mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 13:58:46 +01:00
1084 lines
40 KiB
C++
1084 lines
40 KiB
C++
#include "ggml.h"
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#include "cmpnct_gpt2bpe.hpp"
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#include <cassert>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <cinttypes>
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#include <fstream>
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#include <map>
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#include <string>
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#include <vector>
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#include <thread>
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#include <random>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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// default hparams
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struct gpt_neox_hparams {
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size_t n_merges = 0;
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size_t n_vocab = 0;
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uint32_t n_ctx = 0;
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uint32_t n_embd = 0;
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uint32_t n_head = 0;
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uint32_t n_block = 0;
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uint32_t n_rot = 0; // rotary_pct * (n_embd / n_head)
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bool par_res = true;
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float norm_eps = 1e-5;
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};
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struct gpt_neox_block {
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// pre normalization
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struct ggml_tensor * ln_1_g;
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struct ggml_tensor * ln_1_b;
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// attention
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struct ggml_tensor * c_attn_attn_w;
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struct ggml_tensor * c_attn_attn_b;
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struct ggml_tensor * c_attn_proj_w;
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struct ggml_tensor * c_attn_proj_b;
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// post normalization
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struct ggml_tensor * ln_2_g;
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struct ggml_tensor * ln_2_b;
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// ff
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struct ggml_tensor * c_mlp_fc_w;
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struct ggml_tensor * c_mlp_fc_b;
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struct ggml_tensor * c_mlp_proj_w;
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struct ggml_tensor * c_mlp_proj_b;
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};
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struct gpt_neox_model {
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gpt_neox_hparams hparams;
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// normalization
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struct ggml_tensor * ln_f_g;
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struct ggml_tensor * ln_f_b;
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struct ggml_tensor * wte; // position embedding
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struct ggml_tensor * lmh_g; // language model head
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std::vector<gpt_neox_block> blocks;
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// key + value memory
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struct ggml_tensor * memory_k;
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struct ggml_tensor * memory_v;
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//
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struct gguf_context * ggufctx;
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struct ggml_context * ctx;
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struct ggml_context * kvctx;
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std::map<std::string, struct ggml_tensor *> tensors;
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};
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struct gpt_params {
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int32_t seed = -1; // RNG seed
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int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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uint32_t n_predict = 200; // new tokens to predict
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uint32_t n_batch = 512; // batch size for prompt processing
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// sampling parameters
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int32_t top_k = 40;
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float top_p = 1.0f;
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float temp = 0.8f;
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int32_t repeat_last_n = 64;
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float repeat_penalty = 1.02f;
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std::string model = ""; // model path
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std::string prompt = "";
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std::string token_test = "";
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bool interactive = false;
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int32_t interactive_port = -1;
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int32_t n_gpu_layers = 0;
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};
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void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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fprintf(stderr, "usage: %s [options]\n", argv[0]);
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fprintf(stderr, "\n");
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fprintf(stderr, "options:\n");
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fprintf(stderr, " -h, --help show this help message and exit\n");
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fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
