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-rw-r--r--examples/gptneox-wip/falcon-main.cpp1111
1 files changed, 1111 insertions, 0 deletions
diff --git a/examples/gptneox-wip/falcon-main.cpp b/examples/gptneox-wip/falcon-main.cpp
new file mode 100644
index 00000000..43b6a29f
--- /dev/null
+++ b/examples/gptneox-wip/falcon-main.cpp
@@ -0,0 +1,1111 @@
+#include "ggml.h"
+#include "cmpnct_gpt2bpe.hpp"
+
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <cinttypes>
+#include <fstream>
+#include <map>
+#include <string>
+#include <vector>
+#include <thread>
+#include <random>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+// default hparams
+struct falcon_hparams {
+ size_t n_merges = 0;
+ size_t n_vocab = 0;
+ uint32_t n_ctx = 0;
+ uint32_t n_embd = 0;
+ uint32_t n_head = 0;
+ uint32_t n_head_kv = 1; // Needs to be 1 for 7B model
+ uint32_t n_ff = 0;
+ uint32_t n_block = 0;
+ float norm_eps = 1e-5;
+};
+struct falcon_block {
+ // normalization
+ struct ggml_tensor* input_layernorm;
+ struct ggml_tensor* input_layernorm_b;
+ struct ggml_tensor* attention_norm; // Falcon-40B only
+ struct ggml_tensor* attention_norm_b; // Falcon-40B only
+
+ // attention
+ struct ggml_tensor* query_key_value;
+ struct ggml_tensor* wo;
+
+ // ff
+ struct ggml_tensor* ffn_up;
+ struct ggml_tensor* ffn_down;
+};
+
+struct falcon_model {
+ falcon_hparams hparams;
+
+ struct ggml_tensor* tok_embeddings;
+ struct ggml_tensor* output_norm;
+ struct ggml_tensor* output_norm_b;
+ struct ggml_tensor* lm_head;
+
+ std::vector<falcon_block> blocks;
+
+ // key + value memory
+ struct ggml_tensor* memory_k;
+ struct ggml_tensor* memory_v;
+
+ struct gguf_context * ggufctx;
+ struct ggml_context * ctx;
+ struct ggml_context * kvctx;
+
+ std::map<std::string, struct ggml_tensor*> tensors;
+};
+
+struct gpt_params {
+ int32_t seed = -1; // RNG seed
+ int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
+ uint32_t n_predict = 200; // new tokens to predict
+ uint32_t n_batch = 512; // batch size for prompt processing
+
+ // sampling parameters
+ int32_t top_k = 40;
+ float top_p = 1.0f;
+ float temp = 0.8f;
+ int32_t repeat_last_n = 64;
+ float repeat_penalty = 1.02f;
+
+ std::string model = ""; // model path
+ std::string prompt = "";
+
+ std::string token_test = "";
+ bool interactive = false;
+ int32_t interactive_port = -1;
+ int32_t n_gpu_layers = 0;
+};
+
+void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
+ fprintf(stderr, "usage: %s [options]\n", argv[0]);
+ fprintf(stderr, "\n");
+ fprintf(stderr, "options:\n");
+ fprintf(stderr, " -h, --help show this help message and exit\n");
+ fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
+ fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
+ fprintf(stderr, " -ngl N, --gpu-layers N number of layers to offload to GPU on supported models (default: %d)\n", params.n_gpu_layers);
+ fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");
+ fprintf(stderr, " prompt to start generation with (default: random)\n");
+ fprintf(stderr, " -f FNAME, --file FNAME\n");
+ fprintf(stderr, " load prompt from a file\n");
+ fprintf(stderr, " -tt TOKEN_TEST, --token_test TOKEN_TEST\n");
+ fprintf(stderr, " test tokenization\n");
+ fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);
+ fprintf(stderr, " --top_k N top-k sampling, 0 = n_vocab (default: %d)\n", params.top_k);
+ fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
+ fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
+ fprintf(stderr, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled)\n", params.repeat_last_n);
