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merged llama adapter into the rest of the gpt adapters

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This commit is contained in:
Concedo 2023-04-21 17:47:48 +08:00
parent 82d74ca1a6
commit 5160053e51
4 changed files with 172 additions and 366 deletions
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13
Makefile
View file

@ -234,9 +234,6 @@ common.o: examples/common.cpp examples/common.h
expose.o: expose.cpp expose.h
$(CXX) $(CXXFLAGS) -c $< -o $@
llama_adapter.o: llama_adapter.cpp
$(CXX) $(CXXFLAGS) -c $< -o $@
gpttype_adapter.o: gpttype_adapter.cpp
$(CXX) $(CXXFLAGS) -c $< -o $@
@ -249,19 +246,19 @@ main: examples/main/main.cpp ggml.o llama.o common.o $(OBJS)
@echo '==== Run ./main -h for help. ===='
@echo
koboldcpp: ggml.o ggml_rwkv.o ggml_v1.o expose.o common.o llama_adapter.o gpttype_adapter.o
koboldcpp: ggml.o ggml_rwkv.o ggml_v1.o expose.o common.o gpttype_adapter.o
$(DEFAULT_BUILD)
koboldcpp_openblas: ggml_openblas.o ggml_rwkv.o ggml_v1.o expose.o common.o llama_adapter.o gpttype_adapter.o
koboldcpp_openblas: ggml_openblas.o ggml_rwkv.o ggml_v1.o expose.o common.o gpttype_adapter.o
$(OPENBLAS_BUILD)
koboldcpp_noavx2: ggml_noavx2.o ggml_rwkv.o ggml_v1_noavx2.o expose.o common.o llama_adapter.o gpttype_adapter.o
koboldcpp_noavx2: ggml_noavx2.o ggml_rwkv.o ggml_v1_noavx2.o expose.o common.o gpttype_adapter.o
$(NOAVX2_BUILD)
koboldcpp_openblas_noavx2: ggml_openblas_noavx2.o ggml_rwkv.o ggml_v1_noavx2.o expose.o common.o llama_adapter.o gpttype_adapter.o
koboldcpp_openblas_noavx2: ggml_openblas_noavx2.o ggml_rwkv.o ggml_v1_noavx2.o expose.o common.o gpttype_adapter.o
$(OPENBLAS_NOAVX2_BUILD)
koboldcpp_clblast: ggml_clblast.o ggml_rwkv.o ggml_v1.o expose.o common.o llama_adapter.o gpttype_adapter.o
koboldcpp_clblast: ggml_clblast.o ggml_rwkv.o ggml_v1.o expose.o common.o gpttype_adapter.o
$(CLBLAST_BUILD)
quantize_llama: examples/quantize/quantize.cpp ggml.o llama.o

20
expose.cpp
View file

@ -118,20 +118,20 @@ extern "C"
else
{
printf("\n---\nIdentified as LLAMA model: (ver %d)\nAttempting to Load...\n---\n", file_format);
return llama_load_model(inputs, file_format);
ModelLoadResult lr = gpttype_load_model(inputs, file_format);
if (lr == ModelLoadResult::FAIL || lr == ModelLoadResult::RETRY_LOAD)
{
return false;
}
else
{
return true;
}
}
}
generation_outputs generate(const generation_inputs inputs, generation_outputs &output)
{
if (file_format == FileFormat::GPTJ_1 || file_format == FileFormat::GPTJ_2 || file_format==FileFormat::GPTJ_3
|| file_format==FileFormat::GPT2_1 || file_format==FileFormat::GPT2_2 || file_format==FileFormat::RWKV_1)
{
return gpttype_generate(inputs, output);
}
else
{
return llama_generate(inputs, output);
}
return gpttype_generate(inputs, output);
}
}

225
gpttype_adapter.cpp
View file

@ -11,6 +11,9 @@
#include "model_adapter.h"
#include "otherarch/otherarch.h"
//for easier compilation
#include "llamaextra.cpp"
//concat source files into one file for compilation purposes
#include "otherarch/utils.cpp"
#include "otherarch/gptj_v1.cpp"
@ -21,12 +24,16 @@
//return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt)
static FileFormat file_format = FileFormat::BADFORMAT;
static gpt_vocab vocab;
static gptj_model_v1 model_v1;
static gptj_model model_v2;
static gpt2_v1_model model_gpt2_v1;
static gpt2_model model_gpt2_v2;
static rwkv_context * rwkv_context_v1;
static gptj_model_v1 gptj_ctx_v1;
static gptj_model gptj_ctx_v2;
static gpt2_v1_model gpt2_ctx_v1;
static gpt2_model gpt2_ctx_v2;
static rwkv_context * rwkv_ctx_v1;
