vbatts/llama.cpp
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refactor the interface and fixed the styles

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This commit is contained in:
ningshanwutuobang 2023-06-25 01:21:54 +08:00
parent 53dfbbf553
commit 9d866118c4
11 changed files with 335 additions and 442 deletions
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10
Makefile
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@ -1,5 +1,5 @@
# Define the default target now so that it is always the first target
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple libembd_input.so embd_input_test
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple libembdinput.so embd-input-test
ifdef LLAMA_BUILD_SERVER
BUILD_TARGETS += server
@ -302,12 +302,12 @@ save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.
server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
libembd_input.so: examples/embd_input/embd_input.h examples/embd_input/embd_input_lib.cpp examples/embd_input/embd_input_test.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) --shared $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
libembdinput.so: examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) --shared $(CXXFLAGS) $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
embd_input_test: libembd_input.so examples/embd_input/embd_input_test.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.so,$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -Wl,-rpath=./ -lembd_input
embd-input-test: libembdinput.so examples/embd-input/embd-input-test.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.so,$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -lembdinput
train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

0
examples/embd_input/README.md → examples/embd-input/README.md
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218
examples/embd-input/embd-input-lib.cpp Normal file
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@ -0,0 +1,218 @@
// Defines sigaction on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif
#include "embd-input.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>
static llama_context ** g_ctx;
extern "C" {
struct MyModel* create_mymodel(int argc, char ** argv) {
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
return nullptr;
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed < 0) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
llama_init_backend();
llama_context * ctx;
g_ctx = &ctx;
// load the model and apply lora adapter, if any
ctx = llama_init_from_gpt_params(params);
if (ctx == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
return nullptr;
}
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
}
struct MyModel * ret = new MyModel();
ret->ctx = ctx;
ret->params = params;
ret->n_past = 0;
// printf("ctx: %d\n", ret->ctx);
return ret;
}
void free_mymodel(struct MyModel * mymodel) {
llama_context * ctx = mymodel->ctx;
llama_print_timings(ctx);
llama_free(ctx);
delete mymodel;
}
bool eval_float(void * model, float * input, int N){
MyModel * mymodel = (MyModel*)model;
llama_context * ctx = mymodel->ctx;
gpt_params params = mymodel->params;
int n_emb = llama_n_embd(ctx);
int n_past = mymodel->n_past;
int n_batch = N; // params.n_batch;
for (int i = 0; i < (int) N; i += n_batch) {
int n_eval = (int) N - i;
if (n_eval > n_batch) {
n_eval = n_batch;
}
if (llama_eval_embd(ctx, (input+i*n_emb), n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
n_past += n_eval;
}
mymodel->n_past = n_past;
return true;
}
bool eval_tokens(void * model, std::vector<llama_token> tokens) {
MyModel * mymodel = (MyModel* )model;
llama_context * ctx;
ctx = mymodel->ctx;
gpt_params params = mymodel->params;
int n_past = mymodel->n_past;
for (int i = 0; i < (int) tokens.size(); i += params.n_batch) {
int n_eval = (int) tokens.size() - i;
if (n_eval > params.n_batch) {
n_eval = params.n_batch;
}
if (llama_eval(ctx, &tokens[i], n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
n_past += n_eval;
}
mymodel->n_past = n_past;
return true;
}
bool eval_id(struct MyModel* mymodel, int id) {
std::vector<llama_token> tokens;
tokens.push_back(id);
return eval_tokens(mymodel, tokens);
}
bool eval_string(struct MyModel * mymodel,const char* str){
llama_context * ctx = mymodel->ctx;
std::string str2 = str;
std::vector<llama_token> embd_inp = ::llama_tokenize(ctx, str2, true);
eval_tokens(mymodel, embd_inp);
return true;
}
llama_token sampling_id(struct MyModel* mymodel) {
llama_context* ctx = mymodel->ctx;
gpt_params params = mymodel->params;
// int n_ctx = llama_n_ctx(ctx);
// out of user input, sample next token
const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
const float top_p = params.top_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
// const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
// const float repeat_penalty = params.repeat_penalty;
// const float alpha_presence = params.presence_penalty;
// const float alpha_frequency = params.frequency_penalty;
const int mirostat = params.mirostat;
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
// const bool penalize_nl = params.penalize_nl;
llama_token id = 0;
{
auto logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
// Apply params.logit_bias map
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
logits[it->first] += it->second;
}
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 };
// TODO: Apply penalties
// float nl_logit = logits[llama_token_nl()];
// auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
// llama_sample_repetition_penalty(ctx, &candidates_p,
// last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
// last_n_repeat, repeat_penalty);
// llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
// last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
// last_n_repeat, alpha_frequency, alpha_presence);
// if (!penalize_nl) {
// logits[llama_token_nl()] = nl_logit;
// }
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx, &candidates_p);
} else {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
llama_sample_typical(ctx, &candidates_p, typical_p, 1);
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token(ctx, &candidates_p);
}
}
}
return id;
}
const char * sampling(struct MyModel * mymodel) {
llama_context * ctx = mymodel->ctx;
int id = sampling_id(mymodel);
std::string ret;
if (id == llama_token_eos()) ret = "</s>";
else ret = llama_token_to_str(ctx, id);
eval_id(mymodel, id);
return ret.c_str();
}
}

