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//This is Concedo's shitty adapter for adding python bindings for llama
//Considerations:
//Don't want to use pybind11 due to dependencies on MSVCC
//ZERO or MINIMAL changes as possible to main.cpp - do not move their function declarations here!
//Leave main.cpp UNTOUCHED, We want to be able to update the repo and pull any changes automatically.
//No dynamic memory allocation! Setup structs with FIXED (known) shapes and sizes for ALL output fields
//Python will ALWAYS provide the memory, we just write to it.
#include <time.h>
#include "model_adapter.h"
#include "otherarch.h"
//for easier compilation
//concat source files into one file for compilation purposes
#include "llama_v2.cpp"
#include "llama.cpp"
#include "utils.cpp"
#include "gptj_v1.cpp"
#include "gptj_v2.cpp"
#include "gptj_v3.cpp"
#include "gpt2_v1.cpp"
#include "gpt2_v2.cpp"
#include "gpt2_v3.cpp"
#include "rwkv_v2.cpp"
#include "rwkv_v3.cpp"
#include "neox_v2.cpp"
#include "neox_v3.cpp"
#include "mpt_v3.cpp"
//shared
std::string executable_path = "";
std::string lora_filename = "";
std::string lora_base = "";
bool generation_finished;
std::vector<std::string> generated_tokens;
//return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt)
static FileFormat file_format = FileFormat::BADFORMAT;
static gpt_vocab vocab;
static gptj_v1_model gptj_ctx_v1;
static gptj_v2_model gptj_ctx_v2;
static gptj_model gptj_ctx_v3;
static gpt2_v1_model gpt2_ctx_v1;
static gpt2_v2_model gpt2_ctx_v2;
static gpt2_model gpt2_ctx_v3;
static gpt_neox_v2_model neox_ctx_v2;
static gpt_neox_model neox_ctx_v3;
static mpt_model mpt_ctx_v3;
static rwkv_v2_context * rwkv_ctx_v2;
static rwkv_context * rwkv_ctx_v3;
static llama_v2_context_params llama_ctx_params_v2;
static llama_context_params llama_ctx_params;
static llama_v2_context * llama_ctx_v2;
static llama_context * llama_ctx_v3;
static gpt_params params;
static int n_past = 0;
static int n_threads = 4;
static int n_blasthreads = 4;
static int n_batch = 8;
static bool useSmartContext = false;
static bool unbanTokens = false;
static int blasbatchsize = 512;
static int debugmode = 0; //-1 = hide all, 0 = normal, 1 = showall
static std::string modelname;
static std::vector<gpt_vocab::id> last_n_tokens;
static std::vector<gpt_vocab::id> current_context_tokens;
static size_t mem_per_token = 0;
static std::vector<float> logits;
static std::vector<int> smartcontext;
static std::vector<std::string> stop_sequence;
static std::vector<llama_token_data> top_picks;
static int remaining_tokens = 0;
static int stopper_unused_tokens = 0;
static std::string concat_output = "";
inline bool IsNanCheck(float f)
{
const unsigned int u = *(unsigned int*)&f;
return (u&0x7F800000) == 0x7F800000 && (u&0x7FFFFF); // Both NaN and qNan.
}
inline bool LogitsDuplicated(std::vector<float> & arr1, std::vector<float> & arr2)
{
int compareQty = 5;
if(arr1.size() < compareQty || arr2.size() < compareQty || arr1.size()!=arr2.size())
{
printf("\nError: Logit array sizes are bad!\n");
return false;
}
for(int i=0;i<compareQty;++i)
{
if(arr1[i]!=arr2[i])
{
return false;
}
}
return true;
}
llama_token sample_token(llama_token_data_array * candidates, std::mt19937 & rng)
{
llama_sample_softmax(nullptr, candidates);
std::vector<float> probs;
probs.reserve(candidates->size);
top_picks.clear();
for (size_t i = 0; i < candidates->size; ++i) {
probs.push_back(candidates->data[i].p);
}
std::discrete_distribution<> dist(probs.begin(), probs.end());
int idx = dist(rng);
if(debugmode==1)
{
top_picks.push_back(candidates->data[idx]);
for (size_t i = 0; (i < candidates->size && i<4); ++i)
{
if(i!=idx)
{
top_picks.push_back(candidates->data[i]);
}
}
}
llama_token result = candidates->data[idx].id;
return result;
}
llama_token sample_token_mirostat(int n_vocab, llama_token_data_array * candidates, std::mt19937 & rng, float tau, float eta, int m, float * mu)
{
float N = float(n_vocab);
llama_sample_softmax(nullptr, candidates);
// Estimate s_hat using the most probable m tokens
float s_hat = 0.0;
float sum_ti_bi = 0.0;
float sum_ti_sq = 0.0;
for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
float t_i = logf(float(i + 2) / float(i + 1));
float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
sum_ti_bi += t_i * b_i;
sum_ti_sq += t_i * t_i;
}
s_hat = sum_ti_bi / sum_ti_sq;
// Compute k from the estimated s_hat and target surprise value
float epsilon_hat = s_hat - 1;
float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);
// Sample the next word X using top-k sampling
llama_sample_top_k(nullptr, candidates, int(k),1);
llama_token X = sample_token(candidates, rng); // Compute error as the difference between observed surprise and target surprise value
size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
return candidate.id == X;
}));
