768 lines
26 KiB
C++
768 lines
26 KiB
C++
#include "ggml/ggml.h"
|
|
|
|
#include "common-ggml.h"
|
|
#include "common.h"
|
|
|
|
#include <cassert>
|
|
#include <cmath>
|
|
#include <cstddef>
|
|
#include <cstdio>
|
|
#include <cstring>
|
|
#include <cinttypes>
|
|
|
|
#include <fstream>
|
|
#include <map>
|
|
#include <string>
|
|
#include <unordered_map>
|
|
#include <utility>
|
|
#include <vector>
|
|
|
|
using piece_t = std::pair<std::size_t, float>;
|
|
using piece_map_t = std::unordered_map<std::string, piece_t>;
|
|
|
|
struct replit_tokenizer {
|
|
gpt_vocab raw_vocab;
|
|
piece_map_t piece_map;
|
|
std::vector<std::string> vocab;
|
|
};
|
|
|
|
std::pair<std::vector<std::size_t>, float> encode_word(const std::string & word, const piece_map_t & model) {
|
|
std::vector<int> best_segmentations_starts(word.length() + 1, -1);
|
|
best_segmentations_starts[0] = 0;
|
|
|
|
std::vector<float> best_segmentations_scores(word.length() + 1, -std::numeric_limits<float>::infinity());
|
|
best_segmentations_scores[0] = 1.0;
|
|
|
|
for (int start_idx = 0; start_idx < word.length(); ++start_idx) {
|
|
float best_score_at_start = best_segmentations_scores[start_idx];
|
|
for (int end_idx = start_idx + 1; end_idx <= word.length(); ++end_idx) {
|
|
std::string token = word.substr(start_idx, end_idx - start_idx);
|
|
if (model.count(token) && best_score_at_start != -std::numeric_limits<float>::infinity()) {
|
|
float token_score = model.at(token).second;
|
|
float score = token_score + best_score_at_start;
|
|
if (best_segmentations_scores[end_idx] == -std::numeric_limits<float>::infinity() ||
|
|
best_segmentations_scores[end_idx] > score) {
|
|
best_segmentations_starts[end_idx] = start_idx;
|
|
best_segmentations_scores[end_idx] = score;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (best_segmentations_scores.back() == -std::numeric_limits<float>::infinity()) {
|
|
return std::make_pair(std::vector<std::size_t>{0}, 0.0f);
|
|
}
|
|
|
|
float score = best_segmentations_scores.back();
|
|
int start = best_segmentations_starts.back();
|
|
int end = word.length();
|
|
std::vector<std::size_t> tokens;
|
|
while (start != 0) {
|
|
const auto token_id = model.at(word.substr(start, end - start)).first;
|
|
tokens.insert(tokens.begin(), token_id);
|
|
int next_start = best_segmentations_starts[start];
|
|
end = start;
|
|
start = next_start;
|
|
}
|
|
const auto token_id = model.at(word.substr(start, end - start)).first;
|
|
tokens.insert(tokens.begin(), token_id);
|
|
return std::make_pair(tokens, score);
|
|
}
|
|
|
|
bool replit_tokenizer_load(replit_tokenizer & tokenizer, std::istream & fin, int max_vocab_size) {
|
|
std::string word;
|
|
std::vector<char> buf(128);
|
|
|
|
for (std::size_t i = 0; i < max_vocab_size; i++) {
|
|
uint32_t len;
|
|
fin.read((char *)&len, sizeof(len));
|
|
|
|
buf.resize(len);
|
|
fin.read((char *) buf.data(), len);
|
|
word.assign(buf.data(), len);
|
|
|
|
float score;
|
|
fin.read((char *)&score, sizeof(score));
|
|
|
|
tokenizer.piece_map[word] = std::make_pair(i, -score);
|
|
tokenizer.raw_vocab.id_to_token[i] = word;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
std::string replace_all(const std::string & str, // where to work
|
|
const std::string & find, // substitute 'find'
|
|
const std::string & replace // by 'replace'
|
|
) {
|
|
using namespace std;
|
|
string result;
|
|
size_t find_len = find.size();
|
|
size_t pos, from = 0;
|
|
while (string::npos != (pos = str.find(find, from))) {
|
|
result.append(str, from, pos - from);
|
|
result.append(replace);
|
|
from = pos + find_len;
|
|
}
|
|
result.append(str, from, string::npos);
|
|
return result;
|
|