13
examples/embd_input/embd_input_test.cpp → examples/embd-input/embd-input-test.cpp
View file

@ -1,4 +1,4 @@
#include "embd_input.h"
#include "embd-input.h"
#include <stdlib.h>
#include <random>
#include <string.h>
@ -7,7 +7,10 @@ int main(int argc, char** argv) {
auto mymodel = create_mymodel(argc, argv);
int N = 10;
int max_tgt_len = 500;
int n_embd = llama_n_embd(mymodel->ctx);
// add random float embd to test evaluation
float * data = new float[N*n_embd];
std::default_random_engine e;
std::uniform_real_distribution<float> u(0,1);
@ -16,19 +19,15 @@ int main(int argc, char** argv) {
}
eval_string(mymodel, "user: what is the color of the flag of UN?");
// printf("eval float");
eval_float(mymodel, data, N);
eval_string(mymodel, "assistant:");
// printf("eval float end\n");
eval_string(mymodel, mymodel->params.prompt.c_str());
const char* tmp;
for (int i=0;i < 500; i++) {
// int id = sampling_id(mymodel);
for (int i=0; i<max_tgt_len; i++) {
tmp = sampling(mymodel);
if (strcmp(tmp, "</s>")==0) break;
printf("%s", tmp); // llama_token_to_str(mymodel->ctx, id));
printf("%s", tmp);
fflush(stdout);
// eval_id(mymodel, id);
}
printf("\n");
free_mymodel(mymodel);

0
examples/embd_input/embd_input.h → examples/embd-input/embd-input.h
View file

58
examples/embd_input/embd_input.py → examples/embd-input/embd_input.py
View file

