1028 lines
37 KiB
C++
1028 lines
37 KiB
C++
#include "ggml/ggml.h"
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#include "common-ggml.h"
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#include "common.h"
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#include <cmath>
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#include <cstddef>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <cinttypes>
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#include <map>
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#include <string>
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#include <utility>
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#include <vector>
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// no defaults for now
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struct mpt_hparams {
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int32_t d_model = 0;
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int32_t max_seq_len = 0;
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int32_t n_heads = 0;
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int32_t n_layers = 0;
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int32_t n_vocab = 0;
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float alibi_bias_max = 0;
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float clip_qkv = 0;
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int32_t ftype = 0;
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int32_t n_ctx = 0;
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};
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struct mpt_layer {
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// pre normalization
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struct ggml_tensor * norm_1_weight;
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// attention
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struct ggml_tensor * c_attn_wqkv_weight;
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struct ggml_tensor * c_attn_out_proj_weight;
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// post normalization
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struct ggml_tensor * norm_2_weight;
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// ff
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struct ggml_tensor * ffn_up_proj;
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struct ggml_tensor * ffn_down_proj;
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};
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struct mpt_model {
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mpt_hparams hparams;
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struct ggml_tensor * wte_weight; // position embedding
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struct ggml_tensor * norm_f_weight; // language model head
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std::vector<mpt_layer> layers;
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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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struct ggml_context * ctx;
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std::map<std::string, struct ggml_tensor *> tensors;
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};
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struct mpt_params {
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int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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int32_t seed = -1; // RNG seed
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int32_t n_predict = 200; // new tokens to predict
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int32_t n_batch = 8; // batch size for prompt processing
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int32_t n_ctx = 512;
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std::string model = ""; // model path
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std::string prompt = "";
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bool perplexity = false;
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// sampling parameters
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int32_t top_k = 0;
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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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};
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void mpt_print_usage(int /*argc*/, char ** argv, const mpt_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, " -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, " -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 (default: %d, 0 = n_vocab)\n", params.top_k);
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fprintf(stderr, " --top_p N top-p sampling (default: %.2f)\n", params.top_p);
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fprintf(stderr, " --temp N temperature (default: %.2f)\n", params.temp);
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fprintf(stderr, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\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, " --perplexity compute perplexity over the prompt\n");
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fprintf(stderr, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
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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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bool mpt_params_parse(int argc, char ** argv, mpt_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(argv[++i]);
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} else if (arg == "-t" || arg == "--threads") {
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params.n_threads = std::stoi(argv[++i]);
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} else if (arg == "-p" || arg == "--prompt") {
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params.prompt = argv[++i];
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} else if (arg == "-n" || arg == "--n_predict") {
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params.n_predict = std::stoi(argv[++i]);
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} else if (arg == "--top_k") {
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params.top_k = std::max(1, std::stoi(argv[++i]));