float observed_surprise = -log2f(candidates->data[X_idx].p);
float e = observed_surprise - tau;
// Update mu using the learning rate and error
*mu = *mu - eta * e;
return X;
}
llama_token sample_token_mirostat_v2(llama_token_data_array * candidates, std::mt19937 & rng, float tau, float eta, float * mu)
{
llama_sample_softmax(nullptr, candidates);
// Truncate the words with surprise values greater than mu
candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
return -log2f(candidate.p) > *mu;
}));
// Normalize the probabilities of the remaining words
llama_sample_softmax(nullptr, candidates);
// Sample the next word X from the remaining words
llama_token X = sample_token(candidates,rng);
// Compute error as the difference between observed surprise and target surprise value
size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
return candidate.id == X;
}));
float observed_surprise = -log2f(candidates->data[X_idx].p);
float e = observed_surprise - tau;
// Update mu using the learning rate and error
*mu = *mu - eta * e;
return X;
}
// Top-a (remove all tokens that have softmax probability less than top_a*m^2 where m is the maximum softmax probability)
// top-a 0 is off (no effect)
void sample_top_a(llama_token_data_array * candidates, float a, size_t min_keep) {
if (a <= 0.0f || candidates->size<=1) {
return;
}
llama_sample_softmax(nullptr, candidates);
// Compute the cumulative probabilities
float maxprob = candidates->data[0].p;
float threshold = a * maxprob * maxprob; //tokens with probs less than this are removed
size_t last_idx = candidates->size;
for (size_t i = 0; i < candidates->size; ++i) {
// Go until we reach a value under the threshold
float checkprob = candidates->data[i].p;
if (checkprob < threshold && i >= min_keep) {
last_idx = i;
break;
}
}
// printf("\n\nCandidates: %d, A:%f, MaxProb: %f, Threshold: %f, LastIdx: %d",candidates->size,a,maxprob,threshold,last_idx);
// printf("\nCandidates: %f %f %f %f\n",candidates->data[0].p,candidates->data[1].p,candidates->data[2].p,candidates->data[3].p);
// Resize the output vector to keep only the selected tokens
candidates->size = last_idx;
}
int SampleLogits(const float * logits, int n_ctx, int n_vocab, int rep_pen_range, float rep_pen, float top_k, float top_a, float top_p, float typical_p, float tfs, float temp, std::mt19937 & rng,
int mirostat, float mirostat_tau, float mirostat_eta)
{
int id = 0;
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
// Apply penalties
auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), rep_pen_range), n_ctx);
llama_sample_repetition_penalty(nullptr, &candidates_p,
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
last_n_repeat, rep_pen);
// llama_sample_frequency_and_presence_penalties(nullptr, &candidates_p,
// last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
// last_n_repeat, alpha_frequency, alpha_presence);
if (temp <= 0)
{
// Greedy sampling
id = llama_sample_token_greedy(nullptr, &candidates_p);
}
else
{
if (mirostat == 1)
{
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(nullptr, &candidates_p, temp);
id = sample_token_mirostat(n_vocab, &candidates_p, rng, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
}
else if (mirostat == 2)
{
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(nullptr, &candidates_p, temp);
id = sample_token_mirostat_v2(&candidates_p, rng, mirostat_tau, mirostat_eta, &mirostat_mu);
}
else
{
// Temperature sampling
llama_sample_top_k(nullptr, &candidates_p, top_k,1);
sample_top_a(&candidates_p,top_a,1);
llama_sample_tail_free(nullptr, &candidates_p, tfs,1);
llama_sample_typical(nullptr, &candidates_p, typical_p,1);
llama_sample_top_p(nullptr, &candidates_p, top_p,1);
llama_sample_temperature(nullptr, &candidates_p, temp);
id = sample_token(&candidates_p, rng);
}
}
return id;
}
static std::string FileFormatTokenizeID(int id, FileFormat file_format)
{
if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2)
{
return std::string(llama_v2_token_to_str(llama_ctx_v2, id));
}
else if (file_format == FileFormat::GGJT_3)
{
return std::string(llama_token_to_str(llama_ctx_v3, id));
}
else
{
return vocab.id_to_token[id];
}
}
ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in_file_format)
{
ggml_time_init();
file_format = in_file_format;
n_threads = params.n_threads = inputs.threads;
n_blasthreads = inputs.blasthreads;
n_batch = params.n_batch = inputs.batch_size;
modelname = params.model = inputs.model_filename;
useSmartContext = inputs.use_smartcontext;
debugmode = inputs.debugmode;
unbanTokens = inputs.unban_tokens;
blasbatchsize = inputs.blasbatchsize;
params.memory_f16 = inputs.f16_kv;
params.n_ctx = inputs.max_context_length;
neox_ctx_v2.hparams.n_ctx = neox_ctx_v3.hparams.n_ctx
= gptj_ctx_v1.hparams.n_ctx = gptj_ctx_v2.hparams.n_ctx = gptj_ctx_v3.hparams.n_ctx