static llama_context_params llama_ctx_params;
static llama_context * llama_ctx_v1;
static gpt_params params;
static int n_past = 0;
static int n_threads = 4;
@ -59,21 +66,52 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
blasbatchsize = inputs.blasbatchsize;
params.memory_f16 = inputs.f16_kv;
params.n_ctx = inputs.max_context_length;
model_v1.hparams.n_ctx = model_v2.hparams.n_ctx = model_gpt2_v1.hparams.n_ctx = model_gpt2_v2.hparams.n_ctx = params.n_ctx;
gptj_ctx_v1.hparams.n_ctx = gptj_ctx_v2.hparams.n_ctx = gpt2_ctx_v1.hparams.n_ctx = gpt2_ctx_v2.hparams.n_ctx = params.n_ctx;
if (file_format == FileFormat::RWKV_1)
printf("System Info: %s\n", llama_print_system_info());
if(file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT)
{
rwkv_context_v1 = rwkv_init_from_file(modelname.c_str(), n_threads);
llama_ctx_params = llama_context_default_params();
llama_ctx_params.n_ctx = inputs.max_context_length;
llama_ctx_params.n_parts = -1;//inputs.n_parts_overwrite;
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 = false;
llama_ctx_v1 = llama_init_from_file(modelname.c_str(), llama_ctx_params);
if (llama_ctx_v1 == NULL)
{
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, modelname.c_str());
return ModelLoadResult::FAIL;
}
if (file_format < FileFormat::GGJT)
{
printf("\n---\nWarning: Your model has an INVALID or OUTDATED format (ver %d). Please reconvert it for better results!\n---\n", file_format);
}
//determine mem per token
const std::vector<int> tmp = {0, 1, 2, 3};
llama_eval(llama_ctx_v1, tmp.data(), tmp.size(), 0, params.n_threads);
return ModelLoadResult::SUCCESS;
}
else if (file_format == FileFormat::RWKV_1)
{
rwkv_ctx_v1 = rwkv_init_from_file(modelname.c_str(), n_threads);
//setup buffers for rwkv state
auto padding = 512u;
auto statebufsiz = rwkv_get_state_buffer_element_count(rwkv_context_v1) * sizeof(float) + padding;
auto logitbufsiz = rwkv_get_logits_buffer_element_count(rwkv_context_v1) * sizeof(float) + padding;
auto statebufsiz = rwkv_get_state_buffer_element_count(rwkv_ctx_v1) * sizeof(float) + padding;
auto logitbufsiz = rwkv_get_logits_buffer_element_count(rwkv_ctx_v1) * sizeof(float) + padding;
printf("\nRWKV Init: State Buffer:%u, Logit Buffer:%u\n", statebufsiz, logitbufsiz);
rwkv_context_v1->state_out = (float *)malloc(statebufsiz);
rwkv_context_v1->logits_out = (float *)malloc(logitbufsiz);
rwkv_context_v1->state_in = nullptr;
rwkv_ctx_v1->state_out = (float *)malloc(statebufsiz);
rwkv_ctx_v1->logits_out = (float *)malloc(logitbufsiz);
rwkv_ctx_v1->state_in = nullptr;
n_batch = 1;
std::string word;
@ -87,15 +125,15 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
}
printf("\nRWKV Vocab: %u\n",vocabsiz);
bool testeval = rwkv_eval(rwkv_context_v1, 0, rwkv_context_v1->state_in, rwkv_context_v1->state_out, rwkv_context_v1->logits_out);
bool testeval = rwkv_eval(rwkv_ctx_v1, 0, rwkv_ctx_v1->state_in, rwkv_ctx_v1->state_out, rwkv_ctx_v1->logits_out);
if(!testeval)
{
printf("\nError: RWKV Init Eval Failed!\n");
}
logits.resize(vocabsiz);
memcpy(logits.data(), rwkv_context_v1->logits_out, sizeof(float)*vocabsiz);
memcpy(logits.data(), rwkv_ctx_v1->logits_out, sizeof(float)*vocabsiz);
if (rwkv_context_v1 == NULL)
if (rwkv_ctx_v1 == NULL)
{
return ModelLoadResult::FAIL;
}
@ -103,7 +141,7 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
}
else if (file_format == FileFormat::GPT2_1)
{
ModelLoadResult res = legacy_gpt2_model_load(params.model, model_gpt2_v1, vocab, file_format);
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());
@ -115,12 +153,12 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
