#include "ggml/ggml.h"
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#include "ggml/ggml-alloc.h"
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#include "ggml/ggml-backend.h"
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#ifdef GGML_USE_CUBLAS
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#include "ggml-cuda.h"
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#endif
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#ifdef GGML_USE_METAL
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#include "ggml-metal.h"
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#endif
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#include "common.h"
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#include "common-ggml.h"
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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 <fstream>
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#include <map>
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#include <string>
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#include <vector>
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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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#define GPT2_MAX_NODES 4096
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static void ggml_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
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(void) level;
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(void) user_data;
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fputs(text, stderr);
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fflush(stderr);
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}
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// default hparams (GPT-2 117M)
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struct gpt2_hparams {
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int32_t n_vocab = 50257;
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int32_t n_ctx = 1024;
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int32_t n_embd = 768;
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int32_t n_head = 12;
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int32_t n_layer = 12;
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int32_t ftype = 1;
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float eps = 1e-5f;
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};
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struct gpt2_layer {
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// 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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struct ggml_tensor * ln_2_g;
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struct ggml_tensor * ln_2_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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// mlp
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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 gpt2_model {
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gpt2_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 * wpe; // token embedding
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struct ggml_tensor * lm_head; // language model head
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std::vector<gpt2_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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//
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struct ggml_context * ctx;
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ggml_backend_t backend = NULL;
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ggml_backend_buffer_t buffer_w;
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ggml_backend_buffer_t buffer_kv;
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std::map<std::string, struct ggml_tensor *> tensors;
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};
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// load the model's weights from a file
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bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab, int n_ctx, int n_gpu_layers) {
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printf("%s: loading model from '%s'\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 != GGML_FILE_MAGIC) {
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fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
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return false;
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}
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}
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// load hparams
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{
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auto & hparams = model.hparams;
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fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
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fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
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fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
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fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
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fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
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fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));
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const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
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printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
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printf("%s: n_head = %d\n", __func__, hparams.n_head);
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printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
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printf("%s: ftype = %d\n", __func__, hparams.ftype);
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printf("%s: qntvr = %d\n", __func__, qntvr);
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hparams.ftype %= GGML_QNT_VERSION_FACTOR;
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}
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// load vocab
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{
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int32_t n_vocab = 0;
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fin.read((char *) &n_vocab, sizeof(n_vocab));
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if (n_vocab != model.hparams.n_vocab) {
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fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
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__func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
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return false;
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}
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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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vocab.token_to_id[word] = i;
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vocab.id_to_token[i] = word;
