#include "ggml.h" #include "ggml-alloc.h" #include "ggml-backend.h" #include "common.h" #include "train.h" #include "llama.h" #include #include #include #include #include #include #include #include #include #include #include #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data #endif struct my_llama_hparams { uint32_t n_vocab = 32000; uint32_t n_ctx = 512; uint32_t n_embd = 4096; uint32_t n_head = 32; uint32_t n_layer = 32; uint32_t n_rot = 64; uint32_t n_ff = 11008; // float f_norm_eps = 1e-5f; // falcon float f_norm_rms_eps = 1e-5f; // llama float rope_freq_base = 10000.0f; float rope_freq_scale = 1.0f; }; struct my_llama_layer { // normalization struct ggml_tensor * attention_norm; // attention struct ggml_tensor * wq; struct ggml_tensor * wk; struct ggml_tensor * wv; struct ggml_tensor * wo; // normalization struct ggml_tensor * ffn_norm; // ff struct ggml_tensor * w1; struct ggml_tensor * w2; struct ggml_tensor * w3; }; struct my_llama_model { struct ggml_context * ctx = NULL; ggml_backend_buffer_t data = NULL; my_llama_hparams hparams; struct ggml_tensor * tok_embeddings; struct ggml_tensor * norm; struct ggml_tensor * output; std::vector layers; }; // gguf constants (sync with gguf.py) static const char * LLM_KV_TRAINING_TYPE_TRAIN_MODEL = "train_model"; static const char * LLM_KV_TRAINING_TYPE = "training.type"; static const char * LLM_KV_GENERAL_ARCHITECTURE = "general.architecture"; static const char * LLM_KV_GENERAL_FILE_TYPE = "general.file_type"; static const char * LLM_KV_CONTEXT_LENGTH = "%s.context_length"; static const char * LLM_KV_EMBEDDING_LENGTH = "%s.embedding_length"; static const char * LLM_KV_BLOCK_COUNT = "%s.block_count"; static const char * LLM_KV_FEED_FORWARD_LENGTH = "%s.feed_forward_length"; static const char * LLM_KV_ATTENTION_HEAD_COUNT = "%s.attention.head_count"; static const char * LLM_KV_ATTENTION_LAYERNORM_RMS_EPS = "%s.attention.layer_norm_rms_epsilon"; static const char * LLM_KV_ROPE_DIMENSION_COUNT = "%s.rope.dimension_count"; static const char * LLM_KV_ROPE_FREQ_BASE = "%s.rope.freq_base"; // TODO load in llama.cpp static const char * LLM_KV_ROPE_SCALE_LINEAR = "%s.rope.scale_linear"; static const char * LLM_KV_TOKENIZER_MODEL = "tokenizer.ggml.model"; static const char * LLM_KV_TOKENIZER_LIST = "tokenizer.ggml.tokens"; static const char * LLM_KV_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type"; static const char * LLM_KV_TOKENIZER_SCORES = "tokenizer.ggml.scores"; static const char * LLM_KV_TOKENIZER_MERGES = "tokenizer.ggml.merges"; static const char * LLM_KV_TOKENIZER_BOS_ID = "tokenizer.ggml.bos_token_id"; static const char * LLM_KV_TOKENIZER_EOS_ID = "tokenizer.ggml.eos_token_id"; static const char * LLM_KV_TOKENIZER_UNK_ID = "tokenizer.ggml.unknown_token_id"; static const char * LLM_KV_TOKENIZER_SEP_ID = "tokenizer.ggml.seperator_token_id"; static const char * LLM_KV_TOKENIZER_PAD_ID = "tokenizer.ggml.padding_token_id"; static const char * LLM_TENSOR_TOKEN_EMBD = "token_embd"; static const char * LLM_TENSOR_OUTPUT_NORM = "output_norm"; static const char * LLM_TENSOR_OUTPUT = "output"; static const char * LLM_TENSOR_ATTN_NORM = "blk.%d.attn_norm"; static const char * LLM_TENSOR_ATTN_Q = "blk.%d.attn_q"; static const char * LLM_TENSOR_ATTN_K = "blk.%d.attn_k"; static const char * LLM_TENSOR_ATTN_V = "blk.%d.attn_v"; static const char * LLM_TENSOR_ATTN_OUT = "blk.%d.attn_output"; static const char * LLM_TENSOR_FFN_NORM = "blk.%d.ffn_norm"; static const char * LLM_TENSOR_FFN_GATE = "blk.%d.ffn_gate"; static const char * LLM_TENSOR_FFN_DOWN = "blk.%d.ffn_down"; static const char * LLM_TENSOR_FFN_UP = "blk.%d.ffn_up"; static void print_params(struct my_llama_hparams * params) { printf("%s: n_vocab: %d\n", __func__, params->n_vocab); printf("%s: n_ctx: %d\n", __func__, params->n_ctx); printf("%s: n_embd: %d\n", __func__, params->n_embd); printf("%s: n_head: %d\n", __func__, params->n_head); printf("%s: n_ff: %d\n", __func__, params->n_ff); printf("%s: n_layer: %d\n", __func__, params->n_layer); printf("%s: n_rot: %d\n", __func__, params->n_rot); } static void set_param_model(struct my_llama_model * model) { const auto& hparams = model->hparams; const uint32_t n_layer = hparams.n_layer; struct ggml_context* ctx = model->ctx; ggml_set_param(ctx, model->tok_embeddings); ggml_set_param(ctx, model->norm); ggml_set_param(ctx, model->output); for (uint32_t i = 0; i < n_layer; ++i) { auto & layer = model->layers[i]; ggml_set_param(ctx, layer.attention_norm); ggml_set_param(ctx, layer.wq); ggml_set_param(ctx, layer.wk); ggml_set_param(ctx, layer.wv); ggml_set_param(ctx, layer.wo); ggml_set_param(ctx, layer.ffn_norm); ggml_set_param(ctx, layer.w1); ggml_set_param(ctx, layer.w2); ggml_set_param(ctx, layer.w3); } } static void init_model(struct my_llama_model * model) { const auto & hparams = model->hparams; const uint32_t n_embd = hparams.n_embd; const uint32_t n_layer = hparams.n_layer; const uint32_t n_vocab = hparams.n_vocab; const uint32_t n_ff = hparams.n_ff; std::vector tn_buf; tn_buf.resize(GGML_MAX_NAME); auto tn = [&tn_buf](const char * key) -> const char * { snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key); return tn_buf.data(); }; auto tni = [&tn_buf](const char * key, int bid) -> const char * { snprintf(tn_buf.data(), tn_buf.size(), key, bid); std::string s = tn_buf.data(); snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str()); return tn_buf.data(); }; // context for model tensors without their data struct ggml_init_params ctx_model_params; ctx_model_params.mem_size = ggml_tensor_overhead()*2*(6 + n_layer*18); ctx_model_params.mem_buffer = NULL; ctx_model_params.no_alloc = true; struct ggml_context * ctx = ggml_init(ctx_model_params); model->ctx = ctx; model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); ggml_set_name(model->tok_embeddings, tn(LLM_TENSOR_TOKEN_EMBD)); ggml_set_name(model->norm, tn(LLM_TENSOR_OUTPUT_NORM)); ggml_set_name(model->output, tn(LLM_TENSOR_OUTPUT)); model->layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { auto & layer = model->layers[i]; layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); ggml_set_name(layer.attention_norm, tni(LLM_TENSOR_ATTN_NORM, i)); ggml_set_name(layer.wq, tni(LLM_TENSOR_ATTN_Q, i)); ggml_set_name(layer.wk, tni(LLM_TENSOR_ATTN_K, i)); ggml_set_name(layer.wv, tni(LLM_TENSOR_ATTN_V, i)); ggml_set_name(layer.wo, tni(LLM_TENSOR_ATTN_OUT, i)); ggml_set_name(layer.ffn_norm, tni(LLM_TENSOR_FFN_NORM, i)); ggml_set_name(layer.w1, tni(LLM_TENSOR_FFN_GATE, i)); ggml_set_name(layer.w2, tni(LLM_TENSOR_FFN_DOWN, i)); ggml_set_name(layer.w3, tni(LLM_TENSOR_FFN_UP, i)); } set_param_model(model); // allocate data model->data = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cpu_buffer_type()); } static void randomize_model(struct my_llama_model * model, int seed, float mean, float std, float min, float max) { const auto & hparams = model->hparams; const uint32_t n_layer = hparams.n_layer; struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max); randomize_tensor_normal(model->tok_embeddings, rnd); randomize_tensor_normal(model->norm, rnd); randomize_tensor_normal(model->output, rnd); for (uint32_t i = 0; i < n_layer; ++i) { auto & layer = model->layers[i]; randomize_tensor_normal(layer.attention_norm, rnd); randomize_tensor_normal(layer.wq, rnd); randomize_tensor_normal(layer.wk, rnd); randomize_tensor_normal(layer.wv, rnd); randomize_tensor_normal(layer.wo, rnd); randomize_tensor_normal(layer.ffn_norm, rnd); randomize_tensor_normal(layer.w1, rnd); randomize_tensor_normal(layer.w2, rnd); randomize_tensor_normal(layer.w3, rnd); } free_random_normal_distribution(rnd); } static struct ggml_tensor * llama_build_train_graphs( struct my_llama_model * model, ggml_gallocr_t alloc, struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, struct ggml_cgraph * gb_tmp, struct ggml_tensor * * logits, struct ggml_tensor * tokens_input, struct ggml_tensor * targets, const int n_tokens, const int n_batch, const bool enable_flash_attn, const bool enable_checkpointing, const bool measure_only) { ggml_set_scratch(ctx, { 0, 0, nullptr, }); const int n_past = 0; const int N = n_tokens; const auto & hparams = model->hparams; const int n_ctx = hparams.n_ctx; const int n_vocab = hparams.n_vocab; const int n_embd = hparams.n_embd; const int n_layer = hparams.n_layer; const int n_head = hparams.n_head; const int n_rot = hparams.n_rot; const int n_ff = hparams.n_ff; const float f_norm_rms_eps = hparams.f_norm_rms_eps; const float rope_freq_base = hparams.rope_freq_base; const float rope_freq_scale = hparams.rope_freq_scale; auto set_name = [](struct ggml_tensor * t, const char * n) { ggml_set_name(t, n); if (t->grad) { ggml_format_name(t->grad, "%s->grad", n); } }; // KQ_pos - contains the positions struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N); ggml_set_input(KQ_pos); // rope has so much parameters that we make a custom function for it auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale] (struct ggml_tensor * t) -> struct ggml_tensor * { // not capturing these, to silcence warnings const int rope_mode = 0; return ggml_rope_custom( ctx, t, KQ_pos, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f ); }; set_name(tokens_input, "tokens_input"); set_name(targets, "targets"); GGML_ASSERT(tokens_input->type == GGML_TYPE_I32); struct ggml_tensor * t00 = ggml_reshape_1d(ctx, tokens_input, N*n_batch); set_name(t00, "t00"); assert_shape_1d(t00, N*n_batch); struct ggml_tensor * t01 = ggml_get_rows(ctx, model->tok_embeddings, t00); set_name(t01, "t01"); assert_shape_2d(t01, n_embd, N*n_batch); struct ggml_tensor * cur = t01; std::vector checkpoints; checkpoints.push_back(tokens_input); checkpoints.push_back(targets); checkpoints.push_back(t00); checkpoints.push_back(t01); const float kv_scale = 1.0f/sqrtf(float(n_embd)/n_head); for (int il = 0; il < n_layer; ++il) { struct my_llama_layer & layer = model->layers[il]; struct ggml_tensor * t02 = ggml_rms_norm (ctx, cur, f_norm_rms_eps); set_name(t02, "t02"); assert_shape_2d(t02, n_embd, N*n_batch); struct ggml_tensor * t03 = ggml_repeat (ctx, layer.attention_norm, t02); set_name(t03, "t03"); assert_shape_2d(t03, n_embd, N*n_batch); struct ggml_tensor * t04 = ggml_mul (ctx, t03, t02); set_name(t04, "t04"); assert_shape_2d(t04, n_embd, N*n_batch); struct ggml_tensor * t05 = ggml_mul_mat (ctx, layer.wq, t04); set_name(t05, "t05"); assert_shape_2d(t05, n_embd, N*n_batch); struct ggml_tensor * t06 = ggml_reshape_4d (ctx, t05, n_embd/n_head, n_head, N, n_batch); set_name(t06, "t06"); assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch); struct ggml_tensor * t07 = rope (t06); set_name(t07, "t07"); assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch); struct ggml_tensor * t08 = ggml_mul_mat (ctx, layer.wk, t04); set_name(t08, "t08"); assert_shape_2d(t08, n_embd, N*n_batch); struct ggml_tensor * t09 = ggml_reshape_4d (ctx, t08, n_embd/n_head, n_head, N, n_batch); set_name(t09, "t09"); assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch); struct ggml_tensor * t10 = rope (t09); set_name(t10, "t10"); assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch); struct ggml_tensor * t11 = ggml_mul_mat (ctx, t04, layer.wv); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd); struct ggml_tensor * t12 = ggml_reshape_4d (ctx, t11, N, n_batch, n_embd/n_head, n_head); set_name(t12, "t12"); assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head); struct ggml_tensor * t13 = ggml_permute (ctx, t07, 0, 2, 1, 3); set_name(t13, "t13"); assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch); struct ggml_tensor * t14 = ggml_permute (ctx, t10, 0, 2, 1, 3); set_name(t14, "t14"); assert_shape_4d(t14, n_embd/n_head, N, n_head, n_batch); struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch); struct ggml_tensor * t16; if (enable_flash_attn) { t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); } else { struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch); struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch); struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past); set_name(t16_2, "t16_2"); assert_shape_4d(t16_2, N, N, n_head, n_batch); struct ggml_tensor * t16_3 = ggml_soft_max_inplace (ctx, t16_2); set_name(t16_3, "t16_3"); assert_shape_4d(t16_3, N, N, n_head, n_batch); t16 = ggml_mul_mat(ctx, t15, t16_3); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); } struct ggml_tensor * t17 = ggml_permute (ctx, t16, 0, 2, 1, 3); set_name(t17, "t17"); assert_shape_4d(t17, n_embd/n_head, n_head, N, n_batch); struct ggml_tensor * t18 = ggml_cont (ctx, t17); set_name(t18, "t18"); assert_shape_4d(t18, n_embd/n_head, n_head, N, n_batch); struct ggml_tensor * t19 = ggml_reshape_2d (ctx, t18, n_embd, N*n_batch); set_name(t19, "t19"); assert_shape_2d(t19, n_embd, N*n_batch); struct ggml_tensor * t20 = ggml_mul_mat (ctx, layer.wo, t19); set_name(t20, "t20"); assert_shape_2d(t20, n_embd, N*n_batch); struct ggml_tensor * t21 = ggml_add (ctx, t20, cur); set_name(t21, "t21"); assert_shape_2d(t21, n_embd, N*n_batch); struct ggml_tensor * t22 = ggml_rms_norm (ctx, t21, f_norm_rms_eps); set_name(t22, "t22"); assert_shape_2d(t22, n_embd, N*n_batch); struct ggml_tensor * t23 = ggml_repeat (ctx, layer.ffn_norm, t22); set_name(t23, "t23"); assert_shape_2d(t23, n_embd, N*n_batch); struct ggml_tensor * t24 = ggml_mul (ctx, t23, t22); set_name(t24, "t24"); assert_shape_2d(t24, n_embd, N*n_batch); struct ggml_tensor * t25 = ggml_mul_mat (ctx, layer.w3, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch); struct ggml_tensor * t26 = ggml_mul_mat (ctx, layer.w1, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch); struct ggml_tensor * t27 = ggml_silu (ctx, t26); set_name(t27, "t27"); assert_shape_2d(t27, n_ff, N*n_batch); struct ggml_tensor * t28 = ggml_mul (ctx, t27, t25); set_name(t28, "t28"); assert_shape_2d(t28, n_ff, N*n_batch); struct ggml_tensor * t29 = ggml_mul_mat (ctx, layer.w2, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch); struct ggml_tensor * t30 = ggml_add (ctx, t29, t21); set_name(t30, "t30"); assert_shape_2d(t30, n_embd, N*n_batch); cur = t30; checkpoints.push_back(cur); } struct ggml_tensor * t31 = ggml_rms_norm (ctx, cur, f_norm_rms_eps); set_name(t31, "t31"); assert_shape_2d(t31, n_embd, N*n_batch); struct ggml_tensor * t32 = ggml_repeat (ctx, model->norm, t31); set_name(t32, "t32"); assert_shape_2d(t32, n_embd, N*n_batch); struct ggml_tensor * t33 = ggml_mul (ctx, t32, t31); set_name(t33, "t33"); assert_shape_2d(t33, n_embd, N*n_batch); struct ggml_tensor * t34 = ggml_mul_mat (ctx, model->output, t33); set_name(t34, "t34"); assert_shape_2d(t34, n_vocab, N*n_batch); struct ggml_tensor * t35 = ggml_reshape_3d (ctx, t34, n_vocab, N, n_batch); set_name(t35, "t35"); assert_shape_3d(t35, n_vocab, N, n_batch); struct ggml_tensor * t36 = ggml_cross_entropy_loss(ctx, t35, targets); set_name(t36, "t36"); assert_shape_1d(t36, 1); checkpoints.push_back(t31); checkpoints.push_back(t32); checkpoints.push_back(t33); checkpoints.push_back(t34); checkpoints.push_back(t35); checkpoints.push_back(t36); ggml_build_forward_expand(gf, t36); if (enable_checkpointing) { ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size()); } else { ggml_graph_cpy(gf, gb); ggml_build_backward_expand(ctx, gf, gb, true); } if (alloc) { // make sure some tensors are not reallocated by inserting new temporary nodes depending on them int n_leafs_before = gb->n_leafs; int n_nodes_before = gb->n_nodes; // output tensors ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, 1.0f)); ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, 1.0f)); // input gradient ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f)); // KQ_pos ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f)); GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL); ggml_set_input(t36->grad); // allocating checkpoints in one block to reduce memory fragmentation // note: they will be freed in reverse order for (int i = 0; i < (int) checkpoints.size(); ++i) { if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) { ggml_set_input(checkpoints[i]); } } //int n_leafs_after = gb->n_leafs; //int n_nodes_after = gb->n_nodes; if (measure_only) { // FIXME: will still allocate ggml_gallocr_reserve(alloc, gb); } else { ggml_gallocr_alloc_graph(alloc, gb); if (!measure_only) { int * data = (int *) KQ_pos->data; for (int i = 0; i < N; ++i) { data[i] = n_past + i; } } } // remove the additional nodes and leafs for (int i = n_leafs_before; i < gb->n_leafs; ++i) { gb->leafs[i] = NULL; } for (int i = n_nodes_before; i < gb->n_nodes; ++i) { gb->nodes[i] = NULL; } gb->n_leafs = n_leafs_before; gb->n_nodes = n_nodes_before; } *logits = t35; return t36; } #define GGUF_GET_KEY(ctx, dst, func, type, req, key) \ do { \ const std::string skey(key); \ const int kid = gguf_find_key(ctx, skey.c_str()); \ if (kid >= 0) { \ enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \ if (ktype != (type)) { \ die_fmt("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype)); \ } \ (dst) = func(ctx, kid); \ } else if (req) { \ die_fmt("key not found in model: %s", skey.c_str()); \ } \ } while (0) static