8a88e5855c
* common.h : add hellaswag / remove perplexity-lines * common.cpp : add hellaswag / remove perplexity-lines * perplexity.cpp : add hellswag scores / remove perplexity-lines * perplexity.cpp : clean up * common.h : change default param value * common.cpp : Change default param * perplexity.cpp : alter wording * common.h : alter wording * common.cpp : alter wording
358 lines
13 KiB
C++
358 lines
13 KiB
C++
#include "common.h"
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#include "llama.h"
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#include "build-info.h"
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#include <cmath>
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#include <ctime>
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#include <sstream>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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std::vector<float> softmax(const std::vector<float>& logits) {
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std::vector<float> probs(logits.size());
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float max_logit = logits[0];
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for (float v : logits) max_logit = std::max(max_logit, v);
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double sum_exp = 0.0;
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for (size_t i = 0; i < logits.size(); i++) {
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// Subtract the maximum logit value from the current logit value for numerical stability
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const float logit = logits[i] - max_logit;
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const float exp_logit = expf(logit);
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sum_exp += exp_logit;
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probs[i] = exp_logit;
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}
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for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;
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return probs;
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}
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void perplexity(llama_context * ctx, const gpt_params & params) {
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// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
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// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
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// Output: `perplexity: 13.5106 [114/114]`
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// BOS tokens will be added for each chunk before eval
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auto tokens = ::llama_tokenize(ctx, params.prompt, true);
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const int n_chunk_max = tokens.size() / params.n_ctx;
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const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
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const int n_vocab = llama_n_vocab(ctx);
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const int n_batch = params.n_batch;
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int count = 0;
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double nll = 0.0;
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fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
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for (int i = 0; i < n_chunk; ++i) {
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const int start = i * params.n_ctx;
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const int end = start + params.n_ctx;
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const int num_batches = (params.n_ctx + n_batch - 1) / n_batch;
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std::vector<float> logits;
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const auto t_start = std::chrono::high_resolution_clock::now();
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for (int j = 0; j < num_batches; ++j) {
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const int batch_start = start + j * n_batch;
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const int batch_size = std::min(end - batch_start, n_batch);
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// save original token and restore it after eval
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const auto token_org = tokens[batch_start];
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// add BOS token for the first batch of each chunk
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if (j == 0) {
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tokens[batch_start] = llama_token_bos();
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}
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if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return;
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}
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// restore the original token in case it was set to BOS
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tokens[batch_start] = token_org;
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const auto batch_logits = llama_get_logits(ctx);
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logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
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}
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const auto t_end = std::chrono::high_resolution_clock::now();
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if (i == 0) {
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const float t_total = std::chrono::duration<float>(t_end - t_start).count();
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fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
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int total_seconds = (int)(t_total * n_chunk);
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if (total_seconds >= 60*60) {
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fprintf(stderr, "%d hours ", total_seconds / (60*60));
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total_seconds = total_seconds % (60*60);
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}
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fprintf(stderr, "%d minutes\n", total_seconds / 60);
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}
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// We get the logits for all the tokens in the context window (params.n_ctx)
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// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,
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// calculate the perplexity over the last half of the window (so the model always has
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// some context to predict the token).
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//
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// We rely on the fact that attention in the forward pass only looks at previous
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// tokens here, so the logits returned for each token are an accurate representation
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// of what the model would have predicted at that point.
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//
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// Example, we have a context window of 512, we will compute perplexity for each of the
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// last 256 tokens. Then, we split the input up into context window size chunks to
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// process the entire prompt.
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for (int j = std::min(512, params.n_ctx / 2); j < params.n_ctx - 1; ++j) {
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// Calculate probability of next token, given the previous ones.
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const std::vector<float> tok_logits(
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logits.begin() + (j + 0) * n_vocab,
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logits.begin() + (j + 1) * n_vocab);
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const float prob = softmax(tok_logits)[tokens[start + j + 1]];
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nll += -std::log(prob);
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++count;
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}
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// perplexity is e^(average negative log-likelihood)
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printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
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fflush(stdout);
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}
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printf("\n");
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}
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void hellaswag_score(llama_context * ctx, const gpt_params & params) {
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// Calculates hellaswag score (acc_norm) from prompt
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//
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// Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
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// All used data fields are preprocessed as in https://github.com/EleutherAI/lm-evaluation-harness/blob/df3da98c5405deafd519c2ddca52bb7c3fe36bef/lm_eval/tasks/hellaswag.py#L62-L68
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//
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// All 10042 tasks should be extracted to keep the results standardized like other implementations.
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//
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// Datafile layout:
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// ['??'] denotes json fields
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// 6 lines per task:
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// ['activity_label'] + ": " +['ctx'] - The first part of the query, the context
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// ['label'] - The index the best common sense ending aka gold ending
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// ['endings'][0] - Endings added to the first part of the query
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// ['endings'][1]
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// ['endings'][2]
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// ['endings'][3]
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std::vector<std::string> prompt_lines;
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std::istringstream strstream(params.prompt);
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std::string line;
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while (std::getline(strstream,line,'\n')) {
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prompt_lines.push_back(line);
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}
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if( prompt_lines.size() % 6 != 0) {
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fprintf(stderr, "%s : number of lines in prompt not a multiple of 6.\n", __func__);
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return;
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}
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size_t hs_task_count = prompt_lines.size()/6;
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fprintf(stderr, "%s : loaded %lu tasks from prompt.\n", __func__, hs_task_count);
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// This is needed as usual for LLaMA models
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bool prepend_bos = true;
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// Number of tasks to use when computing the score
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if ( params.hellaswag_tasks < hs_task_count ) {
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hs_task_count = params.hellaswag_tasks;
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}
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// The tasks should be randomized so the score stabilizes quickly.