@ -1,8 +1,9 @@
import ctypes
from ctypes import cdll, c_char_p, c_void_p, POINTER, c_float, c_int
import numpy as np
import os
libc = cdll.LoadLibrary("./libembd_input.so")
libc = cdll.LoadLibrary("./libembdinput.so")
libc.sampling.restype=c_char_p
libc.create_mymodel.restype=c_void_p
libc.eval_string.argtypes=[c_void_p, c_char_p]
@ -16,7 +17,9 @@ class MyModel:
c_str = [c_char_p(i.encode()) for i in args]
args_c = (c_char_p * argc)(*c_str)
self.model = c_void_p(libc.create_mymodel(argc, args_c))
# print("self.model", self.model)
self.max_tgt_len = 512
self.print_string_eval = True
def __del__(self):
libc.free_mymodel(self.model)
@ -25,6 +28,8 @@ class MyModel:
def eval_string(self, x):
libc.eval_string(self.model, x.encode()) # c_char_p(x.encode()))
if self.print_string_eval:
print(x)
def eval_token(self, x):
libc.eval_id(self.model, x)
@ -33,49 +38,34 @@ class MyModel:
s = libc.sampling(self.model)
return s
def generate(self, end="</s>"):
ret = b""
end = end.encode()
for _ in range(500):
tmp = self.sampling() # .decode()
if (ret+tmp).endswith(end):
break
ret += tmp
return ret.decode()
def stream_generate(self, end="</s>"):
ret = b""
end = end.encode()
head = b""
for _ in range(500):
tmp = self.sampling() # .decode()
for _ in range(self.max_tgt_len):
tmp = self.sampling()
ret += tmp
try:
text = (head + tmp).decode()
print(text, end="")
head = b""
except:
head += text
yield tmp
if ret.endswith(end):
break
print("")
return ret.decode()
def generate_with_print(self, end="</s>"):
ret = b""
for i in self.stream_generate(end=end):
ret += i
print(i.decode(errors="replace"), end="", flush=True)
print("")
return ret.decode(errors="replace")
def generate(self, end="</s>"):
text = b"".join(self.stream_generate(end=end))
return text.decode(errors="replace")
if __name__ == "__main__":
model = MyModel(["main", "--model", "../llama.cpp/models/ggml-vic13b-q4_1.bin", "-c", "2048"])
# print(model)
model.eval_string("""user: what is the color of the flag of UN?""")
# model.eval_token(100)
x = np.random.random((5120,10))# , dtype=np.float32)
model.eval_float(x)
model.eval_string("""assistant:""")
# print(x[0,0], x[0,1],x[1,0])
# model.eval_float(x)
# print(libc)
for i in range(500):
tmp = model.sampling().decode()
if tmp == "":
break
print(tmp, end="", flush=True)
for i in model.generate():
print(i.decode(errors="replace"), end="", flush=True)

8
examples/embd_input/llava.py → examples/embd-input/llava.py
View file

@ -31,7 +31,7 @@ class Llava:
self.model.eval_string("user: ")
self.model.eval_string(question)
self.model.eval_string("\nassistant: ")
return self.model.generate()
return self.model.generate_with_print()
def chat_with_image(self, image, question):
with torch.no_grad():
@ -49,7 +49,7 @@ class Llava:
self.model.eval_token(32003-1) # im_end
self.model.eval_string(question)
self.model.eval_string("\nassistant: ")
return self.model.generate()
return self.model.generate_with_print()
if __name__=="__main__":
@ -63,8 +63,8 @@ if __name__=="__main__":
respose = a.chat_with_image(
Image.open("./media/llama1-logo.png").convert('RGB'),
"what is the text in the picture?")
print(respose)
print(a.chat("what is the color of it?"))
respose
a.chat("what is the color of it?")