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fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
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fprintf(stderr, " -ngl N, --gpu-layers N number of layers to offload to GPU on supported models (default: %d)\n", params.n_gpu_layers);
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fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");
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fprintf(stderr, " prompt to start generation with (default: random)\n");
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fprintf(stderr, " -f FNAME, --file FNAME\n");
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fprintf(stderr, " load prompt from a file\n");
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fprintf(stderr, " -tt TOKEN_TEST, --token_test TOKEN_TEST\n");
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fprintf(stderr, " test tokenization\n");
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fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);
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fprintf(stderr, " --top_k N top-k sampling, 0 = n_vocab (default: %d)\n", params.top_k);
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fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
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fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
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fprintf(stderr, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled)\n", params.repeat_last_n);
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fprintf(stderr, " --repeat-penalty N penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)\n", (double)params.repeat_penalty);
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fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
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fprintf(stderr, " -m FNAME, --model FNAME\n");
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fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
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fprintf(stderr, "\n");
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}
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// Function to check if the next argument exists
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std::string get_next_arg(int& i, int argc, char** argv, const std::string& flag, gpt_params& params) {
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if (i + 1 < argc && argv[i + 1][0] != '-') {
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return argv[++i];
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} else {
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fprintf(stderr, "error: %s requires one argument.\n", flag.c_str());
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gpt_print_usage(argc, argv, params);
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exit(0);
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}
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}
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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for (int i = 1; i < argc; i++) {
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std::string arg = argv[i];
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if (arg == "-s" || arg == "--seed") {
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params.seed = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-t" || arg == "--threads") {
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params.n_threads = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
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params.n_gpu_layers = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-p" || arg == "--prompt") {
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params.prompt = get_next_arg(i, argc, argv, arg, params);
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} else if (arg == "-n" || arg == "--n_predict") {
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params.n_predict = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--top_k") {
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params.top_k = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--top_p") {
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params.top_p = std::stof(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--temp") {
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params.temp = std::stof(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--repeat-last-n") {
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params.repeat_last_n = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--repeat-penalty") {
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params.repeat_penalty = std::stof(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-b" || arg == "--batch_size") {
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params.n_batch= std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-m" || arg == "--model") {
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params.model = get_next_arg(i, argc, argv, arg, params);
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} else if (arg == "-i" || arg == "--interactive") {
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params.interactive = true;
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} else if (arg == "-ip" || arg == "--interactive-port") {
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params.interactive = true;
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params.interactive_port = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-h" || arg == "--help") {
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gpt_print_usage(argc, argv, params);
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exit(0);
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} else if (arg == "-f" || arg == "--file") {
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get_next_arg(i, argc, argv, arg, params);
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std::ifstream file(argv[i]);
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if (!file) {
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fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
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break;
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}
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std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
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if (params.prompt.back() == '\n') {
