diff --git a/gpt4all-backend/CMakeLists.txt b/gpt4all-backend/CMakeLists.txt index 736cf47c..0da24dba 100644 --- a/gpt4all-backend/CMakeLists.txt +++ b/gpt4all-backend/CMakeLists.txt @@ -103,11 +103,6 @@ foreach(BUILD_VARIANT IN LISTS BUILD_VARIANTS) # gptj.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h) # prepare_target(gptj ggml-230511) - add_library(falcon-${BUILD_VARIANT} SHARED - falcon.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h) - target_compile_definitions(falcon-${BUILD_VARIANT} PRIVATE LLAMA_VERSIONS=>=3 LLAMA_DATE=999999) - prepare_target(falcon 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) diff --git a/gpt4all-backend/falcon.cpp b/gpt4all-backend/falcon.cpp deleted file mode 100644 index de198358..00000000 --- a/gpt4all-backend/falcon.cpp +++ /dev/null @@ -1,989 +0,0 @@ -#include "ggml.h" -#define FALCON_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE -#include "falcon_impl.h" -#include "llama.h" -#include "utils.h" -#include "llmodel_shared.h" - -#include -#include -#include -#include -#include -#include - -namespace { -const char *modelType_ = "Falcon"; -} - -// commented out 40B support as it presently would require forking ggml/llama.cpp -// can re-add once mainline ggml supports it - -#define FALCON_MAGIC 0x67676a74 - -// default hparams (Falcon 7B) -struct falcon_hparams { - int32_t n_vocab = 65024; - int32_t n_embd = 4544; - int32_t n_head = 71; - int32_t n_head_kv = 1; - int32_t n_layer = 32; - int32_t falcon_version = 7; // 7 for Falcon-7B, 40 for Falcon-40B - int32_t ftype = 1; - int32_t n_ctx = 2048; -}; - -struct falcon_layer { - // normalization - struct ggml_tensor* input_layernorm; - struct ggml_tensor* input_layernorm_b; - //struct ggml_tensor* attention_norm; // Falcon-40B only - //struct ggml_tensor* attention_norm_b; // Falcon-40B only - - // attention - struct ggml_tensor* query_key_value; - struct ggml_tensor* wo; - - // ff - struct ggml_tensor* ffn_up; - struct ggml_tensor* ffn_down; -}; - -struct falcon_model { - falcon_hparams hparams; - - struct ggml_tensor* tok_embeddings; - struct ggml_tensor* output_norm; - struct ggml_tensor* output_norm_b; - struct ggml_tensor* lm_head; - - std::vector layers; - - // key + value memory - llm_kv_cache kv_self; - - struct ggml_context* ctx; - std::map tensors; - - llm_buffer eval_buf; - llm_buffer work_buf; - llm_buffer scr0_buf; - llm_buffer scr1_buf; -}; - -static bool kv_cache_init( - const struct falcon_hparams & hparams, - struct llm_kv_cache & cache, - ggml_type wtype, - int n_ctx) { - const int n_embd = hparams.n_embd; - const int dim_head = n_embd / hparams.n_head; - const int dim_kv = dim_head * hparams.n_head_kv; - const int n_layer = hparams.n_layer; - - const int64_t n_mem = (int64_t)n_layer*n_ctx; - const int64_t n_elements = dim_kv * n_mem; - cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2_MiB); - struct ggml_init_params params; - params.mem_size = cache.buf.size; - params.mem_buffer = cache.buf.addr; - params.no_alloc = false; - - cache.ctx = ggml_init(params); - if (!cache.ctx) { - fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__); - return false; - } - - cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements); - cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements); - return true; -} - -// load the model's weights from a file -bool falcon_model_load(const std::string & fname, falcon_model & model, gpt_vocab & vocab, size_t *mem_req) { - printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str()); - if (mem_req) { - *mem_req = 0; - } - - auto fin = std::ifstream(fname, std::ios::binary); - if (!fin) { - fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str()); - return false; - } - - // verify magic - { - uint32_t magic; - fin.read((char *) &magic, sizeof(magic)); - if (magic != FALCON_MAGIC) { - fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str()); - return false; - } - } - - uint32_t format_version; - fin.read((char *) &format_version, sizeof(format_version)); - - // load hparams - { - auto & hparams = model.hparams; - - fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab)); - 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_head_kv, sizeof(hparams.n_head_kv)); - fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer)); - fin.read((char *) &hparams.falcon_version, sizeof(hparams.falcon_version)); - fin.read((char *) &hparams.ftype, sizeof(hparams.ftype)); - - if (hparams.falcon_version != 7) { // && hparams.falcon_version != 40) { - fprintf(stderr, "%s: invalid model file '%s' (bad Falcon version: %d)\n", __func__, fname.c_str(), hparams.falcon_version); - return false; - } - - const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR; - - printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab); - printf("%s: n_embd = %d\n", __func__, hparams.n_embd); - printf("%s: n_head = %d\n", __func__, hparams.n_head); - printf("%s: n_head_kv = %d\n", __func__, hparams.n_head_kv); - 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 - { - const int32_t n_vocab = model.hparams.n_vocab; - - std::string word; - std::vector buf(128); - - for (int i = 0; i < n_vocab; i++) { - uint32_t len; - fin.read((char *) &len, sizeof(len)); - - buf.resize(len); - fin.read((char *) buf.data(), len); - word.assign(buf.data(), len); - - uint32_t dummy; - fin.read((char *) &dummy, sizeof(dummy)); - - vocab.token_to_id[word] = i; - vocab.id_to_token[i] = word; - } - } - - // for the big tensors, we have the option to store the data in 16-bit floats or quantized - // in order to save memory and also to speed up the computation - ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype)); - if (wtype == GGML_TYPE_COUNT) { - fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n", - __func__, fname.c_str(), model.hparams.ftype); - return false; - } - - auto & ctx = model.ctx; - - size_t ctx_size = 0; - - { - const auto& hparams = model.hparams; - - const int n_embd = hparams.n_embd; - const int n_head = hparams.n_head; - const int n_head_kv = hparams.n_head_kv; - const int n_layer = hparams.n_layer; - const int n_ctx = hparams.n_ctx; - const int n_ff = 4 * model.hparams.n_embd; - const int n_vocab = hparams.n_vocab; - const int head_dim = hparams.n_embd / hparams.n_head; - - ctx_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_vocab; // tok_embeddings - ctx_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd; // output_norm - ctx_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd; // output_norm_b - ctx_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_vocab; // lm_head - - // if (hparams.version == 40) { // Falcon-40B - // ctx_size += n_layer * ggml_sizeof_tensor_1d(GGML_TYPE_F32, n_embd); // attention_norm - // ctx_size += n_layer * ggml_sizeof_tensor_1d(GGML_TYPE_F32, n_embd); // attention_norm_b - // } - ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd); // input_layernorm - ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd); // input_layernorm_b - ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * (n_head_kv * 2 + n_head) * head_dim); // query_key_value - ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_embd); // wo - ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_ff); // ffn_up - ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_ff * n_embd); // ffn_down - - printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0)); - } - - if (mem_req) { - const int n_embd = model.hparams.n_embd; - const int dim_head = n_embd / model.hparams.n_head; - const int dim_kv = dim_head * model.hparams.n_head_kv; - const int n_layer = model.hparams.n_layer; - - const int64_t n_mem = (int64_t)n_layer*model.hparams.n_ctx; - const int64_t n_elements = dim_kv * n_mem; - size_t kv_cache_size = 2u*n_elements*ggml_type_size(wtype) + 2_MiB; - *mem_req = ctx_size + kv_cache_size; - return false; - } - - // create the ggml context - { - struct ggml_init_params params = { - .mem_size = ctx_size, - .mem_buffer = NULL, - .no_alloc = false, - }; - - model.ctx = ggml_init(params); - if (!model.ctx) { - fprintf(stderr, "%s: ggml_init() failed\n", __func__); - return false; - } - } - - // prepare memory for the weights - { - const auto& hparams = model.hparams; - - const int n_embd = hparams.n_embd; - const int n_head = hparams.n_head; - const int n_head_kv = hparams.n_head_kv; - const int n_layer = hparams.n_layer; - const int n_ff = 4 * model.hparams.n_embd; - const int n_vocab = hparams.n_vocab; - const int head_dim = hparams.n_embd / hparams.n_head; - - model.layers.resize(n_layer); - - model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab); - - model.output_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - model.output_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - model.lm_head = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab); - - // map by name - model.tensors["transformer.word_embeddings.weight"] = - model.tok_embeddings; - - model.tensors["transformer.ln_f.weight"] = model.output_norm; - model.tensors["transformer.ln_f.bias"] = model.output_norm_b; - model.tensors["lm_head.weight"] = model.lm_head; - - for (int i = 0; i < n_layer; ++i) { - auto& layer = model.layers[i]; - - layer.input_layernorm = - ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - layer.input_layernorm_b = - ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - - // if (hparams.version == 40) { // for Falcon-40B only - // layer.attention_norm = - // ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - // layer.attention_norm_b = - // ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); - // } - - // query_key_value shape for config.multi_query == True: - layer.query_key_value = ggml_new_tensor_2d( - ctx, wtype, n_embd, (n_head_kv * 2 + n_head) * head_dim); - layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd); - - layer.ffn_up = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff); - layer.ffn_down = ggml_new_tensor_2d(ctx, wtype, n_ff, n_embd); - - // map by name - // if (hparams.version == 40) { - // // Falcon-40B: - // model.tensors["transformer.h." + std::to_string(i) + - // ".ln_mlp.weight"] = layer.input_layernorm; - // model.tensors["transformer.h." + std::to_string(i) + - // ".ln_mlp.bias"] = layer.input_layernorm_b; - // model.tensors["transformer.h." + std::to_string(i) + - // ".ln_attn.weight"] = layer.attention_norm; - // model.tensors["transformer.h." + std::to_string(i) + - // ".ln_attn.bias"] = layer.attention_norm_b; - // } else { - // Falcon-7B: - model.tensors["transformer.h." + std::to_string(i) + - ".input_layernorm.weight"] = layer.input_layernorm; - model.tensors["transformer.h." + std::to_string(i) + - ".input_layernorm.bias"] = layer.input_layernorm_b; - //} - - model.tensors["transformer.h." + std::to_string(i) + - ".self_attention.query_key_value.weight"] = - layer.query_key_value; - model.tensors["transformer.h." + std::to_string(i) + - ".self_attention.dense.weight"] = layer.wo; - - model.tensors["transformer.h." + std::to_string(i) + - ".mlp.dense_h_to_4h.weight"] = layer.ffn_up; - model.tensors["transformer.h." + std::to_string(i) + - ".mlp.dense_4h_to_h.weight"] = layer.ffn_down; - } - } - - // key + value memory - { - const auto & hparams = model.hparams; - - const int n_layer = hparams.n_layer; - const int n_ctx = hparams.n_ctx; - const int n_head_kv = hparams.n_head_kv; - const int head_dim = hparams.n_embd / hparams.n_head; - - const int64_t n_mem = n_layer*n_ctx; - const int64_t n_elements = head_dim*n_mem; - - if (!kv_cache_init(hparams, model.kv_self, GGML_TYPE_F32, model.hparams.n_ctx)) { - fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__); - ggml_free(ctx); - return false; - } - const size_t memory_size = ggml_nbytes(model.kv_self.k) + ggml_nbytes(model.kv_self.v); - - printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size/1024.0/1024.0, n_mem); - } - - // load weights - { - int n_tensors = 0; - size_t total_size = 0; - - printf("%s: ", __func__); - - while (true) { - int32_t n_dims; - int32_t length; - int32_t ttype; - - fin.read(reinterpret_cast(&n_dims), sizeof(n_dims)); - fin.read(reinterpret_cast(&length), sizeof(length)); - fin.read(reinterpret_cast(&ttype), sizeof(ttype)); - - if (fin.eof()) { - break; - } - - int32_t nelements = 1; - int32_t ne[2] = { 1, 1 }; - for (int i = 0; i < n_dims; ++i) { - fin.read(reinterpret_cast(&ne[i]), sizeof(ne[i])); - nelements *= ne[i]; - } - - std::string name(length, 0); - fin.read(&name[0], length); - fin.seekg(-static_cast(fin.tellg()) & 31, std::ios_base::cur); - - if (model.tensors.find(name.data()) == model.tensors.end()) { - fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data()); - return false; - } - - auto tensor = model.tensors[name.data()]; - if (ggml_nelements(tensor) != nelements) { - fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data()); - return false; - } - - if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) { - fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%5d, %5d], expected [%5d, %5d]\n", - __func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]); - return false; - } - - // for debugging - if (0) { - printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor)); - } - - const size_t bpe = ggml_type_size(ggml_type(ttype)); - - if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) { - fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n", - __func__, name.data(), ggml_nbytes(tensor), nelements*bpe); - return false; - } - - fin.read(reinterpret_cast(tensor->data), ggml_nbytes(tensor)); - - total_size += ggml_nbytes(tensor); - if (++n_tensors % 8 == 0) { - printf("."); - fflush(stdout); - } - } - - printf(" done\n"); - - printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors); - } - - fin.close(); - - model.eval_buf.resize(1280u * 1024 * 1024); - model.scr0_buf.resize(256u * 1024 * 1024); - model.scr1_buf.resize(256u * 1024 * 1024); - return true; -} - - -// evaluate the transformer -// -// - model: the model -// - n_threads: number of threads to use -// - n_past: the context size so far -// - embd_inp: the embeddings of the tokens in the context -// - embd_w: the predicted logits for the next token -// -bool falcon_eval( - falcon_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_head_kv = hparams.n_head_kv; - const int n_vocab = hparams.n_vocab; - const int version = hparams.falcon_version; - const size_t head_dim = n_embd / n_head; - - struct ggml_init_params eval_ctx_params = { - .mem_size = model.eval_buf.size, - .mem_buffer = model.eval_buf.addr, - .no_alloc = false, - }; - - struct ggml_context * ctx0 = ggml_init(eval_ctx_params); - struct ggml_cgraph gf = {}; - - 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.tok_embeddings, embd); - struct ggml_tensor* repeat_dummy = ggml_new_tensor_3d(ctx0, inpL->type, head_dim, N + n_past, n_head); - - ggml_type wtype = GGML_TYPE_F32; - const int sizeof_wtype = ggml_type_sizef(wtype); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * cur; - struct ggml_tensor * layernorm_output; - - ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, }); - - // self-attention - { - layernorm_output = ggml_norm(ctx0, inpL, 1e-5f); - - layernorm_output = ggml_add(ctx0, - ggml_mul(ctx0, - ggml_repeat(ctx0, model.layers[il].input_layernorm, layernorm_output), - layernorm_output), - ggml_repeat(ctx0, model.layers[il].input_layernorm_b, layernorm_output)); - - // if (version == 40) { // Falcon-40B only - // cur = ggml_norm(ctx0, inpL); - - // cur = ggml_add(ctx0, - // ggml_mul(ctx0, - // ggml_repeat(ctx0, model.layers[il].attention_norm, cur), - // cur), - // ggml_repeat(ctx0, model.layers[il].attention_norm_b, cur)); - // } - // else { - cur = layernorm_output; - // } - - // compute QKV - - cur = ggml_mul_mat(ctx0, model.layers[il].query_key_value, cur); - - // Note that the strides for Kcur, Vcur are set up so that the - // resulting views are misaligned with the tensor's storage - // (by applying the K/V offset we shift the tensor's original - // view to stick out behind the viewed QKV tensor's allocated - // memory, so to say). This is ok because no actual accesses - // happen to that out-of-range memory, but it can require some - // trickery when trying to accurately dump these views for - // debugging. - - struct ggml_tensor * Qcur = ggml_view_3d( - ctx0, cur, head_dim, n_head, N, - head_dim * sizeof_wtype, - head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype, - 0); - - struct ggml_tensor * Kcur = ggml_view_3d( - ctx0, cur, head_dim, n_head_kv, N, - head_dim * sizeof_wtype, - head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype, - head_dim * n_head * sizeof_wtype); - - struct ggml_tensor * Vcur = ggml_view_3d( - ctx0, cur, head_dim, n_head_kv, N, - head_dim * sizeof_wtype, - head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype, - head_dim * (n_head + n_head_kv) * sizeof_wtype); - - // using mode = 2 for neox mode - Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, head_dim, 2, n_ctx); - Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, head_dim, 2, n_ctx); - - // store key and value to memory - { - struct ggml_tensor* k = ggml_view_1d( - ctx0, model.kv_self.k, N * n_head_kv * head_dim, - (ggml_element_size(model.kv_self.k) * n_head_kv * head_dim) * - (il * n_ctx + n_past)); - struct ggml_tensor* v = ggml_view_1d( - ctx0, model.kv_self.v, N * n_head_kv * head_dim, - (ggml_element_size(model.kv_self.v) * n_head_kv * head_dim) * - (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)); - } - - struct ggml_tensor * K = ggml_permute( - ctx0, - ggml_view_3d( - ctx0, - model.kv_self.k, - head_dim, n_head_kv, n_past + N, - head_dim * sizeof_wtype, - head_dim * n_head_kv * sizeof_wtype, - il * n_ctx * ggml_element_size(model.kv_self.k) * n_head_kv * head_dim), - 0, 2, 1, 3); - - // K * Q - - // changed from repeat2 back to repeat, will not support 40B! - K = ggml_cont(ctx0, ggml_repeat(ctx0, K, repeat_dummy)); - - struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3); - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - struct ggml_tensor * KQ_scaled = - ggml_scale_inplace(ctx0, - KQ, - ggml_new_f32(ctx0, 1.0f/sqrt(float(head_dim))) - ); - - // KQ_masked = mask_past(KQ_scaled) - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past); - - // KQ = soft_max(KQ_masked) - struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked); - - // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous() - struct ggml_tensor* V = ggml_permute( - ctx0, - ggml_view_3d( - ctx0, - model.kv_self.v, - head_dim, n_head_kv, n_past + N, - head_dim * sizeof_wtype, - head_dim * n_head_kv * sizeof_wtype, - il * n_ctx * ggml_element_size(model.kv_self.v) * n_head_kv * head_dim), - 0, 2, 1, 3); - - // changed from repeat2 back to repeat, will not support 40B! - V = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_repeat(ctx0, V, repeat_dummy))); - - // KQV = transpose(V) * KQ_soft_max - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - - // cur = KQV_merged.contiguous().view(n_embd, N) - cur = ggml_cpy(ctx0, - KQV_merged, - ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - // projection - { - cur = ggml_mul_mat(ctx0, - model.layers[il].wo, - cur); - } - } - - ggml_set_scratch(ctx0, {0, model.scr1_buf.size, model.scr1_buf.addr, }); - - struct ggml_tensor* inpFF = layernorm_output; - struct ggml_tensor* attn_out = ggml_cpy( - ctx0, cur, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - { - cur = ggml_mul_mat(ctx0, model.layers[il].ffn_up, inpFF); - cur = ggml_gelu(ctx0, cur); - cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down, cur); - } - - cur = ggml_add(ctx0, cur, attn_out); - cur = ggml_add(ctx0, cur, inpL); - // input for next layer - inpL = cur; - } - - ggml_set_scratch(ctx0, {0, model.scr0_buf.size, model.scr0_buf.addr, }); - - // norm - { - inpL = ggml_norm(ctx0, inpL, 1e-5f); - - // inpL = ln_f_g*inpL + ln_f_b - inpL = ggml_add(ctx0, - ggml_mul(ctx0, - ggml_repeat(ctx0, model.output_norm, inpL), - inpL), - ggml_repeat(ctx0, model.output_norm_b, inpL)); - } - - ggml_set_scratch(ctx0, { 0, 0, nullptr, }); - - // lm_head - { - inpL = ggml_mul_mat(ctx0, model.lm_head, inpL); - - //inpL = ggml_add(ctx0, - // ggml_repeat(ctx0, model.lmh_b, inpL), - // inpL); - } - - // logits -> probs - //inpL = ggml_soft_max_inplace(ctx0, inpL); - - // run the computation - ggml_build_forward_expand(&gf, inpL); - ggml_graph_compute_g4a(model.work_buf, &gf, n_threads); - - - //if (n_past%100 == 0) { - // ggml_graph_print (&gf); - // ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot"); - //} - - //embd_w.resize(n_vocab*N); - //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N); - - // return result for just the last token - embd_w.resize(n_vocab); - memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab); - - if (mem_per_token == 0) { - mem_per_token = ggml_used_mem(ctx0)/N; - } - //printf("used_mem = %zu\n", ggml_used_mem(ctx0)); - - ggml_free(ctx0); - - return true; -} - - -#define MAX_RNG_STATE 64*1024 -size_t falcon_get_state_size(const falcon_model &model) { - const size_t s_rng_size = sizeof(size_t); - const size_t s_rng = MAX_RNG_STATE; - const size_t s_kv_size = sizeof(size_t); - const size_t s_kv_ntok = sizeof(int); - const size_t s_kv = model.kv_self.buf.size; - const size_t s_total = ( - + s_rng_size - + s_rng - + s_kv_size - + s_kv_ntok - + s_kv - ); - return s_total; -} - -size_t falcon_copy_state_data(const falcon_model &model, const std::mt19937 &rng, uint8_t *dest) -{ - uint8_t * out = dest; - // copy rng - { - std::stringstream rng_ss; - rng_ss << rng; - - const size_t rng_size = rng_ss.str().size(); - char rng_buf[MAX_RNG_STATE]; - - memset(&rng_buf[0], 0, MAX_RNG_STATE); - memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size()); - - memcpy(out, &rng_size, sizeof(rng_size)); out += sizeof(rng_size); - memcpy(out, &rng_buf[0], MAX_RNG_STATE); out += MAX_RNG_STATE; - } - - // copy kv cache - { - const size_t kv_size = model.kv_self.buf.size; - const int kv_ntok = model.kv_self.n; - - memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size); - memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok); - - if (kv_size) { - memcpy(out, model.kv_self.buf.addr, kv_size); out += kv_size; - } - } - - const size_t written = out - dest; - assert(written == falcon_get_state_size(model)); - fflush(stdout); - return written; -} - -size_t falcon_set_state_data(falcon_model *model, std::mt19937 *rng, const uint8_t *src) -{ - const uint8_t * in = src; - - // set rng - { - size_t rng_size; - char rng_buf[MAX_RNG_STATE]; - - memcpy(&rng_size, in, sizeof(rng_size)); in += sizeof(rng_size); - memcpy(&rng_buf[0], in, MAX_RNG_STATE); in += MAX_RNG_STATE; - - std::stringstream rng_ss; - rng_ss.str(std::string(&rng_buf[0], rng_size)); - rng_ss >> *rng; - - assert(rng_ss.fail() == false); - } - - // set kv cache - { - size_t kv_size; - int kv_ntok; - - memcpy(&kv_size, in, sizeof(kv_size)); in += sizeof(kv_size); - memcpy(&kv_ntok, in, sizeof(kv_ntok)); in += sizeof(kv_ntok); - - if (kv_size) { - assert(model->kv_self.buf.size == kv_size); - - void * k_data = model->kv_self.k->data; // remember data pointers - void * v_data = model->kv_self.v->data; // because their value is stored in buf and overwritten by memcpy - - memcpy(model->kv_self.buf.addr, in, kv_size); in += kv_size; - - model->kv_self.k->data = k_data; // restore correct data pointers - model->kv_self.v->data = v_data; - - } - - model->kv_self.n = kv_ntok; - } - - const size_t nread = in - src; - assert(nread == falcon_get_state_size(*model)); - fflush(stdout); - return nread; -} - -struct FalconPrivate { - const std::string modelPath; - bool modelLoaded; - gpt_vocab vocab; - falcon_model *model = nullptr; - int64_t n_threads = 0; - size_t mem_per_token = 0; - std::mt19937 rng; -}; - -Falcon::Falcon() : d_ptr(new FalconPrivate) { - d_ptr->model = new falcon_model; - d_ptr->model->ctx = nullptr; - d_ptr->modelLoaded = false; -} - -Falcon::~Falcon() { - if(d_ptr->model->ctx) { - ggml_free(d_ptr->model->ctx); - d_ptr->model->ctx = nullptr; - } - delete d_ptr->model; -} - -bool Falcon::loadModel(const std::string &modelPath) -{ - std::mt19937 rng(time(NULL)); - d_ptr->rng = rng; - - // load the model - if (!falcon_model_load(modelPath, *d_ptr->model, d_ptr->vocab, nullptr)) { - std::cerr << "FALCON ERROR: failed to load model from " << modelPath; - return false; - } - - d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); - d_ptr->modelLoaded = true; - fflush(stdout); - return true; -} - -bool Falcon::isModelLoaded() const -{ - return d_ptr -> modelLoaded; -} - -size_t Falcon::requiredMem(const std::string &modelPath) -{ - falcon_model dummy_model; - gpt_vocab dummy_vocab; - size_t mem_req; - auto fin = std::ifstream(modelPath, std::ios::binary); - falcon_model_load(modelPath, dummy_model, dummy_vocab, &mem_req); - return mem_req; -} - -size_t Falcon::stateSize() const -{ - return falcon_get_state_size(*d_ptr->model); -} - -size_t Falcon::saveState(uint8_t *dest) const -{ - return falcon_copy_state_data(*d_ptr->model, d_ptr->rng, dest); -} - -size_t Falcon::restoreState(const uint8_t *src) -{ - return falcon_set_state_data(d_ptr->model, &d_ptr->rng, src); -} - -void Falcon::setThreadCount(int32_t n_threads) -{ - d_ptr->n_threads = n_threads; -} - -int32_t Falcon::threadCount() const -{ - return d_ptr->n_threads; -} - -std::vector Falcon::tokenize(PromptContext &, const std::string &str) const -{ - return ::gpt_tokenize(d_ptr->vocab, str); -} - -LLModel::Token Falcon::sampleToken(PromptContext &promptCtx) const -{ - const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size()); - return gpt_sample_top_k_top_p(d_ptr->model->hparams.n_vocab, - promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks, - n_prev_toks, - promptCtx.logits, - promptCtx.top_k, promptCtx.top_p, promptCtx.temp, - promptCtx.repeat_penalty, - d_ptr->rng); -} - -std::string Falcon::tokenToString(Token id) const -{ - return d_ptr->vocab.id_to_token[id]; -} - -bool Falcon::evalTokens(PromptContext &ctx, const std::vector &tokens) const -{ - // determine the required inference memory per token: - static bool initialized = false; - if (!initialized) { - falcon_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, ctx.logits, - d_ptr->mem_per_token); - initialized = true; - } - - return falcon_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token); -} - -int32_t Falcon::contextLength() const -{ - return d_ptr->model->hparams.n_ctx; -} - -const std::vector &Falcon::endTokens() const -{ - static const std::vector out = { 11 }; - return out; -} - -#if defined(_WIN32) -#define