aditya/llama.cpp
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perplexity : add Hellaswag calculation (#2389)

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* 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
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klosax 2023-07-28 20:25:36 +02:00 committed by GitHub
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commit 8a88e5855c
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3 changed files with 158 additions and 48 deletions
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15
examples/common.cpp
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@ -402,8 +402,14 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
params.antiprompt.push_back(argv[i]);
} else if (arg == "--perplexity") {
params.perplexity = true;
} else if (arg == "--perplexity-lines") {
params.perplexity_lines = true;
} else if (arg == "--hellaswag") {
params.hellaswag = true;
} else if (arg == "--hellaswag-tasks") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.hellaswag_tasks = std::stoi(argv[i]);
} else if (arg == "--ignore-eos") {
params.logit_bias[llama_token_eos()] = -INFINITY;
} else if (arg == "--no-penalize-nl") {
@ -559,8 +565,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n");
fprintf(stdout, " --temp N temperature (default: %.1f)\n", (double)params.temp);
fprintf(stdout, " --perplexity compute perplexity over each ctx window of the prompt\n");
fprintf(stdout, " --perplexity-lines compute perplexity over each line of the prompt\n");
fprintf(stdout, " --keep number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %d)\n", params.hellaswag_tasks);
fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
if (llama_mlock_supported()) {
fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n");

6
examples/common.h
View file

@ -70,7 +70,10 @@ struct gpt_params {
std::string lora_adapter = ""; // lora adapter path
std::string lora_base = ""; // base model path for the lora adapter
bool low_vram = false; // if true, reduce VRAM usage at the cost of performance
bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
bool low_vram = false; // if true, reduce VRAM usage at the cost of performance
bool memory_f16 = true; // use f16 instead of f32 for memory kv
bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs
@ -86,7 +89,6 @@ struct gpt_params {
bool instruct = false; // instruction mode (used for Alpaca models)
bool penalize_nl = true; // consider newlines as a repeatable token
bool perplexity = false; // compute perplexity over the prompt
bool perplexity_lines = false; // compute perplexity over each line of the prompt
bool use_mmap = true; // use mmap for faster loads
bool use_mlock = false; // use mlock to keep model in memory
bool mem_test = false; // compute maximum memory usage

185
examples/perplexity/perplexity.cpp
View file

@ -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);
}
if( prompt_lines.size() % 6 != 0) {
fprintf(stderr, "%s : number of lines in prompt not a multiple of 6.\n", __func__);
return;
}
size_t hs_task_count = prompt_lines.size()/6;
fprintf(stderr, "%s : loaded %lu tasks from prompt.\n", __func__, hs_task_count);
// 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;
}
// The tasks 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 tasks, 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 tasks.\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 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) );
}
}
fprintf(stderr, "%s : calculating hellaswag score over selected tasks.\n", __func__);
printf("\ntask\tacc_norm\n");
double acc = 0.0f;
const int n_vocab = llama_n_vocab(ctx);
int counttotal = 0;
size_t n_lines = prompt_lines.size();
for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
double nll = 0.0;
// 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();
fprintf(stderr, "%s: calculating perplexity over %lu lines\n", __func__, n_lines);
for (size_t ending_idx=0;ending_idx<4;ending_idx++) {
printf("\nLine\tPPL line\tPPL cumulative\n");
// 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();
for (size_t i = 0; i < n_lines; ++i) {
// 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;
}
// Tokenize and insert BOS at start
std::vector<int> batch_embd = ::llama_tokenize(ctx, prompt_lines[i], true);
// Speedup small evaluations by evaluating atleast 32 tokens
if (query_size < 32) {
query_embd.resize(32);
}
size_t batch_size = batch_embd.size();
// 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;
}
// 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;
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] );
}
if (llama_eval(ctx, batch_embd.data(), batch_size, 0, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
// 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];
}
}
const auto batch_logits = llama_get_logits(ctx);
std::vector<float> logits;
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
// printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_data[task_idx].gold_ending_idx);
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;
// 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;
}
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) );
// 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);
}