diff --git a/gpt4all-backend/CMakeLists.txt b/gpt4all-backend/CMakeLists.txt index b75965b0..8f5ae2fa 100644 --- a/gpt4all-backend/CMakeLists.txt +++ b/gpt4all-backend/CMakeLists.txt @@ -102,10 +102,6 @@ foreach(BUILD_VARIANT IN LISTS BUILD_VARIANTS) gptj.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h) prepare_target(gptj llama-mainline) - add_library(mpt-${BUILD_VARIANT} SHARED - mpt.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h) - prepare_target(mpt llama-mainline) - add_library(bert-${BUILD_VARIANT} SHARED bert.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h) target_compile_definitions(bert-${BUILD_VARIANT} PRIVATE LLAMA_VERSIONS=>=3 LLAMA_DATE=999999) diff --git a/gpt4all-backend/llama.cpp-mainline b/gpt4all-backend/llama.cpp-mainline index ffe96e1e..a8ed8c85 160000 --- a/gpt4all-backend/llama.cpp-mainline +++ b/gpt4all-backend/llama.cpp-mainline @@ -1 +1 @@ -Subproject commit ffe96e1ebf9cdae1dc82b2049d9e45c1875472ab +Subproject commit a8ed8c858985ef94d97a3cf2c97085b680c6d5d0 diff --git a/gpt4all-backend/llamamodel.cpp b/gpt4all-backend/llamamodel.cpp index 23766b62..e743ac00 100644 --- a/gpt4all-backend/llamamodel.cpp +++ b/gpt4all-backend/llamamodel.cpp @@ -397,7 +397,7 @@ DLL_EXPORT bool magic_match(const char * fname) { bool isValid = gguf_get_version(ctx_gguf) <= 2; auto arch = get_arch_name(ctx_gguf); - isValid = isValid && (arch == "llama" || arch == "starcoder" || arch == "falcon"); + isValid = isValid && (arch == "llama" || arch == "starcoder" || arch == "falcon" || arch == "mpt"); gguf_free(ctx_gguf); return isValid; diff --git a/gpt4all-backend/mpt.cpp b/gpt4all-backend/mpt.cpp deleted file mode 100644 index ac4d4282..00000000 --- a/gpt4all-backend/mpt.cpp +++ /dev/null @@ -1,969 +0,0 @@ -#define MPT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE -#include "mpt_impl.h" - -#include "utils.h" -#include "llmodel_shared.h" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#if defined(_WIN32) && defined(_MSC_VER) - #define WIN32_LEAN_AND_MEAN - #ifndef NOMINMAX - #define NOMINMAX - #endif - #include - #include - #include -#else - #include -#endif -#include -#include -#include -#include -#include -#include - - -namespace { -const char *modelType_ = "MPT"; -} - -// default hparams (MPT 7B) -struct mpt_hparams { - int32_t n_vocab = 50432; - int32_t n_ctx = 2048; - int32_t n_embd = 4096; - int32_t n_head = 32; - int32_t n_layer = 32; - float alibi_bias_max = 8; - float clip_qkv = 0; - float norm_eps = 1e-5; - int32_t expand = 4; -}; - -struct mpt_layer { - // normalization - struct ggml_tensor * norm_1_w; - struct ggml_tensor * norm_2_w; - - // attention - struct ggml_tensor * attn_Wqkv_w; - struct ggml_tensor * attn_out_proj_w; - - // ff - struct ggml_tensor * ffn_up_proj_w; - struct ggml_tensor * ffn_down_proj_w; -}; - -struct mpt_model { - mpt_hparams hparams; - - // normalization - struct ggml_tensor * norm_f_w; - - struct ggml_tensor * wte; // position embedding - - // mpt does weight tying - - std::vector layers; - - struct llm_kv_cache kv_self; - struct ggml_context * ctx; - - - llm_buffer eval_buf; - llm_buffer scr0_buf; - llm_buffer scr1_buf; - - ~mpt_model() { - if (ctx) { - ggml_free(ctx); - } - } -}; - -enum mpt_token_type { - MPT_TOKEN_TYPE_NORMAL = 1, - MPT_TOKEN_TYPE_CONTROL = 3, -}; - -using replit_piece_t = std::pair; -using replit_piece_map_t = std::unordered_map; - -static const std::string replit_ws_symbol = "\342\226\201"; - -struct mpt_vocab { - bool is_replit = false; - gpt_vocab raw; - replit_piece_map_t piece_map; - std::vector vocab; - - const char * end_of_text() const { - return is_replit ? "<|endoftext|>" : "<|im_end|>"; - } -}; - -std::pair, float> encode_word(const std::string & word, const replit_piece_map_t & model) { - std::vector best_segmentations_starts(word.length() + 1, -1); - best_segmentations_starts[0] = 0; - - std::vector best_segmentations_scores(word.length() + 1, -std::numeric_limits::infinity()); - best_segmentations_scores[0] = 1.0; - - for (size_t start_idx = 0; start_idx < word.length(); ++start_idx) { - float best_score_at_start = best_segmentations_scores[start_idx]; - for (size_t end_idx = start_idx + 1; end_idx <= word.length(); ++end_idx) { - std::string token = word.substr(start_idx, end_idx - start_idx); - if (model.count(token) && best_score_at_start != -std::numeric_limits::infinity()) { - float token_score = model.at(token).second; - float score = token_score + best_score_at_start; - if (best_segmentations_scores[end_idx] == -std::numeric_limits::infinity() || - best_segmentations_scores[end_idx] > score) { - best_segmentations_starts[end_idx] = start_idx; - best_segmentations_scores[end_idx] = score; - } - } - } - } - - if (best_segmentations_scores.back() == -std::numeric_limits::infinity()) { - return std::make_pair(std::vector{0}, 0.0f); - } - - float score = best_segmentations_scores.back(); - int start = best_segmentations_starts.back(); - int end = word.length(); - std::vector tokens; - while (start != 0) { - const auto token_id = model.at(word.substr(start, end - start)).first; - tokens.insert(tokens.begin(), token_id); - int next_start = best_segmentations_starts[start]; - end = start; - start = next_start; - } - const auto token_id = model.at(word.substr(start, end - start)).first; - tokens.insert(tokens.begin(), token_id); - return std::make_pair(tokens, score); -} - -bool replit_tokenizer_load(mpt_vocab & tokenizer, gguf_context * ggufctx, int tokens_keyidx, int max_vocab_size) { - int scores_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.scores"); - if (scores_keyidx == -1) { - fprintf(stderr, "%s: llama token scores not found!\n", __func__); - return false; - } - const auto *scores = reinterpret_cast(gguf_get_arr_data(ggufctx, scores_keyidx)); - - for (LLModel::Token i = 0; i < max_vocab_size; i++) { - std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i); - tokenizer.piece_map[word] = std::make_pair(i, -scores[i]); - tokenizer.raw.id_to_token[i] = word; - tokenizer.raw.token_to_id[word] = i; - } - - return true; -} - -std::string replace_all(const std::string & str, // where to work - const std::string & find, // substitute 'find' - const std::string & replace // by 'replace' -) { - std::string result; - size_t find_len = find.size(); - size_t pos, from = 0; - while (std::string::npos != (pos = str.find(find, from))) { - result.append(str, from, pos - from); - result.append(replace); - from = pos + find_len; - } - result.append(str, from, std::string::npos); - return result; -} - -std::vector replit_tokenizer_tokenize(mpt_vocab & tokenizer, const std::string & text) { - std::vector tokens; - auto normalized_text = replace_all(text, " ", replit_ws_symbol); - auto tokenized = encode_word(normalized_text, tokenizer.piece_map); - - return tokenized.first; -} - -std::string replit_tokenizer_detokenize(mpt_vocab & tokenizer, const std::vector & tokens) { - std::string text; - for (auto token : tokens) { - text += tokenizer.raw.id_to_token[token]; - } - return replace_all(text, replit_ws_symbol, " "); -} - - -static bool kv_cache_init( - const struct mpt_hparams & hparams, - struct llm_kv_cache & cache, - ggml_type wtype, - int n_ctx) { - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - - const int64_t n_mem = (int64_t)n_layer*n_ctx; - const int64_t n_elements = n_embd*n_mem; - - cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2_MiB); - - struct ggml_init_params params; - params.mem_size = cache.buf.size; - params.mem_buffer = cache.buf.addr; - params.no_alloc = false; - - cache.ctx = ggml_init(params); - - if (!cache.ctx) { - fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__); - return false; - } - - cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements); - cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements); - - return true; -} - -// load the model's weights from a file path. if mem_req ptr is passed the model is -// only partially parsed to estimate required memory -bool mpt_model_load(const std::string &fname, mpt_model & model, mpt_vocab & vocab, size_t * mem_req) { - printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str()); - if (mem_req != nullptr) { - *mem_req = 0; - } - - // create the ggml context - struct gguf_init_params params = { - /*.no_alloc = */ false, - /*.ctx = */ &model.ctx, - }; - - gguf_context *ggufctx = gguf_init_from_file(fname.c_str(), params); - if (!ggufctx) { - fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__); - return false; - } - - printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx)); - printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx)); - printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx)); - - // print