+ fprintf(stderr, " --repeat-penalty N penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)\n", (double)params.repeat_penalty);
+ fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
+ fprintf(stderr, " -m FNAME, --model FNAME\n");
+ fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
+ fprintf(stderr, "\n");
+}
+
+// Function to check if the next argument exists
+std::string get_next_arg(int& i, int argc, char** argv, const std::string& flag, gpt_params& params) {
+ if (i + 1 < argc && argv[i + 1][0] != '-') {
+ return argv[++i];
+ } else {
+ fprintf(stderr, "error: %s requires one argument.\n", flag.c_str());
+ gpt_print_usage(argc, argv, params);
+ exit(0);
+ }
+}
+
+bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
+ for (int i = 1; i < argc; i++) {
+ std::string arg = argv[i];
+
+ if (arg == "-s" || arg == "--seed") {
+ params.seed = std::stoi(get_next_arg(i, argc, argv, arg, params));
+ } else if (arg == "-t" || arg == "--threads") {
+ params.n_threads = std::stoi(get_next_arg(i, argc, argv, arg, params));
+ } else if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
+ params.n_gpu_layers = std::stoi(get_next_arg(i, argc, argv, arg, params));
+ } else if (arg == "-p" || arg == "--prompt") {
+ params.prompt = get_next_arg(i, argc, argv, arg, params);
+ } else if (arg == "-n" || arg == "--n_predict") {
+ params.n_predict = std::stoi(get_next_arg(i, argc, argv, arg, params));
+ } else if (arg == "--top_k") {
+ params.top_k = std::stoi(get_next_arg(i, argc, argv, arg, params));
+ } else if (arg == "--top_p") {
+ params.top_p = std::stof(get_next_arg(i, argc, argv, arg, params));
+ } else if (arg == "--temp") {
+ params.temp = std::stof(get_next_arg(i, argc, argv, arg, params));
+ } else if (arg == "--repeat-last-n") {
+ params.repeat_last_n = std::stoi(get_next_arg(i, argc, argv, arg, params));
+ } else if (arg == "--repeat-penalty") {
+ params.repeat_penalty = std::stof(get_next_arg(i, argc, argv, arg, params));
+ } else if (arg == "-b" || arg == "--batch_size") {
+ params.n_batch= std::stoi(get_next_arg(i, argc, argv, arg, params));
+ } else if (arg == "-m" || arg == "--model") {
+ params.model = get_next_arg(i, argc, argv, arg, params);
+ } else if (arg == "-i" || arg == "--interactive") {
+ params.interactive = true;
+ } else if (arg == "-ip" || arg == "--interactive-port") {
+ params.interactive = true;
+ params.interactive_port = std::stoi(get_next_arg(i, argc, argv, arg, params));
+ } else if (arg == "-h" || arg == "--help") {
+ gpt_print_usage(argc, argv, params);
+ exit(0);
+ } else if (arg == "-f" || arg == "--file") {
+ get_next_arg(i, argc, argv, arg, params);
+ std::ifstream file(argv[i]);
+ if (!file) {
+ fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+ break;
+ }
+ std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
+ if (params.prompt.back() == '\n') {
+ params.prompt.pop_back();
+ }
+ } else if (arg == "-tt" || arg == "--token_test") {
+ params.token_test = get_next_arg(i, argc, argv, arg, params);
+ }
+ else {
+ fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+ gpt_print_usage(argc, argv, params);
+ exit(0);
+ }
+ }
+
+ return true;
+}
+
+gpt2bpe_vocab::id sample_top_k_top_p_repeat(
+ const gpt2bpe_vocab & vocab,
+ const float * logits,
+ const int32_t * last_n_tokens_data,
+ size_t last_n_tokens_data_size,
+ int top_k,
+ double top_p,
+ double temp,
+ int repeat_last_n,
+ float repeat_penalty,
+ std::mt19937 & rng) {
+
+ int n_logits = vocab.id_to_token.size();
+
+ const auto * plogits = logits;
+
+ const auto last_n_tokens = std::vector<int32_t>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_data_size);
+
+ if (temp <= 0) {
