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chat.cpp
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chat.cpp
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#include "chat.h"
#include <algorithm>
#include <cmath>
#include <codecvt>
#include <cstring>
#include <fcntl.h>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <locale>
#include <random>
#include <regex>
#include <string>
#include <functional>
#include <sys/stat.h>
#include <thread>
#ifdef __has_include
#if __has_include(<unistd.h>)
#include <unistd.h>
#if defined(_POSIX_MAPPED_FILES)
#include <sys/mman.h>
#endif
#if defined(_POSIX_MEMLOCK_RANGE)
#include <sys/resource.h>
#endif
#endif
#endif
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <io.h>
#include <stdio.h>
#endif
#ifdef GGML_USE_CLBLAST
#include "ggml-opencl.h"
#endif
namespace chatllm
{
std::string trim(std::string str, const char *spaces)
{
str.erase(str.find_last_not_of(spaces) + 1);
str.erase(0,str.find_first_not_of(spaces));
return str;
}
BaseStreamer::BaseStreamer(BaseTokenizer *tokenizer)
: is_prompt(true), tokenizer(tokenizer), is_first(true), print_len(0),
interceptor(nullptr)
{
}
void BaseStreamer::put(const std::vector<int> &output_ids)
{
is_prompt = false;
token_cache.insert(token_cache.end(), output_ids.begin(), output_ids.end());
std::string text = tokenizer->decode(token_cache);
if (text.empty())
{
return;
}
std::string printable_text;
if ((text.back() == '\n') || (text.back() == '\r'))
{
// flush the cache after newline
printable_text = text.substr(print_len);
token_cache.clear();
print_len = 0;
}
else
{
size_t end = tokenizer::get_end_of_valid_utf8(text, print_len);
if (end > print_len)
{
printable_text = text.substr(print_len, end - print_len);
print_len = end;
}
}
if (printable_text.size() > 0)
{
call_put_chunk(is_first, printable_text);
is_first = false;
}
}
void BaseStreamer::end()
{
if (tokenizer)
{
std::string text = tokenizer->decode(token_cache);
size_t end = tokenizer::get_end_of_valid_utf8(text, print_len);
if (end > print_len)
{
call_put_chunk(is_first, text.substr(print_len));
print_len = end;
}
}
if (interceptor)
{
auto it = interceptor;
interceptor = nullptr;
it->end();
}
is_first = true;
is_prompt = true;
token_cache.clear();
print_len = 0;
}
void BaseStreamer::set_interceptor(ChunkInterceptor *interceptor)
{
this->interceptor = interceptor;
if (interceptor) interceptor->intercept(this);
}
void BaseStreamer::remove_interceptor(void)
{
if (interceptor) interceptor->intercept(nullptr);
interceptor = nullptr;
}
void BaseStreamer::call_put_chunk(bool first, const std::string &chunk)
{
if (interceptor)
interceptor->put_chunk(first, chunk);
else
put_chunk(first, chunk);
}
BaseTokenizer::BaseTokenizer(const BaseConfig &config,
BaseHistoryEncoder *chat_encoder,
BaseHistoryEncoder *qa_encoder,
BaseHistoryEncoder *completion_encoder) :
bos_token_id(config.bos_token_id),
eos_token_id(config.eos_token_id),
pad_token_id(config.pad_token_id),
sep_token_id(config.sep_token_id),
tp(nullptr),
sys_prompt(""),
history_offset(0),
max_length(config.max_length),
format(ChatFormat::CHAT),
chat_encoder(chat_encoder),
completion_encoder(completion_encoder),
qa_encoder(qa_encoder),
auto_add_bos(true)
{
if (chat_encoder)
chat_encoder->set_tokenizer(this);
if (completion_encoder)
completion_encoder->set_tokenizer(this);
