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#include "ggml.h"
#include "cmpnct_gpt2bpe.hpp"
#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <cinttypes>
#include <fstream>
#include <map>
#include <string>
#include <vector>
#include <thread>
#include <random>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
// default hparams
struct falcon_hparams {
size_t n_merges = 0;
size_t n_vocab = 0;
uint32_t n_ctx = 0;
uint32_t n_embd = 0;
uint32_t n_head = 0;
uint32_t n_head_kv = 1; // Needs to be 1 for 7B model
uint32_t n_ff = 0;
uint32_t n_block = 0;
float norm_eps = 1e-5;
};
struct falcon_block {
// normalization
struct ggml_tensor* input_layernorm;
struct ggml_tensor* input_layernorm_b;
struct ggml_tensor* attention_norm; // Falcon-40B only
struct ggml_tensor* attention_norm_b; // Falcon-40B only
// attention
struct ggml_tensor* query_key_value;
struct ggml_tensor* wo;
// ff
struct ggml_tensor* ffn_up;
struct ggml_tensor* ffn_down;
};
struct falcon_model {
falcon_hparams hparams;
struct ggml_tensor* tok_embeddings;
struct ggml_tensor* output_norm;
struct ggml_tensor* output_norm_b;
struct ggml_tensor* lm_head;
std::vector<falcon_block> blocks;
// key + value memory
struct ggml_tensor* memory_k;
struct ggml_tensor* memory_v;
struct gguf_context * ggufctx;
struct ggml_context * ctx;
struct ggml_context * kvctx;
std::map<std::string, struct ggml_tensor*> tensors;
};
struct gpt_params {
int32_t seed = -1; // RNG seed
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
uint32_t n_predict = 200; // new tokens to predict
uint32_t n_batch = 512; // batch size for prompt processing
// sampling parameters
int32_t top_k = 40;
float top_p = 1.0f;
float temp = 0.8f;
int32_t repeat_last_n = 64;
float repeat_penalty = 1.02f;
std::string model = ""; // model path
std::string prompt = "";
std::string token_test = "";
bool interactive = false;
int32_t interactive_port = -1;
int32_t n_gpu_layers = 0;
};
void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stderr, " -ngl N, --gpu-layers N number of layers to offload to GPU on supported models (default: %d)\n", params.n_gpu_layers);
fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");
fprintf(stderr, " prompt to start generation with (default: random)\n");
fprintf(stderr, " -f FNAME, --file FNAME\n");
fprintf(stderr, " load prompt from a file\n");
fprintf(stderr, " -tt TOKEN_TEST, --token_test TOKEN_TEST\n");
fprintf(stderr, " test tokenization\n");
fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);
fprintf(stderr, " --top_k N top-k sampling, 0 = n_vocab (default: %d)\n", params.top_k);
fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
fprintf(stderr, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled)\n", params.repeat_last_n);
fprintf(stderr, " --repeat-penalty N penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)\n", (double)params.repeat_penalty);
fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stderr, " -m FNAME, --model FNAME\n");
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
fprintf(stderr, "\n");
}
// Function to check if the next argument exists
std::string get_next_arg(int& i, int argc, char** argv, const std::string& flag, gpt_params& params) {
if (i + 1 < argc && argv[i + 1][0] != '-') {
return argv[++i];
} else {
fprintf(stderr, "error: %s requires one argument.\n", flag.c_str());
gpt_print_usage(argc, argv, params);
exit(0);
}
}
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
for (int i = 1; i < argc; i++) {
std::string arg = argv[i];
if (arg == "-s" || arg == "--seed") {
params.seed = std::stoi(get_next_arg(i, argc, argv, arg, params));
} else if (arg == "-t" || arg == "--threads") {