+ // select the token with the highest logit directly
+ float max_logit = plogits[0];
+ gpt2bpe_vocab::id max_id = 0;
+
+ for (int i = 1; i < n_logits; ++i) {
+ if (plogits[i] > max_logit) {
+ max_logit = plogits[i];
+ max_id = i;
+ }
+ }
+ return max_id;
+ }
+
+
+ std::vector<std::pair<double, gpt2bpe_vocab::id>> logits_id;
+ logits_id.reserve(n_logits);
+
+ {
+ const float scale = 1.0f/temp;
+ for (int i = 0; i < n_logits; ++i) {
+ // repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
+ // credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
+ if (repeat_last_n > 0 && std::find(last_n_tokens.end()-repeat_last_n, last_n_tokens.end(), i) != last_n_tokens.end()) {
+ // if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
+ if (plogits[i] < 0.0f) {
+ logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
+ } else {
+ logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
+ }
+ } else {
+ logits_id.push_back(std::make_pair(plogits[i]*scale, i));
+ }
+ }
+ }
+
+ // find the top K tokens
+ std::partial_sort(
+ logits_id.begin(),
+ logits_id.begin() + top_k, logits_id.end(),
+ [](const std::pair<double, gpt2bpe_vocab::id> & a, const std::pair<double, gpt2bpe_vocab::id> & b) {
+ return a.first > b.first;
+ });
+
+ logits_id.resize(top_k);
+
+ double maxl = -INFINITY;
+ for (const auto & kv : logits_id) {
+ maxl = std::max(maxl, kv.first);
+ }
+
+ // compute probs for the top K tokens
+ std::vector<double> probs;
+ probs.reserve(logits_id.size());
+
+ double sum = 0.0;
+ for (const auto & kv : logits_id) {
+ double p = exp(kv.first - maxl);
+ probs.push_back(p);
+ sum += p;
+ }
+
+ // normalize the probs
+ for (auto & p : probs) {
+ p /= sum;
+ }
+
+ if (top_p < 1.0f) {
+ double cumsum = 0.0f;
+ for (int i = 0; i < top_k; i++) {
+ cumsum += probs[i];
+ if (cumsum >= top_p) {
+ top_k = i + 1;
+ probs.resize(top_k);
+ logits_id.resize(top_k);
+ break;
+ }
+ }
+
+ cumsum = 1.0/cumsum;
+ for (int i = 0; i < (int) probs.size(); i++) {
+ probs[i] *= cumsum;
+ }
+ }
+
+// printf("\n");
+// for (int i = 0; i < (int) probs.size(); i++) {
+// for (int i = 0; i < 10; i++) {
+// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
+// }
+
+ std::discrete_distribution<> dist(probs.begin(), probs.end());
+ int idx = dist(rng);
+
+ return logits_id[idx].second;
+
+}
+
+struct ggml_tensor * get_tensor_ex( struct ggml_context * ctx, std::string name){
+
+ struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str());
+ if( cur == NULL ) {
+ fprintf(stdout, "%s: tensor '%s' not found!\n", __func__, name.c_str());
+ } else {
+// fprintf(stdout, "%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name);
+ }
+
+ return cur;
+}
+
+// load the model's weights from a file
+bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_vocab & vocab) {
+ printf("%s: loading model from '%s'..\n", __func__, fname.c_str());
+
+ model.ctx = NULL;
+
+ struct gguf_init_params ggufparams = {
+ /*.no_alloc = */ false,
+ /*.ctx = */ &model.ctx,
+ };
+
+ auto & ggufctx = model.ggufctx;
+
+ ggufctx = gguf_init_from_file(fname.c_str(), ggufparams);
+
+ if (!ggufctx) {
+ fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__);
+ return false;
+ }
+
+ fprintf(stdout, "%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx));
+ fprintf(stdout, "%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx));
+ fprintf(stdout, "%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
+
+ // print all kv
+ #if 0
+ {
+ const int n_kv = gguf_get_n_kv(ggufctx);
+
+ fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv);
+
+ for (int i = 0; i < n_kv; ++i) {
+ const char * key = gguf_get_key(ggufctx, i);
+
+ fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key);
+ }
+ }
+ #endif
+
+ // print some standard metadata
+ {
+ int keyidx;
+
+ keyidx = gguf_find_key(ggufctx, "general.name");
+ if (keyidx != -1) { fprintf(stdout, "%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+ keyidx = gguf_find_key(ggufctx, "general.description");
+ if (keyidx != -1) { fprintf(stdout, "%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+ keyidx = gguf_find_key(ggufctx, "general.author");
+ if (keyidx != -1) { fprintf(stdout, "%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+ keyidx = gguf_find_key(ggufctx, "general.license");
+ if (keyidx != -1) { fprintf(stdout, "%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+ keyidx = gguf_find_key(ggufctx, "general.architecture");
+ if (keyidx != -1) { fprintf(stdout, "%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+ keyidx = gguf_find_key(ggufctx, "general.file_type");
+ if (keyidx != -1) { fprintf(stdout, "%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+ keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout");
+ if (keyidx != -1) { fprintf(stdout, "%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+ keyidx = gguf_find_key(ggufctx, "general.source.hugginface.repository");
+ if (keyidx != -1) { fprintf(stdout, "%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
+ }
+
+ // check required metadata
+ {
+ int keyidx;
+
+ // check model architecture kv
+ keyidx = gguf_find_key(ggufctx, "general.architecture");
+ if (keyidx != -1) {
+ if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "falcon") != 0) {
+ fprintf(stdout, "%s: model architecture not supported!\n", __func__);
+ return false;
+ }
+ } else {
+ fprintf(stdout, "%s: gguf model architecture not found!\n", __func__);
+ return false;
+ }
+
+ // check model tensor data layout kv
+ keyidx = gguf_find_key(ggufctx, "falcon.tensor_data_layout");
+ if (keyidx != -1) {
+ if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "jploski") != 0) {
+ fprintf(stdout, "%s: model tensor data layout not supported!\n", __func__);
+ return false;
+ }
+ } else {
+ fprintf(stdout, "%s: gguf model tensor data layout not found!\n", __func__);
+ return false;
+ }
+
+ }
+
+ // load hparams
+ {
+ auto & hparams = model.hparams;
+
+ bool ok = true;
+ int keyidx;
+
+ if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.context_length");
+ if (keyidx != -1) { hparams.n_ctx = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
+
+ if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.embedding_length");
+ if (keyidx != -1) { hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
+
+ if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.attention.head_count");
+ if (keyidx != -1) { hparams.n_head = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
+
+ if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.feed_forward_length");
+ if (keyidx != -1) { hparams.n_ff = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
+
+ if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.block_count");
+ if (keyidx != -1) { hparams.n_block = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
+
+ if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.attention.layer_norm_epsilon");
+ if (keyidx != -1) { hparams.norm_eps= gguf_get_val_f32(ggufctx, keyidx); } else { ok = false; } }
+
+ if (!ok) {
+ fprintf(stderr, "%s: required hparam missing!\n", __func__);
+ return false;
+ }
+
+ keyidx = gguf_find_key(ggufctx, "falcon.attention.head_count_kv");
+ if (keyidx != -1) { hparams.n_head_kv = gguf_get_val_u32(ggufctx, keyidx); }
+
+
+ printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
+ printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
+ printf("%s: n_head = %d\n", __func__, hparams.n_head);
+ printf("%s: n_head_kv = %d\n", __func__, hparams.n_head_kv);
+ printf("%s: n_block = %d\n", __func__, hparams.n_block);
+ printf("%s: norm_eps = %g\n", __func__, hparams.norm_eps);
+
+ }
+
+ // load vocab
+ {
+ auto & hparams = model.hparams;
+
+ int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model");
+
+ if (keyidx != -1) {
+ if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gpt2") != 0) {
+ fprintf(stdout, "%s: tokenizer model not supported!\n", __func__);
+ return false;
+ }
+ } else {
+ fprintf(stdout, "%s: tokenizer model not found!\n", __func__);
+ return false;
+ }
+
+
+ int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
+
+ if (tokens_keyidx == -1) {
+ fprintf(stdout, "%s: gpt2 tokenizer vocab not found!\n", __func__);
+ return false;
+ }
+
+ int merges_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.merges");
+
+ if (merges_keyidx == -1) {
+ fprintf(stdout, "%s: gpt2 tokenizer merges not found!\n", __func__);
+ return false;
+ }
+
+ hparams.n_vocab = gguf_get_arr_n(ggufctx,tokens_keyidx);
+ hparams.n_merges = gguf_get_arr_n(ggufctx,merges_keyidx);
+
+ fprintf(stdout, "%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab);
+ fprintf(stdout, "%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges);
+
+ for (size_t i = 0; i < hparams.n_vocab; i++) {
+ std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
+
+// printf("token %d = '%s'\n",i,word.c_str() );
+
+ vocab.token_to_id[word] = i;
+ vocab.id_to_token[i] = word;
+
+ if( vocab.id_to_token[i] == "\n" ) {
+ vocab.linefeed_id = i;
+ }
+ }
+
+ std::vector<std::pair<std::string, std::string>> bpe_merges;
+
+ for (size_t i = 0; i < hparams.n_merges; i++) {
+
+ std::string word = gguf_get_arr_str(ggufctx, merges_keyidx, i);
+
+ // Split the merges
+ std::string first, second;
+ size_t pos = word.find(' ', 1); // Start the search from the second character
+ if (pos != std::string::npos) {
+ first = word.substr(0, pos);
+ second = word.substr(pos + 1);
+ }
+
+ bpe_merges.push_back(std::make_pair(first, second));
+ }
+
+ vocab.populate_bpe_ranks(bpe_merges);
+
+
+ keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.bos_token_id"); if( keyidx != -1 ) { vocab.special_bos_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
+ keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.eos_token_id"); if( keyidx != -1 ) { vocab.special_eos_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
+ keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.unknown_token_id"); if( keyidx != -1 ) { vocab.special_unk_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
+ keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.separator_token_id"); if( keyidx != -1 ) { vocab.special_sep_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
+ keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.padding_token_id"); if( keyidx != -1 ) { vocab.special_pad_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
+
+ if( vocab.special_bos_id != -1 ) { fprintf(stdout, "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); }
+ if( vocab.special_eos_id != -1 ) { fprintf(stdout, "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); }
+ if( vocab.special_unk_id != -1 ) { fprintf(stdout, "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); }
+ if( vocab.special_sep_id != -1 ) { fprintf(stdout, "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); }
+ if( vocab.special_pad_id != -1 ) { fprintf(stdout, "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); }
