Mirrors/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-24 13:28:50 +01:00
Code Issues Packages Projects Releases Wiki Activity

examples : add "retrieval" (#6193)

Browse Source 
* add `retrieval` example

* add README

* minor fixes

* cast filepos on print

* remove use of variable sized array

* store similarities in separate vector

* print error on insufficient batch size

* fix error message printing

* assign n_batch value to n_ubatch

* fix param definitions

* define retrieval-only parameters in retrieval.cpp

* fix `--context-file` option to be provided multiple times for multiple files

* use vector for `query_emb`

* add usage description in README

* fix merge conflict

* fix usage printing

* remove seed setting

* fix lint

* increase file read buffer size

* retrieval : minor

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Minsoo Cheong 2024-03-25 16:38:22 +09:00 committed by GitHub
parent 7733f0c760
commit 64e7b47c69
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
10 changed files with 435 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
.gitignore vendored
View File

@ -77,6 +77,7 @@ models-mnt
/batched-bench
/export-lora
/finetune
/retrieval
/speculative
/parallel
/train-text-from-scratch

6
Makefile
View File

@ -2,7 +2,7 @@
BUILD_TARGETS = \
main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
simple batched batched-bench save-load-state server gguf gguf-split llama-bench libllava.a llava-cli baby-llama beam-search \
speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o
retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o
# Binaries only useful for tests
TEST_TARGETS = \
@ -804,6 +804,10 @@ export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
retrieval: examples/retrieval/retrieval.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

2
common/common.cpp
View File

@ -157,7 +157,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
return result;
}
static bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
llama_sampling_params& sparams = params.sparams;
if (arg == "-s" || arg == "--seed") {

2
common/common.h
View File

@ -171,6 +171,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param);
std::string get_system_info(const gpt_params & params);
std::string gpt_random_prompt(std::mt19937 & rng);

1
common/log.h
View File

@ -566,6 +566,7 @@ inline void log_print_usage()
printf(" --log-new Create a separate new log file on start. "
"Each log file will have unique name: \"<name>.<ID>.log\"\n");
printf(" --log-append Don't truncate the old log file.\n");
printf("\n");
}
#define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv)

1
examples/CMakeLists.txt
View File

@ -34,6 +34,7 @@ else()
add_subdirectory(perplexity)
add_subdirectory(quantize)
add_subdirectory(quantize-stats)
add_subdirectory(retrieval)
add_subdirectory(save-load-state)
add_subdirectory(simple)
add_subdirectory(passkey)

5
examples/retrieval/CMakeLists.txt Normal file
View File

@ -0,0 +1,5 @@
set(TARGET retrieval)
add_executable(${TARGET} retrieval.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

69
examples/retrieval/README.md Normal file
View File

@ -0,0 +1,69 @@
# llama.cpp/examples/retrieval
Demonstration of simple retrieval technique based on cosine similarity
More info:
https://github.com/ggerganov/llama.cpp/pull/6193
### How to use
`retieval.cpp` has parameters of its own:
- `--context-file`: file to be embedded - state this option multiple times to embed multiple files
- `--chunk-size`: minimum size of each text chunk to be embedded
- `--chunk-separator`: STRING to divide chunks by. newline by default
`retrieval` example can be tested as follows:
```bash
make -j && ./retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .
```
This chunks and embeds all given files and starts a loop requesting query inputs:
```
Enter query:
```
On each query input, top k chunks are shown along with file name, chunk position within file and original text:
```
Enter query: describe the mit license
batch_decode: n_tokens = 6, n_seq = 1
Top 3 similar chunks:
filename: README.md
filepos: 119
similarity: 0.762334
textdata:
png)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[Roadmap](https://github.
--------------------
filename: License
filepos: 0
similarity: 0.725146
textdata:
MIT License
Copyright (c) 2023 Georgi Gerganov
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
--------------------
filename: README.md
filepos: 9178
similarity: 0.621722
textdata:
com/cztomsik/ava) (MIT)
- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
- [pythops/tenere](https://github.
--------------------
```