params.n_threads = std::stoi(get_next_arg(i, argc, argv, arg, params));
} else if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
params.n_gpu_layers = std::stoi(get_next_arg(i, argc, argv, arg, params));
} else if (arg == "-p" || arg == "--prompt") {
params.prompt = get_next_arg(i, argc, argv, arg, params);
} else if (arg == "-n" || arg == "--n_predict") {
params.n_predict = std::stoi(get_next_arg(i, argc, argv, arg, params));
} else if (arg == "--top_k") {
params.top_k = std::stoi(get_next_arg(i, argc, argv, arg, params));
} else if (arg == "--top_p") {
params.top_p = std::stof(get_next_arg(i, argc, argv, arg, params));
} else if (arg == "--temp") {
params.temp = std::stof(get_next_arg(i, argc, argv, arg, params));
} else if (arg == "--repeat-last-n") {
params.repeat_last_n = std::stoi(get_next_arg(i, argc, argv, arg, params));
} else if (arg == "--repeat-penalty") {
params.repeat_penalty = std::stof(get_next_arg(i, argc, argv, arg, params));
} else if (arg == "-b" || arg == "--batch_size") {
params.n_batch= std::stoi(get_next_arg(i, argc, argv, arg, params));
} else if (arg == "-m" || arg == "--model") {
params.model = get_next_arg(i, argc, argv, arg, params);
} else if (arg == "-i" || arg == "--interactive") {
params.interactive = true;
} else if (arg == "-ip" || arg == "--interactive-port") {
params.interactive = true;
params.interactive_port = std::stoi(get_next_arg(i, argc, argv, arg, params));
} else if (arg == "-h" || arg == "--help") {
gpt_print_usage(argc, argv, params);
exit(0);
} else if (arg == "-f" || arg == "--file") {
get_next_arg(i, argc, argv, arg, params);
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
break;
}
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
if (params.prompt.back() == '\n') {
params.prompt.pop_back();
}
} else if (arg == "-tt" || arg == "--token_test") {
params.token_test = get_next_arg(i, argc, argv, arg, params);
}
else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
gpt_print_usage(argc, argv, params);
exit(0);
}
}
return true;
}
gpt2bpe_vocab::id sample_top_k_top_p_repeat(
const gpt2bpe_vocab & vocab,
const float * logits,
const int32_t * last_n_tokens_data,
size_t last_n_tokens_data_size,
int top_k,
double top_p,
double temp,
int repeat_last_n,
float repeat_penalty,
std::mt19937 & rng) {
int n_logits = vocab.id_to_token.size();
const auto * plogits = logits;
const auto last_n_tokens = std::vector<int32_t>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_data_size);
if (temp <= 0) {
// select the token with the highest logit directly
float max_logit = plogits[0];
gpt2bpe_vocab::id max_id = 0;
for (int i = 1; i < n_logits; ++i) {
if (plogits[i] > max_logit) {
max_logit = plogits[i];
max_id = i;
}
}
return max_id;
}
std::vector<std::pair<double, gpt2bpe_vocab::id>> logits_id;
logits_id.reserve(n_logits);
{
const float scale = 1.0f/temp;
for (int i = 0; i < n_logits; ++i) {
// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
if (repeat_last_n > 0 && std::find(last_n_tokens.end()-repeat_last_n, last_n_tokens.end(), i) != last_n_tokens.end()) {
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
if (plogits[i] < 0.0f) {
logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
} else {
logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
}
} else {
logits_id.push_back(std::make_pair(plogits[i]*scale, i));
}
}
}
// find the top K tokens
std::partial_sort(
logits_id.begin(),
logits_id.begin() + top_k, logits_id.end(),
[](const std::pair<double, gpt2bpe_vocab::id> & a, const std::pair<double, gpt2bpe_vocab::id> & b) {
return a.first > b.first;
});
logits_id.resize(top_k);
double maxl = -INFINITY;
for (const auto & kv : logits_id) {
maxl = std::max(maxl, kv.first);
}