some standard metadata - { - int keyidx; - - keyidx = gguf_find_key(ggufctx, "general.name"); - if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.description"); - if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.author"); - if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.license"); - if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.architecture"); - if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.file_type"); - if (keyidx != -1) { printf("%s: model file type = %" PRIu32 "\n", __func__, gguf_get_val_u32(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout"); - if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - keyidx = gguf_find_key(ggufctx, "general.source.huggingface.repository"); - if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } - } - - // check required metadata - { - // check model architecture kv - int keyidx = gguf_find_key(ggufctx, "general.architecture"); - if (keyidx == -1) { - fprintf(stderr, "%s: gguf model architecture not found!\n", __func__); - return false; - } - if (strcmp(gguf_get_val_str(ggufctx, keyidx), "mpt") != 0) { - fprintf(stderr, "%s: model architecture not supported!\n", __func__); - return false; - } - } - - // load hparams - { - auto & hparams = model.hparams; - - bool ok = false; - int keyidx; - - do { - keyidx = gguf_find_key(ggufctx, "mpt.context_length"); - if (keyidx == -1) { break; } - hparams.n_ctx = gguf_get_val_u32(ggufctx, keyidx); - - keyidx = gguf_find_key(ggufctx, "mpt.embedding_length"); - if (keyidx == -1) { break; } - hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx); - - keyidx = gguf_find_key(ggufctx, "mpt.attention.head_count"); - if (keyidx == -1) { break; } - hparams.n_head = gguf_get_val_u32(ggufctx, keyidx); - - keyidx = gguf_find_key(ggufctx, "mpt.block_count"); - if (keyidx == -1) { break; } - hparams.n_layer = gguf_get_val_u32(ggufctx, keyidx); - - keyidx = gguf_find_key(ggufctx, "mpt.attention.max_alibi_bias"); - if (keyidx == -1) { break; } - hparams.alibi_bias_max = gguf_get_val_f32(ggufctx, keyidx); - - keyidx = gguf_find_key(ggufctx, "mpt.attention.clamp_kqv"); - if (keyidx != -1) { // optional - hparams.clip_qkv = gguf_get_val_f32(ggufctx, keyidx); - } - - keyidx = gguf_find_key(ggufctx, "mpt.attention.layer_norm_epsilon"); - if (keyidx == -1) { break; } - hparams.norm_eps = gguf_get_val_f32(ggufctx, keyidx); - - ok = true; - } while (false); - - if (!ok) { - fprintf(stderr, "%s: required hparam missing!\n", __func__); - return false; - } - - 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: alibi_bias_max = %f\n", __func__, hparams.alibi_bias_max); - printf("%s: clip_qkv = %f\n", __func__, hparams.clip_qkv); - } - - // load vocab - { - auto & hparams = model.hparams; - - int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens"); - if (tokens_keyidx == -1) { - fprintf(stderr, "%s: tokenizer vocab not found!\n", __func__); - return false; - } - - int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model"); - if (keyidx == -1) { - fprintf(stderr, "%s: tokenizer model not found!\n", __func__); - return false; - } - std::string tokenizer_model(gguf_get_val_str(ggufctx, keyidx)); - - hparams.n_vocab = gguf_get_arr_n(ggufctx, tokens_keyidx); - printf("%s: %s tokenizer vocab = %d\n", __func__, tokenizer_model.c_str(), int(hparams.n_vocab)); - - if (tokenizer_model == "llama") { // Replit - vocab.is_replit = true; - if (!replit_tokenizer_load(vocab, ggufctx, tokens_keyidx, hparams.n_vocab)) { - return false; - } - } else if (tokenizer_model == "gpt2") { - int toktypes_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.token_type"); - if (toktypes_keyidx == -1) { - fprintf(stderr, "%s: gpt2 token types not found!\n", __func__); - return false; - } - const auto *toktypes = reinterpret_cast(gguf_get_arr_data(ggufctx, toktypes_keyidx)); - - for (int i = 0; i < hparams.n_vocab; i++) { - std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i); - - bool special = false; - if (toktypes[i] == MPT_TOKEN_TYPE_CONTROL) { - special = true; - } else if (toktypes[i] != MPT_TOKEN_TYPE_NORMAL) { - fprintf(stderr, "%s: unknown token type: %d\n", __func__, int(toktypes[i])); - return false; - } - - vocab.raw.token_to_id[word] = i; - vocab.raw.id_to_token[i] = word; - - if (special) { - vocab.raw.add_special_token(word); - } - } - } else { - fprintf(stderr, "%s: tokenizer model not supported!