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} else if (arg == "--top_p") {
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params.top_p = std::stof(argv[++i]);
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} else if (arg == "--temp") {
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params.temp = std::stof(argv[++i]);
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} else if (arg == "--repeat-last-n") {
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params.repeat_last_n = std::stof(argv[++i]);
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} else if (arg == "--repeat-penalty") {
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params.repeat_penalty = std::stof(argv[++i]);
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} else if (arg == "--perplexity") {
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params.perplexity = true;
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} else if (arg == "-c" || arg == "--ctx-size") {
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params.n_ctx = std::stoi(argv[++i]);
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} else if (arg == "-b" || arg == "--batch_size") {
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params.n_batch = std::stoi(argv[++i]);
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} else if (arg == "-m" || arg == "--model") {
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params.model = argv[++i];
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} else if (arg == "-h" || arg == "--help") {
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mpt_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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if (++i > argc) {
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fprintf(stderr, "Invalid file param");
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break;
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}
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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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params.prompt.clear();
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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 {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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mpt_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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// load the model's weights from a file
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bool mpt_model_load(const std::string & fname, mpt_model & model, gpt_vocab & vocab) {
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printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
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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 != 0x67676d6c) {
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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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// load hparams
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{
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auto & hparams = model.hparams;
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fin.read((char *) &hparams.d_model, sizeof(hparams.d_model));
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fin.read((char *) &hparams.max_seq_len, sizeof(hparams.max_seq_len));
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fin.read((char *) &hparams.n_heads, sizeof(hparams.n_heads));
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fin.read((char *) &hparams.n_layers, sizeof(hparams.n_layers));
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fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
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fin.read((char *) &hparams.alibi_bias_max, sizeof(hparams.alibi_bias_max));
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fin.read((char *) &hparams.clip_qkv, sizeof(hparams.clip_qkv));
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fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));
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hparams.n_ctx = std::min(hparams.max_seq_len, hparams.n_ctx);
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const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
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printf("%s: d_model = %d\n", __func__, hparams.d_model);
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printf("%s: max_seq_len = %d\n", __func__, hparams.max_seq_len);
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printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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printf("%s: n_heads = %d\n", __func__, hparams.n_heads);
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printf("%s: n_layers = %d\n", __func__, hparams.n_layers);
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printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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printf("%s: alibi_bias_max = %f\n", __func__, hparams.alibi_bias_max);
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printf("%s: clip_qkv = %f\n", __func__, hparams.clip_qkv);
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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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// Convert token from utf-8
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std::wstring word_multibytes = convert_to_wstring(word);
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word.resize(word_multibytes.size());
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for (int w = 0; w < word_multibytes.size(); w++) {
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word[w] = uint8_t(word_multibytes[w]);
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}
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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
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// floats or quantized in order to save memory and also to speed up the
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// 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", __func__, fname.c_str(),
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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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const auto & hparams = model.hparams;