// compute probs for the top K tokens
std::vector<double> probs;
probs.reserve(logits_id.size());
double sum = 0.0;
for (const auto & kv : logits_id) {
double p = exp(kv.first - maxl);
probs.push_back(p);
sum += p;
}
// normalize the probs
for (auto & p : probs) {
p /= sum;
}
if (top_p < 1.0f) {
double cumsum = 0.0f;
for (int i = 0; i < top_k; i++) {
cumsum += probs[i];
if (cumsum >= top_p) {
top_k = i + 1;
probs.resize(top_k);
logits_id.resize(top_k);
break;
}
}
cumsum = 1.0/cumsum;
for (int i = 0; i < (int) probs.size(); i++) {
probs[i] *= cumsum;
}
}
// printf("\n");
// for (int i = 0; i < (int) probs.size(); i++) {
// for (int i = 0; i < 10; i++) {
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
// }
std::discrete_distribution<> dist(probs.begin(), probs.end());
int idx = dist(rng);
return logits_id[idx].second;
}
struct ggml_tensor * get_tensor_ex( struct ggml_context * ctx, std::string name){
struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str());
if( cur == NULL ) {
printf("%s: tensor '%s' not found!\n", __func__, name.c_str());
} else {
// printf("%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name);
}
return cur;
}
// load the model's weights from a file
bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_vocab & vocab) {
printf("%s: loading model from '%s'..\n", __func__, fname.c_str());
model.ctx = NULL;
struct gguf_init_params ggufparams = {
/*.no_alloc = */ false,
/*.ctx = */ &model.ctx,
};
auto & ggufctx = model.ggufctx;
ggufctx = gguf_init_from_file(fname.c_str(), ggufparams);
if (!ggufctx) {
fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__);
return false;
}
printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx));
printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx));
printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx));
// print all kv
#if 0
{
const int n_kv = gguf_get_n_kv(ggufctx);
printf("%s: n_kv: %d\n", __func__, n_kv);
for (int i = 0; i < n_kv; ++i) {
const char * key = gguf_get_key(ggufctx, i);
printf("%s: kv[%d]: key = %s\n", __func__, i, key);
}
}
#endif
// print some standard metadata
{
int keyidx;
keyidx = gguf_find_key(ggufctx, "general.name");
if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "general.description");
if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "general.author");
if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "general.license");
if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "general.architecture");
if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "general.file_type");
if (keyidx != -1) { printf("%s: model file type = %" PRIu32 "\n", __func__, gguf_get_val_u32(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout");
if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
keyidx = gguf_find_key(ggufctx, "general.source.huggingface.repository");
if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); }
}
// check required metadata
{
int keyidx;
// check model architecture kv
keyidx = gguf_find_key(ggufctx, "general.architecture");
if (keyidx != -1) {
if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "falcon") != 0) {
printf("%s: model architecture not supported!\n", __func__);
return false;
}
} else {
printf("%s: gguf model architecture not found!\n", __func__);
return false;
}
// check model tensor data layout kv
keyidx = gguf_find_key(ggufctx, "falcon.tensor_data_layout");
if (keyidx != -1) {
if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "jploski") != 0) {
printf("%s: model tensor data layout not supported!\n", __func__);
return false;
}
} else {
printf("%s: gguf model tensor data layout not found!\n", __func__);
return false;
}
}
// load hparams
{
auto & hparams = model.hparams;