return res;
}
// determine the required inference memory per token:
legacy_gpt2_eval(model_gpt2_v1, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);
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)
{
ModelLoadResult res = gpt2_model_load(params.model, model_gpt2_v2, vocab, file_format);
ModelLoadResult res = gpt2_model_load(params.model, gpt2_ctx_v2, vocab, file_format);
if(res==ModelLoadResult::FAIL)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
@ -132,12 +170,12 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
return res;
}
// determine the required inference memory per token:
gpt2_eval(model_gpt2_v2, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);
gpt2_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, model_v1, vocab, file_format);
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());
@ -149,13 +187,13 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
return res;
}
// determine the required inference memory per token:
legacy_gptj_eval(model_v1, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);
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, 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(model_v1.ctx);
ggml_v1_free(gptj_ctx_v1.ctx);
return ModelLoadResult::RETRY_LOAD;
}
@ -163,7 +201,7 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
}
else
{
ModelLoadResult loadresult = gptj_model_load(params.model, model_v2, vocab);
ModelLoadResult loadresult = gptj_model_load(params.model, gptj_ctx_v2, vocab);
if (loadresult == ModelLoadResult::FAIL)
{
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
@ -176,14 +214,14 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
}
// determine the required inference memory per token:
gptj_eval(model_v2, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
gptj_eval(gptj_ctx_v2, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
//if the logits are NAN, it means the model is incompatible
if(logits.size()>0 && IsNanCheck(logits[0]))
{
printf("\nBad Logits detected! Retrying GPT-J model loading...");
ggml_free(model_v2.ctx);
ggml_free(gptj_ctx_v2.ctx);
return ModelLoadResult::RETRY_LOAD;
}
@ -229,17 +267,35 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o
{
params.seed = time(NULL);
}
// tokenize the prompt
std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
std::vector<int> embd_inp;
if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT)
{
params.prompt.insert(0, 1, ' ');
if (file_format == FileFormat::GGML)
{
embd_inp = ::legacy_llama_tokenize(llama_ctx_v1, params.prompt, true);
}
else
{
embd_inp = ::llama_tokenize(llama_ctx_v1, 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<llama_token>(embd_inp.begin() + offset, embd_inp.end());
embd_inp = std::vector<int>(embd_inp.begin() + offset, embd_inp.end());
}
//determine how much npast we have to rewind from the current state
@ -261,7 +317,11 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o
}
//if using BLAS and prompt is big enough, switch to single thread and use a huge batch
bool approved_format = (file_format==FileFormat::GPT2_2 || file_format==FileFormat::GPTJ_3);
bool approved_format = (file_format == FileFormat::GGML ||
file_format == FileFormat::GGHF ||
file_format == FileFormat::GGJT ||
file_format == FileFormat::GPT2_2 ||
file_format == FileFormat::GPTJ_3);
bool blasmode = (approved_format && embd_inp.size() >= 32 && ggml_cpu_has_blas());
// bool blasmode = false;
int original_batch = params.n_batch;
@ -269,7 +329,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o
if (blasmode)
{
//for gpttype, GPT2 crashes above 256.