= gpt2_ctx_v1.hparams.n_ctx = gpt2_ctx_v2.hparams.n_ctx = gpt2_ctx_v3.hparams.n_ctx
= mpt_ctx_v3.hparams.n_ctx = params.n_ctx;
//this is used for the mem_per_token eval, openblas needs more RAM
bool use_scratch = ggml_cpu_has_gpublas();
printf("System Info: %s\n", llama_print_system_info());
SetQuantsUnshuffled(false);
if(file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2)
{
//newer format has bit unshuffling
SetQuantsUnshuffled(file_format == FileFormat::GGJT_2);
llama_ctx_params_v2 = llama_v2_context_default_params();
llama_ctx_params_v2.n_ctx = inputs.max_context_length;
//llama_ctx_params.n_parts = -1;
llama_ctx_params_v2.seed = -1;
llama_ctx_params_v2.f16_kv = inputs.f16_kv;
llama_ctx_params_v2.logits_all = false;
llama_ctx_params_v2.use_mmap = inputs.use_mmap;
llama_ctx_params_v2.use_mlock = inputs.use_mlock;
llama_ctx_params_v2.n_gpu_layers = inputs.gpulayers;
llama_ctx_v2 = llama_v2_init_from_file(modelname.c_str(), llama_ctx_params_v2);
if (llama_ctx_v2 == NULL)
{
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, modelname.c_str());
return ModelLoadResult::FAIL;
}
printf("\n---\nWarning: Your model may be an OUTDATED format (ver %d). Please reconvert it for better results!\n---\n", file_format);
if (lora_filename != "")
{
printf("\nAttempting to apply LORA adapter: %s\n", lora_filename.c_str());
const char * lora_base_arg = NULL;
if (lora_base != "") {
printf("Using LORA base model: %s\n", lora_base.c_str());
lora_base_arg = lora_base.c_str();
}
int err = llama_v2_apply_lora_from_file(llama_ctx_v2,
lora_filename.c_str(),
lora_base_arg,
n_threads);
if (err != 0)
{
fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
return ModelLoadResult::FAIL;
}
}
//determine mem per token
const std::vector<int> tmp = {1, 2, 3, 4};
llama_v2_eval(llama_ctx_v2, tmp.data(), tmp.size(), 0, params.n_threads);
return ModelLoadResult::SUCCESS;
}
else if(file_format == FileFormat::GGJT_3)
{
llama_ctx_params = llama_context_default_params();
llama_ctx_params.n_ctx = inputs.max_context_length;
//llama_ctx_paran_parts = -1;
llama_ctx_params.seed = -1;
llama_ctx_params.f16_kv = inputs.f16_kv;
llama_ctx_params.logits_all = false;
llama_ctx_params.use_mmap = inputs.use_mmap;
llama_ctx_params.use_mlock = inputs.use_mlock;
llama_ctx_params.n_gpu_layers = inputs.gpulayers;
llama_ctx_v3 = llama_init_from_file(modelname.c_str(), llama_ctx_params);
if (llama_ctx_v3 == NULL)
{
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, modelname.c_str());
return ModelLoadResult::FAIL;
}
if (lora_filename != "")
{
printf("\nAttempting to apply LORA adapter: %s\n", lora_filename.c_str());
const char * lora_base_arg = NULL;
if (lora_base != "") {
printf("Using LORA base model: %s\n", lora_base.c_str());
lora_base_arg = lora_base.c_str();
}
int err = llama_apply_lora_from_file(llama_ctx_v3,
lora_filename.c_str(),
lora_base_arg,
n_threads);
if (err != 0)
{
fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
return ModelLoadResult::FAIL;
}
}
//determine mem per token
const std::vector<int> tmp = {1, 2, 3, 4};
auto er = llama_eval(llama_ctx_v3, tmp.data(), tmp.size(), 0, params.n_threads);
if(er!=0)
{
printf("\nLLAMA EVAL returned nonzero!\n");
}
return ModelLoadResult::SUCCESS;
}
else if (file_format == FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2)
{
//start loading the models first
bool useWorldTokenizer = false;
if (file_format == FileFormat::RWKV_1)
{
rwkv_ctx_v2 = rwkv_v2_init_from_file(modelname.c_str(), n_threads);
}
else //rwkv_2
{
rwkv_ctx_v3 = rwkv_init_from_file(modelname.c_str(), n_threads);
const struct rwkv_file_header & header = rwkv_ctx_v3->instance->model.header;
const size_t n_vocab = header.n_vocab;
printf("\nDetected Vocab: %d",n_vocab);
if(n_vocab>60000)
{
printf("\nUsing WORLD TOKENIZER");
useWorldTokenizer = true;
}
}
std::string word;
if(useWorldTokenizer)
{
read_rwkv_world_vocab();
}
else
{
read_rwkv_vocab();
}
int vocabsiz = rwkv_vocab.size();
for (int i = 0; i < vocabsiz; i++)
{
uint32_t len;
word = rwkv_vocab[i];
vocab.token_to_id[word] = i;
vocab.id_to_token[i] = word;
}
printf("\nRWKV Vocab: %u\n", vocabsiz);
logits.resize(vocabsiz);
if (file_format == FileFormat::RWKV_1)
{
n_batch = 1;
//setup buffers for rwkv state
auto padding = 512u;
auto statebufsiz = rwkv_v2_get_state_buffer_element_count(rwkv_ctx_v2) * sizeof(float) + padding;
auto logitbufsiz = rwkv_v2_get_logits_buffer_element_count(rwkv_ctx_v2) * sizeof(float) + padding;
printf("\nRWKV old Init: State Buffer:%u, Logit Buffer:%u\n", statebufsiz, logitbufsiz);
rwkv_ctx_v2->state_out = (float *)malloc(statebufsiz);
rwkv_ctx_v2->logits_out = (float *)malloc(logitbufsiz);
rwkv_ctx_v2->state_in = nullptr;
bool testeval = rwkv_v2_eval(rwkv_ctx_v2, 0, rwkv_ctx_v2->state_in, rwkv_ctx_v2->state_out, rwkv_ctx_v2->logits_out);
if (!testeval)
{
printf("\nError: RWKV old Init Eval Failed!\n");
}