\n", __func__); - return false; - } - } - - auto & ctx = model.ctx; - - size_t ctx_size = ggml_get_mem_size(ctx); - printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size / (1024.0 * 1024.0)); - - if (mem_req != nullptr) { - *mem_req = ctx_size; - gguf_free(ggufctx); - return false; - } - - // prepare memory for the weights - { - const auto & hparams = model.hparams; - model.layers.resize(hparams.n_layer); - - model.wte = ggml_get_tensor(ctx, "token_embd.weight"); - model.norm_f_w = ggml_get_tensor(ctx, "output_norm.weight"); - - auto name = [](int i, std::string n) { - static std::string key; - key = "blk." + std::to_string(i) + "." + n; - return key.c_str(); - }; - - for (int i = 0; i < hparams.n_layer; ++i) { - auto &layer = model.layers[i]; - - layer.norm_1_w = ggml_get_tensor(ctx, name(i, "attn_norm.weight")); - layer.norm_2_w = ggml_get_tensor(ctx, name(i, "ffn_norm.weight")); - - layer.attn_Wqkv_w = ggml_get_tensor(ctx, name(i, "attn_qkv.weight")); - layer.attn_out_proj_w = ggml_get_tensor(ctx, name(i, "attn_output.weight")); - layer.ffn_up_proj_w = ggml_get_tensor(ctx, name(i, "ffn_up.weight")); - layer.ffn_down_proj_w = ggml_get_tensor(ctx, name(i, "ffn_down.weight")); - } - } - - // key + value memory - { - const auto &hparams = model.hparams; - if (!kv_cache_init(hparams, model.kv_self, GGML_TYPE_F16, model.hparams.n_ctx)) { - fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__); - ggml_free(ctx); - return false; - } - - const size_t memory_size = ggml_nbytes(model.kv_self.k) + ggml_nbytes(model.kv_self.v); - printf("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0); - } - - model.scr0_buf.resize(256u * 1024 * 1024); - model.scr1_buf.resize(256u * 1024 * 1024); - - return true; -} - -bool mpt_eval( - mpt_model & model, - const int n_threads, - const int n_past, - const std::vector & embd_inp, - std::vector & embd_w, - size_t & mem_per_token) { - const int N = embd_inp.size(); - - const auto & hparams = model.hparams; - - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_ctx = hparams.n_ctx; - const int n_head = hparams.n_head; - const int n_vocab = hparams.n_vocab; - - const size_t init_buf_size = 1024_MiB; - if (!model.eval_buf.addr || model.eval_buf.size < init_buf_size) - model.eval_buf.resize(init_buf_size); - - if (mem_per_token > 0 && mem_per_token*N > model.eval_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__, model.buf.size, buf_size_new); - - // reallocate - model.eval_buf.resize(buf_size_new); - if (model.eval_buf.addr == nullptr) { - fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, model.eval_buf.size); - return false; - } - } - - struct ggml_init_params params = { - .mem_size = model.eval_buf.size, - .mem_buffer = model.eval_buf.addr, - .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) { - ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, }); - - struct ggml_tensor * inpSA = inpL; - struct ggml_tensor * cur = inpSA; - // self-attention - { - - // norm1 - cur = ggml_norm(ctx0, cur, model.hparams.norm_eps); - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model.layers[il].norm_1_w, cur), - cur); - // compute QKV - cur = ggml_mul_mat(ctx0, - model.layers[il].attn_Wqkv_w, - cur); - - // TODO: clip_qkv - struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*ggml_element_size(cur)*n_embd)); - struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*ggml_element_size(cur)*n_embd)); - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*ggml_element_size(cur)*n_embd)); - - // TODO: qk_ln? (seems to be False in MPT-7B configs) - { - Vcur = ggml_transpose(ctx0, Vcur); - - struct ggml_tensor * k = ggml_view_1d(ctx0, model.kv_self.k, N*n_embd, (ggml_element_size(model.kv_self.k)*n_embd)*(il*n_ctx + n_past)); - struct ggml_tensor * v = ggml_view_2d(ctx0, model.kv_self.v, N, n_embd, - ( n_ctx)*ggml_element_size(model.kv_self.v), - (il*n_ctx)*ggml_element_size(model.kv_self.v)*n_embd + n_past*ggml_element_size(model.kv_self.