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}
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}
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// for the big tensors, we have the option to store the data in 16-bit floats or quantized
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// in order to save memory and also to speed up the computation
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ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
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if (wtype == GGML_TYPE_COUNT) {
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fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
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__func__, fname.c_str(), model.hparams.ftype);
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return false;
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}
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auto & ctx = model.ctx;
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// create the ggml context
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{
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size_t n_tensors = 2 + 6 + 12*model.hparams.n_layer;
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struct ggml_init_params params = {
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/*.mem_size =*/ ggml_tensor_overhead() * n_tensors,
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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ctx = ggml_init(params);
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if (!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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// initialize the backend
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#ifdef GGML_USE_CUBLAS
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if (n_gpu_layers > 0) {
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fprintf(stderr, "%s: using CUDA backend\n", __func__);
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model.backend = ggml_backend_cuda_init(0);
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if (!model.backend) {
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fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
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}
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}
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#endif
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#ifdef GGML_USE_METAL
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if (n_gpu_layers > 0) {
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fprintf(stderr, "%s: using Metal backend\n", __func__);
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ggml_backend_metal_log_set_callback(ggml_log_callback_default, nullptr);
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model.backend = ggml_backend_metal_init();
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if (!model.backend) {
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fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
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}
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}
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#endif
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if (!model.backend) {
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// fallback to CPU backend
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fprintf(stderr, "%s: using CPU backend\n", __func__);
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model.backend = ggml_backend_cpu_init();
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}
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if (!model.backend) {
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fprintf(stderr, "%s: ggml_backend_cpu_init() failed\n", __func__);
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return false;
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}
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// create the tensors for the model
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{
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const auto & hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_vocab = hparams.n_vocab;
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model.layers.resize(n_layer);
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model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
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model.lm_head = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
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// map by name
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model.tensors["model/ln_f/g"] = model.ln_f_g;
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model.tensors["model/ln_f/b"] = model.ln_f_b;
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model.tensors["model/wte"] = model.wte;
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model.tensors["model/wpe"] = model.wpe;
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model.tensors["model/lm_head"] = model.lm_head;
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = model.layers[i];
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layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.ln_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.ln_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 3*n_embd);
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layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);
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layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
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layer.c_attn_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd);
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layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
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layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
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layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
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// map by name
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model.tensors["model/h" + std::to_string(i) + "/ln_1/g"] = layer.ln_1_g;
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model.tensors["model/h" + std::to_string(i) + "/ln_1/b"] = layer.ln_1_b;
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model.tensors["model/h" + std::to_string(i) + "/ln_2/g"] = layer.ln_2_g;
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model.tensors["model/h" + std::to_string(i) + "/ln_2/b"] = layer.ln_2_b;
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model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
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model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
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model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
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model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
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model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"] = layer.c_mlp_fc_w;