memcpy(logits.data(), rwkv_ctx_v2->logits_out, sizeof(float) * vocabsiz);
if (rwkv_ctx_v2 == NULL)
{
return ModelLoadResult::FAIL;
}
return ModelLoadResult::SUCCESS;
}
else
{
n_batch = 1; //do not use sequence mode to speedup until it is fixed
//setup buffers for rwkv state
auto padding = 512u;
auto statebufsiz = rwkv_get_state_buffer_element_count(rwkv_ctx_v3) * sizeof(float) + padding;
auto logitbufsiz = rwkv_get_logits_buffer_element_count(rwkv_ctx_v3) * sizeof(float) + padding;
printf("\nRWKV Init: State Buffer:%u, Logit Buffer:%u\n", statebufsiz, logitbufsiz);
rwkv_ctx_v3->state_out = (float *)malloc(statebufsiz);
rwkv_ctx_v3->logits_out = (float *)malloc(logitbufsiz);
rwkv_ctx_v3->state_in = nullptr;
bool testeval = rwkv_eval(rwkv_ctx_v3, 0, rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out);
if (!testeval)
{
printf("\nError: RWKV Init Eval Failed!\n");
}
memcpy(logits.data(), rwkv_ctx_v3->logits_out, sizeof(float) * vocabsiz);
if (rwkv_ctx_v3 == NULL)
{
return ModelLoadResult::FAIL;
}
return ModelLoadResult::SUCCESS;
}
}
else if (file_format == FileFormat::GPT2_1)
{
ModelLoadResult res = legacy_gpt2_model_load(params.model, gpt2_ctx_v1, vocab, file_format);
if(res==ModelLoadResult::FAIL)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return res;
}
else if(res==ModelLoadResult::RETRY_LOAD)
{
printf("\nTensor Transposition Detected! Retrying GPT-2 model loading...");
return res;
}
// determine the required inference memory per token:
legacy_gpt2_eval(gpt2_ctx_v1, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);
return ModelLoadResult::SUCCESS;
}
else if (file_format == FileFormat::GPT2_2 || file_format==FileFormat::GPT2_3 || file_format==FileFormat::GPT2_4)
{
if(file_format==FileFormat::GPT2_4)
{
ModelLoadResult res = gpt2_model_load(params.model, gpt2_ctx_v3, vocab, file_format, inputs.gpulayers);
if(res==ModelLoadResult::FAIL)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return res;
}
else if(res==ModelLoadResult::RETRY_LOAD)
{
printf("\nTensor Transposition Detected! Retrying GPT-2 model loading...");
return res;
}
// determine the required inference memory per token:
gpt2_eval(gpt2_ctx_v3, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, use_scratch);
return ModelLoadResult::SUCCESS;
}
else
{
//newer format has bit unshuffling
SetQuantsUnshuffled(file_format == FileFormat::GPT2_3);
ModelLoadResult res = gpt2_v2_model_load(params.model, gpt2_ctx_v2, vocab, file_format, inputs.gpulayers);
if(res==ModelLoadResult::FAIL)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return res;
}
else if(res==ModelLoadResult::RETRY_LOAD)
{
printf("\nTensor Transposition Detected! Retrying GPT-2 model loading...");
return res;
}
// determine the required inference memory per token:
gpt2_v2_eval(gpt2_ctx_v2, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);
return ModelLoadResult::SUCCESS;
}
}
else if (file_format == FileFormat::GPTJ_1 || file_format == FileFormat::GPTJ_2)
{
ModelLoadResult res = legacy_gptj_model_load(params.model, gptj_ctx_v1, vocab, file_format);
if(res==ModelLoadResult::FAIL)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return res;
}
else if(res==ModelLoadResult::RETRY_LOAD)
{
printf("\nTensor Transposition Detected! Retrying GPT-J model loading...");
return res;
}
// determine the required inference memory per token:
legacy_gptj_eval(gptj_ctx_v1, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);
//if the logits are NAN or duplicated, it means the model is incompatible
if(logits.size()>0 && IsNanCheck(logits[0]))
{
printf("\nBad Logits detected! Retrying GPT-J model loading...");
ggml_v1_free(gptj_ctx_v1.ctx);
return ModelLoadResult::RETRY_LOAD;
}
return ModelLoadResult::SUCCESS;
}
else if(file_format == FileFormat::GPTJ_3 || file_format == FileFormat::GPTJ_4 || file_format == FileFormat::GPTJ_5)
{
if(file_format == FileFormat::GPTJ_5)
{
ModelLoadResult loadresult = gptj_model_load(params.model, gptj_ctx_v3, vocab, inputs.gpulayers);
if (loadresult == ModelLoadResult::FAIL)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return loadresult;
}
else if (loadresult == ModelLoadResult::RETRY_LOAD)
{
printf("\nTensor Transposition Detected! Retrying GPT-J model loading...");
return loadresult;
}
// determine the required inference memory per token:
gptj_eval(gptj_ctx_v3, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, use_scratch);
//if the logits are NAN or duplicated, it means the model is incompatible
std::vector<float> oldlogits(logits);
//this is another hack because they change the library - we run the eval through the model
//twice and compare logits. if they give the same logits for different inputs, model is broken
gptj_eval(gptj_ctx_v3, params.n_threads, 0, {4, 5, 6, 7}, logits, mem_per_token, use_scratch);
if(logits.size()>0 && (IsNanCheck(logits[0]) || LogitsDuplicated(oldlogits,logits)))
