vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

examples : add simple token healing example

Browse source
This commit is contained in:
mare5x 2024-04-30 13:38:14 +02:00
parent 3ea0d36000
commit c77bb3203c
5 changed files with 667 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

1
examples/CMakeLists.txt
View file

@ -38,6 +38,7 @@ else()
add_subdirectory(retrieval)
add_subdirectory(save-load-state)
add_subdirectory(simple)
add_subdirectory(simple-token-healing)
add_subdirectory(passkey)
add_subdirectory(speculative)
add_subdirectory(lookahead)

11
examples/simple-token-healing/CMakeLists.txt Normal file
View file

@ -0,0 +1,11 @@
set(TARGET simple-token-healing)
add_executable(${TARGET} simple-token-healing.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
set(TARGET simple-token-healing-1)
add_executable(${TARGET} simple-token-healing-1.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

70
examples/simple-token-healing/README.md Normal file
View file

@ -0,0 +1,70 @@
# llama.cpp/example/simple-token-healing
This example extends [simple](../simple/README.md) with [token healing](https://github.com/guidance-ai/guidance/blob/main/notebooks/art_of_prompt_design/prompt_boundaries_and_token_healing.ipynb).
Without token healing:
```bash
./simple ./models/phi-2/ggml-model-q4_0.gguf "print('Hel"
...
main: n_len = 32, n_ctx = 2048, n_kv_req = 32
print('Helping the customer')
...
```
Heal the last token (`1`):
```bash
./simple-token-healing ./models/phi-2/ggml-model-q4_0.gguf "print('Hel" 1
...
token_healing: prefix = 'Hel' (1 tokens)
[ 12621] 'Hel'
[ 15496] 'Hello'
[ 22087] 'Help'
[ 28254] 'Hell'
[ 47429] 'Helper'
main: n_len = 32, n_ctx = 2048, n_kv_req = 32
print('Hello, World!')
...
```
Backtrack multiple tokens until there doesn't exist a token which can cover the prompt's suffix (`n`):
```bash
./simple-token-healing ./models/phi-2/ggml-model-q4_0.gguf "print('Hello, worl" n
...
token_healing: prefix = ' worl' (2 tokens)
[ 995] ' world'
[ 8688] ' worldwide'
[ 11621] ' worlds'
[ 29081] ' worldview'
[ 43249] ' worldly'
main: n_len = 32, n_ctx = 2048, n_kv_req = 32
print('Hello, world!')
...
```
Backtrack multiple tokens but don't constrain the decoding to a single token (`m`):
```bash
./simple-token-healing ./models/phi-2/ggml-model-q4_0.gguf "print('Hello, worl" m
...
token_healing: prefix = ' worl' (2 tokens)
main: n_len = 32, n_ctx = 2048, n_kv_req = 32
print('Hello,
token_healing: prefix = ' worl'
[ 220] ' '
[ 266] ' w'
[ 476] ' wor'
[ 995] ' world'
[ 8688] ' worldwide'
[ 11621] ' worlds'
[ 24486] ' wo'
[ 29081] ' worldview'
[ 43249] ' worldly'
world!')
...
```

232
examples/simple-token-healing/simple-token-healing-1.cpp Normal file
View file