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const size_t n_ctx = hparams.n_ctx;
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{
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const size_t n_embd = hparams.d_model;
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const size_t n_layer = hparams.n_layers;
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const size_t n_vocab = hparams.n_vocab;
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ctx_size += n_embd * n_vocab * ggml_type_sizef(wtype); // wte_weight
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ctx_size += n_embd * ggml_type_sizef(GGML_TYPE_F32); // norm_f_weight
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ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_1_weight
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ctx_size += n_layer * (3 * n_embd * n_embd * ggml_type_sizef(wtype)); // attn_Wqkv_weight
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ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // attn_out_proj_weight
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ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_2_weight
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ctx_size += n_layer * (4 * n_embd * n_embd * ggml_type_sizef(wtype)); // mlp_mlp_up_weight
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ctx_size += n_layer * (n_embd * n_embd * 4 * ggml_type_sizef(wtype)); // mlp_mlp_down_weight
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ctx_size += n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16); // memory_k
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ctx_size += n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16); // memory_v
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ctx_size += (1 + 6 * n_layer) * 512; // object overhead
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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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// 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 size_t n_embd = hparams.d_model;
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const size_t n_layer = hparams.n_layers;
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const size_t n_vocab = hparams.n_vocab;
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model.layers.resize(n_layer);
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model.wte_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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model.norm_f_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// map by name
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model.tensors["transformer.wte.weight"] = model.wte_weight;
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model.tensors["transformer.norm_f.weight"] = model.norm_f_weight;
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for (int i = 0; i < (int) n_layer; ++i) {
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auto & layer = model.layers[i];
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layer.norm_1_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.c_attn_wqkv_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, 3 * n_embd);
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layer.c_attn_out_proj_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.norm_2_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.ffn_up_proj = ggml_new_tensor_2d(ctx, wtype, n_embd, 4 * n_embd);
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layer.ffn_down_proj = ggml_new_tensor_2d(ctx, wtype, 4 * n_embd, n_embd);
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// map by name
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model.tensors["transformer.blocks." + std::to_string(i) + ".norm_1.weight"] = layer.norm_1_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".attn.Wqkv.weight"] = layer.c_attn_wqkv_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_out_proj_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".norm_2.weight"] = layer.norm_2_weight;
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model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.up_proj.weight"] = layer.ffn_up_proj;
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model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.down_proj.weight"] = layer.ffn_down_proj;
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}
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}
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// key + value memory
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{
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const auto & hparams = model.hparams;
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const size_t n_embd = hparams.d_model;
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const size_t n_layer = hparams.n_layers;
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const int64_t n_mem = n_layer * n_ctx;
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const int64_t n_elements = n_embd * n_mem;
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model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
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model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
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const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
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printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size / 1024.0 / 1024.0, n_mem);
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}
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// load weights
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{
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int n_tensors = 0;
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size_t total_size = 0;
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printf("%s: ", __func__);
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while (true) {
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int32_t n_dims;
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int32_t length;
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int32_t ttype;
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fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
