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backend: use llamamodel.cpp for Falcon
This commit is contained in:
Cebtenzzre
Adam Treat
parent
cca9e6ce81
commit
ce7be1db48
@ -103,11 +103,6 @@ foreach(BUILD_VARIANT IN LISTS BUILD_VARIANTS)
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# gptj.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
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# gptj.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
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# prepare_target(gptj ggml-230511)
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# prepare_target(gptj ggml-230511)
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add_library(falcon-${BUILD_VARIANT} SHARED
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falcon.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
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target_compile_definitions(falcon-${BUILD_VARIANT} PRIVATE LLAMA_VERSIONS=>=3 LLAMA_DATE=999999)
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prepare_target(falcon llama-mainline)
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add_library(mpt-${BUILD_VARIANT} SHARED
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add_library(mpt-${BUILD_VARIANT} SHARED
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mpt.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
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mpt.cpp utils.h utils.cpp llmodel_shared.cpp llmodel_shared.h)
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prepare_target(mpt llama-mainline)
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prepare_target(mpt llama-mainline)
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@ -1,989 +0,0 @@
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#include "ggml.h"
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#define FALCON_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
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#include "falcon_impl.h"
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#include "llama.h"
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#include "utils.h"
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#include "llmodel_shared.h"
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#include <stdio.h>
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#include <string.h>
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#include <cassert>
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#include <cinttypes>
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#include <iostream>
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#include <sstream>
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namespace {
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const char *modelType_ = "Falcon";
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}
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// commented out 40B support as it presently would require forking ggml/llama.cpp
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// can re-add once mainline ggml supports it
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#define FALCON_MAGIC 0x67676a74
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// default hparams (Falcon 7B)
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struct falcon_hparams {
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int32_t n_vocab = 65024;
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int32_t n_embd = 4544;
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int32_t n_head = 71;
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int32_t n_head_kv = 1;
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int32_t n_layer = 32;
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int32_t falcon_version = 7; // 7 for Falcon-7B, 40 for Falcon-40B
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int32_t ftype = 1;
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int32_t n_ctx = 2048;
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};
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struct falcon_layer {
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// normalization
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struct ggml_tensor* input_layernorm;
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struct ggml_tensor* input_layernorm_b;
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//struct ggml_tensor* attention_norm; // Falcon-40B only
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//struct ggml_tensor* attention_norm_b; // Falcon-40B only
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// attention
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struct ggml_tensor* query_key_value;
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struct ggml_tensor* wo;
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// ff
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struct ggml_tensor* ffn_up;
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struct ggml_tensor* ffn_down;
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};
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struct falcon_model {
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falcon_hparams hparams;
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struct ggml_tensor* tok_embeddings;
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struct ggml_tensor* output_norm;
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struct ggml_tensor* output_norm_b;
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struct ggml_tensor* lm_head;
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std::vector<falcon_layer> layers;
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// key + value memory
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llm_kv_cache kv_self;
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struct ggml_context* ctx;
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std::map<std::string, struct ggml_tensor*> tensors;
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llm_buffer eval_buf;
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llm_buffer work_buf;
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llm_buffer scr0_buf;
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llm_buffer scr1_buf;
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};
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static bool kv_cache_init(
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const struct falcon_hparams & hparams,
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struct llm_kv_cache & cache,
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ggml_type wtype,
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int n_ctx) {
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const int n_embd = hparams.n_embd;
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const int dim_head = n_embd / hparams.n_head;
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const int dim_kv = dim_head * hparams.n_head_kv;
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const int n_layer = hparams.n_layer;
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const int64_t n_mem = (int64_t)n_layer*n_ctx;
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const int64_t n_elements = dim_kv * n_mem;
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cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2_MiB);
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struct ggml_init_params params;
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params.mem_size = cache.buf.size;
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params.mem_buffer = cache.buf.addr;
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params.no_alloc = false;
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cache.ctx = ggml_init(params);
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if (!cache.ctx) {
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fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
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return false;
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}
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cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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return true;
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}