int bbs = (blasbatchsize>256?256:blasbatchsize);
int bbs = blasbatchsize; //(blasbatchsize>256?256:blasbatchsize);
params.n_batch = bbs; //received reports of 1024 and above crashing on some models
params.n_threads = 1;
}
@ -286,34 +346,38 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o
timer_start();
double time1 = 0, time2 = 0;
unsigned int embd_inp_size = embd_inp.size();
int32_t n_vocab = 0;
if(file_format == FileFormat::GPTJ_1||file_format == FileFormat::GPTJ_2)
if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT)
{
n_vocab = model_v1.hparams.n_vocab;
//do nothing
}
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)
{
n_vocab = model_v2.hparams.n_vocab;
n_vocab = gptj_ctx_v2.hparams.n_vocab;
}
else if(file_format == FileFormat::GPT2_1)
{
n_vocab = model_gpt2_v1.hparams.n_vocab;
n_vocab = gpt2_ctx_v1.hparams.n_vocab;
}
else if(file_format == FileFormat::GPT2_2)
{
n_vocab = model_gpt2_v2.hparams.n_vocab;
n_vocab = gpt2_ctx_v2.hparams.n_vocab;
}
else if(file_format == FileFormat::RWKV_1)
{
n_vocab = vocab.id_to_token.size(); //handled seperately
if(n_past==0)
{
rwkv_context_v1->state_in = nullptr;
rwkv_ctx_v1->state_in = nullptr;
}
else
{
rwkv_context_v1->state_in = rwkv_context_v1->state_out;
rwkv_ctx_v1->state_in = rwkv_ctx_v1->state_out;
//if it's empty, push in the final previous token
if(embd_inp.size()==0 && current_context_tokens.size()>0)
{
@ -338,36 +402,40 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o
//print progress
if (!startedsampling)
{
printf("\rProcessing Prompt%s (%d / %d tokens)", (blasmode ? " [BLAS]" : ""), input_consumed, embd_inp_size);
printf("\rProcessing Prompt%s (%d / %d tokens)", (blasmode ? " [BLAS]" : ""), input_consumed, embd_inp.size());
}
else
{
printf("\rGenerating (%d / %d tokens)", (1 + params.n_predict - remaining_tokens), params.n_predict);
}
bool evalres = false;
if(file_format==FileFormat::RWKV_1)
if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT)
{
evalres = rwkv_eval(rwkv_context_v1, embd[0], rwkv_context_v1->state_in, rwkv_context_v1->state_out, rwkv_context_v1->logits_out);
memcpy(logits.data(), rwkv_context_v1->logits_out, sizeof(float)*rwkv_vocab.size());
rwkv_context_v1->state_in = rwkv_context_v1->state_out;
evalres = (llama_eval(llama_ctx_v1, embd.data(), embdsize, n_past, params.n_threads)==0);
}
else if(file_format==FileFormat::RWKV_1)
{
evalres = rwkv_eval(rwkv_ctx_v1, embd[0], rwkv_ctx_v1->state_in, rwkv_ctx_v1->state_out, rwkv_ctx_v1->logits_out);
memcpy(logits.data(), rwkv_ctx_v1->logits_out, sizeof(float)*rwkv_vocab.size());
rwkv_ctx_v1->state_in = rwkv_ctx_v1->state_out;
}
else if(file_format==FileFormat::GPT2_1)
{
evalres = legacy_gpt2_eval(model_gpt2_v1, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
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)
{
evalres = gpt2_eval(model_gpt2_v2, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
evalres = gpt2_eval(gpt2_ctx_v2, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
}
else if(file_format==FileFormat::GPTJ_1 || file_format==FileFormat::GPTJ_2)
{
evalres = legacy_gptj_eval(model_v1, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
evalres = legacy_gptj_eval(gptj_ctx_v1, params.n_threads, n_past, embd, logits, mem_per_token, file_format);
}
else
{
evalres = gptj_eval(model_v2, params.n_threads, n_past, embd, logits, mem_per_token);
evalres = gptj_eval(gptj_ctx_v2, params.n_threads, n_past, embd, logits, mem_per_token);
}
if (!evalres)
{
@ -398,38 +466,59 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o
printf("\n");
}
if(file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT)
{
auto logits = llama_get_logits(llama_ctx_v1);
// set the logit of the eos token (2) to zero to avoid sampling it
logits[llama_token_eos()] = 0;
//set logits of opening square bracket to zero.