if (qa_encoder)
qa_encoder->set_tokenizer(this);
}
bool BaseTokenizer::is_terminate_token_id(int id) const
{
if (id == eos_token_id) return true;
return terminate_ids.find(id) != terminate_ids.end();
}
std::string BaseTokenizer::preprocess(const std::string &text) const
{
return text;
}
std::string BaseTokenizer::postprocess(const std::string &text) const
{
return text;
}
void BaseTokenizer::encode(const std::string &text, std::vector<int> &ids) const
{
std::string input = preprocess(text);
tp->Encode(input, &ids);
}
void BaseTokenizer::encode_qa(const std::string &q, const std::string &a, std::vector<int> &ids) const
{
std::string input = preprocess(q);
tp->Encode(input, &ids);
input = preprocess(a);
tp->Encode(input, &ids);
}
std::vector<int> BaseTokenizer::encode(const std::string &text) const
{
std::vector<int> ids;
encode(text, ids);
return ids;
}
std::string BaseTokenizer::decode(const std::vector<int> &ids) const
{
// filter out special tokens
std::vector<int> normal_ids(ids);
normal_ids.erase(std::remove_if(normal_ids.begin(), normal_ids.end(),
[this](int id)
{ return is_special_id(id); }),
normal_ids.end());
std::string text;
tp->Decode(normal_ids, &text);
text = postprocess(text);
return text;
}
int BaseTokenizer::get_history_start(const std::vector<std::string> &history, int max_length) const
{
int start = (int)history.size() - 1;
size_t total_id_num = encode(history[start]).size();
start--;
while (start >= 1)
{
total_id_num += encode(history[start]).size();
total_id_num += encode(history[start - 1]).size();
if ((int)total_id_num >= max_length)
break;
start -= 2;
}
return start + 1;
}
std::vector<int> BaseTokenizer::encode_history(BaseHistoryEncoder *encoder, const std::vector<std::string> &history, int max_length, const bool incremental)
{
CHATLLM_CHECK(history.size() % 2 == 1) << "invalid history size " << history.size();
std::vector<int> input_ids;
if (!incremental)
{
int start = get_history_start(history, max_length / 2);
history_offset = start;
for (; start <= (int)history.size() - 3; start += 2)
{
std::string user = trim(history[start]);
std::string ai = trim(history[start + 1]);
encoder->append_pair((start - history_offset) / 2, user, ai, input_ids);
}
}
std::string user = trim(history[history.size() - 1]);
encoder->append_user((int)((history.size() - history_offset - 1) / 2), trim(user), input_ids);
return input_ids;
}
std::vector<int> BaseTokenizer::encode_sys_prompt(void)
{
std::vector<int> ids;
switch (format)
{
case ChatFormat::COMPLETION:
if (completion_encoder)
completion_encoder->append_sys_prompt(ids);
else;
break;
case ChatFormat::QA:
if (qa_encoder)
qa_encoder->append_sys_prompt(ids);
else if (chat_encoder)
{
chat_encoder->append_sys_prompt(ids);
}
else;
break;
default:
if (chat_encoder)
chat_encoder->append_sys_prompt(ids);
else;
}
if (auto_add_bos && (bos_token_id >= 0))
{
if ((ids.size() > 0) && (ids[0] != bos_token_id))
ids.insert(ids.begin(), bos_token_id);
}
return ids;
}
std::vector<int> BaseTokenizer::encode_history(const std::vector<std::string> &history, int max_length, const bool incremental)
{
switch (format)
{
case ChatFormat::COMPLETION:
if (completion_encoder)
return encode_history(completion_encoder, history, max_length, incremental);
else;
break;
case ChatFormat::QA:
if (qa_encoder)
return encode_history(qa_encoder, history, max_length, incremental);