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// load the model's weights from a file
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bool falcon_model_load(const std::string & fname, falcon_model & model, gpt_vocab & vocab, size_t *mem_req) {
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printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
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if (mem_req) {
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*mem_req = 0;
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}
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auto fin = std::ifstream(fname, std::ios::binary);
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if (!fin) {
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fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
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return false;
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}
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// verify magic
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{
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uint32_t magic;
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fin.read((char *) &magic, sizeof(magic));
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if (magic != FALCON_MAGIC) {
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fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
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return false;
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}
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}
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uint32_t format_version;
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fin.read((char *) &format_version, sizeof(format_version));
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// load hparams
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{
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auto & hparams = model.hparams;
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fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
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fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
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fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
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fin.read((char *) &hparams.n_head_kv, sizeof(hparams.n_head_kv));
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fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
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fin.read((char *) &hparams.falcon_version, sizeof(hparams.falcon_version));
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fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));
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if (hparams.falcon_version != 7) { // && hparams.falcon_version != 40) {
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fprintf(stderr, "%s: invalid model file '%s' (bad Falcon version: %d)\n", __func__, fname.c_str(), hparams.falcon_version);
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return false;
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}
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const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
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printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
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printf("%s: n_head = %d\n", __func__, hparams.n_head);
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printf("%s: n_head_kv = %d\n", __func__, hparams.n_head_kv);
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printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
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printf("%s: ftype = %d\n", __func__, hparams.ftype);
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printf("%s: qntvr = %d\n", __func__, qntvr);
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hparams.ftype %= GGML_QNT_VERSION_FACTOR;
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}
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// load vocab
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{
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const int32_t n_vocab = model.hparams.n_vocab;
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std::string word;
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std::vector<char> buf(128);
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for (int i = 0; i < n_vocab; i++) {
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uint32_t len;
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fin.read((char *) &len, sizeof(len));
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buf.resize(len);
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fin.read((char *) buf.data(), len);
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word.assign(buf.data(), len);
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uint32_t dummy;
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fin.read((char *) &dummy, sizeof(dummy));
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vocab.token_to_id[word] = i;
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vocab.id_to_token[i] = word;
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}
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}
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// for the big tensors, we have the option to store the data in 16-bit floats or quantized
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// in order to save memory and also to speed up the computation
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ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
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if (wtype == GGML_TYPE_COUNT) {
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fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
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__func__, fname.c_str(), model.hparams.ftype);
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return false;
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}
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auto & ctx = model.ctx;
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size_t ctx_size = 0;
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{
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const auto& hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_head = hparams.n_head;
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const int n_head_kv = hparams.n_head_kv;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_ff = 4 * model.hparams.n_embd;
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const int n_vocab = hparams.n_vocab;
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const int head_dim = hparams.n_embd / hparams.n_head;
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ctx_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_vocab; // tok_embeddings
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ctx_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd; // output_norm
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ctx_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd; // output_norm_b
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ctx_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_vocab; // lm_head
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// if (hparams.version == 40) { // Falcon-40B
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// ctx_size += n_layer * ggml_sizeof_tensor_1d(GGML_TYPE_F32, n_embd); // attention_norm
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// ctx_size += n_layer * ggml_sizeof_tensor_1d(GGML_TYPE_F32, n_embd); // attention_norm_b
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// }
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ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd); // input_layernorm
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ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(GGML_TYPE_F32) * n_embd); // input_layernorm_b
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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
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ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_embd); // wo