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fin.read(reinterpret_cast<char *>(&length), sizeof(length));
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fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));
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if (fin.eof()) {
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break;
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}
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int32_t nelements = 1;
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int32_t ne[2] = {1, 1};
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for (int i = 0; i < n_dims; ++i) {
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fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
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nelements *= ne[i];
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}
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std::string name(length, 0);
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fin.read(&name[0], length);
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if (model.tensors.find(name.data()) == model.tensors.end()) {
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fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
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return false;
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}
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auto tensor = model.tensors[name.data()];
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if (ggml_nelements(tensor) != nelements) {
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fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
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return false;
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}
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if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
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fprintf(stderr,
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"%s: tensor '%s' has wrong shape in model file: got [%5d, "
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"%5d], expected [%5d, %5d]\n",
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__func__, name.data(), (int)tensor->ne[0], (int)tensor->ne[1], ne[0], ne[1]);
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return false;
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}
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// for debugging
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if (0) {
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printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1],
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ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor) / 1024.0 / 1024.0, ggml_nbytes(tensor));
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}
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const size_t bpe = ggml_type_size(ggml_type(ttype));
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if ((nelements * bpe) / ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
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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();
|
|
|
|
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 mpt_eval(const mpt_model & model, const int n_threads, const int n_past,
|
|
const std::vector<gpt_vocab::id> & embd_inp, std::vector<float> & embd_w, bool logits_all, size_t & mem_per_token) {
|
|
const int N = embd_inp.size();
|
|
|
|
const auto & hparams = model.hparams;
|
|
|
|
const int n_embd = hparams.d_model;
|
|
const int n_layer = hparams.n_layers;
|
|
const int n_head = hparams.n_heads;
|
|
const int n_vocab = hparams.n_vocab;
|
|
const int n_ctx = hparams.n_ctx;
|
|
|
|
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 = {.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));
|
|
|
|
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte_weight, embd);
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
|
|
struct ggml_tensor * cur;
|
|
|
|
ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
|
|
|
|
// a = self.ln_1(x)
|
|
{
|
|
cur = ggml_norm(ctx0, inpL);
|
|
|
|
cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].norm_1_weight, cur), cur);
|
|
}
|
|
|
|
// self-attention
|
|
// b, _, past_key_value = self.attn(a, past_key_value=past_key_value,
|
|
// attn_bias=attn_bias, attention_mask=attention_mask,
|
|
// is_causal=is_causal)
|
|
{
|
|
// compute QKV
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_wqkv_weight, cur);
|
|
|
|
if (model.hparams.clip_qkv > 0.0f) {
|
|
cur = ggml_clamp(ctx0, cur, -model.hparams.clip_qkv, model.hparams.clip_qkv);
|
|
}
|
|
|
|
struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0 * sizeof(float) * n_embd);
|
|
struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1 * sizeof(float) * n_embd);
|
|
struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2 * sizeof(float) * n_embd);
|
|
|
|
// store key and value to memory
|
|
{
|
|
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_1d(ctx0, model.memory_v, N * n_embd,
|
|
(ggml_element_size(model.memory_v) * n_embd) * (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));
|
|
}
|
|
|
|
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0,
|
|
// 2, 1, 3) [64, N, 12]
|
|
struct ggml_tensor * Q = ggml_permute(
|
|
ctx0, ggml_cpy(ctx0, Qcur, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd / n_head, n_head, N)), 0, 2,
|
|
1, 3);
|
|
|
|
// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1,
|
|
// 3) [64, n_past + N, 12]
|
|
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(ctx0, KQ, ggml_new_f32(ctx0, 1.0f / sqrt(float(n_embd) / n_head)));
|
|
|
|
struct ggml_tensor * KQ_scaled_alibi =
|
|
ggml_alibi(ctx0, KQ_scaled, n_past, n_head, model.hparams.alibi_bias_max);
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, n_past);
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
|
|
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1,
|
|
// 2, 0, 3).contiguous() [n_past + N, 64, 12]
|
|
struct ggml_tensor * V_trans = ggml_cpy(
|
|
ctx0,
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, model.memory_v, (n_past + N) * n_embd,
|
|
il * n_ctx * ggml_element_size(model.memory_v) * n_embd),
|
|
n_embd / n_head, n_head, n_past + N),
|
|
1, 2, 0, 3),