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model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"] = layer.c_mlp_fc_b;
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model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"] = layer.c_mlp_proj_w;
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model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"] = layer.c_mlp_proj_b;
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}
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}
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// allocate the model tensors in a backend buffer
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model.buffer_w = ggml_backend_alloc_ctx_tensors(ctx, model.backend);
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printf("%s: ggml tensor size = %d bytes\n", __func__, (int) sizeof(ggml_tensor));
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printf("%s: backend buffer size = %6.2f MB\n", __func__, ggml_backend_buffer_get_size(model.buffer_w)/(1024.0*1024.0));
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// override the default training context with the user-provided
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model.hparams.n_ctx = n_ctx;
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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 int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_mem = n_layer*n_ctx;
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const int n_elements = n_embd*n_mem;
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model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
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model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 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 = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
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// create a backend buffer (can be in host or device memory)
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model.buffer_kv = ggml_backend_alloc_buffer(model.backend, memory_size + 256);
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// allocate the tensors into the backend buffer
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{
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ggml_allocr * alloc = ggml_allocr_new_from_buffer(model.buffer_kv);
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// this updates the pointers in the tensors to point to the correct location in the buffer
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// this is necessary since the ggml_context is .no_alloc == true
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// note that the buffer can actually be a device buffer, depending on the backend
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ggml_allocr_alloc(alloc, model.memory_k);
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ggml_allocr_alloc(alloc, model.memory_v);
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ggml_allocr_free(alloc);
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}
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}
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// load weights
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{
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size_t total_size = 0;
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bool has_lm_head = false;
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std::vector<char> read_buf;
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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) == model.tensors.end()) {
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fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.c_str());
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return false;
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}
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auto tensor = model.tensors[name];
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ggml_set_name(tensor, name.c_str());
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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.c_str());
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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, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
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__func__, name.c_str(), (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.c_str(), ne[0], ne[1], 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",
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__func__, name.c_str(), ggml_nbytes(tensor), nelements*bpe);
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return false;
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}
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if (ggml_backend_is_cpu (model.backend)
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#ifdef GGML_USE_METAL
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|| ggml_backend_is_metal(model.backend)
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#endif
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) {
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// for the CPU and Metal backend, we can read directly into the tensor
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fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
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} else {
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// read into a temporary buffer first, then copy to device memory
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read_buf.resize(ggml_nbytes(tensor));
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fin.read(read_buf.data(), ggml_nbytes(tensor));
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ggml_backend_tensor_set(tensor, read_buf.data(), 0, ggml_nbytes(tensor));
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}
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// GPT-2 models share the WTE tensor as the LM head
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if (name == "model/wte" && has_lm_head == false) {
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//ggml_allocr_alloc(alloc, model.lm_head);
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//ggml_backend_tensor_copy(tensor, model.lm_head);
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model.lm_head = tensor;
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}
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if (name == "model/lm_head") {
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has_lm_head = true;
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}
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total_size += ggml_nbytes(tensor);
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}
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printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
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}
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fin.close();
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return true;
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}
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// build the computation graph