bool ok = true;
int keyidx;
if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.context_length");
if (keyidx != -1) { hparams.n_ctx = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.embedding_length");
if (keyidx != -1) { hparams.n_embd = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.attention.head_count");
if (keyidx != -1) { hparams.n_head = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.feed_forward_length");
if (keyidx != -1) { hparams.n_ff = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.block_count");
if (keyidx != -1) { hparams.n_block = gguf_get_val_u32(ggufctx, keyidx); } else { ok = false; } }
if (ok) { keyidx = gguf_find_key(ggufctx, "falcon.attention.layer_norm_epsilon");
if (keyidx != -1) { hparams.norm_eps= gguf_get_val_f32(ggufctx, keyidx); } else { ok = false; } }
if (!ok) {
fprintf(stderr, "%s: required hparam missing!\n", __func__);
return false;
}
keyidx = gguf_find_key(ggufctx, "falcon.attention.head_count_kv");
if (keyidx != -1) { hparams.n_head_kv = gguf_get_val_u32(ggufctx, keyidx); }
printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
printf("%s: n_head = %d\n", __func__, hparams.n_head);
printf("%s: n_head_kv = %d\n", __func__, hparams.n_head_kv);
printf("%s: n_block = %d\n", __func__, hparams.n_block);
printf("%s: norm_eps = %g\n", __func__, hparams.norm_eps);
}
// load vocab
{
auto & hparams = model.hparams;
int keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.model");
if (keyidx != -1) {
if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gpt2") != 0) {
printf("%s: tokenizer model not supported!\n", __func__);
return false;
}
} else {
printf("%s: tokenizer model not found!\n", __func__);
return false;
}
int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens");
if (tokens_keyidx == -1) {
printf("%s: gpt2 tokenizer vocab not found!\n", __func__);
return false;
}
int merges_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.merges");
if (merges_keyidx == -1) {
printf("%s: gpt2 tokenizer merges not found!\n", __func__);
return false;
}
hparams.n_vocab = gguf_get_arr_n(ggufctx,tokens_keyidx);
hparams.n_merges = gguf_get_arr_n(ggufctx,merges_keyidx);
printf("%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab);
printf("%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges);
for (size_t i = 0; i < hparams.n_vocab; i++) {
std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i);
// printf("token %d = '%s'\n",i,word.c_str() );
vocab.token_to_id[word] = i;
vocab.id_to_token[i] = word;
if( vocab.id_to_token[i] == "\n" ) {
vocab.linefeed_id = i;
}
}
std::vector<std::pair<std::string, std::string>> bpe_merges;
for (size_t i = 0; i < hparams.n_merges; i++) {
std::string word = gguf_get_arr_str(ggufctx, merges_keyidx, i);
// Split the merges
std::string first, second;
size_t pos = word.find(' ', 1); // Start the search from the second character
if (pos != std::string::npos) {
first = word.substr(0, pos);
second = word.substr(pos + 1);
}
bpe_merges.push_back(std::make_pair(first, second));
}
vocab.populate_bpe_ranks(bpe_merges);
keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.bos_token_id"); if( keyidx != -1 ) { vocab.special_bos_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.eos_token_id"); if( keyidx != -1 ) { vocab.special_eos_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.unknown_token_id"); if( keyidx != -1 ) { vocab.special_unk_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.separator_token_id"); if( keyidx != -1 ) { vocab.special_sep_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.padding_token_id"); if( keyidx != -1 ) { vocab.special_pad_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); }