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, - ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, N), - 0, 2, 1, 3); - - struct ggml_tensor * K = - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_view_1d(ctx0, model.kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.kv_self.k)*n_embd), - n_embd/n_head, n_head, n_past + N), - 0, 2, 1, 3); - - // 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(ctx0, - KQ, - ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head)) - ); - - - // Alibi - struct ggml_tensor * KQ_scaled_biased = ggml_alibi( - ctx0, ggml_cont(ctx0, KQ_scaled), n_past, n_head, model.hparams.alibi_bias_max - ); - - // KQ_masked = mask_past(KQ_scaled) - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_biased, n_past); - - // KQ = soft_max(KQ_masked) - struct ggml_tensor * KQ_soft_max = ggml_soft_max(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.kv_self.v, - n_past + N, n_embd/n_head, n_head, - n_ctx*ggml_element_size(model.kv_self.v), - n_ctx*ggml_element_size(model.kv_self.v)*n_embd/n_head, - il*n_ctx*ggml_element_size(model.kv_self.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 (no bias) - cur = ggml_mul_mat(ctx0, - model.layers[il].attn_out_proj_w, - cur); - } - - ggml_set_scratch(ctx0, {0, model.scr1_buf.size, model.scr1_buf.addr, }); - // residual - struct ggml_tensor * resSA = ggml_add(ctx0, cur, inpSA); - // feed-forward network - { - cur = resSA; - // norm2 - cur = ggml_norm(ctx0, cur, model.hparams.norm_eps); - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model.layers[il].norm_2_w, cur), - cur); - // ffn - cur = ggml_mul_mat(ctx0, - model.layers[il].ffn_up_proj_w, - cur); - cur = ggml_gelu(ctx0, cur); - cur = ggml_mul_mat(ctx0, - model.layers[il].ffn_down_proj_w, - cur); - - } - - // self-attention + FF - inpL = ggml_add(ctx0, cur, resSA); - } - ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, }); - - struct ggml_tensor * out = inpL; - // -> logits - { - out = ggml_norm(ctx0, out, model.hparams.norm_eps); - out = ggml_mul(ctx0, - ggml_repeat(ctx0, model.norm_f_w, out), - out); - ggml_set_scratch(ctx0, { 0, 0, nullptr, }); - out = ggml_mul_mat(ctx0, model.wte, out); - } - - ggml_build_forward_expand(&gf, out); - - - // run the computation - { - std::unique_ptr data; - auto plan = ggml_graph_plan(&gf, n_threads); - if (plan.work_size > 0) { - data.reset(new uint8_t[plan.work_size]); - plan.work_data = data.get(); - } - ggml_graph_compute(&gf, &plan); - } - - - // return result for just the last token - embd_w.resize(n_vocab); - memcpy(embd_w.data(), (float *) ggml_get_data(out) + (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; -} - - -#define MPT_MAX_RNG_STATE 64*1024 - -size_t mpt_get_state_size(const mpt_model &model) -{ - // we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state. - // for reference, std::mt19937(1337) serializes to 6701 bytes. - const size_t s_rng_size = sizeof(size_t); - const size_t s_rng = MPT_MAX_RNG_STATE; - const size_t s_kv_size = sizeof(size_t); - const size_t s_kv_ntok = sizeof(int); - const size_t s_kv = model.kv_self.buf.size; - const size_t s_total = ( - + s_rng_size - + s_rng - + s_kv_size - + s_kv_ntok - + s_kv - ); - fflush(stdout); - return s_total; -} - -size_t mpt_copy_state_data(const mpt_model &model, const std::mt19937 &rng, uint8_t *dest) -{ - uint8_t * out = dest; - fflush(stdout); - // copy rng - { - std::stringstream rng_ss; - rng_ss << rng; - - const size_t rng_size = rng_ss.str().size(); - char rng_buf[MPT_MAX_RNG_STATE]; - - memset(&rng_buf[0], 0, MPT_MAX_RNG_STATE); - memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size()); - - memcpy(out, &rng_size, sizeof(rng_size)); out += sizeof(rng_size); - memcpy(out, &rng_buf[0], MPT_MAX_RNG_STATE); out += MPT_MAX_RNG_STATE; - } - - // copy kv cache - { - const size_t kv_size = model.kv_self.buf.size; - const int kv_ntok = model.kv_self.n; - - memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size); - memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok); - - if (kv_size) { - memcpy(out, model.kv_self.buf.addr, kv_size); out += kv_size; - } - } - - const size_t written = out - dest; - assert(written == mpt_get_state_size(model)); - fflush(stdout); - return written; -} - -size_t mpt_set_state_data(mpt_model *model, std::mt19937 *rng, const uint8_t *src) -{ - const uint8_t * in = src; - - // set rng - { - size_t rng_size; - char rng_buf[MPT_MAX_RNG_STATE]; - - memcpy(&rng_size, in, sizeof(rng_size)); in += sizeof(rng_size); - memcpy(&rng_buf[0], in, MPT_MAX_RNG_STATE); in += MPT_MAX_RNG_STATE; - - std::stringstream rng_ss; - rng_ss.str(std::string(&rng_buf[0], rng_size)); - rng_ss >> *rng; - - assert(rng_ss.fail() == false); - } - - // set kv cache - { - size_t kv_size; - int kv_ntok; - - memcpy(&kv_size, in, sizeof(kv_size)); in += sizeof(kv_size); - memcpy(&kv_ntok, in, sizeof(kv_ntok)); in += sizeof(kv_ntok); - - if (kv_size) { - assert(model->kv_self.buf.size == kv_size); - - void * k_data = model->kv_self.k->data; // remember data pointers - void * v_data = model->kv_self.v->data; // because their value is stored in buf and overwritten by memcpy - - memcpy(model->kv_self.buf.addr, in, kv_size); in += kv_size; - - model->kv_self.k->data = k_data; // restore correct data pointers - model->kv_self.v->data = v_data; - - } - - model->kv_self.n = kv_ntok; - } - - const size_t nread = in - src; - assert(nread == mpt_get_state_size(*model)); - fflush(stdout); - return nread; -} - -struct MPTPrivate { - const std::string modelPath; - bool modelLoaded; - mpt_vocab vocab; - mpt_model *model = nullptr; - int64_t n_threads = 0; - size_t mem_per_token = 0; - std::mt19937 rng; - bool has_end_of_text = false; -}; - -MPT::MPT() - : d_ptr(new MPTPrivate) { - d_ptr->model = new mpt_model; - d_ptr->model->ctx = nullptr; - d_ptr->modelLoaded = false; -} - -size_t MPT::requiredMem(const std::string &modelPath) { - mpt_model dummy_model; - mpt_vocab dummy_vocab; - size_t mem_req; - mpt_model_load(modelPath, dummy_model, dummy_vocab, &mem_req); - return mem_req; -} - -bool MPT::loadModel(const std::string &modelPath) { - std::mt19937 rng(time(NULL)); - d_ptr->rng = rng; - - // load the model - if (!mpt_model_load(modelPath, *d_ptr->model, d_ptr->vocab, nullptr)) { - std::cerr << "MPT ERROR: failed to load model from " << modelPath; - return false; - } - - d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); - d_ptr->modelLoaded = true; - const auto & vocab = d_ptr->vocab; - d_ptr->has_end_of_text = vocab.raw.token_to_id.find(vocab.end_of_text()) != vocab.raw.token_to_id.end(); - fflush(stdout); - return true; -} - -void MPT::setThreadCount(int32_t n_threads) { - d_ptr->n_threads = n_threads; -} - -int32_t MPT::threadCount() const -{ - return d_ptr->n_threads; -} - -MPT::~MPT() -{ - delete d_ptr->model; -} - -bool MPT::isModelLoaded() const -{ - return d_ptr->modelLoaded; -} - -size_t MPT::stateSize() const -{ - return mpt_get_state_size(*d_ptr->model); -} - -size_t MPT::saveState(uint8_t *dest) const -{ - return mpt_copy_state_data(*d_ptr->model, d_ptr->rng, dest); -} - -size_t MPT::restoreState(const uint8_t *src) -{ - return mpt_set_state_data(d_ptr->model, &d_ptr->rng, src); -} - -std::vector MPT::tokenize(PromptContext &, const std::string &str) const -{ - if (d_ptr->vocab.is_replit) { - return replit_tokenizer_tokenize(d_ptr->vocab, str); - } - return ::gpt_tokenize(d_ptr->vocab.raw, str); -} - -std::string MPT::tokenToString(Token id) const -{ - if (d_ptr->vocab.is_replit) { - return replit_tokenizer_detokenize(d_ptr->vocab, {id}); - } - return d_ptr->vocab.raw.id_to_token[id]; -} - -LLModel::Token MPT::sampleToken(PromptContext &promptCtx) const -{ - const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size()); - return gpt_sample_top_k_top_p(d_ptr->model->hparams.n_vocab, - promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks, - n_prev_toks, - promptCtx.logits, - promptCtx.top_k, promptCtx.top_p, promptCtx.temp, - promptCtx.repeat_penalty, - d_ptr->rng); -} - -bool MPT::evalTokens(PromptContext &ctx, const std::vector &tokens) const -{ - // determine the required inference memory per token: - static bool initialized = false; - if (!initialized) { - mpt_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, ctx.logits, - d_ptr->mem_per_token); - initialized = true; - } - - return mpt_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token); -} - -int32_t MPT::contextLength() const -{ - return d_ptr->model->hparams.n_ctx; -} - -const std::vector &MPT::endTokens() const -{ - static std::vector fres; - if (fres.empty()) { - fres = {0, d_ptr->vocab.raw.token_to_id[d_ptr->vocab.end_of_text()]}; - } - return fres; -} - -std::string get_arch_name(gguf_context *ctx_gguf) { - std::string arch_name; - const int kid = gguf_find_key(ctx_gguf, "general.architecture"); - enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid); - if (ktype != GGUF_TYPE_STRING) { - throw std::runtime_error("ERROR: Can't get general