}
|
|
|
|
std::string ws_symbol = "\342\226\201";
|
|
std::vector<std::size_t> replit_tokenizer_tokenize(replit_tokenizer & tokenizer, const std::string & text) {
|
|
std::vector<std::size_t> tokens;
|
|
auto normalized_text = replace_all(text, " ", ws_symbol);
|
|
auto tokenized = encode_word(normalized_text, tokenizer.piece_map);
|
|
|
|
return tokenized.first;
|
|
}
|
|
|
|
std::string replit_tokenizer_detokenize(replit_tokenizer & tokenizer, const std::vector<std::size_t> & tokens) {
|
|
std::string text;
|
|
for (auto token : tokens) {
|
|
text += tokenizer.raw_vocab.id_to_token[token];
|
|
}
|
|
auto denormalized_text = replace_all(text, ws_symbol, " ");
|
|
return denormalized_text;
|
|
}
|
|
|
|
// no defaults for now
|
|
struct mpt_hparams {
|
|
int32_t d_model = 0;
|
|
int32_t max_seq_len = 0;
|
|
int32_t n_heads = 0;
|
|
int32_t n_layers = 0;
|
|
int32_t n_vocab = 0;
|
|
int32_t ftype = 0;
|
|
};
|
|
|
|
struct replit_layer {
|
|
// pre normalization
|
|
struct ggml_tensor * ln_1_weight;
|
|
|
|
// attention
|
|
struct ggml_tensor * c_attn_wqkv_weight;
|
|
|
|
struct ggml_tensor * c_attn_out_proj_weight;
|
|
|
|
// post normalization
|
|
struct ggml_tensor * ln_2_weight;
|
|
|
|
// ff
|
|
struct ggml_tensor * c_mlp_mlp_up_weight;
|
|
|
|
struct ggml_tensor * c_mlp_mlp_down_weight;
|
|
};
|
|
|
|
struct replit_model {
|
|
mpt_hparams hparams;
|
|
|
|
struct ggml_tensor * wte_weight; // position embedding
|
|
struct ggml_tensor * ln_f_weight; // language model head
|
|
|
|
std::vector<replit_layer> layers;
|
|
|
|
// key + value memory
|
|
struct ggml_tensor * memory_k;
|
|
struct ggml_tensor * memory_v;
|
|
|
|
struct ggml_context * ctx;
|
|
std::map<std::string, struct ggml_tensor *> tensors;
|
|
};
|
|
|
|
// load the model's weights from a file
|
|
bool replit_model_load(const std::string & fname, replit_model & model, replit_tokenizer & vocab) {
|
|
printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
|
|
|
|
auto fin = std::ifstream(fname, std::ios::binary);
|
|
if (!fin) {
|
|
fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
|
|
return false;
|
|
}
|
|
|
|
// verify magic
|
|
{
|
|
uint32_t magic;
|
|
fin.read((char *)&magic, sizeof(magic));
|
|
if (magic != 0x67676d6c) {
|
|
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// load hparams
|
|
{
|
|
auto & hparams = model.hparams;
|
|
|
|
fin.read((char *) &hparams.d_model, sizeof(hparams.d_model));
|
|
fin.read((char *) &hparams.max_seq_len, sizeof(hparams.max_seq_len));
|
|
fin.read((char *) &hparams.n_heads, sizeof(hparams.n_heads));
|
|
fin.read((char *) &hparams.n_layers, sizeof(hparams.n_layers));
|
|
fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
|
|
fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));
|
|
|
|
const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
|
|
|
|
printf("%s: d_model = %d\n", __func__, hparams.d_model);
|
|
printf("%s: max_seq_len = %d\n", __func__, hparams.max_seq_len);
|
|
printf("%s: n_heads = %d\n", __func__, hparams.n_heads);
|
|
printf("%s: n_layers = %d\n", __func__, hparams.n_layers);
|
|
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
|
|
printf("%s: ftype = %d\n", __func__, hparams.ftype);
|
|
printf("%s: qntvr = %d\n", __func__, qntvr);
|
|
|
|
hparams.ftype %= GGML_QNT_VERSION_FACTOR;
|
|
}
|
|
|
|
// load vocab
|
|
replit_tokenizer_load(vocab, fin, model.hparams.n_vocab);
|
|
|
|
// for the big tensors, we have the option to store the data in 16-bit
|
|
// floats or quantized in order to save memory and also to speed up the
|
|
// computation
|
|
ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype)(model.hparams.ftype));
|
|
if (wtype == GGML_TYPE_COUNT) {
