [n_past + N, N, 12] - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past); - - // KQ = soft_max(KQ_masked) - // [n_past + N, N, 12] - struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked); - - // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous() - // [n_past + N, 64, 12] - struct ggml_tensor * V_trans = - ggml_cpy(ctx0, - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_view_1d(ctx0, model.kv_self.v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.v)*n_embd), - n_embd/n_head, n_head, n_past + N), - 1, 2, 0, 3), - ggml_new_tensor_3d(ctx0, model.kv_self.v->type, n_past + N, n_embd/n_head, n_head)); - - // KQV = transpose(V) * KQ_soft_max - // [64, N, 12] - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - // [64, 12, N] - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - - // cur = KQV_merged.contiguous().view(n_embd, N) - // [768, N] - cur = ggml_cpy(ctx0, - KQV_merged, - ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - } - - // projection - // [ 768, 768] - model.layers[il].c_attn_proj_w - // [ 768, 1] - model.layers[il].c_attn_proj_b - // [ 768, N] - cur (in) - // [ 768, N] - cur (out) - // - // cur = proj_w*cur + proj_b - // [768, N] - { - cur = ggml_mul_mat(ctx0, - model.layers[il].c_attn_proj_w, - cur); - - cur = ggml_add(ctx0, - ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur), - cur); - } - - // add the input - cur = ggml_add(ctx0, cur, inpL); - - struct ggml_tensor * inpFF = cur; - - ggml_set_scratch(ctx0, {0, model.scr1_buf.size, model.scr1_buf.addr, }); - - // feed-forward network - { - // norm - { - cur = ggml_norm(ctx0, inpFF, 1e-5f); - - // cur = ln_2_g*cur + ln_2_b - // [ 768, N] - cur = ggml_add(ctx0, - ggml_mul(ctx0, - ggml_repeat(ctx0, model.layers[il].ln_2_g, cur), - cur), - ggml_repeat(ctx0, model.layers[il].ln_2_b, cur)); - } - - // fully connected - // [3072, 768] - model.layers[il].c_mlp_fc_w - // [3072, 1] - model.layers[il].c_mlp_fc_b - // [ 768, N] - cur (in) - // [3072, N] - cur (out) - // - // cur = fc_w*cur + fc_b - // [3072, N] - cur = ggml_mul_mat(ctx0, - model.layers[il].c_mlp_fc_w, - cur); - - cur = ggml_add(ctx0, - ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur), - cur); - - // GELU activation - // [3072, N] - cur = ggml_gelu(ctx0, cur); - - // projection - // [ 768, 3072] - model.layers[il].c_mlp_proj_w - // [ 768, 1] - model.layers[il].c_mlp_proj_b - // [3072, N] - cur (in) - // [ 768, N] - cur (out) - // - // cur = proj_w*cur + proj_b - // [768, N] - cur = ggml_mul_mat(ctx0, - model.layers[il].c_mlp_proj_w, - cur); - - cur = ggml_add(ctx0, - ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur), - cur); - } - - // input for next layer - inpL = ggml_add(ctx0, cur, inpFF); - } - - ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, }); - - // norm - { - // [ 768, N] - inpL = ggml_norm(ctx0, inpL, 1e-5f); - - // inpL = ln_f_g*inpL + ln_f_b - // [ 768, N] - inpL = ggml_add(ctx0, - ggml_mul(ctx0, - ggml_repeat(ctx0, model.ln_f_g, inpL), - inpL), - ggml_repeat(ctx0, model.ln_f_b, inpL)); - } - - ggml_set_scratch(ctx0, { 0, 0, nullptr, }); - - // inpL = WTE * inpL - // [ 768, 50257] - model.lm_head - // [ 768, N] - inpL - inpL = ggml_mul_mat(ctx0, model.lm_head, inpL); - - // logits -> probs - //inpL = ggml_soft_max_inplace(ctx0, inpL); - - // run the computation - ggml_build_forward_expand(&gf, inpL); - ggml_graph_compute_g4a(model.work_buf, &gf, n_threads); - - //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 