38
examples/embd_input/panda_gpt.py → examples/embd-input/panda_gpt.py
View file

@ -7,11 +7,13 @@ from torch import nn
import torch
# use PandaGPT path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "PandaGPT","code","model"))
panda_gpt_path = os.path.join(os.path.dirname(__file__), "PandaGPT")
imagebind_ckpt_path = "./models/panda_gpt/"
sys.path.insert(0, os.path.join(panda_gpt_path,"code","model"))
from ImageBind.models import imagebind_model
from ImageBind import data
imagebind_ckpt_path = "./models/panda_gpt/"
ModalityType = imagebind_model.ModalityType
max_tgt_len = 400
@ -31,25 +33,25 @@ class PandaGPT:
"weight": state["llama_proj.weight"],
"bias": state["llama_proj.bias"]})
def eval_inputs(self, inputs):
self.model.eval_string("<Img>")
embds = self.extract_multimoal_feature(inputs)
for i in embds:
self.model.eval_float(i.T)
self.model.eval_string("</Img> ")
def chat(self, question):
if self.generated_text == "":
self.model.eval_string("###")
self.model.eval_string(" Human: ")
self.model.eval_string(question)
self.model.eval_string("\n### Assistant:")
ret = self.model.stream_generate(end="###")
self.generated_text += ret
return ret
return self.chat_with_image(None, question)
def chat_with_image(self, inputs, question):
if self.generated_text == "":
self.model.eval_string("###")
self.model.eval_string(" Human: <Img>")
embds = self.extract_multimoal_feature(inputs)
for i in embds:
self.model.eval_float(i.T)
self.model.eval_string("</Img> " + question + "\n### Assistant:")
ret = self.model.stream_generate(end="###")
self.model.eval_string(" Human: ")
if inputs:
self.eval_inputs(inputs)
self.model.eval_string(question)
self.model.eval_string("\n### Assistant:")
ret = self.model.generate_with_print(end="###")
self.generated_text += ret
return ret
@ -88,13 +90,9 @@ class PandaGPT:
if __name__=="__main__":
# model form liuhaotian/LLaVA-13b-delta-v1-1
a = PandaGPT(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048", "--lora", "./models/panda_gpt/ggml-adapter-model.bin","--temp", "0"])
# Extract from https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin.
# Also here can use pytorch_model-00003-of-00003.bin directly.
a.load_projection("./models/panda_gpt/adapter_model.bin")
a.chat_with_image(
{"image_paths": ["./media/llama1-logo.png"]},
"what is the text in the picture? 'llama' or 'lambda'?")
a.chat("what is the color of it?")