{
printf("\nBad Logits detected! Retrying GPT-J model loading...");
ggml_free(gptj_ctx_v3.ctx);
return ModelLoadResult::RETRY_LOAD;
}
return ModelLoadResult::SUCCESS;
}
else
{
//newer format has bit unshuffling
SetQuantsUnshuffled(file_format == FileFormat::GPTJ_4);
ModelLoadResult loadresult = gptj_v2_model_load(params.model, gptj_ctx_v2, vocab, inputs.gpulayers);
if (loadresult == ModelLoadResult::FAIL)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return loadresult;
}
else if (loadresult == ModelLoadResult::RETRY_LOAD)
{
printf("\nTensor Transposition Detected! Retrying GPT-J model loading...");
return loadresult;
}
// determine the required inference memory per token:
gptj_v2_eval(gptj_ctx_v2, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
//if the logits are NAN or duplicated, it means the model is incompatible
std::vector<float> oldlogits(logits);
//this is another hack because they change the library - we run the eval through the model
//twice and compare logits. if they give the same logits for different inputs, model is broken
gptj_v2_eval(gptj_ctx_v2, params.n_threads, 0, {4, 5, 6, 7}, logits, mem_per_token);
if(logits.size()>0 && (IsNanCheck(logits[0]) || LogitsDuplicated(oldlogits,logits)))
{
printf("\nBad Logits detected! Retrying GPT-J model loading...");
ggml_v2_free(gptj_ctx_v2.ctx);
return ModelLoadResult::RETRY_LOAD;
}
return ModelLoadResult::SUCCESS;
}
}
else if(file_format==FileFormat::NEOX_1 || file_format==FileFormat::NEOX_2 || file_format==FileFormat::NEOX_3 || file_format==FileFormat::NEOX_4 || file_format==FileFormat::NEOX_5|| file_format==FileFormat::NEOX_6|| file_format==FileFormat::NEOX_7)
{
if(file_format==FileFormat::NEOX_6|| file_format==FileFormat::NEOX_7)
{
ModelLoadResult res = gpt_neox_model_load(params.model, neox_ctx_v3, vocab, file_format, inputs.gpulayers);
if(res==ModelLoadResult::FAIL)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return res;
}
else if(res==ModelLoadResult::RETRY_LOAD)
{
printf("\nIncorrect Tensor Size Detected! Retrying GPT-NeoX model loading...");
return res;
}
// determine the required inference memory per token:
gpt_neox_eval(neox_ctx_v3, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, use_scratch);
return ModelLoadResult::SUCCESS;
}
else
{
//newer format has bit unshuffling
SetQuantsUnshuffled(file_format==FileFormat::NEOX_4 || file_format==FileFormat::NEOX_5);
ModelLoadResult res = gpt_neox_v2_model_load(params.model, neox_ctx_v2, vocab, file_format);
if(res==ModelLoadResult::FAIL)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return res;
}
else if(res==ModelLoadResult::RETRY_LOAD)
{
printf("\nIncorrect Tensor Size Detected! Retrying GPT-NeoX model loading...");
return res;
}
// determine the required inference memory per token:
gpt_neox_v2_eval(neox_ctx_v2, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
if(logits.size()>0 && file_format==FileFormat::NEOX_2 && !IsNanCheck(logits[0]))
{
//run the black magic eval to determine if it's redpajama. VERY UGLY HACK!
std::vector<int> test_embd = ::gpt_tokenize(vocab, "1 2 3 4 5 6 7");
auto orig_par_res = neox_ctx_v2.hparams.par_res;
neox_ctx_v2.hparams.par_res = 0; //test with residual false
gpt_neox_v2_eval(neox_ctx_v2, params.n_threads, 0, test_embd, logits, mem_per_token);
neox_ctx_v2.hparams.par_res = orig_par_res;
int topid = std::max_element(logits.begin(),logits.end())-logits.begin();
std::string predicted = vocab.id_to_token[topid].c_str();
auto findresult = predicted.find("8");
if(findresult != std::string::npos && findresult<2)
{
printf("\n---\nOld RedPajama NeoX Detected! Switching to new format! (use_parallel_residual=False)\n");
ggml_v2_free(neox_ctx_v2.ctx);
return ModelLoadResult::RETRY_LOAD;
}
}
return ModelLoadResult::SUCCESS;
}
}
else if(file_format==FileFormat::MPT_1)
{
bool res = mpt_model_load(params.model, mpt_ctx_v3, vocab, inputs.gpulayers);
if(res==false)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return ModelLoadResult::FAIL;
}
// determine the required inference memory per token:
mpt_eval(mpt_ctx_v3, params.n_threads, 0, { 0, 1, 2, 3 }, logits, false, mem_per_token, use_scratch);
return ModelLoadResult::SUCCESS;
}
else
{
printf("\nUnknown Model, cannot load.\n");
return ModelLoadResult::FAIL;
}
}
bool gpttype_generate_abort()
{
stopper_unused_tokens = remaining_tokens;
remaining_tokens = 0;
return true;
}
const std::string & gpttype_get_pending_output()
{
return concat_output;
}
generation_outputs gpttype_generate(const generation_inputs inputs, generation_outputs &output)
{
stop_sequence.clear();
for(int x=0;x<stop_token_max;++x)
{
std::string stopper = inputs.stop_sequence[x];
if(stopper!="")
{
stop_sequence.push_back(stopper);
}
}
params.prompt = inputs.prompt;
params.seed = inputs.seed;
params.n_predict = inputs.max_length;
params.top_k = inputs.top_k;