void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model) { // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read std::string arch; std::vector keybuf; keybuf.resize(512); auto kv = [&arch, &keybuf](const char * key) -> const char * { snprintf(keybuf.data(), keybuf.size(), key, arch.c_str()); return keybuf.data(); }; std::vector tn_buf; tn_buf.resize(GGML_MAX_NAME); auto tn = [&tn_buf](const char * key) -> const char * { snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key); return tn_buf.data(); }; auto tni = [&tn_buf](const char * key, int bid) -> const char * { snprintf(tn_buf.data(), tn_buf.size(), key, bid); std::string s = tn_buf.data(); snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str()); return tn_buf.data(); }; GGUF_GET_KEY(fctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE); GGML_ASSERT(arch == "llama"); uint32_t ftype_u; GGUF_GET_KEY(fctx, ftype_u, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_GENERAL_FILE_TYPE); GGML_ASSERT((enum llama_ftype) ftype_u == LLAMA_FTYPE_ALL_F32); // n_ctx was not saved in earlier checkpoint file versions, so we make it optional here GGUF_GET_KEY(fctx, model->hparams.n_ctx, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_CONTEXT_LENGTH)); GGUF_GET_KEY(fctx, model->hparams.n_embd, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH)); GGUF_GET_KEY(fctx, model->hparams.n_ff, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH)); GGUF_GET_KEY(fctx, model->hparams.n_head, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT)); GGUF_GET_KEY(fctx, model->hparams.n_layer, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT)); model->hparams.n_rot = model->hparams.n_embd / model->hparams.n_head; GGUF_GET_KEY(fctx, model->hparams.n_rot, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT)); float rope_freq_scale = 1.0f; GGUF_GET_KEY(fctx, model->hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS)); GGUF_GET_KEY(fctx, model->hparams.rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE)); GGUF_GET_KEY(fctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR)); if (rope_freq_scale != 1.0f) { model->hparams.rope_freq_scale = 1.0f / rope_freq_scale; } init_model(model); copy_tensor_by_name(model->tok_embeddings, f_ggml_ctx, tn(LLM_TENSOR_TOKEN_EMBD)); copy_tensor_by_name(model->norm, f_ggml_ctx, tn(LLM_TENSOR_OUTPUT_NORM)); copy_tensor_by_name(model->output, f_ggml_ctx, tn(LLM_TENSOR_OUTPUT)); for (uint32_t i = 0; i < model->hparams.n_layer; ++i) { auto & layer = model->layers[i]; copy_tensor_by_name(layer.attention_norm, f_ggml_ctx, tni(LLM_TENSOR_ATTN_NORM, i)); copy_tensor_by_name(layer.wq, f_ggml_ctx, tni(LLM_TENSOR_ATTN_Q, i)); copy_tensor_by_name(layer.wk, f_ggml_ctx, tni(LLM_TENSOR_ATTN_K, i)); copy_tensor_by_name(layer.wv, f_ggml_ctx, tni(LLM_TENSOR_ATTN_V, i)); copy_tensor_by_name(layer.wo, f_ggml_ctx, tni(LLM_TENSOR_ATTN_OUT, i)); copy_tensor_by_name(layer.ffn_norm, f_ggml_ctx, tni(LLM_TENSOR_FFN_NORM, i)); copy_tensor_by_name(layer.w1, f_ggml_ctx, tni(LLM_TENSOR_FFN_GATE, i)); copy_tensor_by_name(layer.w2, f_ggml_ctx, tni(LLM_TENSOR_FFN_DOWN, i)); copy_tensor_by_name(layer.w3, f_ggml_ctx, tni(LLM_TENSOR_FFN_UP, i)); } } static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model) { const char * arch = "llama"; enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32; std::vector keybuf; keybuf.resize(512); auto kv = [arch, &keybuf](const char * key) -> const char * { snprintf(keybuf.data(), keybuf.size(), key, arch); return keybuf.data(); }; // set arch gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch); gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype); // set hparams gguf_set_val_u32(fctx, kv(LLM_KV_CONTEXT_LENGTH), model->hparams.n_ctx ); gguf_set_val_u32(fctx, kv(LLM_KV_EMBEDDING_LENGTH), model->hparams.n_embd ); gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH), model->hparams.n_ff ); gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT), model->hparams.n_head ); gguf_set_val_u32(fctx, kv(LLM_KV_BLOCK_COUNT), model->hparams.n_layer ); gguf_set_val_u32(fctx, kv(LLM_KV_ROPE_DIMENSION_COUNT), model->hparams.n_rot ); gguf_set_val_f32(fctx, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS), model->hparams.f_norm_rms_eps ); gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_FREQ_BASE), model->hparams.rope_freq_base ); // TODO load in llama.cpp gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_SCALE_LINEAR), 1.0f / model->hparams.rope_freq_scale ); // set vocab by copying from vocab_model gguf file { struct gguf_init_params params = { /*.no_alloc = */ false, /*.ctx = */ NULL, }; struct gguf_context * vctx = gguf_init_from_file(fn_vocab_model, params); const int token_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_LIST)); if (token_idx == -1) { die("cannot find tokenizer vocab in model file"); } const uint32_t n_vocab = gguf_get_arr_n(vctx, token_idx); const int score_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_SCORES)); if (score_idx == -1) { die("cannot find tokenizer scores in model file"); } const float * scores = (const float * ) gguf_get_arr_data(vctx, score_idx); const int toktype_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE)); if (toktype_idx == -1) { die("cannot find token type list in GGUF file"); } const int * toktypes = (const int * ) gguf_get_arr_data(vctx, toktype_idx); std::string tokenizer_name; GGUF_GET_KEY(vctx, tokenizer_name, gguf_get_val_str, GGUF_TYPE_STRING, true, kv(LLM_KV_TOKENIZER_MODEL)); gguf_set_val_str(fctx, kv(LLM_KV_TOKENIZER_MODEL), tokenizer_name.c_str()); gguf_set_arr_data(fctx, kv(LLM_KV_TOKENIZER_SCORES), GGUF_TYPE_FLOAT32, scores, n_vocab); gguf_set_arr_data(fctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE), GGUF_TYPE_INT32, toktypes, n_vocab); int32_t special_bos_id = 