else if (chat_encoder)
{
std::vector<std::string> copied;
copied.push_back(history[history.size() - 1]);
return encode_history(chat_encoder, copied, max_length, incremental);
}
else;
break;
default:
if (chat_encoder)
return encode_history(chat_encoder, history, max_length, incremental);
else;
}
std::vector<int> r = encode(history[history.size() - 1]);
if (auto_add_bos && (bos_token_id >= 0))
{
if ((r.size() > 0) && (r[0] != bos_token_id))
r.insert(r.begin(), bos_token_id);
else
r.push_back(bos_token_id);
}
return r;
}
void BaseTokenizer::set_skip_sys_prompt(bool skip)
{
if (chat_encoder)
chat_encoder->skip_sys_prompt = skip;
if (completion_encoder)
completion_encoder->skip_sys_prompt = skip;
if (qa_encoder)
qa_encoder->skip_sys_prompt = skip;
}
void BaseHistoryEncoder::append_sys_prompt(std::vector<int> &ids) const
{
}
void BaseHistoryEncoder::append_pair(int round_idx, const std::string &user, const std::string &ai, std::vector<int> &ids) const
{
}
void BaseHistoryEncoder::append_user(int round_idx, const std::string &user, std::vector<int> &ids) const
{
if (round_idx == 0)
{
if (!skip_sys_prompt)
append_sys_prompt(ids);
}
do_append_user(round_idx, user, ids);
}
void BaseHistoryEncoder::do_append_user(int round_idx, const std::string &user, std::vector<int> &ids) const
{
tokenizer->encode(user, ids);
}
static std::string shape_to_string(ggml_tensor *tensor)
{
int n_dims = ggml_n_dims(tensor);
std::ostringstream oss;
oss << '[';
for (int i = n_dims - 1; i >= 0; i--)
{
oss << tensor->ne[i] << (i > 0 ? ", " : "");
}
oss << ']';
return oss.str();
}
static std::string strides_to_string(ggml_tensor *tensor)
{
int n_dims = ggml_n_dims(tensor);
std::ostringstream oss;
oss << '[';
for (int i = n_dims - 1; i >= 0; i--)
{
oss << tensor->nb[i] << (i > 0 ? ", " : "");
}
oss << ']';
return oss.str();
}
std::string to_string(ggml_tensor *tensor, bool with_data)
{
std::ostringstream oss;
int n_dims = ggml_n_dims(tensor);
oss << "ggml_tensor(";
if (with_data)
{
if (n_dims > 3)
oss << "[";
for (int i3 = 0; i3 < tensor->ne[3]; i3++)
{
if (n_dims > 2)
oss << (i3 > 0 ? ",\n\n[" : "[");
for (int i2 = 0; i2 < tensor->ne[2]; i2++)
{
if (n_dims > 1)
oss << (i2 > 0 ? ",\n\n[" : "[");
for (int i1 = 0; i1 < tensor->ne[1]; i1++)
{
oss << (i1 > 0 ? ",\n[" : "[");
for (int i0 = 0; i0 < tensor->ne[0]; i0++)
{
auto ptr = (char *)tensor->data + i3 * tensor->nb[3] + i2 * tensor->nb[2] + i1 * tensor->nb[1] +
i0 * tensor->nb[0];
float val;
if (tensor->type == GGML_TYPE_F32)
{
val = *(float *)ptr;
}
else if (tensor->type == GGML_TYPE_F16)
{
val = ggml_fp16_to_fp32(*(ggml_fp16_t *)ptr);
}
else
{
CHATLLM_THROW << "unimplemented";
}
oss << (i0 > 0 ? ", " : "") << std::setw(7) << std::fixed << std::setprecision(4) << val;
}
oss << "]";
}
if (n_dims > 1)
oss << "]";
}
if (n_dims > 2)
oss << "]";
}
if (n_dims > 3)
oss << "]";
}
oss << ", shape=" << shape_to_string(tensor) << ", stride=" << strides_to_string(tensor) << ")";
return oss.str();
}
#ifdef _POSIX_MAPPED_FILES
MappedFile::MappedFile(const std::string &path)
{
int fd = open(path.c_str(), O_RDONLY);
CHATLLM_CHECK(fd > 0) << "cannot open file " << path << ": " << strerror(errno);
struct stat sb;
CHATLLM_CHECK(fstat(fd, &sb) == 0) << strerror(errno);
size = sb.st_size;
data = (char *)mmap(nullptr, size, PROT_READ, MAP_SHARED, fd, 0);