params.top_p = inputs.top_p;
params.typical_p = inputs.typical_p;
params.tfs_z = inputs.tfs;
params.temp = inputs.temperature;
params.repeat_last_n = inputs.rep_pen_range;
params.repeat_penalty = inputs.rep_pen;
params.mirostat = inputs.mirostat;
params.mirostat_eta = inputs.mirostat_eta;
params.mirostat_tau = inputs.mirostat_tau;
params.n_ctx = inputs.max_context_length;
params.n_batch = n_batch;
params.n_threads = n_threads;
bool stream_sse = inputs.stream_sse;
generation_finished = false; // Set current generation status
generated_tokens.clear(); // New Generation, new tokens
if (params.repeat_last_n < 1)
{
params.repeat_last_n = 1;
}
if (params.top_k < 1)
{
params.top_k = 120; //to disable top_k we actually need to increase this value to a very high number
}
if (params.seed <= 0)
{
params.seed = time(NULL);
}
// tokenize the prompt
std::vector<int> embd_inp;
if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2 || file_format == FileFormat::GGJT_3)
{
params.prompt.insert(0, 1, ' ');
if(file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2 )
{
embd_inp = ::llama_v2_tokenize(llama_ctx_v2, params.prompt, true);
}
else if (file_format == FileFormat::GGML)
{
embd_inp = ::legacy_llama_v2_tokenize(llama_ctx_v2, params.prompt, true);
}
else
{
embd_inp = ::llama_tokenize(llama_ctx_v3, params.prompt, true);
}
}
else
{
// tokenize the prompt
embd_inp = ::gpt_tokenize(vocab, params.prompt);
}
//truncate to front of the prompt if its too long
int32_t nctx = params.n_ctx;
if (embd_inp.size() + params.n_predict > nctx)
{
int offset = embd_inp.size() - nctx + params.n_predict;
embd_inp = std::vector<int>(embd_inp.begin() + offset, embd_inp.end());
}
//determine how much npast we have to rewind from the current state
std::vector<gpt_vocab::id> embd;
int last_n_size = params.repeat_last_n;
last_n_tokens.resize(last_n_size);
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
n_past = 0;
if (file_format == FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2)
{
ContextFastForward(current_context_tokens, embd_inp, n_past, last_n_tokens, nctx, smartcontext, false, true);
}
else
{
ContextFastForward(current_context_tokens, embd_inp, n_past, last_n_tokens, nctx, smartcontext, useSmartContext, false);
}
//if using BLAS and prompt is big enough, switch to single thread and use a huge batch
bool approved_format = !(file_format == FileFormat::BADFORMAT ||
file_format == FileFormat::GPT2_1 ||
file_format == FileFormat::GPTJ_1 ||
file_format == FileFormat::GPTJ_2 ||
file_format == FileFormat::RWKV_1 ||
file_format==FileFormat::RWKV_2);
bool blasmode = (approved_format && embd_inp.size() >= 32 && ggml_cpu_has_blas() && blasbatchsize!=-1);
// bool blasmode = false;
int original_batch = params.n_batch;
int original_threads = params.n_threads;
if (blasmode)
{
//for non llama, limit to 256
int bbs = blasbatchsize;
if (file_format != FileFormat::GGML && file_format != FileFormat::GGHF && file_format != FileFormat::GGJT && file_format != FileFormat::GGJT_2 && file_format != FileFormat::GGJT_3)
{
bbs = (blasbatchsize > 256 ? 256 : blasbatchsize);
}
params.n_batch = bbs; //received reports of 1024 and above crashing on some models
if(!ggml_cpu_has_gpublas())
{
params.n_threads = 1; //do not limit here anymore.
}
else
{
params.n_threads = n_blasthreads;
}
}
current_context_tokens.resize(n_past);
remaining_tokens = params.n_predict;
stopper_unused_tokens = 0;
int input_consumed = 0;
std::mt19937 rng(params.seed);
concat_output = "";
bool startedsampling = false;
bool use_scratch = true; //for normal inference always use scratch
timer_start();
double time1 = 0, time2 = 0;
int32_t n_vocab = 0;
if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2)
{
n_vocab = llama_v2_n_vocab(llama_ctx_v2);
}
else if(file_format == FileFormat::GGJT_3)
{
n_vocab = llama_n_vocab(llama_ctx_v3);
}
else if (file_format == FileFormat::GPTJ_1 || file_format == FileFormat::GPTJ_2)
{
n_vocab = gptj_ctx_v1.hparams.n_vocab;
}
else if(file_format == FileFormat::GPTJ_3 || file_format==FileFormat::GPTJ_4)
{
n_vocab = gptj_ctx_v2.hparams.n_vocab;
}
else if(file_format==FileFormat::GPTJ_5)
{
n_vocab = gptj_ctx_v3.hparams.n_vocab;
}
else if(file_format == FileFormat::GPT2_1)
{
n_vocab = gpt2_ctx_v1.hparams.n_vocab;
}
else if(file_format == FileFormat::GPT2_2 || file_format==FileFormat::GPT2_3)
{
n_vocab = gpt2_ctx_v2.hparams.n_vocab;
}
else if(file_format==FileFormat::GPT2_4)
{
n_vocab = gpt2_ctx_v3.hparams.n_vocab;
}
else if(file_format == FileFormat::NEOX_1 || file_format == FileFormat::NEOX_2 || file_format == FileFormat::NEOX_3 || file_format==FileFormat::NEOX_4 || file_format==FileFormat::NEOX_5)
{
n_vocab = neox_ctx_v2.hparams.n_vocab;
}
else if( file_format==FileFormat::NEOX_6|| file_format==FileFormat::NEOX_7)
{
n_vocab = neox_ctx_v3.hparams.n_vocab;
}
else if( file_format==FileFormat::MPT_1)
{