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bool randomize_tasks = true;
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// The random seed should not impact the final result if the computation is done over enough tasks, so kept hardcoded for now
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std::mt19937 rng(1);
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// Dataholder for hellaswag tasks
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struct hs_data_t {
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std::string context;
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size_t gold_ending_idx;
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std::string ending[4];
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size_t ending_logprob_count[4];
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double ending_logprob[4];
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};
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fprintf(stderr, "%s : selecting %lu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first") );
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// Select and read data from prompt lines
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hs_data_t *hs_data = new hs_data_t[hs_task_count];
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for (size_t i=0; i < hs_task_count; i++) {
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size_t idx = i;
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// Select a random example of those left in the prompt
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if (randomize_tasks) {
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std::uniform_int_distribution<size_t> dist(0, prompt_lines.size()/6-1 ) ;
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idx = dist(rng);
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}
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hs_data[i].context = prompt_lines[idx*6];
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hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
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for (size_t j=0; j < 4; j++) {
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hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j];
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}
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// Delete the selected random example from the prompt
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if (randomize_tasks) {
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prompt_lines.erase( std::next(prompt_lines.begin(),idx*6) , std::next(prompt_lines.begin(),idx*6+6) );
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}
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}
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fprintf(stderr, "%s : calculating hellaswag score over selected tasks.\n", __func__);
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printf("\ntask\tacc_norm\n");
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double acc = 0.0f;
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const int n_vocab = llama_n_vocab(ctx);
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for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
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// Tokenize the context to count tokens
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std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, prepend_bos);
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size_t context_size = context_embd.size();
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for (size_t ending_idx=0;ending_idx<4;ending_idx++) {
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// Tokenize the query
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std::vector<int> query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[ending_idx], prepend_bos);
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size_t query_size = query_embd.size();
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// Stop if query wont fit the ctx window
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if (query_size > (size_t)params.n_ctx) {
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fprintf(stderr, "%s : number of tokens in query %lu > n_ctxl\n", __func__, query_size);
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return;
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}
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// Speedup small evaluations by evaluating atleast 32 tokens
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if (query_size < 32) {
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query_embd.resize(32);
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}
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// Evaluate the query
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if (llama_eval(ctx, query_embd.data(), query_embd.size(), 0, params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return;
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}
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const auto query_logits = llama_get_logits(ctx);
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std::vector<float> logits;
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logits.insert(logits.end(), query_logits, query_logits + query_size * n_vocab);
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hs_data[task_idx].ending_logprob_count[ending_idx] = 0;
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hs_data[task_idx].ending_logprob[ending_idx] = 0.0f;
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// Calculate the logprobs over the ending
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for (size_t j = context_size-1; j < query_size - 1; j++) {
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// Calculate probability of next token, given the previous ones.
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const std::vector<float> tok_logits(
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logits.begin() + (j + 0) * n_vocab,
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logits.begin() + (j + 1) * n_vocab);
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const float prob = softmax(tok_logits)[query_embd[ j + 1]];
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hs_data[task_idx].ending_logprob[ending_idx] += std::log(prob);
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hs_data[task_idx].ending_logprob_count[ending_idx]++;
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}
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// Calculate the mean token logprob for acc_norm
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hs_data[task_idx].ending_logprob[ending_idx] /= hs_data[task_idx].ending_logprob_count[ending_idx];
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// printf("task %lu, ending %lu, whole_len %lu, context_len %lu, ending_logprob_count %lu, ending_logprob %.4f\n",
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// 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] );
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}
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// Find the ending with maximum logprob
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size_t ending_logprob_max_idx = -1;
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double ending_logprob_max_val = -INFINITY;
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for (size_t j=0; j < 4; j++) {
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if (hs_data[task_idx].ending_logprob[j] > ending_logprob_max_val) {
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ending_logprob_max_idx = j;
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ending_logprob_max_val = hs_data[task_idx].ending_logprob[j];
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}
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}
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// printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_data[task_idx].gold_ending_idx);
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// If the gold ending got the maximum logprobe add one accuracy point
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if (ending_logprob_max_idx == hs_data[task_idx].gold_ending_idx) {
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acc += 1.0;
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}
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// Print the accumulated accuracy mean x 100
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printf("%li\t%.8lf\n",task_idx+1, acc/double(task_idx+1)*100.0);
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fflush(stdout);
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}
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delete [] hs_data;
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printf("\n");
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}
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int main(int argc, char ** argv) {
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gpt_params params;
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params.n_batch = 512;
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if (gpt_params_parse(argc, argv, params) == false) {
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return 1;
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}
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params.perplexity = true;
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params.n_batch = std::min(params.n_batch, params.n_ctx);
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if (params.n_ctx > 2048) {
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fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
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"expect poor results\n", __func__, params.n_ctx);
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}
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fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
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if (params.seed == LLAMA_DEFAULT_SEED) {
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params.seed = time(NULL);
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}
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fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
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std::mt19937 rng(params.seed);
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if (params.random_prompt) {
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params.prompt = gpt_random_prompt(rng);
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}
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llama_backend_init(params.numa);
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llama_model * model;
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llama_context * ctx;
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// load the model and apply lora adapter, if any
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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if (model == NULL) {
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fprintf(stderr, "%s: error: unable to load model\n", __func__);
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return 1;
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}
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// print system information
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{
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fprintf(stderr, "\n");
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fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
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params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
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}
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if (params.hellaswag) {
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hellaswag_score(ctx, params);
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} else {
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perplexity(ctx, params);
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}
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llama_print_timings(ctx);
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llama_free(ctx);
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llama_free_model(model);
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llama_backend_free();
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return 0;
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}
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