aditya/llama.cpp
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perplexity : add support for batch size to --perplexity (#407)

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* Add support to batch size for perplexity

* Revert "Fix memory allocation issues and seg faults"

This reverts commit 4870e455b3.

* update from merge

* Remove perplexity from main

* updates

* Update batch size for efficiency
This commit is contained in:
Gary Linscott 2023-04-13 14:50:42 -07:00 committed by GitHub
parent 0e07e6a839
commit be87b6ed20
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1 changed files with 21 additions and 15 deletions
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36
examples/perplexity/perplexity.cpp
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@ -27,20 +27,27 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
int count = 0;
int seq_count = tokens.size() / params.n_ctx;
int n_vocab = llama_n_vocab(ctx);
double nll = 0.0;
fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count);
fprintf(stderr, "%s : calculating perplexity over %d chunks, batch_size=%d\n", __func__, seq_count, params.n_batch);
for (int i = 0; i < seq_count; ++i) {
int start = i * params.n_ctx;
int end = start + params.n_ctx - 1; // TODO: this is not optimal, e.g. it makes the batch 511 instead of 512
// it is better to always be power of 2 for better performance
std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);
int end = start + params.n_ctx;
std::vector<float> logits;
int num_batches = (params.n_ctx + params.n_batch - 1) / params.n_batch;
auto start_t = std::chrono::high_resolution_clock::now();
if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
for (int j = 0; j < num_batches; ++j) {
int batch_start = start + j * params.n_batch;
int batch_size = std::min(end - batch_start, params.n_batch);
if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * params.n_batch, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
}
auto batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
}
auto end_t = std::chrono::high_resolution_clock::now();
if (i == 0) {
@ -59,15 +66,12 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
// Example, we have a context window of 512, we will compute perplexity for each of the
// last 256 tokens. Then, we split the input up into context window size chunks to
// process the entire prompt.
auto logits = llama_get_logits(ctx);
for (int j = params.n_ctx / 2; j < params.n_ctx - 1; ++j) {
for (int j = std::min(512, params.n_ctx / 2); j < params.n_ctx - 1; ++j) {
// Calculate probability of next token, given the previous ones.
int n_vocab = llama_n_vocab(ctx);
std::vector<float> tok_logits(
logits + j * n_vocab,
logits + (j + 1) * n_vocab);
const float prob = softmax(tok_logits)[tokens[start + j + 1]];
logits.begin() + j * n_vocab,
logits.begin() + (j + 1) * n_vocab);
float prob = softmax(tok_logits)[tokens[start + j + 1]];
nll += -std::log(prob);
++count;
}
@ -82,11 +86,13 @@ int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
params.n_batch = 512;
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
params.perplexity = true;
params.n_batch = std::min(params.n_batch, params.n_ctx);
if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"