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struct ggml_cgraph * gpt2_graph(
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const gpt2_model & model,
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struct ggml_allocr * allocr,
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const int n_past,
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const std::vector<gpt_vocab::id> & embd_inp) {
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const int N = embd_inp.size();
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const auto & hparams = model.hparams;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_head = hparams.n_head;
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// since we are using ggml-alloc, this buffer only needs enough space to hold the ggml_tensor and ggml_cgraph structs, but not the tensor data
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static size_t buf_size = ggml_tensor_overhead()*GPT2_MAX_NODES + ggml_graph_overhead_custom(GPT2_MAX_NODES, false);
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static std::vector<uint8_t> buf(buf_size);
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struct ggml_init_params params = {
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/*.mem_size =*/ buf_size,
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/*.mem_buffer =*/ buf.data(),
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/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_allocr_alloc_graph()
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};
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struct ggml_context * ctx0 = ggml_init(params);
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, GPT2_MAX_NODES, false);
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struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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ggml_allocr_alloc(allocr, embd);
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// avoid writing to tensors if we are only measuring the memory usage
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if (!ggml_allocr_is_measure(allocr)) {
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ggml_backend_tensor_set(embd, embd_inp.data(), 0, N*ggml_element_size(embd));
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}
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struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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ggml_allocr_alloc(allocr, position);
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if (!ggml_allocr_is_measure(allocr)) {
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for (int i = 0; i < N; ++i) {
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int32_t v = n_past + i;
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ggml_backend_tensor_set(position, &v, i*sizeof(int32_t), sizeof(v));
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}
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}
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// wte + wpe
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struct ggml_tensor * inpL =
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ggml_add(ctx0,
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ggml_get_rows(ctx0, model.wte, embd),
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ggml_get_rows(ctx0, model.wpe, position));
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * cur;
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// norm
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{
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// [ 768, N]
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cur = ggml_norm(ctx0, inpL, hparams.eps);
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// cur = ln_1_g*cur + ln_1_b
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// [ 768, N]
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cur = ggml_add(ctx0,
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ggml_mul(ctx0,
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cur,
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model.layers[il].ln_1_g),
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model.layers[il].ln_1_b);
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}
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// attn
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// [2304, 768] - model.layers[il].c_attn_attn_w
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// [2304, 1] - model.layers[il].c_attn_attn_b
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// [ 768, N] - cur (in)
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// [2304, N] - cur (out)
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//
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// cur = attn_w*cur + attn_b
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// [2304, N]
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{
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cur = ggml_mul_mat(ctx0,
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model.layers[il].c_attn_attn_w,
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cur);
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cur = ggml_add(ctx0,
|
cur,
|
model.layers[il].c_attn_attn_b);
|
}
|
|
// self-attention
|
{
|
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
|
if (N >= 1) {
|
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);
|
|
// GG: flash attention
|
//struct ggml_tensor * V =
|
// 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, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));
|
|
//struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);
|
|
// K * Q
|
// [n_past + N, N, 12]
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
// KQ_scaled = KQ / sqrt(n_embd/n_head)
|
// [n_past + N, N, 12]
|
struct ggml_tensor * KQ_scaled =
|
ggml_scale(ctx0,
|
KQ,
|
1.0f/sqrtf(float(n_embd)/n_head));
|
|
// KQ_masked = mask_past(KQ_scaled)
|
// [n_past + N, N, 12]
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
|
|
// KQ = soft_max(KQ_masked)
|
// [n_past + N, N, 12]
|
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
|
// [64, N, 12]
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
// [64, 12, N]
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
// [768, N]
|
cur = ggml_cpy(ctx0,
|
KQV_merged,
|
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
}
|
|
// projection
|
// [ 768, 768] - model.layers[il].c_attn_proj_w
|
// [ 768, 1] - model.layers[il].c_attn_proj_b
|
// [ 768, N] - cur (in)
|
// [ 768, N] - cur (out)
|
//
|
// cur = proj_w*cur + proj_b
|
// [768, N]
|
{
|
cur = ggml_mul_mat(ctx0,
|
model.layers[il].c_attn_proj_w,
|
cur);
|
|
cur = ggml_add(ctx0,
|
cur,
|
model.layers[il].c_attn_proj_b);
|
}
|
|
// add the input
|
cur = ggml_add(ctx0, cur, inpL);
|
|
struct ggml_tensor * inpFF = cur;
|
|
// feed-forward network
|
{
|
// norm
|
{
|
cur = ggml_norm(ctx0, inpFF, hparams.eps);
|
|
// cur = ln_2_g*cur + ln_2_b
|
// [ 768, N]
|
cur = ggml_add(ctx0,
|
ggml_mul(ctx0,
|
cur,
|
model.layers[il].ln_2_g),
|
model.layers[il].ln_2_b);
|
}
|
|
// fully connected
|
// [3072, 768] - model.layers[il].c_mlp_fc_w
|
// [3072, 1] - model.layers[il].c_mlp_fc_b