+ if( vocab.linefeed_id != -1 ) { fprintf(stdout, "%s: LF token = %d\n", __func__, vocab.linefeed_id ); }
+
+ }
+
+
+ auto & ctx = model.ctx;
+ size_t ctx_size = ggml_get_mem_size(ctx);
+
+ printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
+
+ // print tensor info
+ #if 0
+ {
+ const int n_tensors = gguf_get_n_tensors(ggufctx);
+
+ fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors);
+
+ for (int i = 0; i < n_tensors; ++i) {
+ const char * name = gguf_get_tensor_name (ggufctx, i);
+ const size_t offset = gguf_get_tensor_offset(ggufctx, i);
+
+ fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
+ }
+ }
+ #endif
+
+ // prepare memory for the weights
+ {
+
+ auto & hparams = model.hparams;
+
+ const int n_block = hparams.n_block;
+
+ model.blocks.resize(n_block);
+
+ model.tok_embeddings = ggml_get_tensor(ctx, "token_embd.weight");
+
+ model.output_norm = ggml_get_tensor(ctx, "output_norm.weight");
+ model.output_norm_b = ggml_get_tensor(ctx, "output_norm.bias");
+ model.lm_head = ggml_get_tensor(ctx, "output.weight");
+
+ // map by name
+ model.tensors["token_embd.weight"] = model.tok_embeddings;
+ model.tensors["output_norm.weight"] = model.output_norm;
+ model.tensors["output_norm.bias"] = model.output_norm_b;
+ model.tensors["output.weight"] = model.lm_head;
+
+ for (int i = 0; i < n_block; ++i) {
+
+ auto& block = model.blocks[i];
+ std::string blocknamestart = "blk." + std::to_string(i) + ".";
+
+ block.input_layernorm = get_tensor_ex(ctx, blocknamestart + "attn_norm.weight" );
+ block.input_layernorm_b = get_tensor_ex(ctx, blocknamestart + "attn_norm.bias" );
+
+ if ( hparams.n_head_kv == 8 ) { // Falcon-40B
+ block.attention_norm = get_tensor_ex(ctx, blocknamestart + "attn_norm_2.weight" );
+ block.attention_norm_b = get_tensor_ex(ctx, blocknamestart + "attn_norm_2.bias" );
+ }
+
+ // query_key_value shape for config.multi_query == True:
+ block.query_key_value = get_tensor_ex(ctx, blocknamestart + "attn_qkv.weight" );
+ block.wo = get_tensor_ex(ctx, blocknamestart + "attn_output.weight" );
+
+ block.ffn_up = get_tensor_ex(ctx, blocknamestart + "ffn_up.weight" );
+ block.ffn_down = get_tensor_ex(ctx, blocknamestart + "ffn_down.weight" );
+
+ // map by name
+ if ( hparams.n_head_kv == 8 ) { // Falcon-40B
+ // Falcon-40B:
+ model.tensors[blocknamestart + "attn_norm.weight"] = block.input_layernorm;
+ model.tensors[blocknamestart + "attn_norm.bias"] = block.input_layernorm_b;
+ model.tensors[blocknamestart + "attn_norm_2.weight"] = block.attention_norm;
+ model.tensors[blocknamestart + "attn_norm_2.bias"] = block.attention_norm_b;
+ } else {
+ // Falcon-7B:
+ model.tensors[blocknamestart + "attn_norm.weight"] = block.input_layernorm;
+ model.tensors[blocknamestart + "attn_norm.bias"] = block.input_layernorm_b;
+ }
+
+ model.tensors[blocknamestart + "attn_qkv.weight"] = block.query_key_value;
+ model.tensors[blocknamestart + "attn_output.weight"] = block.wo;
+
+ model.tensors[blocknamestart + "ffn_up.weight"] = block.ffn_up;
+ model.tensors[blocknamestart + "ffn_down.weight"] = block.ffn_down;
+ }
+ }
+
+ // key + value memory
+ {
+ const auto & kvctx = model.kvctx;
+ const auto & hparams = model.hparams;
+
+ const int n_block = hparams.n_block;
+ const int n_ctx = hparams.n_ctx;
+ const int n_embd = hparams.n_embd;
+
+ const int64_t n_mem = n_block*n_ctx;
+ const int64_t n_elements = n_embd*n_mem;
+
+ // create the ggml context
+ {
+ struct ggml_init_params params = {
+ /*.mem_size =*/ size_t(n_elements*4+ggml_tensor_overhead()*2),
+ /*.mem_buffer =*/ NULL,
+ /*.no_alloc =*/ false,
+ };
+
+ model.kvctx = ggml_init(params);
+ if (!model.kvctx) {
