From 198b5e48329611533ae60603708f4c6613902f41 Mon Sep 17 00:00:00 2001 From: Aaron Miller Date: Mon, 26 Jun 2023 19:53:57 -0700 Subject: [PATCH] add Falcon 7B model Tested with https://huggingface.co/TheBloke/falcon-7b-instruct-GGML/blob/main/falcon7b-instruct.ggmlv3.q4_0.bin --- gpt4all-backend/CMakeLists.txt | 4 + gpt4all-backend/falcon.cpp | 1030 ++++++++++++++++++++++++++++++++ gpt4all-backend/falcon_impl.h | 40 ++ gpt4all-backend/llamamodel.cpp | 11 +- gpt4all-chat/CMakeLists.txt | 2 + gpt4all-chat/chatllm.cpp | 1 + gpt4all-chat/chatllm.h | 3 +- 7 files changed, 1085 insertions(+), 6 deletions(-) create mode 100644 gpt4all-backend/falcon.cpp create mode 100644 gpt4all-backend/falcon_impl.h diff --git a/gpt4all-backend/CMakeLists.txt b/gpt4all-backend/CMakeLists.txt index 173c34f5..9e602638 100644 --- a/gpt4all-backend/CMakeLists.txt +++ b/gpt4all-backend/CMakeLists.txt @@ -117,6 +117,10 @@ foreach(BUILD_VARIANT IN LISTS BUILD_VARIANTS) gptj.cpp utils.h utils.cpp llmodel_shared.cpp) prepare_target(gptj ggml-230511) + add_library(falcon-${BUILD_VARIANT} SHARED + falcon.cpp utils.h utils.cpp llmodel_shared.cpp) + prepare_target(falcon llama-mainline) + add_library(mpt-${BUILD_VARIANT} SHARED mpt.cpp utils.h utils.cpp llmodel_shared.cpp) prepare_target(mpt ggml-230511) diff --git a/gpt4all-backend/falcon.cpp b/gpt4all-backend/falcon.cpp new file mode 100644 index 00000000..0cfdbad0 --- /dev/null +++ b/gpt4all-backend/falcon.cpp @@ -0,0 +1,1030 @@ +#define FALCON_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE +#include "falcon_impl.h" +#include "llama.h" +#include "llama-util.h" +#include "utils.h" + +#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_buffer { + uint8_t * addr = NULL; + size_t size = 0; + + void resize(size_t size) { + delete[] addr; + addr = new uint8_t[size]; + this->size = size; + } + + ~falcon_buffer() { + delete[] addr; + } +}; + +struct falcon_kv_cache { + struct ggml_tensor * k; + struct ggml_tensor * v; + + struct ggml_context * ctx = NULL; + + falcon_buffer buf; + + int n; // number of tokens currently in the cache + + ~falcon_kv_cache() { + if (ctx) { + ggml_free(ctx); + } + } +}; + +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 + falcon_kv_cache kv_self; + + struct ggml_context* ctx; + std::map tensors; +}; + +static bool kv_cache_init( + const struct falcon_hparams & hparams, + struct falcon_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_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_vocab; // tok_embeddings + ctx_size += ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd; // output_norm + ctx_size += ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd; // output_norm_b + ctx_size += 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_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd); // input_layernorm + ctx_size += n_layer * (ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd); // input_layernorm_b + ctx_size += n_layer * (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_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_embd); // wo + ctx_size += n_layer * (ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_ff); // ffn_up + ctx_size += n_layer * (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(); + + 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( + const 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; + + static size_t buf_size = 256u*1024*1024; + static void * buf = malloc(buf_size); + + // use 2 scratch buffers + // TODO: very hacky solution - reimplement in a more elegant way + static size_t scr0_size = 256u*1024*1024; + static void * scr0 = malloc(scr0_size); + + static size_t scr1_size = 256u*1024*1024; + static void * scr1 = malloc(scr1_size); + + if (mem_per_token > 0 && mem_per_token*N > buf_size) { + const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead + //printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new); + + // reallocate + buf_size = buf_size_new; + buf = realloc(buf, buf_size); + if (buf == nullptr) { + fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size); + return false; + } + } + + struct ggml_init_params params = { + .mem_size = buf_size, + .mem_buffer = buf, + .no_alloc = false, + }; + + struct ggml_context * ctx0 = ggml_init(params); + struct ggml_cgraph gf = {}; + gf.n_threads = n_threads; + + struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); + memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd)); + + // 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, scr0_size, scr0, }); + + // self-attention + { + layernorm_output = ggml_norm(ctx0, inpL); + + 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); + Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, head_dim, 2); + + // 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, scr1_size, scr1, }); + + 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, scr0_size, scr0, }); + + // norm + { + inpL = ggml_norm(ctx0, inpL); + + // inpL = ln_f_g*inpL + ln_f_b + inpL = ggml_add(ctx0, + ggml_mul(ctx0, + ggml_repeat(ctx0, model.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 (ctx0, &gf); + + //if (n_past%100 == 0) { + // ggml_graph_print (&gf); + // ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot"); + //} + + //embd_w.resize(n_vocab*N); + //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N); + + // return result 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) { + 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(std::istream& f) { + 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; +} + +DLL_EXPORT LLModel *construct() { + std::cerr << "Falcon construct" << std::endl; + return new Falcon; +} +} diff --git a/gpt4all-backend/falcon_impl.h b/gpt4all-backend/falcon_impl.h new file mode 100644 index 00000000..017252ea --- /dev/null +++ b/gpt4all-backend/falcon_impl.h @@ -0,0 +1,40 @@ +#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 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 71d47ef5..5fdc35b2 100644 --- a/gpt4all-backend/llamamodel.cpp +++ b/gpt4all-backend/llamamodel.cpp @@ -282,14 +282,15 @@ DLL_EXPORT bool magic_match(std::istream& f) { if (!(version LLAMA_VERSIONS)) { return false; } + llama_file_hparams hparams; + f.read(reinterpret_cast(&hparams), sizeof(hparams)); + if (!(hparams.n_vocab >= 32000 && hparams.n_vocab <= 32100)) { + return false; // not a llama. + } #ifdef GGML_USE_METAL // Check quant supported on metal // skip fields - off_t offset = sizeof(uint32_t) * 6; // n_vocab, n_embd, n_mult, n_head, n_layer, n_rot - f.seekg(offset, std::ios_base::cur); - uint32_t ftype; - f.read(reinterpret_cast(&ftype), sizeof(ftype)); // ftype - switch((enum llama_ftype) ftype) { + switch(hparams.ftype) { // currently supported on Metal https://github.com/ggerganov/llama.cpp/blob/ae9663f1887513e152839e91f61c513075a19422/ggml-metal.m#L51-L55 case LLAMA_FTYPE_MOSTLY_F16: case LLAMA_FTYPE_MOSTLY_Q2_K: diff --git a/gpt4all-chat/CMakeLists.txt b/gpt4all-chat/CMakeLists.txt index 73907253..d48f6b9a 100644 --- a/gpt4all-chat/CMakeLists.txt +++ b/gpt4all-chat/CMakeLists.txt @@ -178,6 +178,8 @@ 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 falcon-avxonly DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) +install(TARGETS falcon-default DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) install(TARGETS mpt-avxonly DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) install(TARGETS mpt-default DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) install(TARGETS replit-mainline-avxonly DESTINATION lib COMPONENT ${COMPONENT_NAME_MAIN}) diff --git a/gpt4all-chat/chatllm.cpp b/gpt4all-chat/chatllm.cpp index e1a086e4..8a006469 100644 --- a/gpt4all-chat/chatllm.cpp +++ b/gpt4all-chat/chatllm.cpp @@ -224,6 +224,7 @@ bool ChatLLM::loadModel(const ModelInfo &modelInfo) case 'G': m_llModelType = LLModelType::GPTJ_; break; case 'M': m_llModelType = LLModelType::MPT_; break; case 'R': m_llModelType = LLModelType::REPLIT_; break; + case 'F': m_llModelType = LLModelType::FALCON_; break; default: { delete std::exchange(m_llModelInfo.model, nullptr); diff --git a/gpt4all-chat/chatllm.h b/gpt4all-chat/chatllm.h index 83f26ba8..b3fe9b49 100644 --- a/gpt4all-chat/chatllm.h +++ b/gpt4all-chat/chatllm.h @@ -14,7 +14,8 @@ enum LLModelType { GPTJ_, LLAMA_, CHATGPT_, - REPLIT_ + REPLIT_, + FALCON_ }; struct LLModelInfo {