DLL_EXPORT __declspec(dllexport) -#else -#define DLL_EXPORT __attribute__ ((visibility ("default"))) -#endif - -extern "C" { -DLL_EXPORT bool is_g4a_backend_model_implementation() { - return true; -} - -DLL_EXPORT const char *get_model_type() { - return modelType_; -} - -DLL_EXPORT const char *get_build_variant() { - return GGML_BUILD_VARIANT; -} - -DLL_EXPORT bool magic_match(const char* fname) { -#if 0 - uint32_t magic = 0; - f.read(reinterpret_cast(&magic), sizeof(magic)); - uint32_t version = 0; - f.read(reinterpret_cast(&version), sizeof(version)); - if (magic != FALCON_MAGIC) { - return false; - } - falcon_hparams hparams; - f.read(reinterpret_cast(&hparams), sizeof(hparams)); - // we're matching the file format of existing pre-converted models - // compatible with ctransformers llama.cpp based format, which also - // unfortunately shares its magic number what llama uses, so we now - // differentiate by n_vocab - // give some wiggle room over the max to allow for finetunes that expand the - // vocabulary - if (!(hparams.n_vocab >= 65024 && hparams.n_vocab <= 65100)) { - return false; - } - if (hparams.falcon_version != 7) { - return false; - } - return true; -#endif - return false; -} - -DLL_EXPORT LLModel *construct() { - return new Falcon; -} -} diff --git a/gpt4all-backend/falcon_impl.h b/gpt4all-backend/falcon_impl.h deleted file mode 100644 index 2362af9f..00000000 --- a/gpt4all-backend/falcon_impl.h +++ /dev/null @@ -1,42 +0,0 @@ -#ifndef FALCON_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE -#error This file is NOT meant to be included outside of falcon.cpp. Doing so is DANGEROUS. Be sure to know what you are doing before proceeding to #define FALCON_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE -#endif -#ifndef FALCON_H -#define FALCON_H - -#include -#include -#include -#include -#include "llmodel.h" - -struct FalconPrivate; -class Falcon : public LLModel { -public: - Falcon(); - ~Falcon(); - - bool supportsEmbedding() const override { return false; } - bool supportsCompletion() const override { return true; } - bool loadModel(const std::string &modelPath) override; - bool isModelLoaded() const override; - size_t requiredMem(const std::string &modelPath) override; - size_t stateSize() const override; - size_t saveState(uint8_t *dest) const override; - size_t restoreState(const uint8_t *src) override; - void setThreadCount(int32_t n_threads) override; - int32_t threadCount() const override; - -private: - std::unique_ptr d_ptr; - -protected: - std::vector tokenize(PromptContext &, const std::string&) const override; - Token sampleToken(PromptContext &ctx) const override; - std::string tokenToString(Token) const override; - bool evalTokens(PromptContext &ctx, const std::vector &tokens) const override; - int32_t contextLength() const override; - const std::vector& endTokens() const override; -}; - -#endif // Falcon_H diff --git a/gpt4all-backend/llamamodel.cpp b/gpt4all-backend/llamamodel.cpp index 34c93c7b..980a53dc 100644 --- a/gpt4all-backend/llamamodel.cpp +++ b/gpt4all-backend/llamamodel.cpp @@ -393,7 +393,7 @@ 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"); + isValid = isValid && (arch == "llama" || arch == "starcoder" || arch == "falcon"); gguf_free(ctx_gguf); return isValid; diff --git a/gpt4all-backend/scripts/convert_falcon_hf_to_ggml.py b/gpt4all-backend/scripts/convert_falcon_hf_to_ggml.py deleted file mode 100644 index 8aaf8fea..00000000 --- a/gpt4all-backend/scripts/convert_falcon_hf_to_ggml.py +++ /dev/null @@ -1,143 +0,0 @@ -# Based on: https://github.com/KerfuffleV2/ggml-falcon/blob/feat-improve-falcon-convert-hf/examples/falcon/convert-hf-to-ggml.py -# Convert Hugging Face fine-tuned bloom-like models to ggml format -# -# Usage: -# -# python3 convert_falcon_hf_to_ggml.py model_directory output_directory [use-f32] -# -# This script is similar to "convert-pt-to-ggml.py" -# - -import io -import os -import sys -import struct -import json -import code -import torch -import numpy as np -import gc - -from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig - -# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py -def bytes_to_unicode(): - """ - Returns list of utf-8 byte and a corresponding list of unicode strings. - The reversible bpe codes work on unicode strings. - This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. - When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. - This is