|
|
ggml_new_tensor_3d(ctx0, model.memory_v->type, n_past + N, n_embd / n_head, n_head));
|
|
|
|
// KQV = transpose(V) * KQ_soft_max
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, 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].c_attn_out_proj_weight, cur); }
|
|
}
|
|
|
|
inpL = ggml_add(ctx0, inpL, cur);
|
|
|
|
ggml_set_scratch(ctx0, { 0, scr1_size, scr1, });
|
|
|
|
// m = self.ln_2(x)
|
|
{
|
|
cur = ggml_norm(ctx0, inpL);
|
|
|
|
cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].norm_2_weight, cur), cur);
|
|
}
|
|
|
|
// n = self.mlp(m)
|
|
{
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].ffn_up_proj, cur);
|
|
|
|
// GELU activation
|
|
cur = ggml_gelu(ctx0, cur);
|
|
|
|
// projection
|
|
// cur = proj_w*cur + proj_b
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down_proj, cur);
|
|
}
|
|
|
|
// x = x + n
|
|
inpL = ggml_add(ctx0, inpL, cur);
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
|
|
|
|
// norm
|
|
{
|
|
inpL = ggml_norm(ctx0, inpL);
|
|
// inpL = ln_f_g*inpL
|
|
inpL = ggml_mul(ctx0, ggml_repeat(ctx0, model.norm_f_weight, inpL), inpL);
|
|
}
|
|
|
|
ggml_set_scratch(ctx0, { 0, 0, nullptr, });
|
|
|
|
// output embedding weight tied to input embedding
|
|
inpL = ggml_mul_mat(ctx0, model.wte_weight, inpL);
|
|
|
|
// logits -> probs
|
|
// inpL = ggml_soft_max(ctx0, inpL);
|
|
|
|
// run the computation
|
|
ggml_build_forward_expand(&gf, inpL);
|
|
ggml_graph_compute(ctx0, &gf);
|
|
|
|
// std::cout << "Qcur" << std::endl;
|
|
// print_tensor(Qcur);
|
|
|
|
// if (n_past%100 == 0) {
|
|
// ggml_graph_print(&gf);
|
|
// ggml_graph_dump_dot(&gf, NULL, "mpt-model.dot");
|
|
// }
|
|
|
|
if (logits_all) {
|
|
// return result for all tokens
|
|
embd_w.resize(n_vocab *N);
|
|
memcpy(embd_w.data(), (float *)ggml_get_data(inpL) , sizeof(float) * n_vocab * N);
|
|
} else {
|
|
// 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;
|
|
}
|
|
|
|
std::vector<float> softmax(const std::vector<float> & logits) {
|
|
std::vector<float> probs(logits.size());
|
|
float max_logit = logits[0];
|
|
for (float v : logits) max_logit = std::max(max_logit, v);
|
|
double sum_exp = 0.0;
|
|
for (size_t i = 0; i < logits.size(); i++) {
|
|
// Subtract the maximum logit value from the current logit value for numerical stability
|
|
const float logit = logits[i] - max_logit;
|
|
const float exp_logit = expf(logit);
|
|
sum_exp += exp_logit;
|
|
probs[i] = exp_logit;
|
|
}
|
|
for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;
|
|
return probs;
|
|
}
|
|
|
|
int perplexity(mpt_params params) {
|
|
ggml_time_init();
|
|
|
|
const int64_t t_main_start_us = ggml_time_us();
|
|
|
|
printf("%s: n_threads = %d\n", __func__, params.n_threads);
|
|
printf("%s: n_batch = %d\n", __func__, params.n_batch);
|
|
printf("%s: n_ctx = %d\n", __func__, params.n_ctx);
|
|
printf("\n");
|
|
|
|
int64_t t_load_us = 0;
|
|
|
|
gpt_vocab vocab;
|
|
mpt_model model;
|
|
|
|
model.hparams.n_ctx = params.n_ctx;
|
|
|
|
// load the model
|
|
{
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
if (!mpt_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;
|
|
}
|
|
|
|
int64_t t_predict_us = 0;
|
|
|
|
std::vector<float> logits;
|
|
|
|
// tokenize the prompt
|
|
std::vector<int> embd_inp = ::gpt_tokenize(vocab, params.prompt);
|
|
|
|
printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
|
|
|
|
// determine the required inference memory per token:
|
|
size_t mem_per_token = 0;
|
|
mpt_eval(model, params.n_threads, 0, {0, 1, 2, 3}, logits, false, mem_per_token);
|
|
|
|
int count = 0;
|
|
|
|
const int n_chunk = embd_inp.size() / params.n_ctx;
|
|
|
|
const int n_vocab = model.hparams.n_vocab;
|
|
const int n_batch = params.n_batch;
|
|
|
|
double nll = 0.0;
|
|
fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
|
|
|
|
for (int i = 0; i < n_chunk; ++i) {
|
|
|
|
const int start = i * params.n_ctx;
|
|
const int end = start + params.n_ctx;
|
|
|
|
const int num_batches = (params.n_ctx + n_batch - 1) / n_batch;
|
|
|
|
std::vector<float> logits;
|
|
|
|
const auto t_start = std::chrono::high_resolution_clock::now();
|
|
|
|
for (int j = 0; j < num_batches; ++j) {
|
|
|
|
const int batch_start = start + j * n_batch;
|
|
const int batch_size = std::min(end - batch_start, n_batch);
|
|
|
|
std::vector<gpt_vocab::id> embd;
|
|
|
|
for(int p=0;p<batch_size;p++) {
|
|
embd.push_back( embd_inp[batch_start+p] );
|
|
}
|
|
|
|
std::vector<float> batch_logits;// = llama_get_logits(ctx);
|
|
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
if (!mpt_eval(model, params.n_threads, j * batch_size, embd, batch_logits, true, mem_per_token)) {
|
|
printf("%s: failed to evaluate model\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
t_predict_us += ggml_time_us() - t_start_us;
|
|
|
|
logits.insert(logits.end(), batch_logits.data(), batch_logits.data() + batch_size * n_vocab);
|
|
|
|
}
|
|
|
|
const auto t_end = std::chrono::high_resolution_clock::now();
|
|
|
|
if (i == 0) {
|
|
const float t_total = std::chrono::duration<float>(t_end - t_start).count();
|
|
fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
|
|
int total_seconds = (int)(t_total * n_chunk);
|
|
if (total_seconds >= 60*60) {
|
|
fprintf(stderr, "%d hours ", total_seconds / (60*60));
|
|
total_seconds = total_seconds % (60*60);
|
|
}
|
|
fprintf(stderr, "%d minutes\n", total_seconds / 60);
|
|
|
|
printf("\nChunk\tPPL cumulative\tPPL chunk\n");
|
|
}
|
|
|
|
// We get the logits for all the tokens in the context window (params.n_ctx)
|
|
// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,
|
|
// calculate the perplexity over the last half of the window (so the model always has
|
|
// some context to predict the token).
|
|
//
|
|
// We rely on the fact that attention in the forward pass only looks at previous
|
|
// tokens here, so the logits returned for each token are an accurate representation
|
|
// of what the model would have predicted at that point.
|
|
//
|
|
// Example, we have a context window of 512, we will compute perplexity for each of the
|
|
// last 256 tokens. Then, we split the input up into context window size chunks to
|
|
// process the entire prompt.
|
|
|
|
double nllchunk = 0.0;
|
|
int countchunk = 0;
|
|
|
|
for (int j = std::min(512, params.n_ctx / 2); j < params.n_ctx - 1; ++j) {
|
|
// Calculate probability of next token, given the previous ones.