1; int32_t special_eos_id = 2; int32_t special_unk_id = 0; int32_t special_sep_id = -1; int32_t special_pad_id = -1; if (tokenizer_name == "llama") { // default special tokens special_bos_id = 1; special_eos_id = 2; special_unk_id = 0; special_sep_id = -1; special_pad_id = -1; } else if (tokenizer_name == "gpt2") { // read and copy bpe merges const int merges_keyidx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_MERGES)); if (merges_keyidx == -1) { die("cannot find tokenizer merges in model file"); } const int n_merges = gguf_get_arr_n(vctx, merges_keyidx); std::vector merges; merges.resize(n_merges); for (int i = 0; i < n_merges; i++) { merges[i] = gguf_get_arr_str(vctx, merges_keyidx, i); } gguf_set_arr_str(fctx, kv(LLM_KV_TOKENIZER_MERGES), merges.data(), n_merges); // default special tokens special_bos_id = 11; special_eos_id = 11; special_unk_id = -1; special_sep_id = -1; special_pad_id = -1; } else { fprintf(stderr, "%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str()); fprintf(stderr, "%s: using default tokenizer: 'llama'", __func__); } std::vector tokens; tokens.resize(n_vocab); for (uint32_t i = 0; i < n_vocab; i++) { tokens[i] = gguf_get_arr_str(vctx, token_idx, i); } gguf_set_arr_str(fctx, kv(LLM_KV_TOKENIZER_LIST), tokens.data(), n_vocab); GGUF_GET_KEY(vctx, special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID)); GGUF_GET_KEY(vctx, special_eos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_EOS_ID)); GGUF_GET_KEY(vctx, special_unk_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_UNK_ID)); GGUF_GET_KEY(vctx, special_sep_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_SEP_ID)); GGUF_GET_KEY(vctx, special_pad_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_PAD_ID)); gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_BOS_ID), special_bos_id); gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_EOS_ID), special_eos_id); gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_UNK_ID), special_unk_id); gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_SEP_ID), special_sep_id); gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_PAD_ID), special_pad_id); gguf_free(vctx); } // add tensors gguf_add_tensor(fctx, model->tok_embeddings); gguf_add_tensor(fctx, model->norm); gguf_add_tensor(fctx, model->output); for (uint32_t i = 0; i < model->hparams.n_layer; ++i) { auto & layer = model->layers[i]; gguf_add_tensor(fctx, layer.attention_norm); gguf_add_tensor(fctx, layer.wq); gguf_add_tensor(fctx, layer.wk); gguf_add_tensor(fctx, layer.wv); gguf_add_tensor(fctx, layer.wo); gguf_add_tensor(fctx, layer.ffn_norm); gguf_add_tensor(fctx, layer.w1); gguf_add_tensor(fctx, layer.w2); gguf_add_tensor(fctx, layer.w3); } } static void save_llama_model_file(const char * filename, const char * fn_vocab_model, struct my_llama_model * model) { printf("%s: saving to %s\n", __func__, filename); struct gguf_context * fctx = gguf_init_empty(); save_llama_model_gguf(fctx, fn_vocab_model, model); // write file const bool only_meta = false; gguf_write_to_file(fctx, filename, only_meta); gguf_free(fctx); } static void load_checkpoint_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct train_state * train) { load_llama_model_gguf(fctx, f_ggml_ctx, model); if (load_train_state_gguf(fctx, f_ggml_ctx, train)) { std::string train_type = LLM_KV_TRAINING_TYPE_TRAIN_MODEL; GGUF_GET_KEY(fctx, train_type, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_TYPE); GGML_ASSERT(train_type == LLM_KV_TRAINING_TYPE_TRAIN_MODEL); } else { printf("%s: loaded llama model as checkpoint\n", __func__); } } static void save_checkpoint_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model, struct train_state * train) { gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_TRAIN_MODEL); save_llama_model_gguf(fctx, fn_vocab_model, model); save_train_state_gguf(fctx, train); } static bool load_checkpoint_file(const char * filename, struct my_llama_model * model, struct train_state * train) { struct ggml_context * f_ggml_ctx; struct gguf_init_params params; params.no_alloc = false; params.ctx = &f_ggml_ctx; struct gguf_context * fctx = gguf_init_from_file(filename, params); if (fctx == NULL) { return false; } load_checkpoint_gguf(fctx, f_ggml_ctx, model, train); return true; } static void save_checkpoint_file(const char * filename, const char * fn_vocab_model, struct my_llama_model * model, struct train_state * train) { printf("%s: saving to %s\n", __func__, filename); struct gguf_context * fctx = gguf_init_empty(); save_checkpoint_gguf(fctx, fn_vocab_model, model, train); // write file const bool only_meta = false; gguf_write_to_file(fctx, filename, only_meta); gguf_free(fctx); } struct train_params { struct train_params_common common; const char * fn_vocab_model; const char * fn_model_out; bool only_write_model; int n_ctx; int n_embd; int n_head; int n_layer; int n_ff; float f_norm_rms_eps; float rope_freq_base; float rope_freq_scale; }; static struct train_params get_default_train_params() { struct train_params params; params.common = get_default_train_params_common(); params.fn_vocab_model = "ggml-vic7b-uncensored-q4_0.bin"; params.fn_model_out = "ggml-checkpoint-f32.bin"; params.only_write_model = false; params.n_ctx = 128; params.n_embd = 256; params.n_head = 8; params.n_layer = 16; params.n_ff = 768; params.f_norm_rms_eps = 1e-5f; params.rope_freq_base = 10000.0f; params.rope_freq_scale = 1.0f; return params; } static void train_print_usage(int argc, char ** argv, const struct train_params * params) { fprintf(stderr, "usage: %s [options]\n", argv[0]); fprintf(stderr, "\n"); fprintf(stderr, "options:\n"); fprintf(stderr, " -h, --help show this help message and exit\n"); fprintf(stderr, " --vocab-model FNAME model path from which to load vocab (default '%s')\n", params->fn_vocab_model); fprintf(stderr, " --model-out FNAME path to save ggml model (default '%s')\n", params->fn_model_out); fprintf(stderr, " --only-write-model only save llama model, don't do any training. use this if you only want to convert a checkpoint to a model.