if( vocab.special_bos_id != -1 ) { printf("%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); }
if( vocab.special_eos_id != -1 ) { printf("%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); }
if( vocab.special_unk_id != -1 ) { printf("%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); }
if( vocab.special_sep_id != -1 ) { printf("%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); }
if( vocab.special_pad_id != -1 ) { printf("%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); }
if( vocab.linefeed_id != -1 ) { printf("%s: LF token = %d\n", __func__, vocab.linefeed_id ); }
}
auto & ctx = model.ctx;
size_t ctx_size = ggml_get_mem_size(ctx);
printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
// print tensor info
#if 0
{
const int n_tensors = gguf_get_n_tensors(ggufctx);
printf("%s: n_tensors: %d\n", __func__, n_tensors);
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name (ggufctx, i);
const size_t offset = gguf_get_tensor_offset(ggufctx, i);
printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
}
}
#endif
// prepare memory for the weights
{
auto & hparams = model.hparams;
const int n_block = hparams.n_block;
model.blocks.resize(n_block);
model.tok_embeddings = ggml_get_tensor(ctx, "token_embd.weight");
model.output_norm = ggml_get_tensor(ctx, "output_norm.weight");
model.output_norm_b = ggml_get_tensor(ctx, "output_norm.bias");
model.lm_head = ggml_get_tensor(ctx, "output.weight");
// map by name
model.tensors["token_embd.weight"] = model.tok_embeddings;
model.tensors["output_norm.weight"] = model.output_norm;
model.tensors["output_norm.bias"] = model.output_norm_b;
model.tensors["output.weight"] = model.lm_head;
for (int i = 0; i < n_block; ++i) {
auto& block = model.blocks[i];
std::string blocknamestart = "blk." + std::to_string(i) + ".";
block.input_layernorm = get_tensor_ex(ctx, blocknamestart + "attn_norm.weight" );
block.input_layernorm_b = get_tensor_ex(ctx, blocknamestart + "attn_norm.bias" );
if ( hparams.n_head_kv == 8 ) { // Falcon-40B
block.attention_norm = get_tensor_ex(ctx, blocknamestart + "attn_norm_2.weight" );
block.attention_norm_b = get_tensor_ex(ctx, blocknamestart + "attn_norm_2.bias" );
}
// query_key_value shape for config.multi_query == True:
block.query_key_value = get_tensor_ex(ctx, blocknamestart + "attn_qkv.weight" );
block.wo = get_tensor_ex(ctx, blocknamestart + "attn_output.weight" );
block.ffn_up = get_tensor_ex(ctx, blocknamestart + "ffn_up.weight" );
block.ffn_down = get_tensor_ex(ctx, blocknamestart + "ffn_down.weight" );
// map by name
if ( hparams.n_head_kv == 8 ) { // Falcon-40B
// Falcon-40B:
model.tensors[blocknamestart + "attn_norm.weight"] = block.input_layernorm;
model.tensors[blocknamestart + "attn_norm.bias"] = block.input_layernorm_b;
model.tensors[blocknamestart + "attn_norm_2.weight"] = block.attention_norm;
model.tensors[blocknamestart + "attn_norm_2.bias"] = block.attention_norm_b;
} else {
// Falcon-7B:
model.tensors[blocknamestart + "attn_norm.weight"] = block.input_layernorm;
model.tensors[blocknamestart + "attn_norm.bias"] = block.input_layernorm_b;
}
model.tensors[blocknamestart + "attn_qkv.weight"] = block.query_key_value;
model.tensors[blocknamestart + "attn_output.weight"] = block.wo;
model.tensors[blocknamestart + "ffn_up.weight"] = block.ffn_up;
model.tensors[blocknamestart + "ffn_down.weight"] = block.ffn_down;
}
}
// key + value memory
{
const auto & kvctx = model.kvctx;
const auto & hparams = model.hparams;
const int n_block = hparams.n_block;
const int n_ctx = hparams.n_ctx;
const int n_embd = hparams.n_embd;
const int64_t n_mem = n_block*n_ctx;
const int64_t n_elements = n_embd*n_mem;
// create the ggml context