|
// [ 768, N] - cur (in)
|
// [3072, N] - cur (out)
|
//
|
// cur = fc_w*cur + fc_b
|
// [3072, N]
|
cur = ggml_mul_mat(ctx0,
|
model.layers[il].c_mlp_fc_w,
|
cur);
|
|
cur = ggml_add(ctx0,
|
cur,
|
model.layers[il].c_mlp_fc_b);
|
|
// GELU activation
|
// [3072, N]
|
cur = ggml_gelu(ctx0, cur);
|
|
// projection
|
// [ 768, 3072] - model.layers[il].c_mlp_proj_w
|
// [ 768, 1] - model.layers[il].c_mlp_proj_b
|
// [3072, N] - cur (in)
|
// [ 768, N] - cur (out)
|
//
|
// cur = proj_w*cur + proj_b
|
// [768, N]
|
cur = ggml_mul_mat(ctx0,
|
model.layers[il].c_mlp_proj_w,
|
cur);
|
|
cur = ggml_add(ctx0,
|
cur,
|
model.layers[il].c_mlp_proj_b);
|
}
|
|
// input for next layer
|
inpL = ggml_add(ctx0, cur, inpFF);
|
}
|
|
// norm
|
{
|
// [ 768, N]
|
inpL = ggml_norm(ctx0, inpL, hparams.eps);
|
|
// inpL = ln_f_g*inpL + ln_f_b
|
// [ 768, N]
|
inpL = ggml_add(ctx0,
|
ggml_mul(ctx0,
|
inpL,
|
model.ln_f_g),
|
model.ln_f_b);
|
}
|
|
// inpL = WTE * inpL
|
// [ 768, 50257] - model.lm_head
|
// [ 768, N] - inpL
|
inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);
|
|
// logits -> probs
|
//inpL = ggml_soft_max(ctx0, inpL);
|
|
ggml_build_forward_expand(gf, inpL);
|
|
ggml_free(ctx0);
|
|
return gf;
|
}
|
|
// evaluate the transformer
|
//
|
// - model: the model
|
// - allocr: ggml_allocr to use to allocate the compute buffer
|
// - 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 gpt2_eval(
|
const gpt2_model & model,
|
struct ggml_allocr * allocr,
|
const int n_threads,
|
const int n_past,
|
const std::vector<gpt_vocab::id> & embd_inp,
|
std::vector<float> & embd_w) {
|
const int N = embd_inp.size();
|
|
const auto & hparams = model.hparams;
|
|
const int n_vocab = hparams.n_vocab;
|
|
// reset the allocator to free all the memory allocated during the previous inference
|
ggml_allocr_reset(allocr);
|
|
struct ggml_cgraph * gf = gpt2_graph(model, allocr, n_past, embd_inp);
|
|
// allocate tensors
|
ggml_allocr_alloc_graph(allocr, gf);
|
|
// set backend options
|
if (ggml_backend_is_cpu(model.backend)) {
|
ggml_backend_cpu_set_n_threads(model.backend, n_threads);
|
}
|
|
#ifdef GGML_USE_METAL
|
if (ggml_backend_is_metal(model.backend)) {
|
ggml_backend_metal_set_n_cb(model.backend, n_threads);
|
}
|
#endif
|
|
// test
|
#if 0 && defined(GGML_USE_CUBLAS)
|
if (ggml_backend_is_cuda(model.backend)) {
|
auto eval_callback = [](int index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data) {
|
auto tv1 = tensor_to_float(t1);
|
auto tv2 = tensor_to_float(t2);
|
|
#if 1
|
float sim = cosine_similarity(tv1, tv2);
|
float len1 = vec_len(tv1);
|
float len2 = vec_len(tv2);
|
float lenr = len1/len2;
|
float lenrd = std::abs(1.0f-lenr);
|
|
float angle = acosf(sim)*180.0f/M_PI;
|
|
if (angle > 0.5f || lenrd > 0.05f) {
|
printf("%3d [%15s] %s: sim = %f, a = %f, lenrd = %f\n", index, ggml_op_desc(t1), t1->name, sim, angle, lenrd);
|
}
|
assert(sim > 0.90f);
|
#else
|
float dist = distance(tv1, tv2) / vec_len(tv1);
|
if (dist > 0.01f) {
|
printf("%3d [%15s] %s: distance = %f\n", index, ggml_op_desc(t1), t1->name, dist);
|
}
|
#endif
|
|
return true;
|
};
|
ggml_backend_t backend_cpu = ggml_backend_cpu_init();
|
ggml_backend_compare_graph_backend(model.backend, backend_cpu, gf, eval_callback, nullptr);
|
ggml_backend_free(backend_cpu);
|
//printf("done\n");
|
} else
|
#endif
|
{
|
// run the computation
|
ggml_backend_graph_compute(model.backend, gf);
|
}
|
|
//if (n_past%100 == 0) {
|
// ggml_graph_print (&gf);
|
// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
|
//}
|
|
// in this case, the output tensor is the last one in the graph
|
struct ggml_tensor * inpL = gf->nodes[gf->n_nodes - 1];
|
|
//embd_w.resize(n_vocab*N);
|
//ggml_backend_tensor_get(inpL, embd_w.data(), 0, sizeof(float)*n_vocab*N);
|
|
// return result just for the last token
|
embd_w.resize(n_vocab);
|
ggml_backend_tensor_get(inpL, embd_w.data(), (n_vocab*(N-1))*sizeof(float), sizeof(float)*n_vocab);
|
|
return true;
|
}
|
|
int main(int argc, char ** argv) {
|
ggml_time_init();
|
|
const int64_t t_main_start_us = ggml_time_us();
|
|
gpt_params params;
|
params.model = "models/gpt-2-117M/ggml-model.bin";
|
|
if (gpt_params_parse(argc, argv, params) == false) {
|
return 1;
|
}
|
|
if (params.seed < 0) {
|
params.seed = time(NULL);
|
}
|
|
printf("%s: seed = %d\n", __func__, params.seed);
|
|
std::mt19937 rng(params.seed);
|
if (params.prompt.empty()) {
|
params.prompt = gpt_random_prompt(rng);
|
}
|
|
int64_t t_load_us = 0;
|
|
gpt_vocab vocab;
|
gpt2_model model;
|
|
// load the model
|
{
|
const int64_t t_start_us = ggml_time_us();
|
|
if (!gpt2_model_load(params.model, model, vocab, params.n_ctx, params.n_gpu_layers)) {
|
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;
|
|
test_gpt_tokenizer(vocab, params.token_test);
|
}
|
|
// keep this buffer alive while evaluating the model
|
ggml_backend_buffer_t buf_compute;
|
|
struct ggml_allocr * allocr = NULL;
|
// allocate the compute buffer
|
{
|
// create an allocator to measure the memory usage
|
allocr = ggml_allocr_new_measure_from_backend(model.backend);
|
|
// create the worst case graph for memory usage estimation
|
int n_tokens = std::min(model.hparams.n_ctx, params.n_batch);
|
int n_past = model.hparams.n_ctx - n_tokens;
|
struct ggml_cgraph * gf = gpt2_graph(model, allocr, n_past, std::vector<gpt_vocab::id>(n_tokens, 0));
|
|
// compute the required memory
|
size_t mem_size = ggml_allocr_alloc_graph(allocr, gf);
|
|
// recreate the allocator with the required memory
|
ggml_allocr_free(allocr);
|
buf_compute = ggml_backend_alloc_buffer(model.backend, mem_size);
|
allocr = ggml_allocr_new_from_buffer(buf_compute);
|
|
fprintf(stderr, "%s: compute buffer size: %.2f MB\n", __func__, mem_size/1024.0/1024.0);
|
}
|
|
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<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
|
|
params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
|
|
printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
|
printf("%s: number of tokens in prompt = %zu, first 8 tokens: ", __func__, embd_inp.size());
|
for (int i = 0; i < std::min(8, (int) embd_inp.size()); i++) {
|
printf("%d ", embd_inp[i]);
|
}
|
printf("\n\n");
|
|
// submit the input prompt token-by-token
|
// this reduces the memory usage during inference, at the cost of a bit of speed at the beginning
|
std::vector<gpt_vocab::id> embd;
|
|
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 (!gpt2_eval(model, allocr, params.n_threads, n_past, embd, logits)) {
|
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 n_vocab = model.hparams.n_vocab;
|
|
gpt_vocab::id id = 0;
|
|
{
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
id = gpt_sample_top_k_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);
|
|
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 (int32_t(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 (!params.ignore_eos && embd.back() == 50256) {
|
break;
|
}
|
}
|
|
// report timing
|
{
|
const int64_t t_main_end_us = ggml_time_us();
|
|
printf("\n\n");
|
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);
|
|
ggml_backend_buffer_free(model.buffer_w);
|
ggml_backend_buffer_free(model.buffer_kv);
|
ggml_backend_buffer_free(buf_compute);
|
ggml_backend_free(model.backend);
|
|
return 0;
|
}
|