+ fprintf(stderr, "%s: kv ggml_init() failed\n", __func__);
+ return false;
+ }
+
+ }
+
+
+ model.memory_k = ggml_new_tensor_1d(kvctx, GGML_TYPE_F16, n_elements);
+ model.memory_v = ggml_new_tensor_1d(kvctx, GGML_TYPE_F16, n_elements);
+
+ const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
+
+ printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size/1024.0/1024.0, n_mem);
+ }
+
+ 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<gpt2bpe_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_block = hparams.n_block;
+ 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 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_block; ++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.blocks[il].input_layernorm, layernorm_output),
+ layernorm_output),
+ ggml_repeat(ctx0, model.blocks[il].input_layernorm_b, layernorm_output));
+
+ if ( hparams.n_head_kv == 8 ) { // Falcon-40B
+ cur = ggml_norm(ctx0, inpL);
+
+ cur = ggml_add(ctx0,
+ ggml_mul(ctx0,
+ ggml_repeat(ctx0, model.blocks[il].attention_norm, cur),
+ cur),
+ ggml_repeat(ctx0, model.blocks[il].attention_norm_b, cur));
+ }
+ else { // Falcon 7B
+ cur = layernorm_output;
+ }
+
+ // compute QKV
+
+ cur = ggml_mul_mat(ctx0, model.blocks[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, 0);
+ Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, head_dim, 2, 0);
+
+ // store key and value to memory
+ {
+ struct ggml_tensor* k = ggml_view_1d(
+ ctx0, model.memory_k, N * n_head_kv * head_dim,
+ (ggml_element_size(model.memory_k) * n_head_kv * head_dim) *
+ (il * n_ctx + n_past));
+ struct ggml_tensor* v = ggml_view_1d(
+ ctx0, model.memory_v, N * n_head_kv * head_dim,
+ (ggml_element_size(model.memory_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_reshape_3d(
+ ctx0,
+ ggml_view_1d(ctx0, model.memory_k, (n_past + N) * n_head_kv * head_dim,
+ il * n_ctx *
+ ggml_element_size(model.memory_k) *
+ n_head_kv *
+ head_dim),
+ head_dim, n_head_kv, n_past + N),
+ 0, 2, 1, 3);
+
+ // K * Q
+
+// K = ggml_cont(ctx0, ggml_repeat2(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_reshape_3d(
+ ctx0,
+ ggml_view_1d(ctx0, model.memory_v, (n_past + N) * n_head_kv * head_dim,
+ il * n_ctx *
+ ggml_element_size(model.memory_v) *
+ n_head_kv *
+ head_dim),
+ head_dim, n_head_kv, n_past + N),
+ 0, 2, 1, 3);
+
+// V = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_repeat2(ctx0, V, repeat_dummy)));
+ V = ggml_cont(ctx0, ggml_transpose(ctx0, V));
+
+ // 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.blocks[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.blocks[il].ffn_up, inpFF);
+ cur = ggml_gelu(ctx0, cur);
+ cur = ggml_mul_mat(ctx0, model.blocks[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);
+ ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
+
+ //if (n_past%100 == 0) {
+ // ggml_graph_print (&gf);
+ // ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
+ //}
+
+ // 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;
+}
+
+int main(int argc, char ** argv) {
+ ggml_time_init();
+
+ const int64_t t_main_start_us = ggml_time_us();
+
+ gpt_params params;
+
+ if (gpt_params_parse(argc, argv, params) == false) {
+ return 1;
+ }
+
+ int64_t t_load_us = 0;
+
+ gpt2bpe_vocab vocab;
+ falcon_model model;
+
+ // load the model
+ {
+ const int64_t t_start_us = ggml_time_us();
+
+ if (!falcon_model_load(params.model, model, vocab)) {
+ fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
+ return 1;
+ }
+
+ t_load_us = ggml_time_us() - t_start_us;
+
+ }
+
+ if (params.seed < 0) {
+ params.seed = time(NULL);
+ }
+
+ if (params.top_k == 0) {
+ params.top_k = model.hparams.n_vocab;
+ }