{
struct ggml_init_params params = {
/*.mem_size =*/ size_t(n_elements*4+ggml_tensor_overhead()*2),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
model.kvctx = ggml_init(params);
if (!model.kvctx) {
fprintf(stderr, "%s: kv ggml_init() failed\n", __func__);
return false;
}
}
model.memory_k = ggml_new_tensor_1d(kvctx, GGML_TYPE_F16, n_elements);
model.memory_v = ggml_new_tensor_1d(kvctx, GGML_TYPE_F16, n_elements);
const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size/1024.0/1024.0, n_mem);
}
return true;
}
// evaluate the transformer
//
// - model: the model
// - n_threads: number of threads to use
// - n_past: the context size so far
// - embd_inp: the embeddings of the tokens in the context
// - embd_w: the predicted logits for the next token
//
bool falcon_eval(
const falcon_model & model,
const int n_threads,
const int n_past,
const std::vector<gpt2bpe_vocab::id> & embd_inp,
std::vector<float> & embd_w,
size_t & mem_per_token) {
const int N = embd_inp.size();
const auto & hparams = model.hparams;
const int n_embd = hparams.n_embd;
const int n_block = hparams.n_block;
const int n_ctx = hparams.n_ctx;
const int n_head = hparams.n_head;
const int n_head_kv = hparams.n_head_kv;
const int n_vocab = hparams.n_vocab;
const size_t head_dim = n_embd / n_head;
static size_t buf_size = 256u*1024*1024;
static void * buf = malloc(buf_size);
// use 2 scratch buffers
// TODO: very hacky solution - reimplement in a more elegant way
static size_t scr0_size = 256u*1024*1024;
static void * scr0 = malloc(scr0_size);
static size_t scr1_size = 256u*1024*1024;
static void * scr1 = malloc(scr1_size);
if (mem_per_token > 0 && mem_per_token*N > buf_size) {
const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
//printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
// reallocate
buf_size = buf_size_new;
buf = realloc(buf, buf_size);
if (buf == nullptr) {
fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
return false;
}
}
struct ggml_init_params params = {
/*.mem_size =*/ buf_size,
/*.mem_buffer =*/ buf,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph gf = {};
// gf.n_threads = n_threads;
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
// wte
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
// struct ggml_tensor* repeat_dummy = ggml_new_tensor_3d(ctx0, inpL->type, head_dim, N + n_past, n_head);
ggml_type wtype = GGML_TYPE_F32;
const int sizeof_wtype = ggml_type_sizef(wtype);
for (int il = 0; il < n_block; ++il) {
struct ggml_tensor * cur;
struct ggml_tensor * layernorm_output;
ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
// self-attention
{
layernorm_output = ggml_norm(ctx0, inpL);
layernorm_output = ggml_add(ctx0,
ggml_mul(ctx0,
ggml_repeat(ctx0, model.blocks[il].input_layernorm, layernorm_output),
layernorm_output),
ggml_repeat(ctx0, model.blocks[il].input_layernorm_b, layernorm_output));
if ( hparams.n_head_kv == 8 ) { // Falcon-40B
cur = ggml_norm(ctx0, inpL);
cur = ggml_add(ctx0,
ggml_mul(ctx0,
ggml_repeat(ctx0, model.blocks[il].attention_norm, cur),
cur),
ggml_repeat(ctx0, model.blocks[il].attention_norm_b, cur));
}
else { // Falcon 7B
cur = layernorm_output;
}
// compute QKV
cur = ggml_mul_mat(ctx0, model.blocks[il].query_key_value, cur);
// Note that the strides for Kcur, Vcur are set up so that the
// resulting views are misaligned with the tensor's storage
// (by applying the K/V offset we shift the tensor's original
// view to stick out behind the viewed QKV tensor's allocated
// memory, so to say). This is ok because no actual accesses
// happen to that out-of-range memory, but it can require some
// trickery when trying to accurately dump these views for
// debugging.