CHATLLM_CHECK(data != MAP_FAILED) << strerror(errno);
CHATLLM_CHECK(close(fd) == 0) << strerror(errno);
}
MappedFile::~MappedFile() { CHATLLM_CHECK(munmap(data, size) == 0) << strerror(errno); }
#elif defined(_WIN32)
MappedFile::MappedFile(const std::string &path)
{
int fd = open(path.c_str(), O_RDONLY);
CHATLLM_CHECK(fd > 0) << "cannot open file " << path << ": " << strerror(errno);
struct _stat64 sb;
CHATLLM_CHECK(_fstat64(fd, &sb) == 0) << strerror(errno);
size = sb.st_size;
HANDLE hFile = (HANDLE)_get_osfhandle(fd);
HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
CHATLLM_CHECK(hMapping != NULL) << strerror(errno);
data = (char *)MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
CloseHandle(hMapping);
CHATLLM_CHECK(data != NULL) << strerror(errno);
CHATLLM_CHECK(close(fd) == 0) << strerror(errno);
}
MappedFile::~MappedFile()
{
CHATLLM_CHECK(UnmapViewOfFile(data)) << strerror(errno);
}
#endif
void ModelLoader::seek(int64_t offset, int whence)
{
if (whence == SEEK_SET)
ptr = data + offset;
else if (whence == SEEK_CUR)
ptr += offset;
else if (whence == SEEK_END)
ptr = data + size + offset;
else
CHATLLM_THROW << "invalid seek mode " << whence;
}
std::string ModelLoader::read_string(size_t length)
{
std::string s(ptr, ptr + length);
ptr += length;
return s;
}
struct ggml_tensor * ggml_init_tensor(struct ggml_tensor *tensor,
enum ggml_type type,
int n_dims,
const int64_t * ne)
{
struct ggml_tensor *result = tensor;
*result = ggml_tensor {
/*.type =*/ type,
/*.backend =*/ GGML_BACKEND_TYPE_CPU,
/*.buffer =*/ NULL,
/*.ne =*/ { 1, 1, 1, 1 },
/*.nb =*/ { 0, 0, 0, 0 },
/*.op =*/ GGML_OP_NONE,
/*.op_params =*/ { 0 },
/*.flags =*/ 0,
/*.grad =*/ NULL,
/*.src =*/ { NULL },
/*.perf_runs =*/ 0,
/*.perf_cycles =*/ 0,
/*.perf_time_us =*/ 0,
/*.view_src =*/ NULL,
/*.view_offs =*/ 0,
/*.data =*/ NULL,
/*.name =*/ { 0 },
/*.extra =*/ NULL,
/*.padding =*/ { 0 },
};
// TODO: this should not be needed as long as we don't rely on aligned SIMD loads
//ggml_assert_aligned(result->data);
for (int i = 0; i < n_dims; i++) {
result->ne[i] = ne[i];
}
result->nb[0] = ggml_type_size(type);
result->nb[1] = result->nb[0]*(result->ne[0]/ggml_blck_size(type));
for (int i = 2; i < GGML_MAX_DIMS; i++) {
result->nb[i] = result->nb[i - 1]*result->ne[i - 1];
}
return result;
}
void ModelLoader::load_all_tensors(void)
{
tensor_dict.clear(); //return;
struct ggml_tensor t;
while (tell() < (int64_t)size)
{
std::string weight_name;
// read and check tensor name
{
int name_size = read_basic<int>();
weight_name = read_string(name_size);
}
tensor_dict.emplace(weight_name, tell());
int64_t ne[4] = {1,1,1,1};
// read and check tensor shape
int ndim = read_basic<int>();
for (int i = ndim - 1; i >= 0; i--)
{
int dim_size = read_basic<int>();
ne[i] = dim_size;
}
// read and check tensor dtype
ggml_type dtype = (ggml_type)read_basic<int>();
ggml_init_tensor(&t, dtype, ndim, ne);
// map tensor data
{
constexpr int64_t MEM_ALIGNED = 16;
const int64_t data_offset = (tell() + (MEM_ALIGNED - 1)) & ~(MEM_ALIGNED - 1);
seek(data_offset + ggml_nbytes(&t), SEEK_SET);
}
}
}
void ModelLoader::read_tensor(const std::string &name, ggml_tensor *tensor)
{
if (tensor_dict.size() > 0)
{
auto search = tensor_dict.find(name);
CHATLLM_CHECK(search != tensor_dict.end()) << "tensor not exist: " << name;
seek(search->second, SEEK_SET);