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params.prompt.pop_back();
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}
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} else if (arg == "-tt" || arg == "--token_test") {
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params.token_test = get_next_arg(i, argc, argv, arg, params);
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}
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else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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gpt_print_usage(argc, argv, params);
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exit(0);
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}
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}
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return true;
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}
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gpt2bpe_vocab::id sample_top_k_top_p_repeat(
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const gpt2bpe_vocab & vocab,
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const float * logits,
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const int32_t * last_n_tokens_data,
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size_t last_n_tokens_data_size,
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int top_k,
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double top_p,
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double temp,
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int repeat_last_n,
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float repeat_penalty,
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std::mt19937 & rng) {
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int n_logits = vocab.id_to_token.size();
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const auto * plogits = logits;
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const auto last_n_tokens = std::vector<int32_t>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_data_size);
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if (temp <= 0) {
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// select the token with the highest logit directly
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float max_logit = plogits[0];
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gpt2bpe_vocab::id max_id = 0;
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for (int i = 1; i < n_logits; ++i) {
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if (plogits[i] > max_logit) {
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max_logit = plogits[i];
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max_id = i;
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}
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}
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return max_id;
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}
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std::vector<std::pair<double, gpt2bpe_vocab::id>> logits_id;
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logits_id.reserve(n_logits);
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{
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const float scale = 1.0f/temp;
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for (int i = 0; i < n_logits; ++i) {
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// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
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// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
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if (repeat_last_n > 0 && std::find(last_n_tokens.end()-repeat_last_n, last_n_tokens.end(), i) != last_n_tokens.end()) {
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// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
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if (plogits[i] < 0.0f) {
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logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
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} else {
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logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
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}
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} else {
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logits_id.push_back(std::make_pair(plogits[i]*scale, i));
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}
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}
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}
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// find the top K tokens
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std::partial_sort(
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logits_id.begin(),
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logits_id.begin() + top_k, logits_id.end(),
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[](const std::pair<double, gpt2bpe_vocab::id> & a, const std::pair<double, gpt2bpe_vocab::id> & b) {
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return a.first > b.first;
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});
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logits_id.resize(top_k);
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double maxl = -INFINITY;
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for (const auto & kv : logits_id) {
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maxl = std::max(maxl, kv.first);
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}
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// compute probs for the top K tokens
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std::vector<double> probs;
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probs.reserve(logits_id.size());
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double sum = 0.0;
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for (const auto & kv : logits_id) {
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double p = exp(kv.first - maxl);
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probs.push_back(p);
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sum += p;
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}
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// normalize the probs
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for (auto & p : probs) {
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p /= sum;
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}