struct ggml_tensor * Qcur = ggml_view_3d(
ctx0, cur, head_dim, n_head, N,
head_dim * sizeof_wtype,
head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype,
0);
struct ggml_tensor * Kcur = ggml_view_3d(
ctx0, cur, head_dim, n_head_kv, N,
head_dim * sizeof_wtype,
head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype,
head_dim * n_head * sizeof_wtype);
struct ggml_tensor * Vcur = ggml_view_3d(
ctx0, cur, head_dim, n_head_kv, N,
head_dim * sizeof_wtype,
head_dim * (n_head + 2 * n_head_kv) * sizeof_wtype,
head_dim * (n_head + n_head_kv) * sizeof_wtype);
// using mode = 2 for neox mode
Qcur = ggml_rope_inplace(ctx0, Qcur, n_past, head_dim, 2, 0);
Kcur = ggml_rope_inplace(ctx0, Kcur, n_past, head_dim, 2, 0);
// store key and value to memory
{
struct ggml_tensor* k = ggml_view_1d(
ctx0, model.memory_k, N * n_head_kv * head_dim,
(ggml_element_size(model.memory_k) * n_head_kv * head_dim) *
(il * n_ctx + n_past));
struct ggml_tensor* v = ggml_view_1d(
ctx0, model.memory_v, N * n_head_kv * head_dim,
(ggml_element_size(model.memory_v) * n_head_kv * head_dim) *
(il * n_ctx + n_past));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
}
struct ggml_tensor * K = ggml_permute(
ctx0,
ggml_reshape_3d(
ctx0,
ggml_view_1d(ctx0, model.memory_k, (n_past + N) * n_head_kv * head_dim,
il * n_ctx *
ggml_element_size(model.memory_k) *
n_head_kv *
head_dim),
head_dim, n_head_kv, n_past + N),
0, 2, 1, 3);
// K * Q
// K = ggml_cont(ctx0, ggml_repeat2(ctx0, K, repeat_dummy));
struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
// KQ_scaled = KQ / sqrt(n_embd/n_head)
struct ggml_tensor * KQ_scaled =
ggml_scale_inplace(ctx0,
KQ,
ggml_new_f32(ctx0, 1.0f/sqrt(float(head_dim)))
);
// KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
// KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
struct ggml_tensor* V = ggml_permute(
ctx0,
ggml_reshape_3d(
ctx0,
ggml_view_1d(ctx0, model.memory_v, (n_past + N) * n_head_kv * head_dim,
il * n_ctx *
ggml_element_size(model.memory_v) *
n_head_kv *
head_dim),
head_dim, n_head_kv, n_past + N),
0, 2, 1, 3);
// V = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_repeat2(ctx0, V, repeat_dummy)));
V = ggml_cont(ctx0, ggml_transpose(ctx0, V));
// KQV = transpose(V) * KQ_soft_max
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
// KQV_merged = KQV.permute(0, 2, 1, 3)
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
// cur = KQV_merged.contiguous().view(n_embd, N)
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
// projection
{
cur = ggml_mul_mat(ctx0,
model.blocks[il].wo,
cur);
}
}
ggml_set_scratch(ctx0, { 0, scr1_size, scr1, });
struct ggml_tensor* inpFF = layernorm_output;
struct ggml_tensor* attn_out = ggml_cpy(
ctx0, cur, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
{
cur = ggml_mul_mat(ctx0, model.blocks[il].ffn_up, inpFF);
cur = ggml_gelu(ctx0, cur);
cur = ggml_mul_mat(ctx0, model.blocks[il].ffn_down, cur);
}
cur = ggml_add(ctx0, cur, attn_out);
cur = ggml_add(ctx0, cur, inpL);
// input for next layer
inpL = cur;
}
ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
// norm
{
inpL = ggml_norm(ctx0, inpL);
// inpL = ln_f_g*inpL + ln_f_b
inpL = ggml_add(ctx0,
ggml_mul(ctx0,
ggml_repeat(ctx0, model.output_norm, inpL),
inpL),
ggml_repeat(ctx0, model.output_norm_b, inpL));
}
ggml_set_scratch(ctx0, { 0, 0, nullptr, });
// lm_head
{
inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);
//inpL = ggml_add(ctx0,
// ggml_repeat(ctx0, model.lmh_b, inpL),
// inpL);
}
// logits -> probs
//inpL = ggml_soft_max_inplace(ctx0, inpL);
// run the computation
ggml_build_forward_expand(&gf, inpL);
// ggml_graph_compute (ctx0, &gf);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
//if (n_past%100 == 0) {
// ggml_graph_print (&gf);
// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
//}
// return result for just the last token
embd_w.resize(n_vocab);