}
else
{
// read and check tensor name
int name_size = read_basic<int>();
CHATLLM_CHECK(name_size == (int)name.size())
<< "tensor " << name << " name size mismatch: expect " << name.size() << " but got " << name_size
<< "(part of name: " << read_string(name.size()) << ")";
std::string weight_name = read_string(name_size);
CHATLLM_CHECK(weight_name == name) << "tensor name mismatch: expect " << name << " but got " << weight_name;
}
ggml_set_name(tensor, name.c_str());
// read and check tensor shape
{
int ndim = read_basic<int>();
int n_dims = ggml_n_dims(tensor);
// a quick fix
if ((n_dims == 1) && (ndim == 2) && (tensor->ne[1] == 1))
n_dims = 2;
CHATLLM_CHECK(ndim == n_dims)
<< "tensor " << name << " ndim mismatch: expect " << n_dims << " but got " << ndim;
for (int i = ndim - 1; i >= 0; i--)
{
int dim_size = read_basic<int>();
CHATLLM_CHECK(dim_size == tensor->ne[i]) << "tensor " << name << " shape mismatch at dim " << i
<< ": expect " << tensor->ne[i] << " but got " << dim_size;
}
}
// read and check tensor dtype
{
ggml_type dtype = (ggml_type)read_basic<int>();
CHATLLM_CHECK(dtype == tensor->type)
<< "tensor " << name << " dtype mismatch: expect " << tensor->type << " but got " << dtype;
}
// map tensor data
{
constexpr int64_t MEM_ALIGNED = 16;
const int64_t data_offset = (tell() + (MEM_ALIGNED - 1)) & ~(MEM_ALIGNED - 1);
tensor->data = const_cast<char *>(data) + data_offset;
seek(data_offset + ggml_nbytes(tensor), SEEK_SET);
}
}
ModelObject::ModelObject(const std::string &path)
: ModelObject(path, ModelObject::extra_args())
{
}
ModelObject::ModelObject(const std::string &path, const extra_args &args)
: loader(nullptr), loaded(path.size() > 0)
{
ModelFactory::Result result = {nullptr, nullptr};
if (path.size() > 0)
{
loader = std::unique_ptr<ModelLoader>(new ModelLoader(path));
if (!ModelFactory::load(*loader, result, args))
CHATLLM_THROW << "ModelFactory::load() failed";
}
tokenizer = std::move(result.tokenizer);
model = std::move(result.model);
}
void ModelObject::text_embedding(const std::string &input, const GenerationConfig &gen_config, std::vector<float> &result)
{
if (!loaded) return;
std::vector<int> input_ids;
tokenizer->encode(input, input_ids);
model->text_embedding(gen_config, input_ids, result);
}
AbstractModel *ModelObject::fork_model(const extra_args &args)
{
if (!loaded) return nullptr;
return ModelFactory::load_model_again(*loader, args);
}
// ===== pipeline =====
Pipeline::Pipeline(const std::string &path)
: Pipeline(path, ModelObject::extra_args())
{
}
Pipeline::Pipeline(const std::string &path, const ModelObject::extra_args &args)
: gen_max_tokens(-1),
initializing(true),
extending(ExtendingMethod::Restart),
modelobj(path, args)
{
model = modelobj.model.get();
tokenizer = modelobj.tokenizer.get();
}
std::string Pipeline::chat_with_restart(const std::vector<std::string> &history, const GenerationConfig &gen_config,
BaseStreamer *streamer)
{
bool continuous = tokenizer->get_chat_format() == ChatFormat::CHAT;
bool completed = false;
std::vector<int> input_ids;
if (initializing)
{
continuous = false;
initializing = false;
}
else;
input_ids = tokenizer->encode_history(history, gen_config.max_context_length, continuous);
std::vector<int> output_ids = model->generate(input_ids, gen_config, continuous, completed, &performance, gen_max_tokens, streamer);
