GeezerVison

GeezerVison is showing raw.githubusercontent.com — raw.githubusercontent.com

#include "ggml.h" #include "common.h" // #include "common-ggml.h" #include #include #include #include #include #include #include #include #include #include // 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 struct ggml_tensor * memory_k; struct ggml_tensor * memory_v; // struct ggml_context * ctx; std::map tensors; }; // load the model's weights from a file bool starcoder_model_load(const std::string & fname, starcoder_model & model, gpt_vocab & vocab) { printf("%s: loading model from '%s'\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 != 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)); } // 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_embd = hparams.n_embd; const int n_layer = hparams.n_layer; const int n_ctx = hparams.n_ctx; const int n_mem = n_layer*n_ctx; const int n_elements = n_embd*n_mem; model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements); model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements); const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v); printf("%s: memory size = %8.2f MB, n_mem = %d\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(); 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; 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 = {}; 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, scr0_size, scr0, }); // 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.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past)); struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past)); ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k)); ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v)); } // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3) // [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.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); //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.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, model.memory_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, scr1_size, scr1, }); // 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, scr0_size, scr0, }); // 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; } int main(int argc, char ** argv) { ggml_time_init(); const int64_t t_main_start_us = ggml_time_us(); gpt_params params; params.model = "models/bigcode/gpt_bigcode-santacoder-ggml.bin"; if (gpt_params_parse(argc, argv, params) == false) { return 1; } if (params.seed < 0) { params.seed = time(NULL); } printf("%s: seed = %d\n", __func__, params.seed); std::mt19937 rng(params.seed); if (params.prompt.empty()) { if( !isatty(STDIN_FILENO) ){ std::string line; while( std::getline(std::cin, line) ){ params.prompt = params.prompt + "\n" + line; } } else { params.prompt = gpt_random_prompt(rng); } } int64_t t_load_us = 0; gpt_vocab vocab; starcoder_model model; // load the model { const int64_t t_start_us = ggml_time_us(); if (!starcoder_model_load(params.model, model, vocab)) { fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str()); return 1; } t_load_us = ggml_time_us() - t_start_us; } int n_past = 0; int64_t t_sample_us = 0; int64_t t_predict_us = 0; std::vector logits; // tokenize the prompt std::vector embd_inp = ::gpt_tokenize(vocab, params.prompt); params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size()); printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str()); printf("%s: number of tokens in prompt = %zu, first 8 tokens: ", __func__, embd_inp.size()); for (int i = 0; i < std::min(8, (int) embd_inp.size()); i++) { printf("%d ", embd_inp[i]); } printf("\n\n"); // submit the input prompt token-by-token // this reduces the memory usage during inference, at the cost of a bit of speed at the beginning std::vector embd; // determine the required inference memory per token: size_t mem_per_token = 0; starcoder_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token); for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) { // predict if (embd.size() > 0) { const int64_t t_start_us = ggml_time_us(); if (!starcoder_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) { printf("Failed to predict\n"); return 1; } 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 = params.top_k; const float top_p = params.top_p; const float temp = params.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); } 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 (embd.size() >= params.n_batch) { break; } } i += embd.size() - 1; } // display text for (auto id : embd) { printf("%s", vocab.id_to_token[id].c_str()); } fflush(stdout); // check if model is santacoder if (model.hparams.n_layer <= 30 && embd.back() == 49152) { break; } // check if model is starcoder else if (embd.back() == 0) { //TODO: this is only for starcoder break; } } // report timing { const int64_t t_main_end_us = ggml_time_us(); printf("\n\n"); printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token); printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f); printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f); printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past); printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f); } ggml_free(model.ctx); return 0; }