a significant percentage of your normal, say, 32K bpe vocab. - To avoid that, we want lookup tables between utf-8 bytes and unicode strings. - And avoids mapping to whitespace/control characters the bpe code barfs on. - """ - bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) - cs = bs[:] - n = 0 - for b in range(2**8): - if b not in bs: - bs.append(b) - cs.append(2**8+n) - n += 1 - cs = [chr(n) for n in cs] - return dict(zip(bs, cs)) - -if len(sys.argv) < 3: - print("INFO: GGML V1 files produced are meant to be finalized through examples/falcon_quantize which will bring them to latest version and precision of choice"); - print("Usage: python convert_falcon_hf_to_ggml.py model_directory output_directory [use-f32]") - print(" model_directory: name of the directory and model you convert (it should be a subdirectory)") - print(" output-directory: directory where the output file will be written") - print(" use-f32: if present, use float32 instead of float16 (f32 is recommended)") - sys.exit(1) - -# num_parts = int(sys.argv[1]) -dir_model = sys.argv[1] # name and dir of model -dir_out = sys.argv[2] # output directory - -# make sure the output directory exists -os.makedirs(dir_out, exist_ok=True) - - -# possible data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 -# -# map from ftype to string -ftype_str = ["f32", "f16"] -ftype = 1 -if len(sys.argv) > 3: - ftype = 0 - -tokenizer = AutoTokenizer.from_pretrained(dir_model) -# print(tokenizer) -config = AutoConfig.from_pretrained(dir_model, trust_remote_code=True) -model = AutoModelForCausalLM.from_pretrained(dir_model, trust_remote_code=True, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True) -hparams = config.to_dict() - -n_head = hparams["n_head"] -n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1 -head_dim = hparams["hidden_size"] // n_head -print("* Loading model from: ", dir_model) - -fname_out = dir_out + f"/ggml-model-{dir_model.split('/')[-1]}-{ftype_str[ftype]}.bin" -fout = open(fname_out, "wb") -fout.write(struct.pack("i", 0x67676a74)) # magic: ggmf in hex (version 1) - possibly change to ggfc ? -fout.write(struct.pack("i", 1)) # version -fout.write(struct.pack("i", hparams["vocab_size"])) -fout.write(struct.pack("i", hparams["hidden_size"])) -fout.write(struct.pack("i", n_head)) -fout.write(struct.pack("i", n_head_kv)) -fout.write(struct.pack("i", hparams["n_layer"])) -fout.write(struct.pack("i", 40 if "n_head_kv" in hparams else 7)) # obsolete field that breaks ggml compatibility - todo again remove one day -fout.write(struct.pack("i", ftype)) - -reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} -byte_encoder = bytes_to_unicode() -byte_decoder = {v:k for k, v in byte_encoder.items()} - -for i in range(hparams["vocab_size"]): - text = bytearray([byte_decoder[c] for c in reverse_vocab[i]]) - fout.write(struct.pack("i", len(text))) - fout.write(text) - fout.write(struct.pack("f", 0.0)) # falcon uses bpe on RefinedWeb - no probability scores used - -model = model.state_dict() -for name in model.keys(): - src = name - # The original query_key_value tensor contains n_head_kv "kv groups", - # each consisting of n_head/n_head_kv query weights followed by one key - # and one value weight (shared by all query heads in the kv group). - # This layout makes it a big pain to work with in GGML. - # So we rearrange them here,, so that we have n_head query weights - # followed by n_head_kv key weights followed by n_head_kv value weights, - # in contiguous fashion. - - if "query_key_value" in src: - qkv = model[src].view( - n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) - - q = qkv[:, :-2 ].reshape(n_head * head_dim, head_dim * n_head) - k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) - v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) - - model[src] = torch.cat((q,k,v)).reshape_as(model[src]) - data = model[src].squeeze() - n_dims = len(data.shape) - # default type is fp32 - ftype_cur = 1 if ftype == 1 and n_dims > 1 else 0 - data = data.to(dtype = torch.float16 if ftype_cur == 1 else torch.float32).numpy() - print(f' |', name, data.shape, '->', data.dtype) - # header - str = name.encode('utf-8') - fout.write(struct.pack("iii", n_dims, len(str), ftype_cur)) - for i in range(n_dims): - fout.write(struct.pack("i", data.shape[n_dims - 1 - i])) - fout.write(str) - - # data - data.tofile(fout) - -fout.close() - -print("Done. Output file: " + fname_out) -print("")