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ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_embd * n_ff); // ffn_up
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ctx_size += n_layer * (GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_sizef(wtype) * n_ff * n_embd); // ffn_down
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printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
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}
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if (mem_req) {
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const int n_embd = model.hparams.n_embd;
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const int dim_head = n_embd / model.hparams.n_head;
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const int dim_kv = dim_head * model.hparams.n_head_kv;
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const int n_layer = model.hparams.n_layer;
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const int64_t n_mem = (int64_t)n_layer*model.hparams.n_ctx;
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const int64_t n_elements = dim_kv * n_mem;
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size_t kv_cache_size = 2u*n_elements*ggml_type_size(wtype) + 2_MiB;
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*mem_req = ctx_size + kv_cache_size;
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return false;
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}
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// create the ggml context
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{
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struct ggml_init_params params = {
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.mem_size = ctx_size,
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.mem_buffer = NULL,
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.no_alloc = false,
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};
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model.ctx = ggml_init(params);
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if (!model.ctx) {
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fprintf(stderr, "%s: ggml_init() failed\n", __func__);
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return false;
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}
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}
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// prepare memory for the weights
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{
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const auto& hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_head = hparams.n_head;
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const int n_head_kv = hparams.n_head_kv;
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const int n_layer = hparams.n_layer;
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const int n_ff = 4 * model.hparams.n_embd;
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const int n_vocab = hparams.n_vocab;
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const int head_dim = hparams.n_embd / hparams.n_head;
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model.layers.resize(n_layer);
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model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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model.output_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model.output_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model.lm_head = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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// map by name
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model.tensors["transformer.word_embeddings.weight"] =
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model.tok_embeddings;
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model.tensors["transformer.ln_f.weight"] = model.output_norm;
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model.tensors["transformer.ln_f.bias"] = model.output_norm_b;
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model.tensors["lm_head.weight"] = model.lm_head;
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for (int i = 0; i < n_layer; ++i) {
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auto& layer = model.layers[i];
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layer.input_layernorm =
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ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.input_layernorm_b =
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ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// if (hparams.version == 40) { // for Falcon-40B only
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// layer.attention_norm =
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// ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// layer.attention_norm_b =
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// ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// }
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// query_key_value shape for config.multi_query == True:
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layer.query_key_value = ggml_new_tensor_2d(
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ctx, wtype, n_embd, (n_head_kv * 2 + n_head) * head_dim);
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layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.ffn_up = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);
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layer.ffn_down = ggml_new_tensor_2d(ctx, wtype, n_ff, n_embd);
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// map by name
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// if (hparams.version == 40) {
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// // Falcon-40B:
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|
||||||
// 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<char *>(&n_dims), sizeof(n_dims));
|
|
||||||
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
|
||||||
fin.read(reinterpret_cast<char *>(&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<char *>(&ne[i]), sizeof(ne[i]));
|
|
||||||
nelements *= ne[i];
|
|
||||||
}
|
|
||||||
|
|
||||||
std::string name(length, 0);
|
|
||||||
fin.read(&name[0], length);
|
|
||||||
fin.seekg(-static_cast<ptrdiff_t>(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<char *>(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<gpt_vocab::id> & embd_inp,
|
|
||||||
std::vector<float> & 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<LLModel::Token> 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<int32_t> &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<LLModel::Token> &Falcon::endTokens() const
|
|
||||||
{
|
|
||||||
static const std::vector<LLModel::Token> 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<char*>(&magic), sizeof(magic));
|
|
||||||
uint32_t version = 0;
|
|
||||||
f.read(reinterpret_cast<char*>(&version), sizeof(version));
|
|
||||||
if (magic != FALCON_MAGIC) {
|
|
||||||
return false;
|
|
||||||
}
|
|
||||||
falcon_hparams hparams;
|
|
||||||
f.read(reinterpret_cast<char*>(&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;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@ -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 <string>
|
|
||||||
#include <functional>
|
|
||||||
#include <vector>
|
|
||||||
#include <memory>
|
|
||||||
#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<FalconPrivate> d_ptr;
|
|
||||||
|
|
||||||
protected:
|
|
||||||
std::vector<Token> 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<int32_t> &tokens) const override;
|
|
||||||
int32_t contextLength() const override;
|
|
||||||
const std::vector<Token>& endTokens() const override;
|
|
||||||
};
|
|
||||||
|
|
||||||
#endif // Falcon_H
|
|
||||||
@ -393,7 +393,7 @@ DLL_EXPORT bool magic_match(const char * fname) {
|
|||||||
|
|
||||||
bool isValid = gguf_get_version(ctx_gguf) <= 2;
|
bool isValid = gguf_get_version(ctx_gguf) <= 2;
|
||||||
auto arch = get_arch_name(ctx_gguf);
|
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);
|
gguf_free(ctx_gguf);
|
||||||
return isValid;
|
return isValid;
|
||||||
|
|||||||
@ -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("")
|
|
||||||
Loading…
Reference in New Issue
Block a user