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if (top_p < 1.0f) {
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double cumsum = 0.0f;
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for (int i = 0; i < top_k; i++) {
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cumsum += probs[i];
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if (cumsum >= top_p) {
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top_k = i + 1;
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probs.resize(top_k);
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logits_id.resize(top_k);
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break;
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}
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}
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cumsum = 1.0/cumsum;
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for (int i = 0; i < (int) probs.size(); i++) {
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probs[i] *= cumsum;
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}
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}
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// printf("\n");
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// for (int i = 0; i < (int) probs.size(); i++) {
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// for (int i = 0; i < 10; i++) {
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// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
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// }
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std::discrete_distribution<> dist(probs.begin(), probs.end());
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int idx = dist(rng);
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return logits_id[idx].second;
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}
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struct ggml_tensor * get_tensor_ex( struct ggml_context * ctx, std::string name){
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struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str());
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if( cur == NULL ) {
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printf("%s: tensor '%s' not found!\n", __func__, name.c_str());
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} else {
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// printf("%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name);
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}
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return cur;
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}
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// load the model's weights from a file
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bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2bpe_vocab & vocab) {
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printf("%s: loading model from '%s'..\n", __func__, fname.c_str());
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model.ctx = NULL;
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struct gguf_init_params ggufparams = {
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/*.no_alloc = */ false,
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/*.ctx = */ &model.ctx,
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};
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auto & ggufctx = model.ggufctx;
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ggufctx = gguf_init_from_file(fname.c_str(), ggufparams);
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if (!ggufctx) {
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fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__);
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return false;
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}
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printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx));
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printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx));
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printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
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// print all kv
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#if 0
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{
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const int n_kv = gguf_get_n_kv(ggufctx);
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printf("%s: n_kv: %d\n", __func__, n_kv);
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for (int i = 0; i < n_kv; ++i) {
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const char * key = gguf_get_key(ggufctx, i);
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printf("%s: kv[%d]: key = %s\n", __func__, i, key);
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}
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}
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#endif
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// print some standard metadata
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{
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int keyidx;
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keyidx = gguf_find_key(ggufctx, "general.name");
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if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "general.description");
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if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "general.author");
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if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "general.license");
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if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "general.architecture");
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if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "general.file_type");
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if (keyidx != -1) { printf("%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout");
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if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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keyidx = gguf_find_key(ggufctx, "general.source.hugginface.repository");
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if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
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}