architecture from gguf file."); - } - return gguf_get_val_str(ctx_gguf, kid); -} - -#if defined(_WIN32) -#define DLL_EXPORT __declspec(dllexport) -#else -#define DLL_EXPORT __attribute__ ((visibility ("default"))) -#endif - -extern "C" { -DLL_EXPORT bool is_g4a_backend_model_implementation() { - return true; -} - -DLL_EXPORT const char *get_model_type() { - return modelType_; -} - -DLL_EXPORT const char *get_build_variant() { - return GGML_BUILD_VARIANT; -} - -DLL_EXPORT bool magic_match(const char * fname) { - struct ggml_context * ctx_meta = NULL; - struct gguf_init_params params = { - /*.no_alloc = */ true, - /*.ctx = */ &ctx_meta, - }; - gguf_context *ctx_gguf = gguf_init_from_file(fname, params); - if (!ctx_gguf) - return false; - - bool isValid = gguf_get_version(ctx_gguf) <= 2; - isValid = isValid && get_arch_name(ctx_gguf) == "mpt"; - - gguf_free(ctx_gguf); - return isValid; -} - -DLL_EXPORT LLModel *construct() { - return new MPT; -} -} diff --git a/gpt4all-backend/mpt_impl.h b/gpt4all-backend/mpt_impl.h deleted file mode 100644 index df7b7718..00000000 --- a/gpt4all-backend/mpt_impl.h +++ /dev/null @@ -1,41 +0,0 @@ -#ifndef MPT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE -#error This file is NOT meant to be included outside of mpt.cpp. Doing so is DANGEROUS. Be sure to know what you are doing before proceeding to #define MPT_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE -#endif -#ifndef MPT_H -#define MPT_H - -#include -#include -#include -#include "llmodel.h" - -struct MPTPrivate; -class MPT : public LLModel { -public: - MPT(); - ~MPT(); - - bool supportsEmbedding() const override { return false; } - bool supportsCompletion() const override { return true; } - bool loadModel(const std::string &modelPath) override; - bool isModelLoaded() const override; - size_t requiredMem(const std::string &modelPath) override; - size_t stateSize() const override; - size_t saveState(uint8_t *dest) const override; - size_t restoreState(const uint8_t *src) override; - void setThreadCount(int32_t n_threads) override; - int32_t threadCount() const override; - -private: - MPTPrivate *d_ptr; - -protected: - std::vector tokenize(PromptContext &, const std::string&) const override; - std::string tokenToString(Token) const override; - Token sampleToken(PromptContext &ctx) const override; - bool evalTokens(PromptContext &ctx, const std::vector &tokens) const override; - int32_t contextLength() const override; - const std::vector& endTokens() const override; -}; - -#endif // MPT_H diff --git a/gpt4all-backend/scripts/convert_mpt_hf_to_gguf.py b/gpt4all-backend/scripts/convert_mpt_hf_to_gguf.py index 9db33c61..b2688e56 100755 --- a/gpt4all-backend/scripts/convert_mpt_hf_to_gguf.py +++ b/gpt4all-backend/scripts/convert_mpt_hf_to_gguf.py @@ -18,7 +18,7 @@ from pathlib import Path import gguf import numpy as np import torch -from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig +from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, MptConfig from transformers.models.gpt2 import tokenization_gpt2 @@ -30,7 +30,7 @@ if not 3 <= len(sys.argv) < 5: print(" ftype == 1 -> float16") sys.exit(1) -model_name = sys.argv[1] +dir_model = Path(sys.argv[1]) dir_out = Path(sys.argv[2]) # make sure the output directory exists @@ -50,7 +50,7 @@ if len(sys.argv) > 3: print("Invalid ftype: " + str(ftype)) sys.exit(1) -fname_out = dir_out / f"ggml-model-{Path(model_name).name}-{ftype_str[ftype]}.gguf" +fname_out = dir_out / f"ggml-model-{dir_model.name}-{ftype_str[ftype]}.gguf" ARCH = gguf.MODEL_ARCH.MPT @@ -58,7 +58,7 @@ gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) print("gguf: get model metadata") -config = AutoConfig.from_pretrained(model_name) +config = AutoConfig.from_pretrained(dir_model, trust_remote_code=True) block_count = config.n_layers gguf_writer.add_name("MPT") @@ -67,17 +67,19 @@ gguf_writer.add_embedding_length(config.d_model) gguf_writer.add_block_count(block_count) gguf_writer.add_feed_forward_length(4 * config.d_model) gguf_writer.add_head_count(config.n_heads) -gguf_writer.add_max_alibi_bias(config.attn_config.alibi_bias_max) -gguf_writer.add_layer_norm_eps(config.layer_norm_epsilon) +if kv_n_heads := config.attn_config.get('kv_n_heads'): + gguf_writer.add_head_count_kv(kv_n_heads) +gguf_writer.add_max_alibi_bias(config.attn_config['alibi_bias_max']) +gguf_writer.add_layer_norm_eps(MptConfig().layer_norm_epsilon) # use