|
|
const std::vector<float> tok_logits(
|
|
logits.begin() + (j + 0) * n_vocab,
|
|
logits.begin() + (j + 1) * n_vocab);
|
|
|
|
const float prob = softmax(tok_logits)[embd_inp[ start+ j + 1]];
|
|
|
|
nllchunk += -std::log(prob);
|
|
++countchunk;
|
|
}
|
|
|
|
nll += nllchunk;
|
|
count += countchunk;
|
|
|
|
// perplexity is e^(average negative log-likelihood)
|
|
printf("%d\t%.8lf\t%.8lf\n", i + 1, std::exp(nll / count), std::exp(nllchunk/countchunk) );
|
|
fflush(stdout);
|
|
}
|
|
|
|
// 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: eval time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us / 1000.0f, t_predict_us / 1000.0f / (n_chunk * params.n_ctx));
|
|
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;
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
mpt_params params;
|
|
|
|
if (mpt_params_parse(argc, argv, params) == false) {
|
|
return 1;
|
|
}
|
|
|
|
if (params.perplexity) {
|
|
return perplexity(params);
|
|
}
|
|
|
|
ggml_time_init();
|
|
|
|
const int64_t t_main_start_us = ggml_time_us();
|
|
|
|
if (params.seed < 0) {
|
|
params.seed = time(NULL);
|
|
}
|
|
|
|
if (params.n_predict < 0) {
|
|
params.n_predict = 0;
|
|
}
|
|
|
|
printf("%s: seed = %d\n", __func__, params.seed);
|
|
printf("%s: n_threads = %d\n", __func__, params.n_threads);
|
|
printf("%s: n_batch = %d\n", __func__, params.n_batch);
|
|
printf("%s: n_ctx = %d\n", __func__, params.n_ctx);
|
|
printf("%s: n_predict = %d\n\n", __func__, params.n_predict);
|
|
|
|
std::mt19937 rng(params.seed);
|
|
if (params.prompt.empty()) {
|
|
params.prompt = gpt_random_prompt(rng);
|
|
}
|
|
|
|
int64_t t_load_us = 0;
|
|
|
|
gpt_vocab vocab;
|
|
mpt_model model;
|
|
|
|
model.hparams.n_ctx = params.n_ctx;
|
|
|
|
// load the model
|
|
{
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
if (!mpt_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.top_k == 0) {
|
|
params.top_k = model.hparams.n_vocab;
|
|
}
|
|
|
|
if (params.repeat_last_n == -1) {
|
|
params.repeat_last_n = params.n_ctx;
|
|
}
|
|
|
|
printf("\n");
|
|
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);
|
|
|
|
int64_t t_sample_us = 0;
|
|
int64_t t_predict_us = 0;
|
|
|
|
std::vector<int32_t> last_n_tokens(params.n_ctx);
|
|
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
|
|
|
|
// tokenize the prompt
|
|
std::vector<int> embd_inp = ::gpt_tokenize(vocab, params.prompt);
|
|
|
|
printf("\n");
|
|
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[%lu] = %6d\n", __func__, i, embd_inp[i]);
|
|
}
|
|
printf("\n");
|
|
|
|
std::vector<gpt_vocab::id> embd;
|
|
std::vector<float> logits;
|
|
|
|
// determine the required inference memory per token:
|
|
size_t mem_per_token = 0;
|
|
mpt_eval(model, params.n_threads, 0, {0, 1, 2, 3}, logits, false, mem_per_token);
|
|
|
|
int n_past = 0;
|
|
int n_consumed = 0;
|
|
int n_sampled = 0;
|
|
|
|
while (n_sampled < params.n_predict) {
|
|
// predict
|
|
if (embd.size() > 0) {
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
if (!mpt_eval(model, params.n_threads, n_past, embd, logits, false, mem_per_token)) {
|
|
printf("%s: failed to predict\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
t_predict_us += ggml_time_us() - t_start_us;
|
|
|
|
n_past += embd.size();
|
|
embd.clear();
|
|
}
|
|
|
|
if ((int)embd_inp.size() <= n_consumed) {
|
|
// 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;
|
|
|
|
gpt_vocab::id id = 0;
|
|
|
|
{
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
id = gpt_sample_top_k_top_p_repeat(vocab, logits.data() + (logits.size() - model.hparams.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);
|
|
++n_sampled;
|
|
|
|
} else {
|
|
// if here, it means we are still processing the input prompt
|
|
while ((int) embd_inp.size() > n_consumed) {
|
|
embd.push_back(embd_inp[n_consumed]);
|
|
|
|
last_n_tokens.erase(last_n_tokens.begin());
|
|
last_n_tokens.push_back(embd_inp[n_consumed]);
|
|
|
|
++n_consumed;
|
|
if ((int) embd.size() >= params.n_batch) {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
// display text
|
|
for (auto id : embd) {
|
|
printf("%s", vocab.id_to_token[id].c_str());
|
|
}
|
|
fflush(stdout);
|
|
|
|
// end of text token
|
|
if (embd.back() == 0) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
// report timing
|
|
{
|
|
const int64_t t_main_end_us = ggml_time_us();
|
|
|
|
printf("\n\n\n");
|
|
printf("%s: sampled tokens = %8d\n", __func__, n_sampled);
|
|
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 / %.2f ms per token\n", __func__, t_sample_us / 1000.0f, t_sample_us / 1000.0f / n_sampled);
|
|
printf("%s: eval 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;
|
|
}
|