From 84869ff0f3558080cf394b21296b528381383010 Mon Sep 17 00:00:00 2001 From: James Ravenscroft Date: Thu, 10 Aug 2023 08:39:14 +0100 Subject: [PATCH] added support for gptneox models --- include/turbopilot/gptneox.hpp | 87 ++++ src/CMakeLists.txt | 2 + src/gptneox.cpp | 707 +++++++++++++++++++++++++++++++++ src/main.cpp | 6 +- 4 files changed, 801 insertions(+), 1 deletion(-) create mode 100644 include/turbopilot/gptneox.hpp create mode 100644 src/gptneox.cpp diff --git a/include/turbopilot/gptneox.hpp b/include/turbopilot/gptneox.hpp new file mode 100644 index 0000000..66aeb95 --- /dev/null +++ b/include/turbopilot/gptneox.hpp @@ -0,0 +1,87 @@ +#ifndef __TURBOPILOT_GPTNEOX_H +#define __TURBOPILOT_GPTNEOX_H + +#include + +#include +#include + +// default hparams (StableLM 3B) +struct gpt_neox_hparams { + int32_t n_vocab = 50257; + int32_t n_ctx = 4096; + int32_t n_embd = 4096; + int32_t n_head = 32; + int32_t n_layer = 16; + int32_t n_rot = 32; // rotary_pct * (n_embd / n_head) + int32_t par_res = 1; // 1 = true, 0 = false + int32_t ftype = 1; +}; + +struct gpt_neox_layer { + // pre normalization + struct ggml_tensor * ln_1_g; + struct ggml_tensor * ln_1_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; + + // post normalization + struct ggml_tensor * ln_2_g; + struct ggml_tensor * ln_2_b; + + // ff + 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 gpt_neox_model { + gpt_neox_hparams hparams; + + // normalization + struct ggml_tensor * ln_f_g; + struct ggml_tensor * ln_f_b; + + struct ggml_tensor * wte; // position embedding + + struct ggml_tensor * lmh_g; // language model head + //struct ggml_tensor * lmh_b; // language model bias + + std::vector layers; + + // key + value memory + struct ggml_tensor * memory_k; + struct ggml_tensor * memory_v; + + // + struct ggml_context * ctx; + std::map tensors; +}; + + +class GPTNEOXModel : public TurbopilotModel { + +public: + GPTNEOXModel(ModelConfig config, std::mt19937 &rng) : TurbopilotModel(config, rng){ + this->model = new gpt_neox_model{}; + this->vocab = new gpt_vocab{}; + } + virtual ~GPTNEOXModel(); + bool load_model(std::string path); + virtual std::stringstream predict(std::string prompt, int max_length, bool include_prompt); + +private: + gpt_neox_model *model = NULL; + gpt_vocab *vocab = NULL; + + +}; + +#endif // __TURBOPILOT_GPTNEOX_H \ No newline at end of file diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index fbba207..78f3618 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -6,11 +6,13 @@ include_directories(${Boost_INCLUDE_DIRS}) add_executable(${TURBOPILOT_TARGET} main.cpp gptj.cpp + gptneox.cpp common.cpp server.cpp starcoder.cpp ../include/turbopilot/model.hpp ../include/turbopilot/gptj.hpp + ../include/turbopilot/gptneox.hpp ../include/turbopilot/starcoder.hpp ) diff --git a/src/gptneox.cpp b/src/gptneox.cpp new file mode 100644 index 0000000..fc3fcf0 --- /dev/null +++ b/src/gptneox.cpp @@ -0,0 +1,707 @@ +#include +#include + +#include + +#include + +#include +#include + +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + + +// feed-forward network +ggml_tensor * gpt_neox_ff( + const gpt_neox_layer &layer, + ggml_context * ctx0, + ggml_tensor * inp) { + ggml_tensor * cur = ggml_norm(ctx0, inp); + + cur = ggml_add(ctx0, + ggml_mul(ctx0, + ggml_repeat(ctx0, layer.ln_2_g, cur), + cur), + ggml_repeat(ctx0, layer.ln_2_b, cur)); + + cur = ggml_mul_mat(ctx0, + layer.c_mlp_fc_w, + cur); + + cur = ggml_add(ctx0, + ggml_repeat(ctx0, layer.c_mlp_fc_b, cur), + cur); + + // GELU activation + cur = ggml_gelu(ctx0, cur); + + // projection + // cur = proj_w*cur + proj_b + cur = ggml_mul_mat(ctx0, + layer.c_mlp_proj_w, + cur); + + cur = ggml_add(ctx0, + ggml_repeat(ctx0, layer.c_mlp_proj_b, cur), + cur); + return cur; +} + + +// 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 gpt_neox_eval( + const gpt_neox_model & model, + const int n_threads, + const int n_past, + const std::vector & embd_inp, + std::vector & embd_w, + size_t & mem_per_token) { + const int N = embd_inp.size(); + + const auto & hparams = model.hparams; + + const int n_embd = hparams.n_embd; + const int n_layer = hparams.n_layer; + const int n_ctx = hparams.n_ctx; + const int n_head = hparams.n_head; + const int n_vocab = hparams.n_vocab; + const int n_rot = hparams.n_rot; + + static size_t