logits[518] = 0;
logits[29961] = 0;
id = llama_sample_top_p_top_k(llama_ctx_v1, last_n_tokens.data(), last_n_tokens.size(), top_k, top_p, temp, repeat_penalty);
}
else
{
// set the logit of the eos token (2) to zero to avoid sampling it
if(logits.size()>50256)
{
logits[50256] = (logits[50256]<0?logits[50256]:0);
{
logits[50256] = (logits[50256] < 0 ? logits[50256] : 0);
}
//gpt2 uses negative logits, so we cant zero it
id = gptj_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_k, top_p, temp, rng);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
current_context_tokens.push_back(id);
}
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)
if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT)
{
concat_output += vocab.id_to_token[id].c_str();
for (const auto &matched : stop_sequence)
concat_output += llama_token_to_str(llama_ctx_v1, id);
}
else
{
for (auto id : embd)
{
if (concat_output.find(matched) != std::string::npos)
{
stopper_unused_tokens = remaining_tokens;
remaining_tokens = 0;
printf("\n(Stop sequence triggered: <%s>)",matched.c_str());
break;
}
concat_output += vocab.id_to_token[id].c_str();
}
}
for (const auto &matched : stop_sequence)
{
if (concat_output.find(matched) != std::string::npos)
{
stopper_unused_tokens = remaining_tokens;
remaining_tokens = 0;
printf("\n(Stop sequence triggered: <%s>)", matched.c_str());
break;
}
}
}
@ -451,7 +540,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o
}
}
time2 = timer_check();
float pt1 = (time1*1000.0/(embd_inp_size==0?1:embd_inp_size));
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));
printf("\nTime Taken - Processing:%.1fs (%.0fms/T), Generation:%.1fs (%.0fms/T), Total:%.1fs", time1, pt1, time2, pt2, (time1 + time2));

280
llama_adapter.cpp
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@ -1,280 +0,0 @@
//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.
// Defines sigaction on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif
#include <time.h>
#include "./examples/main/main.cpp"
#include "ggml.h"
#include "model_adapter.h"
//for easier compilation
#include "llamaextra.cpp"
//return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt)
static FileFormat file_format = FileFormat::BADFORMAT;
static llama_context_params ctx_params;
static gpt_params params;
static int n_past = 0;
static int n_threads = 4;
static int n_batch = 8;
static bool useSmartContext = false;
static int blasbatchsize = 512;
static std::string modelname;
static llama_context *ctx;
static std::vector<llama_token> last_n_tokens;
static std::vector<llama_token> current_context_tokens;
static std::vector<llama_token> smartcontext;
static std::vector<std::string> stop_sequence;
bool llama_load_model(const load_model_inputs inputs, FileFormat in_file_format)
{
printf("System Info: %s\n", llama_print_system_info());
ctx_params = llama_context_default_params();
n_threads = inputs.threads;
n_batch = inputs.batch_size;
modelname = inputs.model_filename;
useSmartContext = inputs.use_smartcontext;
blasbatchsize = inputs.blasbatchsize;
ctx_params.n_ctx = inputs.max_context_length;
ctx_params.n_parts = -1;//inputs.n_parts_overwrite;
ctx_params.seed = -1;
ctx_params.f16_kv = inputs.f16_kv;
ctx_params.logits_all = false;
ctx_params.use_mmap = inputs.use_mmap;
ctx_params.use_mlock = false;
file_format = in_file_format;
ctx = llama_init_from_file(modelname.c_str(), ctx_params);
if (ctx == NULL)
{
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, modelname.c_str());
return false;
}
if (file_format < FileFormat::GGJT)
{
printf("\n---\nWarning: Your model has an INVALID or OUTDATED format (ver %d). Please reconvert it for better results!\n---\n", file_format);
}
//determine mem per token