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// check required metadata
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{
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int keyidx;
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// check model architecture kv
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keyidx = gguf_find_key(ggufctx, "general.architecture");
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if (keyidx != -1) {
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if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gptneox") != 0) {
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printf("%s: model architecture not supported!\n", __func__);
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return false;
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}
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} else {
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printf("%s: gguf model architecture not found!\n", __func__);
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return false;
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}
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}
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// load hparams
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{
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auto & hparams = model.hparams;
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bool ok = true;
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int keyidx;
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if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.context_length");
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if (keyidx != -1) { hparams.n_ctx = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
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if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.embedding_length");
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if (keyidx != -1) { hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
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if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.attention.head_count");
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if (keyidx != -1) { hparams.n_head = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
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if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.block_count");
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|
if (keyidx != -1) { hparams.n_block = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
|
|
|
|
if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.rope.dimension_count");
|
|
if (keyidx != -1) { hparams.n_rot = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
|
|
|
|
if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.use_parallel_residual");
|
|
if (keyidx != -1) { hparams.par_res = gguf_get_val_bool(ggufctx, keyidx); } else { ok = false; } }
|
|
|
|
if (ok) { keyidx = gguf_find_key(ggufctx, "gptneox.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;
|
|
}
|
|
|
|
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_block = %d\n", __func__, hparams.n_block);
|
|
printf("%s: n_rot = %d\n", __func__, hparams.n_rot);
|
|
printf("%s: par_res = %d\n", __func__, hparams.par_res);
|
|
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) {
|
|
printf("%s: tokenizer model not supported!\n", __func__);
|
|
return false;
|
|
}
|
|
} else {
|
|
printf("%s: tokenizer model not found!\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
|
|
int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
|
|
|
|
if (tokens_keyidx == -1) {
|
|
printf("%s: gpt2 tokenizer vocab not found!\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
int merges_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.merges");
|
|
|
|
if (merges_keyidx == -1) {
|
|
printf("%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);
|
|
|
|
printf("%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab);
|
|
printf("%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 ) { printf("%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 ) { printf("%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 ) { printf("%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 ) { printf("%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 ) { printf("%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 ) { printf("%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);
|
|
|
|
printf("%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);
|
|
|
|
printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
|
|
}
|
|
}
|
|
#endif
|
|
|
|
// prepare memory for the weights
|
|
{
|
|
const int n_block = model.hparams.n_block;
|
|
|
|
model.blocks.resize(n_block);
|
|
|
|
model.wte = ggml_get_tensor(ctx, "token_embd.weight");
|
|
model.ln_f_g = ggml_get_tensor(ctx, "output_norm.weight");
|
|
model.ln_f_b = ggml_get_tensor(ctx, "output_norm.bias");
|
|
model.lmh_g = ggml_get_tensor(ctx, "output.weight");
|
|
|
|
// map by name
|
|
model.tensors["token_embd.weight"] = model.wte;
|
|
model.tensors["output_norm.weight"] = model.ln_f_g;
|
|
model.tensors["output_norm.bias"] = model.ln_f_b;
|
|
model.tensors["output.weight"] = model.lmh_g;
|
|
|
|
for (int i = 0; i < n_block; ++i) {
|
|
auto & block = model.blocks[i];
|
|
|
|
std::string blocknamestart = "blk." + std::to_string(i) + ".";
|
|
|
|
block.ln_1_g = get_tensor_ex(ctx, blocknamestart + "attn_norm.weight" );
|
|
block.ln_1_b = get_tensor_ex(ctx, blocknamestart + "attn_norm.bias" );
|
|
|
|
block.c_attn_attn_w = get_tensor_ex(ctx, blocknamestart + "attn_qkv.weight" );
|
|
block.c_attn_attn_b = get_tensor_ex(ctx ,blocknamestart + "attn_qkv.bias" );
|
|
|
|
block.c_attn_proj_w = get_tensor_ex(ctx, blocknamestart + "attn_output.weight" );
|
|
block.c_attn_proj_b = get_tensor_ex(ctx, blocknamestart + "attn_output.bias" );
|
|
|
|
block.ln_2_g = get_tensor_ex(ctx, blocknamestart + "ffn_norm.weight" );
|
|
block.ln_2_b = get_tensor_ex(ctx, blocknamestart + "ffn_norm.bias");
|
|
|
|
block.c_mlp_fc_w = get_tensor_ex(ctx, blocknamestart + "ffn_up.weight" );
|
|
block.c_mlp_fc_b = get_tensor_ex(ctx, blocknamestart + "ffn_up.bias" );
|
|
|
|
block.c_mlp_proj_w = get_tensor_ex(ctx, blocknamestart + "ffn_down.weight" );
|
|
block.c_mlp_proj_b = get_tensor_ex(ctx, blocknamestart + "ffn_down.bias" );
|
|
|
|
// map by name
|
|
model.tensors[blocknamestart + "attn_norm.weight"] = block.ln_1_g;
|
|
model.tensors[blocknamestart + "attn_norm.bias"] = block.ln_1_b;
|
|
|
|
model.tensors[blocknamestart + "attn_qkv.weight"] = block.c_attn_attn_w;
|
|
