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author | klosax <131523366+klosax@users.noreply.github.com> | 2023-07-22 14:21:24 +0200 |
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committer | GitHub <noreply@github.com> | 2023-07-22 14:21:24 +0200 |
commit | b5fe67f8c69113bd9354bc1adcfe2df6be323740 (patch) | |
tree | 705c322554824091c35e885214ae19462773eab0 /examples/perplexity | |
parent | 24baa54ac1ff3d4156a2360deb1473af04a9b1a2 (diff) |
Perplexity: Compute scores correlated to HellaSwag (#2312)
* Add parameter --perplexity-lines to perplexity.cpp
Diffstat (limited to 'examples/perplexity')
-rw-r--r-- | examples/perplexity/perplexity.cpp | 78 |
1 files changed, 77 insertions, 1 deletions
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index bfad999..d23b7e7 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -4,6 +4,7 @@ #include <cmath> #include <ctime> +#include <sstream> #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -120,6 +121,77 @@ void perplexity(llama_context * ctx, const gpt_params & params) { printf("\n"); } +void perplexity_lines(llama_context * ctx, const gpt_params & params) { + // Calculates perplexity over each line of the prompt + + std::vector<std::string> prompt_lines; + std::istringstream strstream(params.prompt); + std::string line; + + while (std::getline(strstream,line,'\n')) { + prompt_lines.push_back(line); + } + + const int n_vocab = llama_n_vocab(ctx); + + int counttotal = 0; + size_t n_lines = prompt_lines.size(); + + double nll = 0.0; + + fprintf(stderr, "%s: calculating perplexity over %lu lines\n", __func__, n_lines); + + printf("\nLine\tPPL line\tPPL cumulative\n"); + + for (size_t i = 0; i < n_lines; ++i) { + + // Tokenize and insert BOS at start + std::vector<int> batch_embd = ::llama_tokenize(ctx, prompt_lines[i], true); + + size_t batch_size = batch_embd.size(); + + // Stop if line is too long + if( batch_size > (size_t)params.n_ctx ) { + fprintf(stderr, "%s : tokens in line %lu > n_ctxl\n", __func__, i); + return; + } + + if (llama_eval(ctx, batch_embd.data(), batch_size, 0, params.n_threads)) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return; + } + + const auto batch_logits = llama_get_logits(ctx); + std::vector<float> logits; + logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); + + double nllline = 0.0; + int countline = 0; + + // Perplexity over second half of the line + for (size_t j = batch_size/2; j < batch_size - 1; ++j) { + // Calculate probability of next token, given the previous ones. + const std::vector<float> tok_logits( + logits.begin() + (j + 0) * n_vocab, + logits.begin() + (j + 1) * n_vocab); + + const float prob = softmax(tok_logits)[batch_embd[ j + 1]]; + + nllline += -std::log(prob); + ++countline; + } + + nll += nllline; + counttotal += countline; + + // perplexity is e^(average negative log-likelihood) + printf("%lu\t%.8lf\t%.8lf\n", i + 1, std::exp(nllline/countline), std::exp(nll / counttotal) ); + fflush(stdout); + } + + printf("\n"); +} + int main(int argc, char ** argv) { gpt_params params; @@ -168,7 +240,11 @@ int main(int argc, char ** argv) { params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); } - perplexity(ctx, params); + if (params.perplexity_lines) { + perplexity_lines(ctx, params); + } else { + perplexity(ctx, params); + } llama_print_timings(ctx); llama_free(ctx); |