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Diffstat (limited to 'llama.cpp/tools/perplexity/perplexity.cpp')
| -rw-r--r-- | llama.cpp/tools/perplexity/perplexity.cpp | 2070 |
1 files changed, 2070 insertions, 0 deletions
diff --git a/llama.cpp/tools/perplexity/perplexity.cpp b/llama.cpp/tools/perplexity/perplexity.cpp new file mode 100644 index 0000000..1ead9c8 --- /dev/null +++ b/llama.cpp/tools/perplexity/perplexity.cpp @@ -0,0 +1,2070 @@ +#include "arg.h" +#include "common.h" +#include "log.h" +#include "llama.h" + +#include <chrono> +#include <algorithm> +#include <array> +#include <atomic> +#include <cmath> +#include <cstdio> +#include <cstring> +#include <ctime> +#include <fstream> +#include <mutex> +#include <random> +#include <sstream> +#include <thread> +#include <vector> + +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +struct results_perplexity { + std::vector<llama_token> tokens; + double ppl_value; + std::vector<float> logits; + std::vector<float> probs; +}; + +struct results_log_softmax { + double log_softmax; + float logit; + float prob; +}; + +static std::vector<float> softmax(const std::vector<float>& logits) { + std::vector<float> probs(logits.size()); + float max_logit = logits[0]; + for (float v : logits) { + max_logit = std::max(max_logit, v); + } + double sum_exp = 0.0; + for (size_t i = 0; i < logits.size(); i++) { + // Subtract the maximum logit value from the current logit value for numerical stability + const float logit = logits[i] - max_logit; + const float exp_logit = expf(logit); + sum_exp += exp_logit; + probs[i] = exp_logit; + } + for (size_t i = 0; i < probs.size(); i++) { + probs[i] /= sum_exp; + } + return probs; +} + +static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) { + float max_logit = logits[0]; + for (int i = 1; i < n_vocab; ++i) { + max_logit = std::max(max_logit, logits[i]); + } + double sum_exp = 0.0; + for (int i = 0; i < n_vocab; ++i) { + sum_exp += expf(logits[i] - max_logit); + } + return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp}; +} + +static inline int nearest_int(float fval) { + //assert(fval <= 4194303.f); + float val = fval + 12582912.f; + int i; memcpy(&i, &val, sizeof(int)); + return (i & 0x007fffff) - 0x00400000; +} + +static double log_softmax(int n_vocab, const float * logits, uint16_t * log_prob, int tok) { + float max_logit = logits[0]; + float min_logit = logits[0]; + for (int i = 1; i < n_vocab; ++i) { + max_logit = std::max(max_logit, logits[i]); + min_logit = std::min(min_logit, logits[i]); + } + min_logit = std::max(min_logit, max_logit - 16); + double sum_exp = 0.0; + for (int i = 0; i < n_vocab; ++i) { + sum_exp += expf(logits[i] - max_logit); + } + const float log_sum_exp = log(sum_exp); + const float min_log_prob = min_logit - max_logit - log_sum_exp; + const float scale = (max_logit - min_logit)/65535.f; + float * d = (float *)log_prob; + d[0] = scale; + d[1] = min_log_prob; + log_prob += 4; + if (scale) { + const float inv_scale = 1/scale; + for (int i = 0; i < n_vocab; ++i) { + log_prob[i] = logits[i] > min_logit ? nearest_int(inv_scale*(logits[i] - min_logit)) : 0; + } + } else { + std::memset(log_prob, 0, n_vocab*sizeof(uint16_t)); + } + return max_logit + log_sum_exp - logits[tok]; +} + +static void process_logits( + int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers, + double & nll, double & nll2, float * logit_history, float * prob_history +) { + std::mutex mutex; + int counter = 0; + auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () { + double local_nll = 0; + double local_nll2 = 0; + while (true) { + std::unique_lock<std::mutex> lock(mutex); + int i = counter++; + if (i >= n_token) { + nll += local_nll; nll2 += local_nll2; + break; + } + lock.unlock(); + const results_log_softmax results = log_softmax(n_vocab, logits + size_t(i)*n_vocab, tokens[i+1]); + const double v = -results.log_softmax; + local_nll += v; + local_nll2 += v*v; + + logit_history[i] = results.logit; + prob_history[i] = results.prob; + } + }; + for (auto & w : workers) { + w = std::thread(compute); + } + compute(); + for (auto & w : workers) { + w.join(); + } +} + +static void process_logits(std::ostream& out, int