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Strided perplexity (#2714)
* Implementing strided computation of perplexity * Alternative way to output PPL results --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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@ -417,6 +417,18 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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params.antiprompt.push_back(argv[i]);
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} else if (arg == "--perplexity") {
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params.perplexity = true;
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} else if (arg == "--ppl-stride") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.ppl_stride = std::stoi(argv[i]);
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} else if (arg == "--ppl-output-type") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.ppl_output_type = std::stoi(argv[i]);
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} else if (arg == "--hellaswag") {
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params.hellaswag = true;
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} else if (arg == "--hellaswag-tasks") {
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@ -64,6 +64,10 @@ struct gpt_params {
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std::string lora_adapter = ""; // lora adapter path
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std::string lora_base = ""; // base model path for the lora adapter
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int ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
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int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
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// (which is more convenient to use for plotting)
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//
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bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
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size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
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@ -27,7 +27,121 @@ std::vector<float> softmax(const std::vector<float>& logits) {
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return probs;
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}
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void perplexity_v2(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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if (params.ppl_stride <= 0) {
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fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride);
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return;
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}
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auto tokens = ::llama_tokenize(ctx, params.prompt, true);
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const int calc_chunk = params.n_ctx;
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fprintf(stderr, "%s: have %zu tokens. Calculation chunk = %d\n", __func__, tokens.size(), calc_chunk);
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if (int(tokens.size()) <= calc_chunk) {
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fprintf(stderr, "%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__,
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tokens.size(), params.n_ctx, params.ppl_stride);
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return;
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}
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const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1) / params.ppl_stride;
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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.ppl_stride;
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const int end = start + calc_chunk;
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const int num_batches = (calc_chunk + n_batch - 1) / n_batch;
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//fprintf(stderr, "%s: evaluating %d...%d using %d batches\n", __func__, start, end, num_batches);
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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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//fprintf(stderr, " Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
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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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// 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(ctx);
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}
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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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if (j == 0) {
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tokens[batch_start] = token_org;
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}
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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, "%.2f minutes\n", total_seconds / 60.0);
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}
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//fprintf(stderr, "%s: using tokens %d...%d\n",__func__,params.n_ctx - params.ppl_stride + start, params.n_ctx + start);
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for (int j = params.n_ctx - params.ppl_stride - 1; 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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if (params.ppl_output_type == 0) {
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printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
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} else {
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printf("%8d %.4lf\n", i*params.ppl_stride, std::exp(nll / count));
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}
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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 perplexity(llama_context * ctx, const gpt_params & params) {
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if (params.ppl_stride > 0) {
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perplexity_v2(ctx, params);
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return;
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}
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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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@ -116,7 +230,11 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
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++count;
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}
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// perplexity is e^(average negative log-likelihood)
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if (params.ppl_output_type == 0) {
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printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
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} else {
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printf("%8d %.4lf\n", i*params.n_ctx, std::exp(nll / count));
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}
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fflush(stdout);
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}
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printf("\n");
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@ -369,6 +487,12 @@ int main(int argc, char ** argv) {
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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.ppl_stride > 0) {
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fprintf(stderr, "Will perform strided perplexity calculation -> adjusting context size from %d to %d\n",
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params.n_ctx, params.n_ctx + params.ppl_stride/2);
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params.n_ctx += params.ppl_stride/2;
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}
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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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