just for 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; - } - //printf("used_mem = %zu MB\n", ggml_used_mem(ctx0)/(1024*1024)); - - ggml_free(ctx0); - - return true; -} - -#define MAX_RNG_STATE 64*1024 -size_t starcoder_get_state_size(const starcoder_model &model) { - const size_t s_rng_size = sizeof(size_t); - const size_t s_rng = MAX_RNG_STATE; - const size_t s_kv_size = sizeof(size_t); - const size_t s_kv_ntok = sizeof(int); - const size_t s_kv = model.kv_self.buf.size; - const size_t s_total = ( - + s_rng_size - + s_rng - + s_kv_size - + s_kv_ntok - + s_kv - ); - return s_total; -} - -size_t starcoder_copy_state_data(const starcoder_model &model, const std::mt19937 &rng, uint8_t *dest) -{ - uint8_t * out = dest; - // copy rng - { - std::stringstream rng_ss; - rng_ss << rng; - - const size_t rng_size = rng_ss.str().size(); - char rng_buf[MAX_RNG_STATE]; - - memset(&rng_buf[0], 0, MAX_RNG_STATE); - memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size()); - - memcpy(out, &rng_size, sizeof(rng_size)); out += sizeof(rng_size); - memcpy(out, &rng_buf[0], MAX_RNG_STATE); out += MAX_RNG_STATE; - } - - // copy kv cache - { - const size_t kv_size = model.kv_self.buf.size; - const int kv_ntok = model.kv_self.n; - - memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size); - memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok); - - if (kv_size) { - memcpy(out, model.kv_self.buf.addr, kv_size); out += kv_size; - } - } - - const size_t written = out - dest; - assert(written == starcoder_get_state_size(model)); - fflush(stdout); - return written; -} - -size_t starcoder_set_state_data(starcoder_model *model, std::mt19937 *rng, const uint8_t *src) -{ - const uint8_t * in = src; - - // set rng - { - size_t rng_size; - char rng_buf[MAX_RNG_STATE]; - - memcpy(&rng_size, in, sizeof(rng_size)); in += sizeof(rng_size); - memcpy(&rng_buf[0], in, MAX_RNG_STATE); in += MAX_RNG_STATE; - - std::stringstream rng_ss; - rng_ss.str(std::string(&rng_buf[0], rng_size)); - rng_ss >> *rng; - - assert(rng_ss.fail() == false); - } - - // set kv cache - { - size_t kv_size; - int kv_ntok; - - memcpy(&kv_size, in, sizeof(kv_size)); in += sizeof(kv_size); - memcpy(&kv_ntok, in, sizeof(kv_ntok)); in += sizeof(kv_ntok); - - if (kv_size) { - assert(model->kv_self.buf.size == kv_size); - - void * k_data = model->kv_self.k->data; // remember data pointers - void * v_data = model->kv_self.v->data; // because their value is stored in buf and overwritten by memcpy - - memcpy(model->kv_self.buf.addr, in, kv_size); in += kv_size; - - model->kv_self.k->data = k_data; // restore correct data pointers - model->kv_self.v->data = v_data; - - } - - model->kv_self.n = kv_ntok; - } - - const size_t nread = in - src; - assert(nread == starcoder_get_state_size(*model)); - fflush(stdout); - return nread; -} - -struct StarcoderPrivate { - const std::string modelPath; - bool modelLoaded; - gpt_vocab vocab; - starcoder_model *model = nullptr; - int64_t n_threads = 0; - size_t mem_per_token = 0; - std::mt19937 rng; -}; - -Starcoder::Starcoder() : d_ptr(new StarcoderPrivate) { - d_ptr->model = new starcoder_model; - d_ptr->model->ctx = nullptr; - d_ptr->modelLoaded = false; -} - -Starcoder::~Starcoder() { - if(d_ptr->model->ctx) { - ggml_free(d_ptr->model->ctx); - d_ptr->model->ctx = nullptr; - } - delete d_ptr->model; -} - -bool