@ -0,0 +1,232 @@
#include "common.h"
#include "llama.h"
#include <cmath>
#include <cstdio>
#include <string>
#include <vector>
static std::vector<llama_token> heal_last_token(const llama_context * ctx, const std::vector<llama_token> & tokens_list) {
const llama_token last_token_id = tokens_list.back();
const llama_model * model = llama_get_model(ctx);
const int32_t n_vocab = llama_n_vocab(model);
// Don't roll back e.g. <|endoftext|> (set parse_special=true in llama_tokenize)
if (llama_token_get_type(model, last_token_id) != LLAMA_TOKEN_TYPE_NORMAL) {
return {};
}
const std::string last_piece = llama_token_to_piece(ctx, last_token_id);
fprintf(stderr, "token_healing: prefix = '%s'\n", last_piece.c_str());
fprintf(stderr, "token_healing: candidates:\n");
fprintf(stderr, " [%6d] '%s'\n", last_token_id, last_piece.c_str());
std::vector<llama_token> candidates = { last_token_id };
for (llama_token token_id = 0; token_id < n_vocab; ++token_id) {
if (token_id == last_token_id) {
continue;
}
std::string token_piece = llama_token_to_piece(ctx, token_id);
if (token_piece.rfind(last_piece, 0) != std::string::npos) {
candidates.push_back(token_id);
fprintf(stderr, " [%6d] '%s'\n", token_id, token_piece.c_str());
}
}
if (candidates.size() == 1) {
// No healing necessary if the last token is the only candidate.
return {};
}
return candidates;
}
int main(int argc, char ** argv) {
gpt_params params;
if (argc == 1 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]);
return 1 ;
}
if (argc >= 2) {
params.model = argv[1];
}
if (argc >= 3) {
params.prompt = argv[2];
}
if (params.prompt.empty()) {
params.prompt = "Hello my name is";
}
// total length of the sequence including the prompt
const int n_len = 32;
// init LLM
llama_backend_init();
llama_numa_init(params.numa);
// initialize the model
llama_model_params model_params = llama_model_default_params();
// model_params.n_gpu_layers = 99; // offload all layers to the GPU
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
if (model == NULL) {
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
return 1;
}
// initialize the context
llama_context_params ctx_params = llama_context_default_params();
ctx_params.seed = 1234;
ctx_params.n_ctx = 2048;
ctx_params.n_threads = params.n_threads;
ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
if (ctx == NULL) {
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
return 1;
}
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(ctx, params.prompt, true);
// Roll back the last token and constrain tokens to generate in the next step to match the removed last token.
std::vector<llama_token> token_healing_candidates = heal_last_token(ctx, tokens_list);
if (!token_healing_candidates.empty()) {
tokens_list.pop_back();
}
if (tokens_list.empty()) {
// If we remove the first token, llama_decode would crash with an empty sequence, so add bos.
tokens_list.emplace_back(llama_token_bos(model));
}
const int n_ctx = llama_n_ctx(ctx);
const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_kv_req);
// make sure the KV cache is big enough to hold all the prompt and generated tokens
if (n_kv_req > n_ctx) {
LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
LOG_TEE("%s: either reduce n_len or increase n_ctx\n", __func__);
return 1;
}
// print the prompt token-by-token
fprintf(stderr, "\n");
for (auto id : tokens_list) {
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
}
fflush(stderr);
// create a llama_batch with size 512
// we use this object to submit token data for decoding
llama_batch batch = llama_batch_init(512, 0, 1);
// evaluate the initial prompt
for (size_t i = 0; i < tokens_list.size(); i++) {
llama_batch_add(batch, tokens_list[i], i, { 0 }, false);
}
// llama_decode will output logits only for the last token of the prompt
batch.logits[batch.n_tokens - 1] = true;
if (llama_decode(ctx, batch) != 0) {
LOG_TEE("%s: llama_decode() failed\n", __func__);
return 1;
}
// main loop
int n_cur = batch.n_tokens;
int n_decode = 0;
const auto t_main_start = ggml_time_us();
while (n_cur <= n_len) {
// sample the next token
{
auto n_vocab = llama_n_vocab(model);
auto * logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
if (n_decode == 0 && !token_healing_candidates.empty()) {
for (const llama_token token_id : token_healing_candidates) {
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
}
} else {
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 };
// sample the most likely token
const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
// is it an end of generation?
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
LOG_TEE("\n");
break;
}
LOG_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str());
fflush(stdout);
// prepare the next batch
llama_batch_clear(batch);
// push this new token for next evaluation
llama_batch_add(batch, new_token_id, n_cur, { 0 }, true);
n_decode += 1;
}
n_cur += 1;
// evaluate the current batch with the transformer model
if (llama_decode(ctx, batch)) {
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
return 1;
}
}
LOG_TEE("\n");
const auto t_main_end = ggml_time_us();
LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
__func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
llama_print_timings(ctx);
fprintf(stderr, "\n");
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}