\n"); fprintf(stderr, " --embd N Embedding size used for new models (default %d)\n", params->n_embd); fprintf(stderr, " --ff N Feedforward size used for new models. (default %d)\n", params->n_ff); fprintf(stderr, " --head N Number of heads for new models (default %d)\n", params->n_head); fprintf(stderr, " --layer N Number of layers for new models (default %d)\n", params->n_layer); fprintf(stderr, " --norm-rms-eps F RMS-Norm epsilon value (default %f)\n", params->f_norm_rms_eps); fprintf(stderr, " --rope-freq-base F Frequency base for ROPE (default %f)\n", params->rope_freq_base); fprintf(stderr, " --rope-freq-scale F Frequency scale for ROPE (default %f)\n", params->rope_freq_scale); print_common_train_usage(argc, argv, ¶ms->common); } static bool train_params_parse(int argc, char ** argv, struct train_params * params) { bool invalid_param = false; std::string arg; struct train_params default_params = get_default_train_params(); const std::string arg_prefix = "--"; for (int i = 1; i < argc; i++) { arg = argv[i]; if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { std::replace(arg.begin(), arg.end(), '_', '-'); } if (consume_common_train_arg(argc, argv, &i, ¶ms->common, &invalid_param)) { if (invalid_param) { break; } else if (params->common.print_usage) { train_print_usage(argc, argv, &default_params); exit(0); } } else if (arg == "--vocab-model") { if (++i >= argc) { invalid_param = true; break; } params->fn_vocab_model = argv[i]; } else if (arg == "--model-out") { if (++i >= argc) { invalid_param = true; break; } params->fn_model_out = argv[i]; } else if (arg == "--only-write-model") { params->only_write_model = true; } else if (arg == "--embd") { if (++i >= argc) { invalid_param = true; break; } params->n_embd = std::stoi(argv[i]); } else if (arg == "--ff") { if (++i >= argc) { invalid_param = true; break; } params->n_ff = std::stoi(argv[i]); } else if (arg == "--head") { if (++i >= argc) { invalid_param = true; break; } params->n_head = std::stoi(argv[i]); } else if (arg == "--layer") { if (++i >= argc) { invalid_param = true; break; } params->n_layer = std::stoi(argv[i]); } else if (arg == "--norm-rms-eps") { if (++i >= argc) { invalid_param = true; break; } params->f_norm_rms_eps = std::stof(argv[i]); } else if (arg == "--rope-freq-base") { if (++i >= argc) { invalid_param = true; break; } params->rope_freq_base = std::stof(argv[i]); } else if (arg == "--rope-freq-scale") { if (++i >= argc) { invalid_param = true; break; } params->rope_freq_scale = std::stof(argv[i]); } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); train_print_usage(argc, argv, &default_params); exit(1); } } if (invalid_param) { fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); train_print_usage(argc, argv, &default_params); exit(1); } finish_processing_train_args(¶ms->common); return true; } struct save_train_files_data { const char * fn_checkpoint_out; const char * fn_model_out; const char * fn_vocab_model; const char * pattern_fn_it; const char * fn_latest; struct my_llama_model * model; }; static void save_train_files(void * vdata, struct train_state * train) { struct save_train_files_data * data = (struct save_train_files_data *) vdata; int64_t iter = train->opt->iter; if (strlen(data->fn_checkpoint_out) > 0) { save_checkpoint_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->fn_vocab_model, data->model, train); save_checkpoint_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, -1 ).c_str(), data->fn_vocab_model, data->model, train); } if (strlen(data->fn_model_out) > 0) { save_llama_model_file(get_train_filename(data->fn_model_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->fn_vocab_model, data->model); save_llama_model_file(get_train_filename(data->fn_model_out, data->pattern_fn_it, data->fn_latest, -1 ).c_str(), data->fn_vocab_model, data->model); } } static int64_t get_parameter_count(struct my_llama_model* model) { int64_t nx = 0; nx += ggml_nelements(model->tok_embeddings); nx += ggml_nelements(model->norm); nx += ggml_nelements(model->output); for (uint32_t i = 0; i < model->layers.size(); ++i) { auto & layer = model->layers[i]; nx += ggml_nelements(layer.attention_norm); nx += ggml_nelements(layer.wq); nx += ggml_nelements(layer.wk); nx += ggml_nelements(layer.wv); nx += ggml_nelements(layer.wo); nx += ggml_nelements(layer.ffn_norm); nx += ggml_nelements(layer.w1); nx += ggml_nelements(layer.w2); nx += ggml_nelements(layer.w3); } return nx; } int main(int argc, char ** argv) { struct train_params params = get_default_train_params(); if (!train_params_parse(argc, argv, ¶ms)) { return 1; } if (params.common.seed == LLAMA_DEFAULT_SEED) { params.common.seed = time(NULL); } printf("%s: seed: %u\n", __func__, params.common.seed); srand(params.common.seed); struct llama_model_params mparams = llama_model_default_params(); mparams.vocab_only = true; struct llama_context_params cparams = llama_context_default_params(); struct llama_model * lmodel = llama_load_model_from_file(params.fn_vocab_model, mparams); struct llama_context * lctx = llama_new_context_with_model(lmodel, cparams); struct my_llama_model model; model.hparams.n_vocab = llama_n_vocab(lmodel); model.hparams.n_ctx = params.common.n_ctx; model.hparams.n_embd = params.n_embd; model.hparams.n_head = params.n_head; model.hparams.n_layer = params.n_layer; model.hparams.n_ff = params.n_ff; // llama.cpp requires n_rot to be exactly n_embd / n_head model.hparams.n_rot = model.hparams.n_embd / model.hparams.n_head; model.hparams.f_norm_rms_eps = params.f_norm_rms_eps; model.hparams.rope_freq_base = params.rope_freq_base; model.hparams.rope_freq_scale = params.rope_freq_scale; struct train_state * train = init_train_state(); struct ggml_opt_context * opt = train->opt; // set opt params from command line opt->params = ggml_opt_default_params(GGML_OPT_ADAM); opt->params.print_forward_graph = false; opt->params.print_backward_graph = false; opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; opt->params.n_threads = params.common.n_threads; opt->params.past = params.common.opt_past; opt->params.delta = params.common.opt_delta; opt->params.max_no_improvement = params.common.opt_max_no_improvement; opt->params.n_gradient_accumulation = params.common.n_gradient_accumulation; opt->params.adam.n_iter = params.common.adam_n_iter; opt->params.adam.sched = 1.0f; opt->params.adam.alpha = params.common.adam_alpha; opt->params.adam.decay = params.common.adam_decay; opt->params.adam.decay_min_ndim = params.common.adam_decay_min_ndim; opt->params.adam.beta1 = params.common.adam_beta1; opt->params.adam.beta2 = params.common.adam_beta2; opt->params.adam.gclip = params.common.adam_gclip; opt->params.adam.eps_f = params.common.adam_eps_f; printf("%s: init model\n", __func__); bool existed = load_checkpoint_file(params.common.fn_checkpoint_in, &model, train); if (existed) { // overwrite last n_ctx with user provided n_ctx if (params.common.custom_n_ctx) { model.hparams.n_ctx = params.common.n_ctx; } const bool opt_past_changed = opt->params.past != params.common.opt_past; if (opt_past_changed) { die("Optimizer parameter '--opt-past N' differs from checkpoint file. To use different value train from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting"); // need to discard previous optimizer past function value statistics and opt_init with new shapes // TODO } } else { init_model(&model); randomize_model(&model, params.common.seed, 0.0f, 1.0f, -1.0f, +1.0f); if (!params.only_write_model) { ggml_opt_init(opt->ctx, opt, opt->params, get_parameter_count(&model)); } } opt->iter = train->train_its; print_params(&model.hparams); printf("%s: total train_iterations %llu\n", __func__, (long long unsigned) train->train_its); printf("%s: seen train_samples %llu\n", __func__, (long long unsigned) train->train_samples); printf("%s: seen train_tokens %llu\n", __func__, (long long unsigned) train->train_tokens); printf("%s: completed train_epochs %llu\n", __func__, (long long unsigned) train->train_epochs); printf("%s: model_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(model.ctx) + ggml_backend_buffer_get_size(model.data)), (float) (ggml_used_mem(model.ctx) + ggml_backend_buffer_get_size(model.data)) / (1024.0f*1024.0f)); if (params.only_write_model) { save_train_files_data save_data; save_data.fn_checkpoint_out = ""; save_data.fn_model_out = params.fn_model_out; save_data.fn_vocab_model = params.fn_vocab_model; save_data.pattern_fn_it = params.common.pattern_fn_it; save_data.fn_latest = params.common.fn_latest; save_data.model = &model; save_train_files(&save_data, train); free_train_state(train); ggml_free(model.ctx); llama_free(lctx); llama_free_model(lmodel); return 0; } printf("%s: opt_size = %zu bytes (%.1f MB)\n", __func__, ggml_get_mem_size(opt->ctx), (float) ggml_get_mem_size(opt->ctx) / (1024.0f*1024.0f)); printf("%s: opt iter %d\n", __func__, opt->iter); int n_tokens = model.hparams.n_ctx; int n_vocab = model.hparams.n_vocab; int n_batch = params.common.n_batch; // context for input tensors without their data struct ggml_init_params ctx_input_params = { ggml_tensor_overhead() * 2, // mem_size NULL, // mem_buffer true, // no_alloc }; struct ggml_context * ctx_input = ggml_init(ctx_input_params); // the input tensors struct ggml_tensor * tokens_input = ggml_new_tensor_2d(ctx_input, GGML_TYPE_I32, n_tokens, n_batch); struct ggml_tensor * target_probs = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab, n_tokens, n_batch); // measure required memory for input tensors // allocate input tensors ggml_backend_buffer_t input_data = ggml_backend_alloc_ctx_tensors_from_buft(ctx_input, ggml_backend_cpu_buffer_type()); size_t max_input_size = ggml_backend_buffer_get_size(input_data); printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f)); // context for compute tensors without their data const size_t estimated_compute_size_wo_data = ( 2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() + (params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true)) ); struct ggml_init_params ctx_compute_params = { estimated_compute_size_wo_data, // mem_size NULL, // mem_buffer true, // no_alloc }; struct ggml_context * ctx_compute = NULL; struct ggml_tensor * loss = NULL; struct ggml_tensor * logits = NULL; struct ggml_cgraph * gf = NULL; struct ggml_cgraph * gb = NULL; struct ggml_cgraph * gb_tmp = NULL; // measure required memory for compute tensors size_t best_compute_size = SIZE_MAX; enum ggml_cgraph_eval_order best_order = GGML_CGRAPH_EVAL_ORDER_COUNT; // find best evaluation order for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) { ctx_compute = ggml_init(ctx_compute_params); ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type()); gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = (enum ggml_cgraph_eval_order) order; gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gb_tmp = params.common.use_checkpointing ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true) : NULL; loss = llama_build_train_graphs( &model, alloc, ctx_compute, gf, gb, gb_tmp, &logits, tokens_input, target_probs, n_tokens, n_batch, params.common.use_flash, params.common.use_checkpointing, true ); size_t max_compute_size = ggml_gallocr_get_buffer_size(alloc, 0); // FIXME: this will still allocate the buffer if (max_compute_size < best_compute_size) { best_compute_size = max_compute_size; best_order = gf->order; } ggml_free(ctx_compute); } size_t max_compute_size = best_compute_size; printf("%s: compute_size = %zu bytes (%.1f MB)\n", __func__, max_compute_size, (float) max_compute_size / (1024.0f*1024.0f)); printf("%s: evaluation order = %s\n", __func__, (best_order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? "LEFT_TO_RIGHT" : (best_order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? "RIGHT_TO_LEFT" : "invalid"); // allocate compute tensors ctx_compute = ggml_init(ctx_compute_params); ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type()); gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = best_order; gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gb_tmp = params.common.use_checkpointing ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true) : NULL; loss = llama_build_train_graphs( &model, alloc, ctx_compute, gf, gb, gb_tmp, &logits, tokens_input, target_probs, n_tokens, n_batch, params.common.use_flash, params.common.use_checkpointing, false ); std::vector train_tokens; std::vector train_samples_begin; std::vector train_samples_size; printf("%s: tokenize training data\n", __func__); tokenize_file(lctx, params.common.fn_train_data, params.common.sample_start, params.common.include_sample_start, params.common.overlapping_samples, n_tokens, train_tokens, train_samples_begin, train_samples_size); GGML_ASSERT(train_samples_begin.size() == train_samples_size.size()); printf("%s: number of training tokens: %zu\n", __func__, train_tokens.size()); size_t shuffle_samples_hash = compute_samples_hash(params.common.fn_train_data, train_samples_begin.data(), train_samples_size.data(), train_samples_size.size()); const bool changed_train_data = (shuffle_samples_hash != train->shuffle_samples_hash) || (train->shuffle_sample_count != train_samples_size.size()); if (changed_train_data) { printf("%s: train data seems to have changed. restarting shuffled epoch.\n", __func__); } if (params.common.force_reshuffle) { printf("%s: forced reshuffling of data. restarting with newly shuffled epoch.\n", __func__); } if ((train->shuffle_rng_state_current == "") || changed_train_data || params.common.force_reshuffle) { train->shuffle_rng_state_current = mt19937_seed_to_state(params.common.seed); train->shuffle_sample_count = train_samples_size.size(); train->shuffle_next_sample = 0; train->shuffle_samples_hash = shuffle_samples_hash; } std::vector train_shuffled_samples_offs; std::vector train_shuffled_samples_begin; std::vector train_shuffled_samples_size; train_shuffled_samples_offs.resize(train_samples_begin.size()); train_shuffled_samples_begin.resize(train_samples_begin.size()); train_shuffled_samples_size.resize(train_samples_size.size()); train->shuffle_rng_state_next = shuffle_samples( train->shuffle_rng_state_current, train_shuffled_samples_offs.data(), train_shuffled_samples_begin.data(), train_shuffled_samples_size.data(), train_samples_begin.data(), train_samples_size.data(), train_samples_size.size()); printf("%s: begin training\n", __func__); save_train_files_data save_data; save_data.fn_checkpoint_out = params.common.fn_checkpoint_out; save_data.fn_model_out = params.fn_model_out; save_data.fn_vocab_model = params.fn_vocab_model; save_data.pattern_fn_it = params.common.pattern_fn_it; save_data.fn_latest = params.common.fn_latest; save_data.model = &model; struct train_opt_callback_data opt_cb_data; opt_cb_data.params = ¶ms.common; opt_cb_data.train = train; opt_cb_data.save_cb = &save_train_files; opt_cb_data.save_data = &save_data; opt_cb_data.lctx = lctx; opt_cb_data.last_save_iter = opt->iter; opt_cb_data.tokens_data = train_tokens.data(); opt_cb_data.tokens_size = train_tokens.size(); opt_cb_data.samples_begin = train_samples_begin.data(); opt_cb_data.samples_size = train_samples_size.data(); opt_cb_data.shuffled_samples_offs = train_shuffled_samples_offs.data(); opt_cb_data.shuffled_samples_begin = train_shuffled_samples_begin.data(); opt_cb_data.shuffled_samples_size = train_shuffled_samples_size.data(); opt_cb_data.samples_count = train_samples_size.size(); opt_cb_data.tokens_input = tokens_input; opt_cb_data.target_probs = target_probs; opt_cb_data.first_iter = opt->iter; opt_cb_data.first_epoch = train->train_epochs; opt_cb_data.iter_at_last_epoch = -1; opt_cb_data.last_time = ggml_time_ms(); opt_cb_data.millis_per_iter = 0.0; // measure required memory for work buffer size_t max_work_size = ggml_graph_plan(gb, params.common.n_threads).work_size + GGML_OBJECT_SIZE; printf("%s: work_size = %zu bytes (%.1f MB)\n", __func__, max_work_size, (float) max_work_size / (1024.0f*1024.0f)); // context for work buffer struct ggml_init_params ctx_work_params = { max_work_size, // mem_size NULL, // mem_buffer false, // no_alloc }; struct ggml_context * ctx_work = ggml_init(ctx_work_params); int64_t t0 = ggml_time_ms(); ggml_opt_resume_g(ctx_work, opt, loss, gf, gb, &train_opt_callback, (void *) &opt_cb_data); ggml_free(ctx_work); ggml_free(ctx_compute); ggml_free(ctx_input); int64_t t1 = ggml_time_ms(); printf("%s: total training time: ", __func__); print_duration((double) (t1 - t0)); printf("\n"); int new_iters = opt->iter - opt_cb_data.last_save_iter; if (new_iters > 0) { train->train_its += new_iters; train->train_tokens += new_iters * opt->params.n_gradient_accumulation * n_batch * n_tokens; save_train_files(&save_data, train); opt_cb_data.last_save_iter = opt->iter; } ggml_free(opt->ctx); free_train_state(train); ggml_free(model.ctx); llama_free(lctx); llama_free_model(lmodel); return 0; }