+
+ printf("%s: seed = %d\n", __func__, params.seed);
+ printf("%s: temp = %.3f\n", __func__, params.temp);
+ printf("%s: top_k = %d\n", __func__, params.top_k);
+ printf("%s: top_p = %.3f\n", __func__, params.top_p);
+ printf("%s: repeat_last_n = %d\n", __func__, params.repeat_last_n);
+ printf("%s: repeat_penalty = %.3f\n", __func__, params.repeat_penalty);
+
+ std::mt19937 rng(params.seed);
+
+ if (params.prompt.empty()) {
+ params.prompt = "Once upon";
+ }
+
+ std::vector<int32_t> last_n_tokens(model.hparams.n_ctx);
+ std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
+
+ int n_past = 0;
+
+ int64_t t_sample_us = 0;
+ int64_t t_predict_us = 0;
+
+ std::vector<float> logits;
+
+ // tokenize the prompt
+ std::vector<gpt2bpe_vocab::id> embd_inp = gpt2bpe_tokenize(vocab, params.prompt,false, false);
+
+ params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
+
+ printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
+// for (size_t i = 0; i < embd_inp.size(); i++) {
+// printf("%s: token[%zu] = %6d, %s\n", __func__, i, embd_inp[i], vocab.id_to_token[embd_inp[i]].c_str());
+// }
+
+ if( model.hparams.n_ctx < params.n_predict+embd_inp.size() ) {
+ params.n_predict = model.hparams.n_ctx-embd_inp.size();
+ }
+
+ printf("%s: n_predict = %d\n", __func__, params.n_predict);
+ printf("\n");
+
+ std::vector<gpt2bpe_vocab::id> embd;
+
+ // determine the required inference memory per token:
+ size_t mem_per_token = 0;
+ falcon_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
+
+ for (size_t i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
+ // predict
+ if (embd.size() > 0) {
+ const int64_t t_start_us = ggml_time_us();
+
+ if (!falcon_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
+ printf("Failed to predict\n");
+ return 1;
+ }
+
+ t_predict_us += ggml_time_us() - t_start_us;
+ }
+
+ n_past += embd.size();
+ embd.clear();
+
+ if (i >= embd_inp.size()) {
+ // sample next token
+ const int top_k = params.top_k;
+ const float top_p = params.top_p;
+ const float temp = params.temp;
+ const int repeat_last_n = params.repeat_last_n;
+ const float repeat_penalty = params.repeat_penalty;
+
+ const int n_vocab = model.hparams.n_vocab;
+
+ gpt2bpe_vocab::id id = 0;
+
+ {
+ const int64_t t_start_sample_us = ggml_time_us();
+
+ id = sample_top_k_top_p_repeat(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens.data(), last_n_tokens.size(), top_k, top_p, temp, repeat_last_n, repeat_penalty, rng);
+
+ last_n_tokens.erase(last_n_tokens.begin());
+ last_n_tokens.push_back(id);
+
+ t_sample_us += ggml_time_us() - t_start_sample_us;
+ }
+
+ // add it to the context
+ embd.push_back(id);
+ } else {
+ // if here, it means we are still processing the input prompt
+ for (size_t k = i; k < embd_inp.size(); k++) {
+ embd.push_back(embd_inp[k]);
+ if (embd.size() > params.n_batch) {
+ break;
+ }
+ }
+ i += embd.size() - 1;
+ }
+
+ // display text
+ for (auto id : embd) {
+ printf("%s", vocab.id_to_token[id].c_str() );
+ }
+ fflush(stdout);
+
+ // end of text token
+ if (vocab.special_eos_id != -1 && embd.back() == vocab.special_eos_id) {
+ break;
+ }
+ }
+
+ // report timing
+ {
+ const int64_t t_main_end_us = ggml_time_us();
+
+ printf("\n\n");
+ printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
+ printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
+ printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
+ printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
+ printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
+ }
+
+ ggml_free(model.ctx);
+
+ return 0;
+}
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-15 14:35:41 +0000