diff --git a/gpt4all-backend/CMakeLists.txt b/gpt4all-backend/CMakeLists.txt index 830a3b86..385a691c 100644 --- a/gpt4all-backend/CMakeLists.txt +++ b/gpt4all-backend/CMakeLists.txt @@ -129,6 +129,10 @@ foreach(BUILD_VARIANT IN LISTS BUILD_VARIANTS) add_library(bert-${BUILD_VARIANT} SHARED bert.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h) prepare_target(bert llama-mainline) + + add_library(starcoder-${BUILD_VARIANT} SHARED + starcoder.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h) + prepare_target(starcoder llama-mainline) endif() endforeach() diff --git a/gpt4all-backend/gptj.cpp b/gpt4all-backend/gptj.cpp index 385171e1..1a6574d6 100644 --- a/gpt4all-backend/gptj.cpp +++ b/gpt4all-backend/gptj.cpp @@ -961,6 +961,11 @@ DLL_EXPORT const char *get_build_variant() { DLL_EXPORT bool magic_match(std::istream& f) { uint32_t magic = 0; f.read(reinterpret_cast(&magic), sizeof(magic)); + gptj_hparams hparams; + f.read(reinterpret_cast(&hparams), sizeof(hparams)); + if (!(hparams.n_vocab >= 50300 && hparams.n_vocab <= 50400)) { + return false; // not a gptj. + } return magic == 0x67676d6c; } diff --git a/gpt4all-backend/starcoder.cpp b/gpt4all-backend/starcoder.cpp new file mode 100644 index 00000000..322405ea --- /dev/null +++ b/gpt4all-backend/starcoder.cpp @@ -0,0 +1,1023 @@ +#define STARCODER_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE +#include "starcoder_impl.h" +#include "llama.h" +#include "llama-util.h" +#include "utils.h" +#include "llmodel_shared.h" + +#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; +}; + +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( + const 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 = {}; + 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 * 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); + + // 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); + + // 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); + + // 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 (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 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(std::istream& f) { + 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; +} + +DLL_EXPORT LLModel *construct() { + return new Starcoder; +} +} diff --git a/gpt4all-backend/starcoder_impl.h b/gpt4all-backend/starcoder_impl.h new file mode 100644 index 00000000..1a0136e7 --- /dev/null +++ b/gpt4all-backend/starcoder_impl.h @@ -0,0 +1,42 @@ +#ifndef STARCODER_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE +#error This file is NOT meant to be included outside of starcoder.cpp. Doing so is DANGEROUS. Be sure to know what you are doing before proceeding to #define STARCODER_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE +#endif +#ifndef STARCODER_H +#define STARCODER_H + +#include +#include +#include +#include +#include "llmodel.h" + +struct StarcoderPrivate; +class Starcoder : public LLModel { +public: + Starcoder(); + ~Starcoder(); + + bool supportsEmbedding() const override { return false; } + bool supportsCompletion() const override { return true; } + bool loadModel(const std::string &modelPath) override; + bool isModelLoaded() const override; + size_t requiredMem(const std::string &modelPath) override; + size_t stateSize() const override; + size_t saveState(uint8_t *dest) const override; + size_t restoreState(const uint8_t *src) override; + void setThreadCount(int32_t n_threads) override; + int32_t threadCount() const override; + +private: + std::unique_ptr d_ptr; + +protected: + std::vector tokenize(PromptContext &, const std::string&) const override; + Token sampleToken(PromptContext &ctx) const override; + std::string tokenToString(Token) const