memcpy(embd_w.data(), (float *)ggml_get_data(inpL) + (n_vocab * (N - 1)), sizeof(float) * n_vocab);
if (mem_per_token == 0) {
mem_per_token = ggml_used_mem(ctx0)/N;
}
//printf("used_mem = %zu\n", ggml_used_mem(ctx0));
ggml_free(ctx0);
return true;
}
int main(int argc, char ** argv) {
ggml_time_init();
const int64_t t_main_start_us = ggml_time_us();
gpt_params params;
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}
int64_t t_load_us = 0;
gpt2bpe_vocab vocab;
falcon_model model;
// load the model
{
const int64_t t_start_us = ggml_time_us();
if (!falcon_model_load(params.model, model, vocab)) {
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return 1;
}
t_load_us = ggml_time_us() - t_start_us;
}
if (params.seed < 0) {
params.seed = time(NULL);
}
if (params.top_k == 0) {
params.top_k = model.hparams.n_vocab;
}
printf("%s: seed = %d\n", __func__, params.seed);
printf("%s: temp = %.3f\n", __func__, params.temp);
printf("%s: top_k = %d\n", __func__, params.top_k);
printf("%s: top_p = %.3f\n", __func__, params.top_p);
printf("%s: repeat_last_n = %d\n", __func__, params.repeat_last_n);
printf("%s: repeat_penalty = %.3f\n", __func__, params.repeat_penalty);
std::mt19937 rng(params.seed);
if (params.prompt.empty()) {
params.prompt = "Once upon";
}
std::vector<int32_t> last_n_tokens(model.hparams.n_ctx);
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
int n_past = 0;
int64_t t_sample_us = 0;
int64_t t_predict_us = 0;
std::vector<float> logits;
// tokenize the prompt
std::vector<gpt2bpe_vocab::id> embd_inp = gpt2bpe_tokenize(vocab, params.prompt,false, false);
params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
// for (size_t i = 0; i < embd_inp.size(); i++) {
// printf("%s: token[%zu] = %6d, %s\n", __func__, i, embd_inp[i], vocab.id_to_token[embd_inp[i]].c_str());
// }
if( model.hparams.n_ctx < params.n_predict+embd_inp.size() ) {
params.n_predict = model.hparams.n_ctx-embd_inp.size();
}
printf("%s: n_predict = %d\n", __func__, params.n_predict);
printf("\n");
std::vector<gpt2bpe_vocab::id> embd;
// determine the required inference memory per token:
size_t mem_per_token = 0;
falcon_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
for (size_t i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
// predict
if (embd.size() > 0) {
const int64_t t_start_us = ggml_time_us();
if (!falcon_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
printf("Failed to predict\n");
return 1;
}
t_predict_us += ggml_time_us() - t_start_us;
}
n_past += embd.size();
embd.clear();
if (i >= embd_inp.size()) {
// sample next token
const int top_k = params.top_k;
const float top_p = params.top_p;
const float temp = params.temp;
const int repeat_last_n = params.repeat_last_n;
const float repeat_penalty = params.repeat_penalty;
const int n_vocab = model.hparams.n_vocab;
gpt2bpe_vocab::id id = 0;
{
const int64_t t_start_sample_us = ggml_time_us();
id = sample_top_k_top_p_repeat(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens.data(), last_n_tokens.size(), top_k, top_p, temp, repeat_last_n, repeat_penalty, rng);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
t_sample_us += ggml_time_us() - t_start_sample_us;
}
// add it to the context
embd.push_back(id);
} else {
// if here, it means we are still processing the input prompt
for (size_t k = i; k < embd_inp.size(); k++) {
embd.push_back(embd_inp[k]);
if (embd.size() > params.n_batch) {
break;
}
}
i += embd.size() - 1;
}
// display text
for (auto id : embd) {
printf("%s", vocab.id_to_token[id].c_str() );
}
fflush(stdout);
// end of text token
if (vocab.special_eos_id != -1 && embd.back() == vocab.special_eos_id) {
break;
}
}
// report timing
{
const int64_t t_main_end_us = ggml_time_us();
printf("\n\n");
printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
}
ggml_free(model.ctx);
return 0;
}
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