if (!completed)
{
if (continuous)
{
streamer->putln("\nRUN OUT OF CONTEXT. Let me forget something and try again ...\n");
input_ids = tokenizer->encode_history(history, gen_config.max_context_length);
output_ids = model->generate(input_ids, gen_config, false, completed, &performance, gen_max_tokens, streamer);
}
else
streamer->putln("\nRUN OUT OF CONTEXT. I have to stop now.\n");
}
std::string output = tokenizer->decode(output_ids);
return output;
}
std::string Pipeline::chat_without_extending(const std::vector<std::string> &history, const GenerationConfig &gen_config,
BaseStreamer *streamer)
{
bool continuous = tokenizer->get_chat_format() == ChatFormat::CHAT;
bool completed = false;
std::vector<int> input_ids;
if (initializing)
{
continuous = false;
initializing = false;
}
else;
input_ids = tokenizer->encode_history(history, gen_config.max_context_length, continuous);
std::vector<int> output_ids = model->generate(input_ids, gen_config, continuous, completed, &performance, gen_max_tokens, streamer);
if (!completed)
{
streamer->putln("\nRUN OUT OF CONTEXT. I have to stop now.\n");
}
std::string output = tokenizer->decode(output_ids);
return output;
}
std::string Pipeline::chat_with_shift(const std::vector<std::string> &history, const GenerationConfig &gen_config,
BaseStreamer *streamer)
{
bool continuous = true;
bool completed = false;
if (initializing)
{
continuous = false;
initializing = false;
}
else;
std::vector<int> input_ids = tokenizer->encode_history(history, gen_config.max_context_length, continuous);
std::vector<int> output_ids = model->generate(input_ids, gen_config, continuous, completed, &performance, gen_max_tokens, streamer);
while (!completed)
{
streamer->putln("\nRUN OUT OF CONTEXT. Try to forget something and continue ...\n");
model->shift_memory(gen_config.max_context_length);
if (output_ids.size() > 0)
input_ids = {output_ids[output_ids.size() - 1]};
else
input_ids.clear();
auto ids = model->generate(input_ids, gen_config, true, completed, &performance, gen_max_tokens, streamer);
output_ids.insert(output_ids.end(), ids.begin(), ids.end());
}
std::string output = tokenizer->decode(output_ids);
return output;
}
void Pipeline::eval_sys_prompt(const GenerationConfig &gen_config)
{
bool completed = false;
std::vector<int> input_ids = tokenizer->encode_sys_prompt();
model->generate(input_ids, gen_config, false, completed, &performance, 1, nullptr);
// just in case that chatting is continued
tokenizer->set_skip_sys_prompt(true);
initializing = false;
return;
}
std::string Pipeline::chat(std::vector<std::string> &history, const GenerationConfig &gen_config,
BaseStreamer *streamer)
{
std::string r;
if (modelobj.loaded && streamer)
{
ChunkInterceptor *interceptor = model->get_interceptor();
if (interceptor)
{
streamer->set_interceptor(interceptor);
}
}
before_chat(history, gen_config, streamer);
if (modelobj.loaded)
{
switch (extending)
{
case ExtendingMethod::Shift:
r = chat_with_shift(history, gen_config, streamer);
break;
case ExtendingMethod::Restart:
r = chat_with_restart(history, gen_config, streamer);
break;
default:
r = chat_without_extending(history, gen_config, streamer);
break;
}
}
post_chat(history, gen_config, streamer);
if (streamer)
streamer->end();
return r;
}
void Pipeline::abort_generation(void)
{
if (modelobj.loaded)
modelobj.model->abort_generation();
