vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

perplexity.cpp : add hellswag scores / remove perplexity-lines

Browse source
This commit is contained in:
klosax 2023-07-25 17:43:34 +02:00 • committed by GitHub
parent a40f608249
commit ae4d116bdf
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 142 additions and 42 deletions
Download patch file Download diff file Expand all files Collapse all files

178
examples/perplexity/perplexity.cpp
View file

@ -121,8 +121,23 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
printf("\n"); printf("\n");
} }
void perplexity_lines(llama_context * ctx, const gpt_params & params) { void hellaswag_score(llama_context * ctx, const gpt_params & params) {
// Calculates perplexity over each line of the prompt // 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 examples 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::vector<std::string> prompt_lines;
std::istringstream strstream(params.prompt); std::istringstream strstream(params.prompt);
@ -132,63 +147,148 @@ void perplexity_lines(llama_context * ctx, const gpt_params & params) {
prompt_lines.push_back(line); 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__);
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; return;
} }
if (llama_eval(ctx, batch_embd.data(), batch_size, 0, params.n_threads)) { size_t hs_task_count = prompt_lines.size()/6;
fprintf(stderr, "%s : loaded %lu examples from prompt.\n", __func__, hs_task_count);
// This is needed as usual for LLaMA models
bool prepend_bos = true;
// Number of examples to use when computing the score
if ( params.hellaswag_tasks < hs_task_count )
hs_task_count = params.hellaswag_tasks;
// The examples should be randomized so the score stabilizes quickly.
bool randomize_tasks = true;
// The random seed should not impact the final result if the computation is done over enough examples, so kept hardcoded for now
std::mt19937 rng(1);
// 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];
};
fprintf(stderr, "%s : selecting %lu %s examples.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first") );
// 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;
// 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);
}
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];
}
// Delete the select 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) );
}
}
fprintf(stderr, "%s : calculating hellaswag score over selected examples.\n", __func__);
printf("\ntask\tacc_norm\n");
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;
}
// 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__); fprintf(stderr, "%s : failed to eval\n", __func__);
return; return;
} }
const auto batch_logits = llama_get_logits(ctx); const auto query_logits = llama_get_logits(ctx);
std::vector<float> logits; std::vector<float> logits;
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); logits.insert(logits.end(), query_logits, query_logits + query_size * n_vocab);
double nllline = 0.0; hs_data[task_idx].ending_logprob_count[ending_idx] = 0;
int countline = 0; hs_data[task_idx].ending_logprob[ending_idx] = 0.0f;
// Perplexity over second half of the line // Calculate the logprobs over the ending
for (size_t j = batch_size/2; j < batch_size - 1; ++j) { for (size_t j = context_size-1; j < query_size - 1; j++) {
// Calculate probability of next token, given the previous ones. // Calculate probability of next token, given the previous ones.
const std::vector<float> tok_logits( const std::vector<float> tok_logits(
logits.begin() + (j + 0) * n_vocab, logits.begin() + (j + 0) * n_vocab,
logits.begin() + (j + 1) * n_vocab); logits.begin() + (j + 1) * n_vocab);
const float prob = softmax(tok_logits)[batch_embd[ j + 1]]; const float prob = softmax(tok_logits)[query_embd[ j + 1]];
nllline += -std::log(prob); hs_data[task_idx].ending_logprob[ending_idx] += std::log(prob);
++countline; hs_data[task_idx].ending_logprob_count[ending_idx]++;
} }
nll += nllline; // Calculate the mean token logprob for acc_norm
counttotal += countline; hs_data[task_idx].ending_logprob[ending_idx] /= hs_data[task_idx].ending_logprob_count[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) ); // 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] );
}
// 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];
}
}
// printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_data[task_idx].gold_ending_idx);
// 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); fflush(stdout);
} }
delete [] hs_data;
printf("\n"); printf("\n");
} }
@ -240,8 +340,8 @@ int main(int argc, char ** argv) {
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
} }
if (params.perplexity_lines) { if (params.hellaswag) {
perplexity_lines(ctx, params); hellaswag_score(ctx, params);
} else { } else {
perplexity(ctx, params); perplexity(ctx, params);
} }