353
examples/simple-token-healing/simple-token-healing.cpp Normal file
View file

@ -0,0 +1,353 @@
#include "common.h"
#include "llama.h"
#include <cmath>
#include <cstdio>
#include <string>
#include <vector>
#define TH_VERBOSE // print token healing candidates
enum class token_healing_type : uint8_t {
LAST, // replace last token only
MULTI_ONCE, // replace multiple last tokens with a single token
MULTI // replace multiple last tokens with multiple decoding steps
};
struct token_healing_context {
std::string prefix; // remaining prefix to generate (the input prompt's suffix)
std::vector<std::string> vocab; // map token id to token piece
// TODO consider using a prefix tree
};
static inline bool startswith(const std::string & str, const std::string & prefix) {
return str.rfind(prefix, 0) != std::string::npos;
}
static bool token_healing_prefix_exists(const token_healing_context * th_ctx, const std::string & prefix) {
for (const std::string & token : th_ctx->vocab) {
if (startswith(token, prefix)) {
return true;
}
}
return false;
}
static std::vector<llama_token> token_healing_find_prefix(
const token_healing_context * th_ctx,
const std::string & prefix,
const bool include_partial_prefix) {
// Example: prefix=" world" -> " world", " worldwide", ...
// If `include_partial_prefix`, include also: " w", " wo", ...
std::vector<llama_token> candidates;
const auto & vocab = th_ctx->vocab;
for (size_t token_id = 0; token_id < vocab.size(); ++token_id) {
if (startswith(vocab[token_id], prefix)
|| (include_partial_prefix && startswith(prefix, vocab[token_id]))) {
candidates.push_back((llama_token)token_id);
}
}
return candidates;
}
static token_healing_context * token_healing_init(const llama_context * ctx) {
auto * th_ctx = new token_healing_context;
const llama_model * model = llama_get_model(ctx);
const int32_t n_vocab = llama_n_vocab(model);
std::vector<std::string> & vocab = th_ctx->vocab;
vocab.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
vocab.emplace_back(llama_token_to_piece(ctx, token_id, true));
}
return th_ctx;
}
static void token_healing_free(token_healing_context * th_ctx) {
delete th_ctx;
}
static int token_healing_start(
const llama_context * ctx,
std::vector<llama_token> & tokens_list,
const token_healing_type th_type,
token_healing_context * th_ctx) {
if (tokens_list.empty()) {
return 0;
}
const llama_model * model = llama_get_model(ctx);
const int n_ctx = tokens_list.size();
const int max_to_remove = (th_type == token_healing_type::LAST) ? 1 : n_ctx;
int n_removed = 0;
std::string prefix;
// Backtrack tokens until there does not exist a token that can cover the prompt
while (n_removed < max_to_remove) {
const llama_token next_token = tokens_list[n_ctx - n_removed - 1];
if (llama_token_get_type(model, next_token) != LLAMA_TOKEN_TYPE_NORMAL) {
// Don't roll back e.g. <|endoftext|> (if parse_special=true in llama_tokenize)
break;
}
std::string new_prefix = llama_token_to_piece(ctx, next_token) + prefix;
if (!token_healing_prefix_exists(th_ctx, new_prefix)) {
break;
}
n_removed += 1;
prefix = new_prefix;
}
th_ctx->prefix = prefix;
if (n_removed == 0) {
return 0;
}
const std::vector<llama_token> candidates = token_healing_find_prefix(th_ctx, prefix, false);
fprintf(stderr, "token_healing: prefix = '%s' (%d tokens)\n", prefix.c_str(), n_removed);
if (n_removed == 1 && candidates.size() == 1) {
fprintf(stderr, "token_healing: nothing to heal\n");
return 0;
}
#ifdef TH_VERBOSE
if (th_type != token_healing_type::MULTI) {
for (const llama_token token_id : candidates) {
fprintf(stderr, " [%6d] '%s'\n", token_id, th_ctx->vocab[token_id].c_str());
}
}
#endif
for (int i = 0; i < n_removed; ++i) {
tokens_list.pop_back();
}
if (tokens_list.empty()) {
// If the first token was removed, llama_decode would crash with an empty sequence, so add bos.
tokens_list.emplace_back(llama_token_bos(model));
}
return n_removed;
}
int main(int argc, char ** argv) {
gpt_params params;
if (argc == 1 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH [PROMPT] [TOKEN_HEALING 0|1|n|m]\n" , argv[0]);
return 1 ;
}
if (argc >= 2) {
params.model = argv[1];
}
if (argc >= 3) {
params.prompt = argv[2];
}
bool token_healing_enabled = true;
auto th_type = token_healing_type::LAST;
if (argc >= 4) {
std::string value(argv[3]);
/**/ if (value == "0") { token_healing_enabled = false; }
else if (value == "1") { th_type = token_healing_type::LAST; }
else if (value == "n") { th_type = token_healing_type::MULTI_ONCE; }
else if (value == "m") { th_type = token_healing_type::MULTI; }
else {
printf("usage: %s MODEL_PATH [PROMPT] [TOKEN_HEALING 0|1|n|m]\n" , argv[0]);
