vbatts/llama.cpp
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common : use common_ prefix for common library functions (#9805)

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* common : use common_ prefix for common library functions

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Diego Devesa 2024-10-10 22:57:42 +02:00 • committed by GitHub
parent 0e9f760eb1
commit 7eee341bee
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45 changed files with 1284 additions and 1284 deletions
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58
examples/perplexity/perplexity.cpp
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@ -35,7 +35,7 @@ struct results_log_softmax {
};
static void write_logfile(
const llama_context * ctx, const gpt_params & params, const llama_model * model,
const llama_context * ctx, const common_params & params, const llama_model * model,
const struct results_perplexity & results
) {
if (params.logdir.empty()) {
@ -339,7 +339,7 @@ static void process_logits(int n_vocab, const float * logits, const int * tokens
}
}
static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) {
static results_perplexity perplexity_v2(llama_context * ctx, const common_params & params) {
// Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
// Output: `perplexity: 13.5106 [114/114]`
@ -350,7 +350,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
LOG_INF("%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true);
const int n_ctx = llama_n_ctx(ctx);
@ -474,7 +474,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
return {tokens, std::exp(nll / count), logit_history, prob_history};
}
static results_perplexity perplexity(llama_context * ctx, const gpt_params & params, const int32_t n_ctx) {
static results_perplexity perplexity(llama_context * ctx, const common_params & params, const int32_t n_ctx) {
if (params.ppl_stride > 0) {
return perplexity_v2(ctx, params);
}
@ -502,7 +502,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
auto tim1 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true);
auto tim2 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
@ -772,7 +772,7 @@ static void compute_logprobs(const float * batch_logits, int n_vocab, std::vecto
}
}
static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
static void hellaswag_score(llama_context * ctx, const common_params & params) {
// Calculates hellaswag score (acc_norm) from prompt
//
// Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
@ -853,7 +853,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
for (size_t j = 0; j < 4; j++) {
hs_cur.ending[j] = prompt_lines[idx*6+2+j];
hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true);
hs_cur.seq_tokens[j] = common_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true);
}
// determine the common prefix of the endings
@ -910,7 +910,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
size_t i1 = i0;
size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
llama_batch_clear(batch);
common_batch_clear(batch);
// batch as much tasks as possible into the available context
// each task has 4 unique sequence ids - one for each ending
@ -926,7 +926,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
}
for (size_t i = 0; i < hs_cur.common_prefix; ++i) {
llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false);
common_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false);
}
batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
n_logits += 1;
@ -936,7 +936,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
// TODO: don't evaluate the last token of each sequence
for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) {
const bool needs_logits = i < seq_tokens_size - 1;
llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits);
common_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits);
n_logits += needs_logits;
}
}
@ -1112,7 +1112,7 @@ static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string
* 0,Sarah was a much better surgeon than Maria so _ always got the easier cases.,Sarah,Maria,2
*
*/
static void winogrande_score(llama_context * ctx, const gpt_params & params) {
static void winogrande_score(llama_context * ctx, const common_params & params) {
constexpr int k_min_trailing_ctx = 3;
@ -1146,8 +1146,8 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
LOG_INF("%s : tokenizing selected tasks\n", __func__);
for (auto & task : data) {
task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, true);
task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, true);
task.seq_tokens[0] = common_tokenize(ctx, task.first + task.choices[0] + task.second, true);
task.seq_tokens[1] = common_tokenize(ctx, task.first + task.choices[1] + task.second, true);
task.common_prefix = 0;
for (size_t k = 0; k < task.seq_tokens[0].size(); k++) {
@ -1162,8 +1162,8 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
task.seq_tokens[0].size() - task.common_prefix +
task.seq_tokens[1].size() - task.common_prefix;
task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], true).size();
task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], true).size();
task.n_base1 = common_tokenize(ctx, task.first + task.choices[0], true).size();
task.n_base2 = common_tokenize(ctx, task.first + task.choices[1], true).size();
}
LOG_INF("%s : calculating winogrande score over selected tasks.\n", __func__);
@ -1195,7 +1195,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
size_t i1 = i0;
size_t i_logits = 0;
llama_batch_clear(batch);
common_batch_clear(batch);
while (n_cur + (int) data[i1].required_tokens <= n_ctx) {
int n_logits = 0;
@ -1205,7 +1205,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
}
for (size_t i = 0; i < data[i1].common_prefix; ++i) {
llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false);
common_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false);
}
batch.logits[batch.n_tokens - 1] = true;
n_logits += 1;
@ -1213,7 +1213,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
for (int s = 0; s < 2; ++s) {
// TODO: end before the last token, no need to predict past the end of the sequences
for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) {
llama_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true);
common_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true);
n_logits += 1;
}
}
@ -1370,7 +1370,7 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choic
}
return false;
}
task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, true));
task.seq_tokens.emplace_back(::common_tokenize(ctx, task.question + " " + answer, true));
}
auto min_len = task.seq_tokens.front().size();
for (auto& seq : task.seq_tokens) {
@ -1414,7 +1414,7 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choic
// git@hf.co:datasets/Stevross/mmlu
// https://huggingface.co/datasets/truthful_qa
//
static void multiple_choice_score(llama_context * ctx, const gpt_params & params) {
static void multiple_choice_score(llama_context * ctx, const common_params & params) {
std::istringstream strstream(params.prompt);
uint32_t n_task;
@ -1548,7 +1548,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
size_t i1 = i0;
size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
llama_batch_clear(batch);
common_batch_clear(batch);
// batch as much tasks as possible into the available context
// each task has 4 unique sequence ids - one for each ending
@ -1571,7 +1571,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
for (size_t i = 0; i < cur_task.common_prefix; ++i) {
//llama_batch_add(batch, cur_task.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
llama_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
common_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
}
batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
n_logits += 1;
@ -1581,7 +1581,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
// TODO: don't evaluate the last token of each sequence
for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) {
const bool needs_logits = i < seq_tokens_size - 1;
llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits);
common_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits);
n_logits += needs_logits;
}
}
@ -1695,7 +1695,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
LOG_INF("\n");
}
static void kl_divergence(llama_context * ctx, const gpt_params & params) {
static void kl_divergence(llama_context * ctx, const common_params & params) {
if (params.logits_file.empty()) {
LOG_ERR("%s: you must provide a name of a file containing the log probabilities of the base model\n", __func__);
return;
@ -1968,17 +1968,17 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
params.n_ctx = 512;
params.logits_all = true;
params.escape = false;
if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
return 1;
}
gpt_init();
common_init();
const int32_t n_ctx = params.n_ctx;
@ -2017,7 +2017,7 @@ int main(int argc, char ** argv) {
llama_numa_init(params.numa);
// load the model and apply lora adapter, if any
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
@ -2036,7 +2036,7 @@ int main(int argc, char ** argv) {
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", gpt_params_get_system_info(params).c_str());
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
}
struct results_perplexity results;