283
examples/embd_input/embd_input_lib.cpp
View file

@ -1,283 +0,0 @@
// Defines sigaction on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif
#include "embd_input.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined (_WIN32)
#define WIN32_LEAN_AND_MEAN
#define NOMINMAX
#include <windows.h>
#include <signal.h>
#endif
static console_state con_st;
static llama_context ** g_ctx;
static bool is_interacting = false;
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
void sigint_handler(int signo) {
if (signo == SIGINT) {
if (!is_interacting) {
is_interacting=true;
} else {
console_cleanup(con_st);
printf("\n");
llama_print_timings(*g_ctx);
_exit(130);
}
}
}
#endif
extern "C" {
struct MyModel* create_mymodel(int argc, char ** argv) {
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
return nullptr;
}
if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
"expect poor results\n", __func__, params.n_ctx);
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed < 0) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
llama_init_backend();
llama_context * ctx;
g_ctx = &ctx;
// load the model and apply lora adapter, if any
ctx = llama_init_from_gpt_params(params);
if (ctx == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
return nullptr;
}
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
}
struct MyModel* ret= new MyModel();
ret->ctx = ctx;
ret->params = params;
ret->n_past = 0;
// printf("ctx: %d\n", ret->ctx);
return ret;
}
void free_mymodel(struct MyModel* mymodel) {
llama_context* ctx = mymodel->ctx;
llama_print_timings(ctx);
llama_free(ctx);
delete mymodel;
}
bool eval_float(void* model, float* input, int N){
MyModel* mymodel = (MyModel* )model;
llama_context* ctx = mymodel->ctx;
gpt_params params = mymodel->params;
int n_emb = llama_n_embd(ctx);
int n_past = mymodel->n_past;
// printf("%f,%f\n", *input, *(input+1));
int n_batch = N; // params.n_batch;
for (int i = 0; i < (int) N; i += n_batch) {
int n_eval = (int) N - i;
if (n_eval > n_batch) {
n_eval = n_batch;
}
if (llama_eval_float(ctx, (input+i*n_emb), n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
n_past += n_eval;
}
mymodel->n_past = n_past;
return true;
}
bool eval_tokens(void* model, std::vector<llama_token> tokens) {
MyModel* mymodel = (MyModel* )model;
// printf("model: %d\n", mymodel);
llama_context* ctx;// = mymodel->ctx;
// printf("ctx2: %d\n", ctx);
// printf("ctx2: %d\n", mymodel->ctx);
ctx = mymodel->ctx;
// printf("ctx2: %d\n", ctx);
gpt_params params = mymodel->params;
// printf("\n%d\n", params);
int n_past = mymodel->n_past;
for (int i = 0; i < (int) tokens.size(); i += params.n_batch) {
int n_eval = (int) tokens.size() - i;
if (n_eval > params.n_batch) {
n_eval = params.n_batch;
}
// printf("%d, %d, %d\n", i, n_eval, n_past);
if (llama_eval(ctx, &tokens[i], n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
n_past += n_eval;
}
mymodel->n_past = n_past;
return true;
}
bool eval_id(struct MyModel* mymodel, int id) {
// printf("%d\n", id);
std::vector<llama_token> tokens;
tokens.push_back(id);
// printf("%d\n", tokens.size());
// printf("%d\n", tokens[0]);
return eval_tokens(mymodel, tokens);
}
bool eval_string(struct MyModel* mymodel,const char* str){
// std::cout << "eval " << std::endl;
// printf("%s", str);
llama_context* ctx = mymodel->ctx;
std::string str2 = str;
// printf("%s", str2.c_str());
std::cout << str2 << std::endl;
std::vector<llama_token> embd_inp = ::llama_tokenize(ctx, str2, true);
eval_tokens(mymodel, embd_inp);
return true;
}
llama_token sampling_id(struct MyModel* mymodel) {
llama_context* ctx = mymodel->ctx;
gpt_params params = mymodel->params;
// int n_ctx = llama_n_ctx(ctx);
// out of user input, sample next token
const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
const float top_p = params.top_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
// const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
// const float repeat_penalty = params.repeat_penalty;
// const float alpha_presence = params.presence_penalty;
// const float alpha_frequency = params.frequency_penalty;
const int mirostat = params.mirostat;
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
// const bool penalize_nl = params.penalize_nl;
llama_token id = 0;
{
auto logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
// Apply params.logit_bias map
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
logits[it->first] += it->second;
}
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
// float nl_logit = logits[llama_token_nl()];
// auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
// llama_sample_repetition_penalty(ctx, &candidates_p,
// last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
// last_n_repeat, repeat_penalty);
// llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
// last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
// last_n_repeat, alpha_frequency, alpha_presence);
// if (!penalize_nl) {
// logits[llama_token_nl()] = nl_logit;
// }
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx, &candidates_p);
} else {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
llama_sample_typical(ctx, &candidates_p, typical_p, 1);
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token(ctx, &candidates_p);
}
}
}
return id;
}
const char* sampling(struct MyModel* mymodel) {
llama_context* ctx = mymodel->ctx;
int id = sampling_id(mymodel);
std::string ret;
if (id == llama_token_eos()) ret = "</s>";
else ret = llama_token_to_str(ctx, id);
eval_id(mymodel, id);
return ret.c_str();
}
}