n_vocab = mpt_ctx_v3.hparams.n_vocab;
}
else if(file_format == FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2)
{
n_vocab = vocab.id_to_token.size(); //handled seperately
if(n_past==0)
{
if(file_format == FileFormat::RWKV_1)
{
rwkv_ctx_v2->state_in = nullptr;
}
else
{
rwkv_ctx_v3->state_in = nullptr;
}
}
else
{
if (file_format == FileFormat::RWKV_1)
{
rwkv_ctx_v2->state_in = rwkv_ctx_v2->state_out;
}
else
{
rwkv_ctx_v3->state_in = rwkv_ctx_v3->state_out;
}
//if it's empty, push in the final previous token
if(embd_inp.size()==0 && current_context_tokens.size()>0)
{
embd_inp.push_back(current_context_tokens[current_context_tokens.size()-1]);
current_context_tokens.pop_back();
}
}
}
else
{
printf("Bad format!");
}
if(debugmode!=-1)
{
printf("\n");
}
if (debugmode==1)
{
std::string outstr = "";
printf("\n[Debug: Dump Input Tokens, format: %d]\n", file_format);
std::string tmp = "";
for (auto id : embd_inp)
{
tmp += "'" + FileFormatTokenizeID(id, file_format) + " (" + std::to_string(id) + ")', ";
}
::utreplace(tmp, "\n", "\\n");
outstr += tmp;
outstr += "\n\n[Debug: Context Size = " + std::to_string(current_context_tokens.size()) + "]\n";
tmp = "";
for (auto id : current_context_tokens)
{
tmp += "'" + FileFormatTokenizeID(id, file_format) + " (" + std::to_string(id) + ")', ";
}
::utreplace(tmp, "\n", "\\n");
outstr += tmp;
printf("%s\n\n", outstr.c_str());
}
while (remaining_tokens > 0)
{
gpt_vocab::id id = 0;
// predict
unsigned int embdsize = embd.size();
//print progress
if (!startedsampling && debugmode!=-1)
{
printf("\rProcessing Prompt%s (%d / %d tokens)", (blasmode ? " [BLAS]" : ""), input_consumed, embd_inp.size());
}
fflush(stdout);
if (embdsize > 0)
{
bool evalres = false;
if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2)
{
evalres = (llama_v2_eval(llama_ctx_v2, embd.data(), embdsize, n_past, params.n_threads)==0);
}
else if(file_format == FileFormat::GGJT_3)
{
evalres = (llama_eval(llama_ctx_v3, embd.data(), embdsize, n_past, params.n_threads)==0);
}
else if(file_format==FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2)
{
if (file_format == FileFormat::RWKV_1)
{
evalres = rwkv_v2_eval(rwkv_ctx_v2, embd[0], rwkv_ctx_v2->state_in, rwkv_ctx_v2->state_out, rwkv_ctx_v2->logits_out);
memcpy(logits.data(), rwkv_ctx_v2->logits_out, sizeof(float) * rwkv_vocab.size());
rwkv_ctx_v2->state_in = rwkv_ctx_v2->state_out;
}
else
{
if(embd.size()>1)
{
evalres = rwkv_eval_sequence(rwkv_ctx_v3, (uint32_t*)embd.data(), embd.size(), rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out);
}
else
{
evalres = rwkv_eval(rwkv_ctx_v3, embd[0], rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out);
}
memcpy(logits.data(), rwkv_ctx_v3->logits_out, sizeof(float) * rwkv_vocab.size());
rwkv_ctx_v3->state_in = rwkv_ctx_v3->state_out;
}
}
else if(file_format==FileFormat::GPT2_1)
{
evalres = legacy_gpt2_eval(gpt2_ctx_v1, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
}
else if(file_format==FileFormat::GPT2_2 || file_format==FileFormat::GPT2_3)
{
evalres = gpt2_v2_eval(gpt2_ctx_v2, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
}
else if(file_format==FileFormat::GPT2_4)
{
evalres = gpt2_eval(gpt2_ctx_v3, params.n_threads, n_past, embd, logits, mem_per_token, use_scratch);
}
else if(file_format==FileFormat::NEOX_1 || file_format == FileFormat::NEOX_2 || file_format == FileFormat::NEOX_3 || file_format==FileFormat::NEOX_4 || file_format==FileFormat::NEOX_5)
{
evalres = gpt_neox_v2_eval(neox_ctx_v2, params.n_threads, n_past, embd, logits, mem_per_token);
}
else if(file_format==FileFormat::NEOX_6|| file_format==FileFormat::NEOX_7)
{
evalres = gpt_neox_eval(neox_ctx_v3, params.n_threads, n_past, embd, logits, mem_per_token, use_scratch);
}
else if(file_format==FileFormat::GPTJ_1 || file_format==FileFormat::GPTJ_2)
{
evalres = legacy_gptj_eval(gptj_ctx_v1, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
}
else if(file_format==FileFormat::GPTJ_3 || file_format==FileFormat::GPTJ_4)
{
evalres = gptj_v2_eval(gptj_ctx_v2, params.n_threads, n_past, embd, logits, mem_per_token);
}
else if(file_format==FileFormat::GPTJ_5)
{
evalres = gptj_eval(gptj_ctx_v3, params.n_threads, n_past, embd, logits, mem_per_token, use_scratch);
}
else if(file_format==FileFormat::MPT_1)
{
evalres = mpt_eval(mpt_ctx_v3, params.n_threads, n_past, embd, logits, false, mem_per_token, use_scratch);
}
else
{
printf("\nCannot find eval function\n");
}
if (!evalres)
{
fprintf(stderr, "Failed to predict\n");
snprintf(output.text, sizeof(output.text), "%s", "");
output.status = 0;
generation_finished = true;
return output;
}
}
n_past += embd.size();
embd.clear();
if ((int)embd_inp.size() <= input_consumed)
{
// out of user input, sample next token
const float top_k = params.top_k;
const float top_p = params.top_p;
const float temp = params.temp;
const float top_a = inputs.top_a;
