vbatts/llama.cpp
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examples : migrate to gpt_params

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ggml-ci
This commit is contained in:
Georgi Gerganov 2024-06-04 10:50:34 +03:00
parent b47e91ed75
commit e7e0381411
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GPG key ID: 449E073F9DC10735
5 changed files with 53 additions and 83 deletions
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9
common/common.cpp
View file

@ -1563,7 +1563,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "*", " --keep N", "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep });
options.push_back({ "*", " --chunks N", "max number of chunks to process (default: %d, -1 = all)", params.n_chunks });
options.push_back({ "*", "-fa, --flash-attn", "enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" });
options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with (default: empty)" });
options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with (default: '%s')", params.prompt.c_str() });
options.push_back({ "*", "-f, --file FNAME", "a file containing the prompt (default: none)" });
options.push_back({ "*", "-bf, --binary-file FNAME", "binary file containing the prompt (default: none)" });
options.push_back({ "*", "-e, --escape", "process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)" });
@ -1626,6 +1626,10 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
"JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\n"
"For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead" });
options.push_back({ "embedding" });
options.push_back({ "embedding", " --pooling {none,mean,cls}",
"pooling type for embeddings, use model default if unspecified" });
options.push_back({ "context hacking" });
options.push_back({ "*", " --rope-scaling {none,linear,yarn}",
"RoPE frequency scaling method, defaults to linear unless specified by the model" });
@ -1644,9 +1648,6 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "*", "-ctk, --cache-type-k TYPE", "KV cache data type for K (default: %s)", params.cache_type_k.c_str() });
options.push_back({ "*", "-ctv, --cache-type-v TYPE", "KV cache data type for V (default: %s)", params.cache_type_v.c_str() });
options.push_back({ "embedding", " --pooling {none,mean,cls}",
"pooling type for embeddings, use model default if unspecified" });
options.push_back({ "perplexity" });
options.push_back({ "perplexity", " --all-logits", "return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false" });
options.push_back({ "perplexity", " --hellaswag", "compute HellaSwag score over random tasks from datafile supplied with -f" });

2
examples/batched/README.md
View file

@ -3,7 +3,7 @@
The example demonstrates batched generation from a given prompt
```bash
./batched ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" 4
./batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4
...

69
examples/batched/batched.cpp
View file

@ -7,48 +7,31 @@
#include <string>
#include <vector>
static void print_usage(int argc, char ** argv, const gpt_params & params) {
gpt_params_print_usage(argc, argv, params);
LOG_TEE("\nexample usage:\n");
LOG_TEE("\n %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]);
LOG_TEE("\n");
}
int main(int argc, char ** argv) {
gpt_params params;
if (argc == 1 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN] [NGL]\n" , argv[0]);
params.prompt = "Hello my name is";
params.n_predict = 32;
if (!gpt_params_parse(argc, argv, params)) {
print_usage(argc, argv, params);
return 1;
}
// number of parallel batches
int n_parallel = 1;
int n_parallel = params.n_parallel;
// total length of the sequences including the prompt
int n_len = 32;
// number of layers to offload to the GPU
int n_gpu_layers = 0;
if (argc >= 2) {
params.model = argv[1];
}
if (argc >= 3) {
params.prompt = argv[2];
}
if (argc >= 4) {
n_parallel = std::atoi(argv[3]);
}
if (argc >= 5) {
n_len = std::atoi(argv[4]);
}
if (argc >= 6) {
n_gpu_layers = std::atoi(argv[5]);
}
if (params.prompt.empty()) {
params.prompt = "Hello my name is";
}
string_process_escapes(params.prompt);
int n_predict = 32;
// init LLM
@ -57,9 +40,7 @@ int main(int argc, char ** argv) {
// initialize the model
llama_model_params model_params = llama_model_default_params();
model_params.n_gpu_layers = n_gpu_layers;
llama_model_params model_params = llama_model_params_from_gpt_params(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
@ -73,18 +54,14 @@ int main(int argc, char ** argv) {
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize(model, params.prompt, true);
const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
// initialize the context
llama_context_params ctx_params = llama_context_default_params();
llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
ctx_params.seed = 1234;
ctx_params.n_ctx = n_kv_req;
ctx_params.n_batch = std::max(n_len, n_parallel);
ctx_params.n_seq_max = n_parallel;
ctx_params.n_threads = params.n_threads;
ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
ctx_params.n_batch = std::max(n_predict, n_parallel);
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
@ -95,7 +72,7 @@ int main(int argc, char ** argv) {
const int n_ctx = llama_n_ctx(ctx);
LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
LOG_TEE("\n%s: n_predict = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, ctx_params.n_batch, n_parallel, 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) {
@ -156,7 +133,7 @@ int main(int argc, char ** argv) {
const auto t_main_start = ggml_time_us();
while (n_cur <= n_len) {
while (n_cur <= n_predict) {
// prepare the next batch
llama_batch_clear(batch);
@ -192,7 +169,7 @@ int main(int argc, char ** argv) {
//const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
// is it an end of generation? -> mark the stream as finished
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
i_batch[i] = -1;
LOG_TEE("\n");
if (n_parallel > 1) {

2
examples/simple/README.md
View file

@ -3,7 +3,7 @@
The purpose of this example is to demonstrate a minimal usage of llama.cpp for generating text with a given prompt.
```bash
./simple ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is"
./simple -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is"
...

50
examples/simple/simple.cpp
View file

@ -6,28 +6,27 @@
#include <string>
#include <vector>
static void print_usage(int argc, char ** argv, const gpt_params & params) {
gpt_params_print_usage(argc, argv, params);
LOG_TEE("\nexample usage:\n");
LOG_TEE("\n %s -m model.gguf -p \"Hello my name is\" -n 32\n", argv[0]);
LOG_TEE("\n");
}
int main(int argc, char ** argv) {
gpt_params params;
if (argc == 1 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]);
params.prompt = "Hello my name is";
params.n_predict = 32;
if (!gpt_params_parse(argc, argv, params)) {
print_usage(argc, argv, params);
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;
const int n_predict = params.n_predict;
// init LLM
@ -36,9 +35,7 @@ int main(int argc, char ** argv) {
// 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_params model_params = llama_model_params_from_gpt_params(params);
llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
@ -49,12 +46,7 @@ int main(int argc, char ** argv) {
// 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_params ctx_params = llama_context_params_from_gpt_params(params);
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
@ -69,14 +61,14 @@ int main(int argc, char ** argv) {
tokens_list = ::llama_tokenize(ctx, params.prompt, true);
const int n_ctx = llama_n_ctx(ctx);
const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size());
const int n_kv_req = tokens_list.size() + (n_predict - 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);
LOG_TEE("\n%s: n_predict = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_predict, 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__);
LOG_TEE("%s: either reduce n_predict or increase n_ctx\n", __func__);
return 1;
}
@ -115,7 +107,7 @@ int main(int argc, char ** argv) {
const auto t_main_start = ggml_time_us();
while (n_cur <= n_len) {
while (n_cur <= n_predict) {
// sample the next token
{
auto n_vocab = llama_n_vocab(model);
@ -134,7 +126,7 @@ int main(int argc, char ** argv) {
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) {
if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
LOG_TEE("\n");
break;