350
examples/retrieval/retrieval.cpp Normal file
View File

@ -0,0 +1,350 @@
#include "common.h"
#include "llama.h"
#include <algorithm>
#include <fstream>
struct retrieval_params {
std::vector<std::string> context_files; // context files to embed
int32_t chunk_size = 64; // chunk size for context embedding
std::string chunk_separator = "\n"; // chunk separator for context embedding
};
static void retrieval_params_print_usage(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & params) {
gpt_print_usage(argc, argv, gpt_params);
printf("retrieval options:\n");
printf(" --context-file FNAME file containing context to embed.\n");
printf(" specify multiple files by providing --context-file option multiple times.\n");
printf(" --chunk-size N minimum length of embedded text chunk (default:%d)\n", params.chunk_size);
printf(" --chunk-separator STRING\n");
printf(" string to separate chunks (default: \"\\n\")\n");
printf("\n");
}
static void retrieval_params_parse(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & retrieval_params) {
int i = 1;
std::string arg;
while (i < argc) {
arg = argv[i];
bool invalid_gpt_param = false;
if(gpt_params_find_arg(argc, argv, argv[i], gpt_params, i, invalid_gpt_param)) {
if (invalid_gpt_param) {
fprintf(stderr, "error: invalid argument: %s\n", arg.c_str());
retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
exit(1);
}
// option was parsed by gpt_params_find_arg
} else if (arg == "--context-file") {
if (++i >= argc) {
fprintf(stderr, "error: missing argument for --context-file\n");
retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
exit(1);
}
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
exit(1);
}
// store the external file name in params
retrieval_params.context_files.push_back(argv[i]);
} else if (arg == "--chunk-size") {
if (++i >= argc) {
fprintf(stderr, "error: missing argument for --chunk-size\n");
retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
exit(1);
}
retrieval_params.chunk_size = std::stoi(argv[i]);
} else if (arg == "--chunk-separator") {
if (++i >= argc) {
fprintf(stderr, "error: missing argument for --chunk-separator\n");
retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
exit(1);
}
retrieval_params.chunk_separator = argv[i];
} else {
// unknown argument
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
exit(1);
}
i++;
}
}
struct chunk {
// filename
std::string filename;
// original file position
size_t filepos;
// original text data
std::string textdata = "";
// tokenized text data
std::vector<llama_token> tokens;
// embedding
std::vector<float> embedding;
};
// chunk file data to chunks of size >= chunk_size
// chunk_separator is the separator between chunks
static std::vector<chunk> chunk_file(const std::string & filename, int chunk_size, const std::string & chunk_separator) {
std::vector<chunk> chunks;
std::ifstream f(filename.c_str());
if (!f.is_open()) {
fprintf(stderr, "Error: could not open file %s\n", filename.c_str());
return chunks;
}
chunk current_chunk;
char buffer[1024];
int64_t filepos = 0;
std::string current = "";
while (f.read(buffer, 1024)) {
current += std::string(buffer, f.gcount());
size_t pos;
while ((pos = current.find(chunk_separator)) != std::string::npos) {
current_chunk.textdata += current.substr(0, pos + chunk_separator.size());
if ((int) current_chunk.textdata.size() > chunk_size) {
// save chunk
current_chunk.filepos = filepos;
current_chunk.filename = filename;
chunks.push_back(current_chunk);
// update filepos
filepos += (int) current_chunk.textdata.size();
// reset current_chunk
current_chunk = chunk();
}
current = current.substr(pos + chunk_separator.size());
}
}
// add leftover data to last chunk
if (current_chunk.textdata.size() > 0) {
if (chunks.empty()) {
current_chunk.filepos = filepos;
current_chunk.filename = filename;
chunks.push_back(current_chunk);
} else {
chunks.back().textdata += current_chunk.textdata;
}
}
f.close();
return chunks;
}
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
for (size_t i = 0; i < tokens.size(); i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
}
}
static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
// clear previous kv_cache values (irrelevant for embeddings)
llama_kv_cache_clear(ctx);
// run model
fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
if (llama_decode(ctx, batch) < 0) {
fprintf(stderr, "%s : failed to decode\n", __func__);
}
for (int i = 0; i < batch.n_tokens; i++) {
if (!batch.logits[i]) {
continue;
}
// try to get sequence embeddings - supported only when pooling_type is not NONE
const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
if (embd == NULL) {
embd = llama_get_embeddings_ith(ctx, i);
if (embd == NULL) {
fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
continue;
}
}
float * out = output + batch.seq_id[i][0] * n_embd;
llama_embd_normalize(embd, out, n_embd);
}
}
int main(int argc, char ** argv) {
