diff options
Diffstat (limited to 'examples/perplexity')
| -rw-r--r-- | examples/perplexity/perplexity.cpp | 179 |
1 files changed, 140 insertions, 39 deletions
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index d23b7e7..6870a11 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -121,8 +121,23 @@ 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 +void hellaswag_score(llama_context * ctx, const gpt_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 + // All used data fields are preprocessed as in https://github.com/EleutherAI/lm-evaluation-harness/blob/df3da98c5405deafd519c2ddca52bb7c3fe36bef/lm_eval/tasks/hellaswag.py#L62-L68 + // + // All 10042 tasks should be extracted to keep the results standardized like other implementations. + // + // Datafile layout: + // ['??'] denotes json fields + // 6 lines per task: + // ['activity_label'] + ": " +['ctx'] - The first part of the query, the context + // ['label'] - The index the best common sense ending aka gold ending + // ['endings'][0] - Endings added to the first part of the query + // ['endings'][1] + // ['endings'][2] + // ['endings'][3] std::vector<std::string> prompt_lines; std::istringstream strstream(params.prompt); @@ -132,63 +147,149 @@ void perplexity_lines(llama_context * ctx, const gpt_params & params) { prompt_lines.push_back(line); } - const int n_vocab = llama_n_vocab(ctx); + if( prompt_lines.size() % 6 != 0) { + fprintf(stderr, "%s : number of lines in prompt not a multiple of 6.\n", __func__); + return; + } - int counttotal = 0; - size_t n_lines = prompt_lines.size(); + size_t hs_task_count = prompt_lines.size()/6; + fprintf(stderr, "%s : loaded %lu tasks from prompt.\n", __func__, hs_task_count); - double nll = 0.0; + // This is needed as usual for LLaMA models + bool prepend_bos = true; + + // Number of tasks to use when computing the score + if ( params.hellaswag_tasks < hs_task_count ) { + hs_task_count = params.hellaswag_tasks; + } - fprintf(stderr, "%s: calculating perplexity over %lu lines\n", __func__, n_lines); + // The tasks should be randomized so the score stabilizes quickly. + bool randomize_tasks = true; - printf("\nLine\tPPL line\tPPL cumulative\n"); + // The random seed should not impact the final result if the computation is done over enough tasks, so kept hardcoded for now + std::mt19937 rng(1); - for (size_t i = 0; i < n_lines; ++i) { + // Dataholder for hellaswag tasks + struct hs_data_t { + std::string context; + size_t gold_ending_idx; + std::string ending[4]; + size_t ending_logprob_count[4]; + double ending_logprob[4]; + }; - // Tokenize and insert BOS at start - std::vector<int> batch_embd = ::llama_tokenize(ctx, prompt_lines[i], true); + fprintf(stderr, "%s : selecting %lu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first") ); - size_t batch_size = batch_embd.size(); + // Select and read data from prompt lines + hs_data_t *hs_data = new hs_data_t[hs_task_count]; + for (size_t i=0; i < hs_task_count; i++) { + size_t idx = i; - // 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; + // Select a random example of those left in the prompt + if (randomize_tasks) { + std::uniform_int_distribution<size_t> dist(0, prompt_lines.size()/6-1 ) ; + idx = dist(rng); } - if (llama_eval(ctx, batch_embd.data(), batch_size, 0, params.n_threads)) { - fprintf(stderr, "%s : failed to eval\n", __func__); - return; + hs_data[i].context = prompt_lines[idx*6]; + hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] ); + for (size_t j=0; j < 4; j++) { + hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j]; } - const auto batch_logits = llama_get_logits(ctx); - std::vector<float> logits; - logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); + // Delete the selected random example from the prompt + if (randomize_tasks) { + prompt_lines.erase( std::next(prompt_lines.begin(),idx*6) , std::next(prompt_lines.begin(),idx*6+6) ); + } + } - double nllline = 0.0; - int countline = 0; + fprintf(stderr, "%s : calculating hellaswag score over selected tasks.\n", __func__); + printf("\ntask\tacc_norm\n"); - // 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); + double acc = 0.0f; + const int n_vocab = llama_n_vocab(ctx); + + for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) { + + // Tokenize the context to count tokens + std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, prepend_bos); + size_t context_size = context_embd.size(); + + for (size_t ending_idx=0;ending_idx<4;ending_idx++) { + + // Tokenize the query + std::vector<int> query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[ending_idx], prepend_bos); + size_t query_size = query_embd.size(); + + // Stop if query wont fit the ctx window + if (query_size > (size_t)params.n_ctx) { + fprintf(stderr, "%s : number of tokens in query %lu > n_ctxl\n", __func__, query_size); + return; + } - const float prob = softmax(tok_logits)[batch_embd[ j + 1]]; + // Speedup small evaluations by evaluating atleast 32 tokens + if (query_size < 32) { + query_embd.resize(32); + } + + // Evaluate the query + if (llama_eval(ctx, query_embd.data(), query_embd.size(), 0, params.n_threads)) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return; + } + + const auto query_logits = llama_get_logits(ctx); + std::vector<float> logits; + logits.insert(logits.end(), query_logits, query_logits + query_size * n_vocab); + + hs_data[task_idx].ending_logprob_count[ending_idx] = 0; + hs_data[task_idx].ending_logprob[ending_idx] = 0.0f; + + // Calculate the logprobs over the ending + for (size_t j = context_size-1; j < query_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)[query_embd[ j + 1]]; + + hs_data[task_idx].ending_logprob[ending_idx] += std::log(prob); + hs_data[task_idx].ending_logprob_count[ending_idx]++; + } + + // Calculate the mean token logprob for acc_norm + hs_data[task_idx].ending_logprob[ending_idx] /= hs_data[task_idx].ending_logprob_count[ending_idx]; + + +// printf("task %lu, ending %lu, whole_len %lu, context_len %lu, ending_logprob_count %lu, ending_logprob %.4f\n", +// task_idx,ending_idx,whole_size,context_size, hs_data[task_idx].ending_logprob_count[ending_idx], hs_data[task_idx].ending_logprob[ending_idx] ); + } - nllline += -std::log(prob); - ++countline; + // Find the ending with maximum logprob + size_t ending_logprob_max_idx = -1; + double ending_logprob_max_val = -INFINITY; + for (size_t j=0; j < 4; j++) { + if (hs_data[task_idx].ending_logprob[j] > ending_logprob_max_val) { + ending_logprob_max_idx = j; + ending_logprob_max_val = hs_data[task_idx].ending_logprob[j]; + } } - nll += nllline; - counttotal += countline; +// printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_data[task_idx].gold_ending_idx); - // perplexity is e^(average negative log-likelihood) - printf("%lu\t%.8lf\t%.8lf\n", i + 1, std::exp(nllline/countline), std::exp(nll / counttotal) ); + // If the gold ending got the maximum logprobe add one accuracy point + if (ending_logprob_max_idx == hs_data[task_idx].gold_ending_idx) { + acc += 1.0; + } + + // Print the accumulated accuracy mean x 100 + printf("%li\t%.8lf\n",task_idx+1, acc/double(task_idx+1)*100.0); fflush(stdout); } + delete [] hs_data; + printf("\n"); } @@ -240,8 +341,8 @@ int main(int argc, char ** argv) { params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); } - if (params.perplexity_lines) { - perplexity_lines(ctx, params); + if (params.hellaswag) { + hellaswag_score(ctx, params); } else { perplexity(ctx, params); } |