}
void Pipeline::text_embedding(const std::string &input, const GenerationConfig &gen_config, std::vector<float> &result)
{
modelobj.text_embedding(input, gen_config, result);
}
int Pipeline::get_text_embedding_dim(void)
{
if (!modelobj.loaded) return 0;
return model->get_text_embedding_dim();
}
std::string Pipeline::get_additional_description(void) const
{
return "";
}
void Pipeline::restart(void)
{
initializing = true;
}
void Pipeline::rewind(int n_past)
{
if (!modelobj.loaded) return;
model->set_n_past(n_past);
}
int Pipeline::save_session(const std::vector<std::string> &history, const std::string &file_name)
{
if (!modelobj.loaded) return -1000;
FILE *f = fopen(file_name.c_str(), "wb");
file_header header = {0};
memcpy(header.magic, head_magic, sizeof(header.magic));
header.history_len = history.size();
if (fwrite(&header, sizeof(header), 1, f) != 1)
return -1;
for (auto &s : history)
{
size_t len = s.size();
fwrite(&len, sizeof(len), 1, f);
fwrite(s.data(), 1, len, f);
}
model->save_session(f);
fclose(f);
return 0;
}
int Pipeline::load_session(std::vector<std::string> &history, const std::string &file_name, BaseStreamer *streamer, int *n_past)
{
if (!modelobj.loaded) return -1000;
int r = -1;
FILE *f = fopen(file_name.c_str(), "rb");
file_header header = {0};
if (fread(&header, sizeof(header), 1, f) != 1)
return -1;
history.clear();
if (memcmp(header.magic, head_magic, sizeof(header.magic)) == 0)
{
for (size_t i = 0; i < header.history_len; i++)
{
std::string s;
size_t len;
if (fread(&len, sizeof(len), 1, f) != 1)
return -2;
s.resize(len);
if (fread(s.data(), 1, len, f) != len)
return -3;
history.push_back(s);
if (streamer)
{
if ((history.size() % 2) == 0)
streamer->put_history_ai(s);
else
streamer->put_history_user(s);
}
}
r = model->load_session(f);
if (r != 0) goto exit;
}
exit:
fclose(f);
if (r == 0)
{
initializing = false;
tokenizer->set_skip_sys_prompt(true);
if (n_past != nullptr)
*n_past = model->get_n_past();
}
return r;
}
float Pipeline::qa_rank(const std::string &q, const std::string &a, const GenerationConfig &gen_config)
{
if (!modelobj.loaded) return -1.0f;
std::vector<int> input_ids;
tokenizer->encode_qa(q, a, input_ids);
return model->qa_rank(gen_config, input_ids);
}
void Pipeline::set_system_prompt(const std::string &prompt)
{
if (!modelobj.loaded) return;
tokenizer->set_system_prompt(prompt);
}
void Pipeline::set_extending_method(ExtendingMethod method)
{
extending = method;
}
void Pipeline::set_additional_args(const std::map<std::string, std::string> &args)
{
if (!modelobj.loaded) return;
tokenizer->set_additional_args(args);
}
void Pipeline::before_chat(std::vector<std::string> &history, const GenerationConfig &gen_config, BaseStreamer *streamer)
{
}
void Pipeline::post_chat(std::vector<std::string> &history, const GenerationConfig &gen_config, BaseStreamer *streamer)
{
}
RAGPipeline::RAGPipeline(const std::string &path, const ModelObject::extra_args &args,
DistanceStrategy vec_cmp, const std::vector<std::string> &vector_stores,
const std::string &embedding_model, const std::string &reranker_model)
: Pipeline(path, args),
composer(),
hide_reference(false),
retrieve_top_n(5), rerank_top_n(3),
dump(false),
rerank_score_threshold(0.5f),
rag_post_extending(0),
rerank_rewrite(false),
vs(vec_cmp, vector_stores),
embedding(embedding_model),