const std::vector<llama_token> tmp = {0, 1, 2, 3};
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
return true;
}
generation_outputs llama_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.temp = inputs.temperature;
params.repeat_last_n = inputs.rep_pen_range;
params.repeat_penalty = inputs.rep_pen;
params.n_ctx = inputs.max_context_length;
params.n_batch = n_batch;
params.n_threads = n_threads;
if (params.repeat_last_n < 1)
{
params.repeat_last_n = 1;
}
if (params.top_k < 1)
{
params.top_k = 300; //to disable top_k we actually need to increase this value to a very high number
}
if (params.seed <= 0)
{
params.seed = time(NULL);
}
params.prompt.insert(0, 1, ' ');
// tokenize the prompt
std::vector<llama_token> embd_inp;
if (file_format == 1)
{
embd_inp = ::legacy_llama_tokenize(ctx, params.prompt, true);
}
else
{
embd_inp = ::llama_tokenize(ctx, params.prompt, true);
}
//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<llama_token>(embd_inp.begin() + offset, embd_inp.end());
}
//determine how much npast we have to rewind from the current state
std::vector<llama_token> 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;
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 blasmode = (embd_inp.size() >= 32 && ggml_cpu_has_blas());
int original_batch = params.n_batch;
int original_threads = params.n_threads;
if (blasmode)
{
params.n_batch = blasbatchsize; //received reports of 1024 and above crashing on some models
params.n_threads = 1;
}
current_context_tokens.resize(n_past);
int remaining_tokens = params.n_predict;
int stopper_unused_tokens = 0;
int input_consumed = 0;
std::mt19937 rng(params.seed);
std::string concat_output = "";
bool startedsampling = false;
timer_start();
double time1 = 0, time2 = 0;
unsigned int embd_inp_size = embd_inp.size();
printf("\n");
while (remaining_tokens > 0)
{
llama_token id = 0;
// predict
unsigned int embdsize = embd.size();
if (embdsize > 0)
{
//print progress
if (!startedsampling)
{
printf("\rProcessing Prompt%s (%d / %d tokens)", (blasmode ? " [BLAS]" : ""), input_consumed, embd_inp_size);
}
else
{
printf("\rGenerating (%d / %d tokens)", (1 + params.n_predict - remaining_tokens), params.n_predict);
}
if (llama_eval(ctx, embd.data(), embdsize, n_past, params.n_threads))
{
fprintf(stderr, "Failed to predict\n");
snprintf(output.text, sizeof(output.text), "%s", "");
output.status = 0;
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 repeat_penalty = params.repeat_penalty;
if (!startedsampling)
{
startedsampling = true;
params.n_batch = original_batch;
params.n_threads = original_threads;
time1 = timer_check();
timer_start();
printf("\n");
}
{
auto logits = llama_get_logits(ctx);
// set the logit of the eos token (2) to zero to avoid sampling it
logits[llama_token_eos()] = 0;
//set logits of opening square bracket to zero.
logits[518] = 0;
logits[29961] = 0;
id = llama_sample_top_p_top_k(ctx, last_n_tokens.data(), last_n_tokens.size(), top_k, top_p, temp, repeat_penalty);
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;
//printf("\nid:%d word:%s\n",id,llama_token_to_str(ctx, id));
concat_output += llama_token_to_str(ctx, id);
for (const auto &matched : stop_sequence)
{
if (concat_output.find(matched) != std::string::npos)
{
stopper_unused_tokens = remaining_tokens;
remaining_tokens = 0;
printf("\n(Stop sequence triggered: <%s>)",matched.c_str());
break;
}
}
}
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));
printf("\nTime Taken - Processing:%.1fs (%.0fms/T), Generation:%.1fs (%.0fms/T), Total:%.1fs", time1, pt1, time2, pt2, (time1 + time2));
fflush(stdout);
output.status = 1;
snprintf(output.text, sizeof(output.text), "%s", concat_output.c_str());
return output;
}