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") {
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");
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);
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");
}
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);
}
overview / pkg / ggml / sources / llama.cpp / commitdiff