const float repeat_penalty = params.repeat_penalty;
const float typical_p = params.typical_p;
const float tfs_z = params.tfs_z;
if (!startedsampling)
{
startedsampling = true;
params.n_batch = original_batch;
params.n_threads = original_threads;
time1 = timer_check();
timer_start();
if(debugmode!=-1)
{
printf("\n");
}
}
unsigned int eosID = 0;
float * logitsPtr;
if(file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2 || file_format == FileFormat::GGJT_3)
{
if(file_format == FileFormat::GGJT_3)
{
logitsPtr = llama_get_logits(llama_ctx_v3);
}
else
{
logitsPtr = llama_v2_get_logits(llama_ctx_v2);
}
eosID = llama_token_eos();
if (!unbanTokens)
{
// set the logit of the eos token (2) to zero to avoid sampling it
logitsPtr[eosID] = 0;
}
}
else
{
logitsPtr = logits.data();
if (!unbanTokens)
{
//gpt2 uses negative logits, so we cant zero it
// set the logit of the eos token to minimum to avoid sampling it
if (file_format == FileFormat::GPT2_1 ||
file_format == FileFormat::GPT2_2 ||
file_format == FileFormat::GPT2_3 ||
file_format == FileFormat::GPT2_4 ||
file_format == FileFormat::GPTJ_1 ||
file_format == FileFormat::GPTJ_2 ||
file_format == FileFormat::GPTJ_3 ||
file_format == FileFormat::GPTJ_4 ||
file_format == FileFormat::GPTJ_5)
{
eosID = 50256;
if(logits.size() > eosID)
{
int topid = std::min_element(logits.begin(),logits.end())-logits.begin();
logits[eosID] = (logits[topid] < 0 ? logits[topid] : 0);
}
else
{
//special case, starcoder models use ID 0 for EOS
if (file_format == FileFormat::GPT2_3 || file_format == FileFormat::GPT2_4)
{
eosID = 0;
int topid = std::min_element(logits.begin(), logits.end()) - logits.begin();
logits[eosID] = (logits[topid] < 0 ? logits[topid] : 0);
}
}
}
// set the logit of the eos token (0) to minimum to avoid sampling it
if (file_format == FileFormat::RWKV_1 ||
file_format == FileFormat::RWKV_2 ||
file_format == FileFormat::NEOX_1 ||
file_format == FileFormat::NEOX_2 ||
file_format == FileFormat::NEOX_3 ||
file_format == FileFormat::NEOX_4 ||
file_format == FileFormat::NEOX_5 ||
file_format == FileFormat::NEOX_6 ||
file_format == FileFormat::NEOX_7 ||
file_format == FileFormat::MPT_1)
{
eosID = 0;
int topid = std::min_element(logits.begin(),logits.end())-logits.begin();
logits[eosID] = (logits[topid] < 0 ? logits[topid] : 0);
}
}
}
id = SampleLogits(logitsPtr, nctx, n_vocab, last_n_size, repeat_penalty,
top_k, top_a, top_p, typical_p, tfs_z, temp, rng,
params.mirostat,params.mirostat_tau,params.mirostat_eta);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
current_context_tokens.push_back(id);
// add it to the context
embd.push_back(id);
// decrement remaining sampling budget
--remaining_tokens;
for (auto id : embd)
{
std::string tokenizedstr = FileFormatTokenizeID(id, file_format);
if(stream_sse)
{
generated_tokens.push_back(tokenizedstr);
}
concat_output += tokenizedstr;
}
if (startedsampling && debugmode!=-1)
{
printf("\rGenerating (%d / %d tokens)", (params.n_predict - remaining_tokens), params.n_predict);
}
if(debugmode==1 && top_picks.size()>0)
{
printf(" [");
bool firstloop = true;
for (auto & pick : top_picks)
{
if (!firstloop)
{
printf(" ");
}
firstloop = false;
std::string tokenizedstr = FileFormatTokenizeID(pick.id, file_format);
::utreplace(tokenizedstr, "\n", "\\n");
printf("(%s %.2f%%)", tokenizedstr.c_str(), pick.p*100);
}
printf("]\n");
}
if(unbanTokens && id==eosID)
{
stopper_unused_tokens = remaining_tokens;
printf("\n(EOS token triggered!)");
remaining_tokens = 0;
}
for (const auto &matched : stop_sequence)
{
if (concat_output.find(matched) != std::string::npos)
{
stopper_unused_tokens = remaining_tokens;
remaining_tokens = 0;
if(debugmode!=-1)
{
printf("\n(Stop sequence triggered: <%s>)", matched.c_str());
}
break;
}
}
fflush(stdout);
}
else
{
// some user input remains from prompt or interaction, forward it to processing
while ((int)embd_inp.size() > input_consumed)
{
embd.push_back(embd_inp[input_consumed]);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(embd_inp[input_consumed]);
current_context_tokens.push_back(embd_inp[input_consumed]);
++input_consumed;
if ((int)embd.size() >= params.n_batch)
{
break;
}
}
}
}
time2 = timer_check();
float pt1 = (time1*1000.0/(embd_inp.size()==0?1:embd_inp.size()));
int realnpredict = params.n_predict-stopper_unused_tokens;
float pt2 = (time2*1000.0/(realnpredict==0?1:realnpredict));
float tokens_per_second = (realnpredict == 0 ? 0 : realnpredict / (time1 + time2));
printf("\nTime Taken - Processing:%.1fs (%.0fms/T), Generation:%.1fs (%.0fms/T), Total:%.1fs (%.1fT/s)", time1, pt1, time2, pt2, (time1 + time2), tokens_per_second);
fflush(stdout);
output.status = 1;
generation_finished = true;
snprintf(output.text, sizeof(output.text), "%s", concat_output.c_str());
return output;
}
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