default from upstream transformers gguf_writer.add_file_type(ftype) -clip_qkv = config.attn_config.clip_qkv +clip_qkv = config.attn_config['clip_qkv'] if clip_qkv is not None: gguf_writer.add_clamp_kqv(clip_qkv) print("gguf: get gpt2 tokenizer vocab") -tokenizer = AutoTokenizer.from_pretrained(model_name) +tokenizer = AutoTokenizer.from_pretrained(dir_model) special_ids = tokenizer.all_special_ids @@ -111,13 +113,17 @@ gguf_writer.add_tokenizer_model("gpt2") gguf_writer.add_token_list(tokens) gguf_writer.add_token_types(toktypes) +special_vocab = gguf.SpecialVocab(dir_model, load_merges=True) +special_vocab.add_to_gguf(gguf_writer) + print("gguf: get tensor metadata") -print("Loading model:", model_name) +print("Loading model:", dir_model) model = AutoModelForCausalLM.from_pretrained( - model_name, config=config, torch_dtype=torch.float16 if ftype == 1 else torch.float32, low_cpu_mem_usage=True, + dir_model, config=config, torch_dtype=torch.float16 if ftype == 1 else torch.float32, + low_cpu_mem_usage=True, trust_remote_code=True, ) -print("Model loaded:", model_name) +print("Model loaded:", dir_model) tensor_map = gguf.get_tensor_name_map(ARCH, block_count) diff --git a/gpt4all-chat/CMakeLists.txt b/gpt4all-chat/CMakeLists.txt index 103188bc..f7884eb6 100644 --- a/gpt4all-chat/CMakeLists.txt +++ b/gpt4all-chat/CMakeLists.txt @@ -189,8 +189,6 @@ install(TARGETS llamamodel-mainline-default DESTINATION lib COMPONENT ${COMPONEN if(APPLE) install(TARGETS llamamodel-mainline-metal DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) endif() -install(TARGETS mpt-avxonly DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) -install(TARGETS mpt-default DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) install(TARGETS bert-avxonly DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) install(TARGETS bert-default DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) diff --git a/gpt4all-chat/chatllm.cpp b/gpt4all-chat/chatllm.cpp index 3f26f483..efb43ee4 100644 --- a/gpt4all-chat/chatllm.cpp +++ b/gpt4all-chat/chatllm.cpp @@ -9,7 +9,6 @@ //#define DEBUG //#define DEBUG_MODEL_LOADING -#define MPT_INTERNAL_STATE_VERSION 0 #define GPTJ_INTERNAL_STATE_VERSION 0 #define LLAMA_INTERNAL_STATE_VERSION 0 #define BERT_INTERNAL_STATE_VERSION 0 @@ -325,7 +324,6 @@ bool ChatLLM::loadModel(const ModelInfo &modelInfo) switch (m_llModelInfo.model->implementation().modelType()[0]) { case 'L': m_llModelType = LLModelType::LLAMA_; break; case 'G': m_llModelType = LLModelType::GPTJ_; break; - case 'M': m_llModelType = LLModelType::MPT_; break; case 'B': m_llModelType = LLModelType::BERT_; break; default: { @@ -760,7 +758,6 @@ bool ChatLLM::serialize(QDataStream &stream, int version, bool serializeKV) if (version > 1) { stream << m_llModelType; switch (m_llModelType) { - case MPT_: stream << MPT_INTERNAL_STATE_VERSION; break; case GPTJ_: stream << GPTJ_INTERNAL_STATE_VERSION; break; case LLAMA_: stream << LLAMA_INTERNAL_STATE_VERSION; break; case BERT_: stream << BERT_INTERNAL_STATE_VERSION; break; diff --git a/gpt4all-chat/chatllm.h b/gpt4all-chat/chatllm.h index 4e07e48f..367915f6 100644 --- a/gpt4all-chat/chatllm.h +++ b/gpt4all-chat/chatllm.h @@ -10,7 +10,6 @@ #include "../gpt4all-backend/llmodel.h" enum LLModelType { - MPT_, GPTJ_, LLAMA_, CHATGPT_, diff --git a/gpt4all-chat/metadata/models2.json b/gpt4all-chat/metadata/models2.json index 1f3f8a7d..70933da3 100644 --- a/gpt4all-chat/metadata/models2.json +++ b/gpt4all-chat/metadata/models2.json @@ -126,20 +126,20 @@ }, { "order": "j", - "md5sum": "51c627fac9062e208f9b386f105cbd48", + "md5sum": "e30579a1b109882f10e2a5e75ea388fb", "disableGUI": "true", "name": "Replit", - "filename": "replit-code-v1-3b-q4_0.gguf", - "filesize": "1532949760", + "filename": "replit-code-v1_5-3b-q4_0.gguf", + "filesize": "1870449696", "requires": "2.5.0", "ramrequired": "4", "parameters": "3 billion", - "quant": "f16", + "quant": "q4_0", "type": "Replit", "systemPrompt": " ", "promptTemplate": "%1", "description": "Trained on subset of the Stack
  • Code completion based
  • Licensed for commercial use
", - "url": "https://gpt4all.io/models/gguf/replit-code-v1-3b-q4_0.gguf" + "url": "https://gpt4all.io/models/gguf/replit-code-v1_5-3b-q4_0.gguf" }, { "order": "k",