Starcoder::loadModel(const std::string &modelPath) -{ - std::mt19937 rng(time(NULL)); - d_ptr->rng = rng; - - // load the model - if (!starcoder_model_load(modelPath, *d_ptr->model, d_ptr->vocab, nullptr)) { - std::cerr << "STARCODER ERROR: failed to load model from " << modelPath; - return false; - } - - d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); - d_ptr->modelLoaded = true; - fflush(stdout); - return true; -} - -bool Starcoder::isModelLoaded() const -{ - return d_ptr->modelLoaded; -} - -size_t Starcoder::requiredMem(const std::string &modelPath) -{ - starcoder_model dummy_model; - gpt_vocab dummy_vocab; - size_t mem_req; - auto fin = std::ifstream(modelPath, std::ios::binary); - starcoder_model_load(modelPath, dummy_model, dummy_vocab, &mem_req); - return mem_req; -} - -size_t Starcoder::stateSize() const -{ - return starcoder_get_state_size(*d_ptr->model); -} - -size_t Starcoder::saveState(uint8_t *dest) const -{ - return starcoder_copy_state_data(*d_ptr->model, d_ptr->rng, dest); -} - -size_t Starcoder::restoreState(const uint8_t *src) -{ - return starcoder_set_state_data(d_ptr->model, &d_ptr->rng, src); -} - -void Starcoder::setThreadCount(int32_t n_threads) -{ - d_ptr->n_threads = n_threads; -} - -int32_t Starcoder::threadCount() const -{ - return d_ptr->n_threads; -} - -std::vector Starcoder::tokenize(PromptContext &, const std::string &str) const -{ - return ::gpt_tokenize(d_ptr->vocab, str); -} - -LLModel::Token Starcoder::sampleToken(PromptContext &promptCtx) const -{ - const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size()); - return gpt_sample_top_k_top_p(d_ptr->model->hparams.n_vocab, - promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks, - n_prev_toks, - promptCtx.logits, - promptCtx.top_k, promptCtx.top_p, promptCtx.temp, - promptCtx.repeat_penalty, - d_ptr->rng); -} - -std::string Starcoder::tokenToString(Token id) const -{ - return d_ptr->vocab.id_to_token[id]; -} - -bool Starcoder::evalTokens(PromptContext &ctx, const std::vector &tokens) const -{ - // determine the required inference memory per token: - static bool initialized = false; - if (!initialized) { - starcoder_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, ctx.logits, - d_ptr->mem_per_token); - initialized = true; - } - - return starcoder_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token); -} - -int32_t Starcoder::contextLength() const -{ - return d_ptr->model->hparams.n_ctx; -} - -const std::vector &Starcoder::endTokens() const -{ - static const std::vector out = { 0 }; - return out; -} - -#if defined(_WIN32) -#define DLL_EXPORT __declspec(dllexport) -#else -#define DLL_EXPORT __attribute__ ((visibility ("default"))) -#endif - -extern "C" { -DLL_EXPORT bool is_g4a_backend_model_implementation() { - return true; -} - -DLL_EXPORT const char *get_model_type() { - return modelType_; -} - -DLL_EXPORT const char *get_build_variant() { - return GGML_BUILD_VARIANT; -} - -DLL_EXPORT bool magic_match(const char *fname) { -#if 0 - uint32_t magic = 0; - f.read(reinterpret_cast(&magic), sizeof(magic)); - if (magic != STARCODER_MAGIC) { - return false; - } - starcoder_hparams hparams; - f.read(reinterpret_cast(&hparams), sizeof(hparams)); - if (!(hparams.n_vocab >= 49100 && hparams.n_vocab <= 49290)) { - return false; - } - return true; -#endif - return false; -} - -DLL_EXPORT LLModel *construct() { - return new Starcoder; -} -}