buf_size = 256u*1024*1024; + static void * buf = malloc(buf_size); + + // use 2 scratch buffers + // TODO: very hacky solution - reimplement in a more elegant way + static size_t scr0_size = 256u*1024*1024; + static void * scr0 = malloc(scr0_size); + + static size_t scr1_size = 256u*1024*1024; + static void * scr1 = malloc(scr1_size); + + if (mem_per_token > 0 && mem_per_token*N > buf_size) { + const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead + //printf("\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, + /*.no_alloc =*/ false, + }; + + struct ggml_context * ctx0 = ggml_init(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)); + + // wte + struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * cur; + + ggml_set_scratch(ctx0, { 0, scr0_size, scr0, }); + + // self-attention + { + { + cur = ggml_norm(ctx0, inpL); + + 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)); + } + + // compute QKV + { + 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); + } + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 0*sizeof(float)*n_embd/n_head)); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 1*sizeof(float)*n_embd/n_head)); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 2*sizeof(float)*n_embd/n_head)); + + // using mode = 2 for GPT-NeoX mode + Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, n_rot, 2, 0); + Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, n_rot, 2, 0); + + // store key and value to memory + { + Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd, N)); + + 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_2d(ctx0, model.memory_v, N, n_embd, + ( n_ctx)*ggml_element_size(model.memory_v), + (il*n_ctx)*ggml_element_size(model.memory_v)*n_embd + n_past*ggml_element_size(model.memory_v)); + + 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, + Qcur, + 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_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), + 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_inplace(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_inplace(ctx0, KQ_scaled, n_past); + + // KQ = soft_max(KQ_masked) + 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() + struct ggml_tensor * V = + ggml_view_3d(ctx0, model.memory_v, + n_past + N, n_embd/n_head, n_head, + n_ctx*ggml_element_size(model.memory_v), + n_ctx*ggml_element_size(model.memory_v)*n_embd/n_head, + il*n_ctx*ggml_element_size(model.memory_v)*n_embd); + + // KQV = transpose(V) * KQ_soft_max + struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, 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 + { + 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); + } + } + + ggml_set_scratch(ctx0, { 0, scr1_size, scr1, }); + + if (hparams.par_res == 0) { + struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpL); + + cur = gpt_neox_ff(model.layers[il], ctx0, inpFF); + + // input for next layer + inpL = ggml_add(ctx0, cur, inpFF); + } else { + struct ggml_tensor * inpFF = cur; + + // this is independent of the self-attention result, so it could be done in parallel to the self-attention + // note here we pass inpL instead of cur + cur = gpt_neox_ff(model.layers[il], ctx0, inpL); + + // layer input + FF + cur = ggml_add(ctx0, cur, inpFF); + + // input for next layer + inpL = ggml_add(ctx0, cur, inpL); + } + } + + ggml_set_scratch(ctx0, { 0, scr0_size, scr0, }); + + // norm + { + inpL = ggml_norm(ctx0, inpL); + + // inpL = ln_f_g*inpL + ln_f_b + 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, }); + + // lm_head + { + inpL = ggml_mul_mat(ctx0, model.lmh_g, inpL); + + //inpL = ggml_add(ctx0, + // ggml_repeat(ctx0, model.lmh_b, inpL), + // inpL); + } + + // logits -> probs + //inpL = ggml_soft_max_inplace(ctx0, inpL); + + // run the computation + ggml_build_forward_expand(&gf, inpL); + ggml_graph_compute_with_ctx(ctx0, &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 for just the last token + embd_w.resize(n_vocab); + memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab); + + if (mem_per_token == 0) { + mem_per_token = ggml_used_mem(ctx0)/N; + } + //printf("used_mem = %zu\n", ggml_used_mem(ctx0)); + + ggml_free(ctx0); + + return true; +} + + +GPTNEOXModel::~GPTNEOXModel(){ + ggml_free(model->ctx); + free(model); + free(vocab); +} + +bool GPTNEOXModel::load_model(std::string