gpt_params params;
retrieval_params retrieval_params;
retrieval_params_parse(argc, argv, params, retrieval_params);
// For BERT models, batch size must be equal to ubatch size
params.n_ubatch = params.n_batch;
if (retrieval_params.chunk_size <= 0) {
fprintf(stderr, "chunk_size must be positive\n");
return 1;
}
if (retrieval_params.context_files.empty()) {
fprintf(stderr, "context_files must be specified\n");
return 1;
}
params.embedding = true;
print_build_info();
printf("processing files:\n");
for (auto & context_file : retrieval_params.context_files) {
printf("%s\n", context_file.c_str());
}
std::vector<chunk> chunks;
for (auto & context_file : retrieval_params.context_files) {
std::vector<chunk> file_chunk = chunk_file(context_file, retrieval_params.chunk_size, retrieval_params.chunk_separator);
chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end());
}
printf("Number of chunks: %ld\n", chunks.size());
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model;
llama_context * ctx;
// load the model
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
return 1;
}
const int n_ctx_train = llama_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);
if (n_ctx > n_ctx_train) {
fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
__func__, n_ctx_train, n_ctx);
}
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "%s\n", get_system_info(params).c_str());
}
// max batch size
const uint64_t n_batch = params.n_batch;
GGML_ASSERT(params.n_batch >= params.n_ctx);
// tokenize the prompts and trim
for (auto & chunk : chunks) {
auto inp = ::llama_tokenize(ctx, chunk.textdata, true, false);
if (inp.size() > n_batch) {
fprintf(stderr, "%s: error: chunk size (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
__func__, (long long int) inp.size(), (long long int) n_batch);
return 1;
}
// add eos if not present
if (inp.empty() || inp.back() != llama_token_eos(model)) {
inp.push_back(llama_token_eos(model));
}
chunk.tokens = inp;
}
// tokenization stats
if (params.verbose_prompt) {
for (int i = 0; i < (int) chunks.size(); i++) {
fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size());
for (int j = 0; j < (int) chunks[i].tokens.size(); j++) {
fprintf(stderr, "%6d -> '%s'\n", chunks[i].tokens[j], llama_token_to_piece(ctx, chunks[i].tokens[j]).c_str());
}
fprintf(stderr, "\n\n");
}
}
// initialize batch
const int n_chunks = chunks.size();
struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
// allocate output
const int n_embd = llama_n_embd(model);
std::vector<float> embeddings(n_chunks * n_embd, 0);
float * emb = embeddings.data();
// break into batches
int p = 0; // number of prompts processed already
int s = 0; // number of prompts in current batch
for (int k = 0; k < n_chunks; k++) {
// clamp to n_batch tokens
auto & inp = chunks[k].tokens;
const uint64_t n_toks = inp.size();
// encode if at capacity
if (batch.n_tokens + n_toks > n_batch) {
float * out = emb + p * n_embd;
batch_decode(ctx, batch, out, s, n_embd);
llama_batch_clear(batch);
p += s;
s = 0;
}
// add to batch
batch_add_seq(batch, inp, s);
s += 1;
}
// final batch
float * out = emb + p * n_embd;
batch_decode(ctx, batch, out, s, n_embd);
// save embeddings to chunks
for (int i = 0; i < n_chunks; i++) {
chunks[i].embedding = std::vector<float>(emb + i * n_embd, emb + (i + 1) * n_embd);
// clear tokens as they are no longer needed
chunks[i].tokens.clear();
}
// start loop, receive query and return top k similar chunks based on cosine similarity
std::string query;
while (true) {
printf("Enter query: ");
std::getline(std::cin, query);
std::vector<int32_t> query_tokens = llama_tokenize(ctx, query, true);
struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1);
batch_add_seq(query_batch, query_tokens, 0);
std::vector<float> query_emb(n_embd, 0);
batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd);
llama_batch_clear(query_batch);
// compute cosine similarities
{
std::vector<std::pair<int, float>> similarities;
for (int i = 0; i < n_chunks; i++) {
float sim = llama_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd);
similarities.push_back(std::make_pair(i, sim));
}
// sort similarities
std::sort(similarities.begin(), similarities.end(), [](const std::pair<int, float> & a, const std::pair<int, float> & b) {
return a.second > b.second;
});
printf("Top %d similar chunks:\n", params.sparams.top_k);
for (int i = 0; i < std::min(params.sparams.top_k, (int) chunks.size()); i++) {
printf("filename: %s\n", chunks[similarities[i].first].filename.c_str());
printf("filepos: %lld\n", (long long int) chunks[similarities[i].first].filepos);
printf("similarity: %f\n", similarities[i].second);
printf("textdata:\n%s\n", chunks[similarities[i].first].textdata.c_str());
printf("--------------------\n");
}
}
}
// clean up
llama_print_timings(ctx);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
}

BIN
retrieval
View File

Binary file not shown.