125
llama.cpp
View file

@ -1342,15 +1342,33 @@ static bool llama_model_load(
}
}
static bool llama_eval_internal_tensor(
// evaluate the transformer
//
// - lctx: llama context
// - tokens: new batch of tokens to process
// - n_tokens number of tokens
// - embd embeddings input
// - n_past: the context size so far
// - n_threads: number of threads to use
//
static bool llama_eval_internal(
llama_context & lctx,
ggml_context* ctx0,
ggml_tensor* inpL,
const llama_token * tokens,
const int n_tokens,
const float * embd,
const int n_past,
const int n_threads,
const char * cgraph_fname,
const int64_t t_start_us) {
const char * cgraph_fname) {
LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
// enforce that the first token is BOS
if (tokens && n_past == 0 && tokens[0] != llama_token_bos()) {
fprintf(stderr, "%s: first token must be BOS\n", __func__);
return false;
}
const int64_t t_start_us = ggml_time_us();
const int N = n_tokens;
@ -1359,7 +1377,6 @@ static bool llama_eval_internal_tensor(
const auto & kv_self = model.kv_self;
LLAMA_ASSERT(!!kv_self.ctx);
const int n_embd = hparams.n_embd;
@ -1371,6 +1388,15 @@ static bool llama_eval_internal_tensor(
const int n_gpu_layers = model.n_gpu_layers;
auto & mem_per_token = lctx.mem_per_token;
auto & buf_compute = lctx.buf_compute;
struct ggml_init_params params = {
/*.mem_size =*/ buf_compute.size,
/*.mem_buffer =*/ buf_compute.addr,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(params);
// for big prompts, if BLAS is enabled, it is better to use only one thread
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
@ -1378,6 +1404,17 @@ static bool llama_eval_internal_tensor(
gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
if (tokens) {
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
ggml_set_name(embd, "embd");
memcpy(embd->data, tokens, N*ggml_element_size(embd));
inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
} else {
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
}
const int i_gpu_start = n_layer - n_gpu_layers;
(void) i_gpu_start;
@ -1746,53 +1783,6 @@ static bool llama_eval_internal_tensor(
return true;
}
// evaluate the transformer
//
// - lctx: llama context
// - tokens: new batch of tokens to process
// - n_past: the context size so far
// - n_threads: number of threads to use
//
static bool llama_eval_internal(
llama_context & lctx,
const llama_token * tokens,
const int n_tokens,
const int n_past,
const int n_threads,
const char * cgraph_fname) {
// enforce that the first token is BOS
if (n_past == 0 && tokens[0] != llama_token_bos()) {
fprintf(stderr, "%s: first token must be BOS\n", __func__);
return false;
}
const auto & model = lctx.model;
const int64_t t_start_us = ggml_time_us();
const int N = n_tokens;
auto & buf_compute = lctx.buf_compute;
struct ggml_init_params params = {
/*.mem_size =*/ buf_compute.size,
/*.mem_buffer =*/ buf_compute.addr,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
ggml_set_name(embd, "embd");
memcpy(embd->data, tokens, N*ggml_element_size(embd));
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
return llama_eval_internal_tensor(lctx, ctx0, inpL, N, n_past, n_threads, cgraph_fname, t_start_us);
}
//
// tokenizer
//
@ -3357,7 +3347,7 @@ int llama_eval(
int n_tokens,
int n_past,
int n_threads) {
if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads, nullptr)) {
if (!llama_eval_internal(*ctx, tokens, n_tokens, nullptr, n_past, n_threads, nullptr)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
@ -3373,32 +3363,13 @@ int llama_eval(
}
int llama_eval_float(
int llama_eval_embd(
struct llama_context * ctx,
const float * input,
const float * embd,
int n_tokens,
int n_past,
int n_threads) {
const auto & model = ctx->model;
const int64_t t_start_us = ggml_time_us();
const int N = n_tokens;
auto & buf_compute = ctx->buf_compute;
struct ggml_init_params params = {
/*.mem_size =*/ buf_compute.size,
/*.mem_buffer =*/ buf_compute.addr,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_tensor *inpL =
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, model.hparams.n_embd, N);
memcpy(inpL->data, input, N * model.hparams.n_embd * ggml_element_size(inpL));
if (!llama_eval_internal_tensor(*ctx, ctx0, inpL, N, n_past, n_threads, nullptr, t_start_us)) {
if (!llama_eval_internal(*ctx, nullptr, n_tokens, embd, n_past, n_threads, nullptr)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
@ -3419,7 +3390,7 @@ int llama_eval_export(struct llama_context * ctx, const char * fname) {
const std::vector<llama_token> tmp(n_batch, llama_token_bos());
if (!llama_eval_internal(*ctx, tmp.data(), tmp.size(), n_ctx, 1, fname)) {
if (!llama_eval_internal(*ctx, tmp.data(), tmp.size(), nullptr, n_ctx, 1, fname)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}

4
llama.h
View file

@ -200,9 +200,9 @@ extern "C" {
int n_threads);
// Same as llama_eval, but use float matrix input directly.
LLAMA_API int llama_eval_float(
LLAMA_API int llama_eval_embd(
struct llama_context * ctx,
const float * embds,
const float * embd,
int n_tokens,
int n_past,
int n_threads);