model.tensors[blocknamestart + "attn_qkv.bias"] = block.c_attn_attn_b;
|
|
|
|
model.tensors[blocknamestart + "attn_output.weight"] = block.c_attn_proj_w;
|
|
model.tensors[blocknamestart + "attn_output.bias"] = block.c_attn_proj_b;
|
|
|
|
model.tensors[blocknamestart + "ffn_norm.weight"] = block.ln_2_g;
|
|
model.tensors[blocknamestart + "ffn_norm.bias"] = block.ln_2_b;
|
|
|
|
model.tensors[blocknamestart + "ffn_up.weight"] = block.c_mlp_fc_w;
|
|
model.tensors[blocknamestart + "ffn_up.bias"] = block.c_mlp_fc_b;
|
|
|
|
model.tensors[blocknamestart + "ffn_down.weight"] = block.c_mlp_proj_w;
|
|
model.tensors[blocknamestart + "ffn_down.bias"] = block.c_mlp_proj_b;
|
|
}
|
|
}
|
|
|
|
// key + value memory
|
|
{
|
|
const auto & kvctx = model.kvctx;
|
|
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 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;
|
|
}
|
|
|
|
|
|
// feed-forward network
|
|
ggml_tensor * gpt_neox_ff(
|
|
const gpt_neox_block &block,
|
|
ggml_context * ctx0,
|
|
ggml_tensor * inp,
|
|
const gpt_neox_hparams &hparams) {
|
|
|
|
ggml_tensor * cur = ggml_norm(ctx0, inp, hparams.norm_eps);
|
|
|
|
cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, block.ln_2_g, cur), cur), ggml_repeat(ctx0, block.ln_2_b, cur));
|
|
cur = ggml_mul_mat(ctx0, block.c_mlp_fc_w, cur);
|
|
cur = ggml_add(ctx0, ggml_repeat(ctx0, block.c_mlp_fc_b, cur), cur);
|
|
|
|
// GELU activation
|
|
cur = ggml_gelu(ctx0, cur);
|
|
|
|
// projection
|
|
// cur = proj_w*cur + proj_b
|
|
cur = ggml_mul_mat(ctx0, block.c_mlp_proj_w, cur);
|
|
|
|
cur = ggml_add(ctx0, ggml_repeat(ctx0, block.c_mlp_proj_b, cur), cur);
|
|
return cur;
|
|
}
|
|
|
|
// 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 gpt_neox_eval(
|
|
const gpt_neox_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_vocab = hparams.n_vocab;
|
|
const int n_rot = hparams.n_rot;
|
|
|
|
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 = {};
|
|
|
|
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
|
memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
|
|
|
|
|
|
// wte
|
|
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd);
|
|
|
|
for (int il = 0; il < n_block; ++il) {
|
|
struct ggml_tensor * cur;
|
|
|
|
ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
|
|
|
|
// self-attention
|
|
{
|
|
{
|
|
cur = ggml_norm(ctx0, inpL, hparams.norm_eps);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0, ggml_repeat(ctx0, model.blocks[il].ln_1_g, cur), cur),
|
|
ggml_repeat(ctx0, model.blocks[il].ln_1_b, cur));
|
|
}
|
|
|
|
// compute QKV
|
|
{
|
|
|
|
cur = ggml_mul_mat(ctx0, model.blocks[il].c_attn_attn_w, cur);
|
|
cur = ggml_add(ctx0, ggml_repeat(ctx0, model.blocks[il].c_attn_attn_b, cur), cur);
|
|
}
|
|
|
|
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 0*sizeof(float)*n_embd/n_head));
|
|
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 1*sizeof(float)*n_embd/n_head));
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 2*sizeof(float)*n_embd/n_head));
|
|
|
|
// using mode = 2 for GPT-NeoX mode
|
|
Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, n_rot, 2, 0);
|
|
Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, n_rot, 2, 0);
|
|
|
|
// store key and value to memory
|
|
{
|
|
Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd, N));
|
|
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
|
|
struct ggml_tensor * v = ggml_view_2d(ctx0, model.memory_v, N, n_embd,
|
|
( n_ctx)*ggml_element_size(model.memory_v),
|
|
(il*n_ctx)*ggml_element_size(model.memory_v)*n_embd + n_past*ggml_element_size(model.memory_v));
|
|
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
|
|
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
|
|
struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
|
|
|
|
// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
|
|
struct ggml_tensor * K =
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
|
|
n_embd/n_head, n_head, n_past + N),
|
|
0, 2, 1, 3);
|
|
|
|
// K * Q
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd/n_head)
|
|
struct ggml_tensor * KQ_scaled =
|
|
ggml_scale_inplace(ctx0,
|
|
KQ,
|
|
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
|
|
);
|
|
|
|
// 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_view_3d(ctx0, model.memory_v,
|
|
n_past + N, n_embd/n_head, n_head,
|
|
n_ctx*ggml_element_size(model.memory_v),
|
|
n_ctx*ggml_element_size(model.memory_v)*n_embd/n_head,
|
|
il*n_ctx*ggml_element_size(model.memory_v)*n_embd);
|
|
|
|
// KQV = transpose(V) * KQ_soft_max
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
|
cur = ggml_cpy(ctx0, KQV_merged, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
|
|
|
// projection
|
|
{
|
|
cur = ggml_mul_mat(ctx0, model.blocks[il].c_attn_proj_w, cur);
|
|
cur = ggml_add(ctx0, ggml_repeat(ctx0, model.blocks[il].c_attn_proj_b, cur), cur);
|
|
}
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, { 0, scr1_size, scr1, });
|
|
|
|
if (hparams.par_res == 0) {
|
|
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpL);
|
|
|
|
cur = gpt_neox_ff(model.blocks[il], ctx0, inpFF, hparams);
|
|
|
|
// input for next layer
|
|
inpL = ggml_add(ctx0, cur, inpFF);
|
|
} else {
|
|
struct ggml_tensor * inpFF = cur;
|
|
|
|
// this is independent of the self-attention result, so it could be done in parallel to the self-attention
|
|
// note here we pass inpL instead of cur
|
|
cur = gpt_neox_ff(model.blocks[il], ctx0, inpL, hparams);
|
|
|
|
// layer input + FF
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
|
|
// input for next layer
|
|
inpL = ggml_add(ctx0, cur, inpL);
|
|
}
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
|
|
|
|
// norm
|
|
{
|
|
inpL = ggml_norm(ctx0, inpL, hparams.norm_eps);
|
|
|
|
// inpL = ln_f_g*inpL + ln_f_b
|
|
inpL = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.ln_f_g, inpL),
|
|
inpL),
|
|
ggml_repeat(ctx0, model.ln_f_b, inpL));
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, { 0, 0, nullptr, });
|
|
|
|
// lm_head
|
|
{
|
|
inpL = ggml_mul_mat(ctx0, model.lmh_g, 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_with_ctx(ctx0, &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;
|
|
}
|
|
|
|
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)) {
|
|
return 1;
|
|
}
|
|
|
|
int64_t t_load_us = 0;
|
|
|
|
gpt2bpe_vocab vocab;
|
|
gpt_neox_model model;
|
|
|
|
// load the model
|
|
{
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
if (!gpt_neox_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;
|
|
gpt_neox_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 (!gpt_neox_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;
|
|
}
|