|
|
fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n", __func__, fname.c_str(),
|
|
model.hparams.ftype);
|
|
return false;
|
|
}
|
|
|
|
auto & ctx = model.ctx;
|
|
|
|
size_t ctx_size = 0;
|
|
|
|
{
|
|
const auto & hparams = model.hparams;
|
|
|
|
const int n_embd = hparams.d_model;
|
|
const int n_layer = hparams.n_layers;
|
|
const int n_ctx = hparams.max_seq_len;
|
|
const int n_vocab = hparams.n_vocab;
|
|
|
|
ctx_size += n_embd * n_vocab * ggml_type_sizef(wtype); // wte_weight
|
|
ctx_size += n_embd * ggml_type_sizef(GGML_TYPE_F32); // ln_f_weight
|
|
|
|
ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_1_weight
|
|
ctx_size += n_layer * (3 * n_embd * n_embd * ggml_type_sizef(wtype)); // attn_Wqkv_weight
|
|
ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype)); // attn_out_proj_weight
|
|
ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32)); // ln_2_weight
|
|
ctx_size += n_layer * (4 * n_embd * n_embd * ggml_type_sizef(wtype)); // mlp_mlp_up_weight
|
|
ctx_size += n_layer * (n_embd * n_embd * 4 * ggml_type_sizef(wtype)); // mlp_mlp_down_weight
|
|
|
|
ctx_size += n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16); // memory_k
|
|
ctx_size += n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16); // memory_v
|
|
|
|
ctx_size += (1 + 6 * n_layer) * 512; // object overhead
|
|
|
|
printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size / (1024.0 * 1024.0));
|
|
}
|
|
|
|
// create the ggml context
|
|
{
|
|
struct ggml_init_params params = {
|
|
.mem_size = ctx_size,
|
|
.mem_buffer = NULL,
|
|
.no_alloc = false,
|
|
};
|
|
|
|
model.ctx = ggml_init(params);
|
|
if (!model.ctx) {
|
|
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// prepare memory for the weights
|
|
{
|
|
const auto & hparams = model.hparams;
|
|
|
|
const int n_embd = hparams.d_model;
|
|
const int n_layer = hparams.n_layers;
|
|
const int n_vocab = hparams.n_vocab;
|
|
|
|
model.layers.resize(n_layer);
|
|
|
|
model.wte_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
|
|
model.ln_f_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
|
|
// map by name
|
|
model.tensors["transformer.wte.weight"] = model.wte_weight;
|
|
model.tensors["transformer.ln_f.weight"] = model.ln_f_weight;
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.ln_1_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
layer.c_attn_wqkv_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, 3 * n_embd);
|
|
layer.c_attn_out_proj_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
|
layer.ln_2_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
layer.c_mlp_mlp_up_weight = ggml_new_tensor_2d(ctx, wtype, n_embd, 4 * n_embd);
|
|
layer.c_mlp_mlp_down_weight = ggml_new_tensor_2d(ctx, wtype, 4 * n_embd, n_embd);
|
|
|
|
// map by name
|
|
model.tensors["transformer.blocks." + std::to_string(i) + ".ln_1.weight"] = layer.ln_1_weight;
|
|
model.tensors["transformer.blocks." + std::to_string(i) + ".attn.Wqkv.weight"] = layer.c_attn_wqkv_weight;
|
|
model.tensors["transformer.blocks." + std::to_string(i) + ".attn.out_proj.weight"] =
|
|
layer.c_attn_out_proj_weight;
|
|
model.tensors["transformer.blocks." + std::to_string(i) + ".ln_2.weight"] = layer.ln_2_weight;
|
|
model.tensors["transformer.blocks." + std::to_string(i) + ".mlp.mlp_up.weight"] = layer.c_mlp_mlp_up_weight;
|
|
model.tensors["transformer.blocks." + std::to_string(i) + ".mlp.mlp_down.weight"] =
|
|
layer.c_mlp_mlp_down_weight;
|
|
}
|
|
}
|
|
|
|
// key + value memory
|
|
{
|
|
const auto & hparams = model.hparams;
|
|
|
|
const int n_embd = hparams.d_model;
|
|
const int n_layer = hparams.n_layers;
|
|
const int n_ctx = hparams.max_seq_len;