return 1;
}
}
if (params.prompt.empty()) {
params.prompt = "Hello my name is";
}
// total length of the sequence including the prompt
const int n_len = 32;
// init LLM
llama_backend_init();
llama_numa_init(params.numa);
// initialize the model
llama_model_params model_params = llama_model_default_params();
// model_params.n_gpu_layers = 99; // offload all layers to the GPU
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
if (model == NULL) {
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
return 1;
}
// initialize the context
llama_context_params ctx_params = llama_context_default_params();
ctx_params.seed = 1234;
ctx_params.n_ctx = 2048;
ctx_params.n_threads = params.n_threads;
ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
if (ctx == NULL) {
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
return 1;
}
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(ctx, params.prompt, true);
token_healing_context * th_ctx = nullptr;
if (token_healing_enabled) {
th_ctx = token_healing_init(ctx);
int th_n_tokens_removed = token_healing_start(ctx, tokens_list, th_type, th_ctx);
if (th_n_tokens_removed == 0) {
token_healing_enabled = false;
}
}
const int n_ctx = llama_n_ctx(ctx);
const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_kv_req);
// make sure the KV cache is big enough to hold all the prompt and generated tokens
if (n_kv_req > n_ctx) {
LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
LOG_TEE("%s: either reduce n_len or increase n_ctx\n", __func__);
return 1;
}
// print the prompt token-by-token
fprintf(stderr, "\n");
for (auto id : tokens_list) {
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
}
fflush(stderr);
// create a llama_batch with size 512
// we use this object to submit token data for decoding
llama_batch batch = llama_batch_init(512, 0, 1);
// evaluate the initial prompt
for (size_t i = 0; i < tokens_list.size(); i++) {
llama_batch_add(batch, tokens_list[i], i, { 0 }, false);
}
// llama_decode will output logits only for the last token of the prompt
batch.logits[batch.n_tokens - 1] = true;
if (llama_decode(ctx, batch) != 0) {
LOG_TEE("%s: llama_decode() failed\n", __func__);
return 1;
}
// main loop
int n_cur = batch.n_tokens;
int n_decode = 0;
const auto t_main_start = ggml_time_us();
while (n_cur <= n_len) {
// sample the next token
{
auto n_vocab = llama_n_vocab(model);
auto * logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
if (token_healing_enabled) {
// Constrain tokens based on the remaining token healing prefix
// N.B. We could also set token constraints by setting rejected tokens' logits to -inf
std::vector<llama_token> th_candidates;
if (th_type == token_healing_type::LAST || th_type == token_healing_type::MULTI_ONCE) {
th_candidates = token_healing_find_prefix(th_ctx, th_ctx->prefix, false);
} else {
th_candidates = token_healing_find_prefix(th_ctx, th_ctx->prefix, true);
#ifdef TH_VERBOSE
fprintf(stderr, "\ntoken_healing: prefix = '%s'\n", th_ctx->prefix.c_str());
for (const llama_token token_id : th_candidates) {
fprintf(stderr, " [%6d] '%s'\n", token_id, th_ctx->vocab[token_id].c_str());
}
#endif
}
for (const llama_token token_id: th_candidates) {
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
}
} else {
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 };
// sample the most likely token
const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
// is it an end of generation?
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
LOG_TEE("\n");
break;
}
std::string new_token_piece = llama_token_to_piece(ctx, new_token_id);
LOG_TEE("%s", new_token_piece.c_str());
fflush(stdout);
if (token_healing_enabled) {
if (new_token_piece.size() < th_ctx->prefix.size()) {
// Shift prefix constraint (for multi step token healing)
th_ctx->prefix = th_ctx->prefix.substr(new_token_piece.size());
} else {
th_ctx->prefix.clear();
token_healing_enabled = false;
}
}
// prepare the next batch
llama_batch_clear(batch);
// push this new token for next evaluation
llama_batch_add(batch, new_token_id, n_cur, { 0 }, true);
n_decode += 1;
}
n_cur += 1;
// evaluate the current batch with the transformer model
if (llama_decode(ctx, batch)) {
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
return 1;
}
}
LOG_TEE("\n");
const auto t_main_end = ggml_time_us();
LOG_TEE("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
__func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
llama_print_timings(ctx);
fprintf(stderr, "\n");
token_healing_free(th_ctx);
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}