override; + bool evalTokens(PromptContext &ctx, const std::vector &tokens) const override; + int32_t contextLength() const override; + const std::vector& endTokens() const override; +}; + +#endif // STARCODER_H diff --git a/gpt4all-chat/CMakeLists.txt b/gpt4all-chat/CMakeLists.txt index c380cccd..9a5fbc21 100644 --- a/gpt4all-chat/CMakeLists.txt +++ b/gpt4all-chat/CMakeLists.txt @@ -208,6 +208,8 @@ install(TARGETS replit-mainline-metal DESTINATION lib COMPONENT ${COMPONENT_NAME endif() install(TARGETS bert-avxonly DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) install(TARGETS bert-default DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) +install(TARGETS starcoder-avxonly DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) +install(TARGETS starcoder-default DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) set(CPACK_GENERATOR "IFW") set(CPACK_VERBATIM_VARIABLES YES) diff --git a/gpt4all-chat/chatllm.cpp b/gpt4all-chat/chatllm.cpp index 80910274..f26191bc 100644 --- a/gpt4all-chat/chatllm.cpp +++ b/gpt4all-chat/chatllm.cpp @@ -15,6 +15,7 @@ #define LLAMA_INTERNAL_STATE_VERSION 0 #define FALCON_INTERNAL_STATE_VERSION 0 #define BERT_INTERNAL_STATE_VERSION 0 +#define STARCODER_INTERNAL_STATE_VERSION 0 class LLModelStore { public: @@ -266,6 +267,7 @@ bool ChatLLM::loadModel(const ModelInfo &modelInfo) case 'R': m_llModelType = LLModelType::REPLIT_; break; case 'F': m_llModelType = LLModelType::FALCON_; break; case 'B': m_llModelType = LLModelType::BERT_; break; + case 'S': m_llModelType = LLModelType::STARCODER_; break; default: { delete std::exchange(m_llModelInfo.model, nullptr); @@ -673,6 +675,7 @@ bool ChatLLM::serialize(QDataStream &stream, int version) case LLAMA_: stream << LLAMA_INTERNAL_STATE_VERSION; break; case FALCON_: stream << FALCON_INTERNAL_STATE_VERSION; break; case BERT_: stream << BERT_INTERNAL_STATE_VERSION; break; + case STARCODER_: stream << STARCODER_INTERNAL_STATE_VERSION; break; default: Q_UNREACHABLE(); } } diff --git a/gpt4all-chat/chatllm.h b/gpt4all-chat/chatllm.h index f75d24e2..be478c25 100644 --- a/gpt4all-chat/chatllm.h +++ b/gpt4all-chat/chatllm.h @@ -16,7 +16,8 @@ enum LLModelType { CHATGPT_, REPLIT_, FALCON_, - BERT_ + BERT_, + STARCODER_ }; struct LLModelInfo { diff --git a/gpt4all-chat/cmake/deploy-qt-mac.cmake.in b/gpt4all-chat/cmake/deploy-qt-mac.cmake.in index 24205990..488335aa 100644 --- a/gpt4all-chat/cmake/deploy-qt-mac.cmake.in +++ b/gpt4all-chat/cmake/deploy-qt-mac.cmake.in @@ -8,6 +8,7 @@ file(GLOB MYLLAMALIBS ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_ file(GLOB MYREPLITLIBS ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/lib/libreplit*) file(GLOB MYFALCONLLIBS ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/lib/libfalcon*) file(GLOB MYBERTLLIBS ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/lib/libbert*) +file(GLOB MYSTARCODERLLIBS ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/lib/libstarcoder*) file(GLOB MYLLMODELLIBS ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/lib/libllmodel.*) file(COPY ${MYGPTJLIBS} DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks) @@ -21,6 +22,10 @@ file(COPY ${MYFALCONLLIBS} DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks) file(COPY ${MYBERTLLIBS} DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks) +file(COPY ${MYSTARCODERLLIBS} + DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks) +file(COPY ${MYLLAMALIBS} + DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks) file(COPY ${MYLLMODELLIBS} DESTINATION ${CPACK_TEMPORARY_INSTALL_DIRECTORY}/packages/${COMPONENT_NAME_MAIN}/data/bin/gpt4all.app/Contents/Frameworks) file(COPY "${CMAKE_CURRENT_SOURCE_DIR}/icons/favicon.icns"