|
|
|
|
const int64_t n_mem = n_layer * n_ctx;
|
|
const int64_t n_elements = n_embd * n_mem;
|
|
|
|
model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
|
|
model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
|
|
|
|
const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
|
|
|
|
printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size / 1024.0 / 1024.0, n_mem);
|
|
}
|
|
|
|
// load weights
|
|
{
|
|
int n_tensors = 0;
|
|
size_t total_size = 0;
|
|
|
|
printf("%s: ", __func__);
|
|
|
|
while (true) {
|
|
int32_t n_dims;
|
|
int32_t length;
|
|
int32_t ttype;
|
|
|
|
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
|
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
|
fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));
|
|
|
|
if (fin.eof()) {
|
|
break;
|
|
}
|
|
|
|
int32_t nelements = 1;
|
|
int32_t ne[2] = {1, 1};
|
|
for (int i = 0; i < n_dims; ++i) {
|
|
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
|
nelements *= ne[i];
|
|
}
|
|
|
|
std::string name(length, 0);
|
|
fin.read(&name[0], length);
|
|
|
|
if (model.tensors.find(name.data()) == model.tensors.end()) {
|
|
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
|
|
return false;
|
|
}
|
|
|
|
auto tensor = model.tensors[name.data()];
|
|
if (ggml_nelements(tensor) != nelements) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
|
return false;
|
|
}
|
|
|
|
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
|
|
fprintf(stderr,
|
|
"%s: tensor '%s' has wrong shape in model file: got [%5d, "
|
|
"%5d], expected [%5d, %5d]\n",
|
|
__func__, name.data(), (int)tensor->ne[0], (int)tensor->ne[1], ne[0], ne[1]);
|
|
return false;
|
|
}
|
|
|
|
// for debugging
|
|
if (0) {
|
|
printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1],
|
|
ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor) / 1024.0 / 1024.0, ggml_nbytes(tensor));
|
|
}
|
|
|
|
const size_t bpe = ggml_type_size(ggml_type(ttype));
|
|
|
|
if ((nelements * bpe) / ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
|
|
fprintf(stderr,
|
|
"%s: tensor '%s' has wrong size in model file: got %zu, "
|
|
"expected %zu\n",
|
|
__func__, name.data(), ggml_nbytes(tensor), nelements * bpe);
|
|
return false;
|
|
}
|
|
|
|
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
|
|
|
|
total_size += ggml_nbytes(tensor);
|
|
if (++n_tensors % 8 == 0) {
|
|
printf(".");
|
|
fflush(stdout);
|
|
}
|
|
}
|
|
|
|
printf(" done\n");
|
|
|
|
printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size / 1024.0 / 1024.0, n_tensors);
|
|
}
|
|
|
|
fin.close();
|
|
|
|
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 replit_eval(const replit_model & model, const int n_threads, const int n_past,
|
|
const std::vector<gpt_vocab::id> & embd_inp, std::vector<float> & embd_w, size_t & mem_per_token) {
|
|
const int N = embd_inp.size();
|
|
|
|
const auto & hparams = model.hparams;
|
|
|
|
const int n_embd = hparams.d_model;
|
|
const int n_layer = hparams.n_layers;
|
|
const int n_ctx = hparams.max_seq_len;
|
|
const int n_head = hparams.n_heads;
|
|
const int n_vocab = hparams.n_vocab;
|
|
|
|
static size_t buf_size = 256u * 1024 * 1024;
|
|
static void * buf = malloc(buf_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;
|
|
|
|
// a = self.ln_1(x)
|
|
{
|
|
cur = ggml_norm(ctx0, inpL);
|
|
|
|
cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_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); }
|
|
|
|
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, 8.0);
|
|
|
|
// 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);
|
|
|
|
// m = self.ln_2(x)
|
|
{
|
|
cur = ggml_norm(ctx0, inpL);
|
|
|
|
cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_2_weight, cur), cur);
|
|
}
|
|
|
|
// n = self.mlp(m)
|
|
{
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].c_mlp_mlp_up_weight, cur);
|
|
|
|
// GELU activation
|
|
cur = ggml_gelu(ctx0, cur);
|
|
|
|
// projection
|
|
// cur = proj_w*cur + proj_b
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].c_mlp_mlp_down_weight, cur);
|
|
}
|
|
|
|
// x = x + n
|
|
inpL = ggml_add(ctx0, inpL, cur);
|
|
}
|
|
|
|
// norm
|
|
{
|
|
inpL = ggml_norm(ctx0, inpL);
|
|
// inpL = ln_f_g*inpL
|
|
inpL = ggml_mul(ctx0, ggml_repeat(ctx0, model.ln_f_weight, inpL), inpL);
|
|
}
|
|
|
|
// 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, "replit-model.dot");
|
|
// }
|
|
|
|
// return result for just the last token
|
|
embd_w.resize(n_vocab);
|
|
memcpy(embd_w.data(), (float *)ggml_get_data(inpL) + (n_vocab * (N - 1)), sizeof(float) * n_vocab);
|
|
|
|
if (mem_per_token == 0) {
|
|
mem_per_token = ggml_used_mem(ctx0) / N;
|
|
}
|
|
// printf("used_mem = %zu\n", ggml_used_mem(ctx0));
|
|
|
|
ggml_free(ctx0);
|
|
|
|
return true;
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
ggml_time_init();
|
|
|
|
const int64_t t_main_start_us = ggml_time_us();
|
|
|
|
gpt_params params;
|
|
params.model = "";
|
|
|
|
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;
|
|
|
|
replit_tokenizer vocab;
|
|
replit_model model;
|
|
|
|
// load the model
|
|
{
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
if (!replit_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;
|
|
}
|
|
|
|
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<std::size_t> embd_inp = replit_tokenizer_tokenize(vocab, params.prompt);
|
|
|
|
printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
|
|
|
|
for (int i = 0; i < embd_inp.size(); i++) {
|
|
printf("%s: token[%d] = %6lu\n", __func__, i, embd_inp[i]);
|
|
// vocab.id_to_token.at(embd_inp[i]).c_str()
|
|
}
|
|
printf("\n");
|
|
|
|
params.n_predict = std::min(params.n_predict, model.hparams.max_seq_len - (int)embd_inp.size());
|
|
|
|
std::vector<gpt_vocab::id> embd;
|
|
|
|
// determine the required inference memory per token:
|
|
size_t mem_per_token = 0;
|
|
replit_eval(model, params.n_threads, 0, {0, 1, 2, 3}, logits, mem_per_token);
|
|
|
|
for (int 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 (!replit_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
|
|
printf("Failed to predict\n");
|
|
return 1;
|
|
}
|
|
|
|
t_predict_us += ggml_time_us() - t_start_us;
|
|
}
|
|
|
|
n_past += embd.size();
|
|
embd.clear();
|
|
|
|
if (i >= embd_inp.size()) {
|
|
// sample next token
|
|
const int top_k = params.top_k;
|
|
const float top_p = params.top_p;
|
|
const float temp = params.temp;
|
|
|
|
const int 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.raw_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 (int k = i; k < embd_inp.size(); k++) {
|
|
embd.push_back(embd_inp[k]);
|
|
if (embd.size() > params.n_batch) {
|
|
break;
|
|
}
|
|
}
|
|
i += embd.size() - 1;
|
|
}
|
|
|
|
// display text
|
|
for (auto id : embd) {
|
|
printf("%s", replit_tokenizer_detokenize(vocab, {static_cast<std::size_t>(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");
|
|
printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
|
|
printf("%s: load time = %8.2f ms\n", __func__, t_load_us / 1000.0f);
|
|
printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us / 1000.0f);
|
|
printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us / 1000.0f,
|
|
t_predict_us / 1000.0f / n_past);
|
|
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f);
|
|
}
|
|
|
|
ggml_free(model.ctx);
|
|
|
|
return 0;
|
|
}
|