fname) { + printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str()); + + 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 != GGML_FILE_MAGIC) { + 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.n_rot, sizeof(hparams.n_rot)); + fin.read((char *) &hparams.par_res, sizeof(hparams.par_res)); + 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: n_rot = %d\n", __func__, hparams.n_rot); + printf("%s: par_res = %d\n", __func__, hparams.par_res); + printf("%s: ftype = %d\n", __func__, hparams.ftype); + printf("%s: qntvr = %d\n", __func__, qntvr); + + hparams.ftype %= GGML_QNT_VERSION_FACTOR; + } + + // load vocab + { + const int32_t n_vocab = model->hparams.n_vocab; + + 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; + } + } + + // 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 size_t n_embd = hparams.n_embd; + const size_t n_layer = hparams.n_layer; + const size_t n_ctx = hparams.n_ctx; + const size_t n_vocab = hparams.n_vocab; + + 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_embd*n_vocab*ggml_type_sizef(wtype); // wte + + ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // lmh_g + //ctx_size += n_vocab*ggml_type_sizef(GGML_TYPE_F32); // lmh_b + + 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*(3*n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_attn_w + ctx_size += n_layer*( 3*n_embd*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*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_proj_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*(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 + 16*n_layer)*1024; // object overhead + + printf("%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, + /*.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_vocab = hparams.n_vocab; + + model->layers.resize(n_layer); + + model->wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab); + + 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->lmh_g = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab); + //model->lmh_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_vocab); + + // map by name + model->tensors["gpt_neox.embed_in.weight"] = model->wte; + + model->tensors["gpt_neox.final_layer_norm.weight"] = model->ln_f_g; + model->tensors["gpt_neox.final_layer_norm.bias"] = model->ln_f_b; + + model->tensors["embed_out.weight"] = model->lmh_g; + //model->tensors["lm_head.bias"] = model->lmh_b; + + 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.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 3*n_embd); + layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd); + + 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.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_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd); + 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["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.weight"] = layer.ln_1_g; + model->tensors["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.bias"] = layer.ln_1_b; + + model->tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.weight"] = layer.c_attn_attn_w; + model->tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.bias"] = layer.c_attn_attn_b; + + model->tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.weight"] = layer.c_attn_proj_w; + model->tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.bias"] = layer.c_attn_proj_b; + + model->tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.weight"] = layer.ln_2_g; + model->tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.bias"] = layer.ln_2_b; + + model->tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.weight"] = layer.c_mlp_fc_w; + model->tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.bias"] = layer.c_mlp_fc_b; + + model->tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.weight"] = layer.c_mlp_proj_w; + model->tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.bias"] = layer.c_mlp_proj_b; + } + } + + // 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 int64_t n_mem = n_layer*n_ctx; + const int64_t n_elements = n_embd*n_mem; + + model->memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements); + model->memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements); + + const size_t memory_size = ggml_nbytes(model->memory_k) + ggml_nbytes(model->memory_v); + + printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size/1024.0/1024.0, n_mem); + } + + // load weights + { + int n_tensors = 0; + size_t total_size = 0; + + printf("%s: ", __func__); + + 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 (ggml_nelements(tensor) != nelements) { + fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data()); + return false; + } + + if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) { + fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%5d, %5d], expected [%5d, %5d]\n", + __func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]); + 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)); + + total_size += ggml_nbytes(tensor); + if (++n_tensors % 8 == 0) { + printf("."); + fflush(stdout); + } + } + + printf(" done\n"); + + printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors); + } + + fin.close(); + + return true; +} + +std::stringstream GPTNEOXModel::predict(std::string prompt, int max_length, bool include_prompt) { + + std::stringstream result; + // tokenize the prompt + std::vector embd_inp = ::gpt_tokenize((*vocab), prompt); + + auto END_TOKEN_ID = vocab->token_to_id["<|endoftext|>"]; + + int n_past = 0; + + int64_t t_sample_us = 0; + int64_t t_predict_us = 0; + + int n_predict = std::min(max_length, model->hparams.n_ctx - (int) embd_inp.size()); + + spdlog::debug("{}: number of tokens in prompt = {}", __func__, embd_inp.size()); + + std::vector embd; + + // determine the required inference memory per token: + size_t mem_per_token = 0; + + std::vector logits; + + gpt_neox_eval((*model), config.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token); + + for (int i = embd.size(); i < embd_inp.size() + n_predict; i++) { + // predict + if (embd.size() > 0) { + const int64_t t_start_us = ggml_time_us(); + + if (!gpt_neox_eval((*model), config.n_threads, n_past, embd, logits, mem_per_token)) { + throw std::runtime_error("Failed to predict"); + } + + t_predict_us += ggml_time_us() - t_start_us; + } + + n_past += embd.size(); + embd.clear(); + + if (i >= embd_inp.size()) { + // sample next token + const int top_k = config.top_k; + const float top_p = config.top_p; + const float temp = config.temp; + + const int n_vocab = model->hparams.n_vocab; + + gpt_vocab::id id = 0; + + { + const int64_t t_start_sample_us = ggml_time_us(); + + id = gpt_sample_top_k_top_p((*vocab), logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng); + + t_sample_us += ggml_time_us() - t_start_sample_us; + } + + // add it to the context + embd.push_back(id); + + // do not actually add endoftext char to the end string + if(id != END_TOKEN_ID){ + result << vocab->id_to_token[id].c_str(); + } + + } else { + // if here, it means we are still processing the input prompt + for (int k = i; k < embd_inp.size(); k++) { + embd.push_back(embd_inp[k]); + + if(include_prompt){ + result << vocab->id_to_token[embd_inp[k]].c_str(); + } + + if (embd.size() > config.n_batch) { + break; + } + } + i += embd.size() - 1; + } + + + // end of text token + //if (embd.back() == 50256) { + if(embd.back() == END_TOKEN_ID){ + break; + } + } + + return result; +} diff --git a/src/main.cpp b/src/main.cpp index 939d820..d2ce02e 100644 --- a/src/main.cpp +++ b/src/main.cpp @@ -11,6 +11,7 @@ #include "turbopilot/model.hpp" #include "turbopilot/starcoder.hpp" #include "turbopilot/gptj.hpp" +#include "turbopilot/gptneox.hpp" #include "turbopilot/server.hpp" int main(int argc, char **argv) @@ -23,7 +24,7 @@ int main(int argc, char **argv) .required(); program.add_argument("-m", "--model-type") - .help("The type of model to load. Can be codegen,starcoder,wizardcoder") + .help("The type of model to load. Can be codegen,starcoder,wizardcoder,stablecode") .default_value("codegen"); program.add_argument("-t", "--threads") @@ -76,6 +77,9 @@ int main(int argc, char **argv) }else if(model_type.compare("starcoder") == 0 || model_type.compare("wizardcoder") == 0){ spdlog::info("Initializing Starcoder/Wizardcoder type model for '{}' model type", model_type); model = new StarcoderModel(config, rng); + }else if(model_type.compare("stablecode") == 0){ + spdlog::info("Initializing StableLM type model for '{}' model type", model_type); + model = new GPTNEOXModel(config, rng); }else{ spdlog::error("Invalid model type: {}", model_type); }