n_vocab, const float * logits, const int * tokens, int n_token, + std::vector<std::thread> & workers, std::vector<uint16_t> & log_probs, double & nll, double & nll2) { + std::mutex mutex; + const int nv = 2*((n_vocab + 1)/2) + 4; + int counter = 0; + auto compute = [&mutex, &counter, &log_probs, &nll, &nll2, n_vocab, logits, tokens, n_token, nv] () { + double local_nll = 0; + double local_nll2 = 0; + while (true) { + std::unique_lock<std::mutex> lock(mutex); + int i = counter++; + if (i >= n_token) { + nll += local_nll; nll2 += local_nll2; + break; + } + lock.unlock(); + const double v = log_softmax(n_vocab, logits + size_t(i)*n_vocab, log_probs.data() + i*nv, tokens[i+1]); + local_nll += v; + local_nll2 += v*v; + } + }; + for (auto & w : workers) { + w = std::thread(compute); + } + compute(); + for (auto & w : workers) { + w.join(); + } + out.write((const char *)log_probs.data(), n_token*nv*sizeof(uint16_t)); +} + +struct kl_divergence_result { + double sum_nll = 0.0; + double sum_nll2 = 0.0; + double sum_nll_base = 0.0; + double sum_nll_base2 = 0.0; + double sum_nll_nll_base = 0.0; + double sum_kld = 0.0; + double sum_kld2 = 0.0; + double sum_p_diff = 0.0; + double sum_p_diff2 = 0.0; + double sum_p_diff4 = 0.0; + float max_p_diff = 0.0f; + size_t n_same_top = 0.0; + size_t count = 0.0; +}; + +static std::pair<double, float> log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) { + float max_logit = logits[0]; + int imax = 0; + for (int i = 1; i < n_vocab; ++i) { + if (logits[i] > max_logit) { + max_logit = logits[i]; + imax = i; + } + } + double sum_exp = 0.0; + for (int i = 0; i < n_vocab; ++i) { + sum_exp += expf(logits[i] - max_logit); + } + const float log_sum_exp = log(sum_exp); + const float * d = (const float *)base_log_prob; + const float scale = d[0]; + const float min_log_prob = d[1]; + base_log_prob += 4; + + const float nll = max_logit + log_sum_exp - logits[tok]; + kld.sum_nll += nll; + kld.sum_nll2 += nll*nll; + + const float nll_base = -(scale*base_log_prob[tok] + min_log_prob); + kld.sum_nll_base += nll_base; + kld.sum_nll_base2 += nll_base*nll_base; + + kld.sum_nll_nll_base += nll*nll_base; + + max_logit += log_sum_exp; + double sum = 0; + int imax_base = -1; + float p_log_base_max = 0; + for (int i = 0; i < n_vocab; ++i) { + const float p_log_base = scale*base_log_prob[i] + min_log_prob; + if (i == 0 || p_log_base > p_log_base_max) { + p_log_base_max = p_log_base; + imax_base = i; + } + if (p_log_base > -16.f) { + const float p_base = expf(p_log_base); + sum += p_base * (p_log_base - logits[i] + max_logit); + } + } + kld.sum_kld += sum; + kld.sum_kld2 += sum*sum; + ++kld.count; + if (imax == imax_base) { + ++kld.n_same_top; + } + + const float p_base = expf(-nll_base); + const float p = expf(-nll); + const float p_diff = p - p_base; + kld.sum_p_diff += p_diff; + const double p_diff2 = p_diff*p_diff; + kld.sum_p_diff2 += p_diff2; + kld.sum_p_diff4 += p_diff2*p_diff2; + kld.max_p_diff = std::max(kld.max_p_diff, std::fabs(p_diff)); + + return std::make_pair(sum, p_diff); +} + +static void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, + std::vector<std::thread> & workers, const std::vector<uint16_t> & base_log_probs, kl_divergence_result & kld, + float * kld_values, float * p_diff_values) { + std::mutex mutex; + const int nv = 2*((n_vocab + 1)/2) + 4; + int counter = 0; + auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values, p_diff_values] () { + kl_divergence_result local_kld; + while (true) { + std::unique_lock<std::mutex> lock(mutex); + int i = counter++; + if (i >= n_token) { + kld.sum_nll += local_kld.sum_nll; + kld.sum_nll2 += local_kld.sum_nll2; + kld.sum_nll_base += local_kld.sum_nll_base; + kld.sum_nll_base2 += local_kld.sum_nll_base2; + kld.sum_nll_nll_base += local_kld.sum_nll_nll_base; + kld.sum_kld += local_kld.sum_kld; + kld.sum_kld2 += local_kld.sum_kld2; + kld.sum_p_diff += local_kld.sum_p_diff; + kld.sum_p_diff2 += local_kld.sum_p_diff2; + kld.sum_p_diff4 += local_kld.sum_p_diff4; + kld.n_same_top += local_kld.n_same_top; + kld.max_p_diff = std::max(kld.max_p_diff, local_kld.max_p_diff); + kld.count += local_kld.count; + break; + } + lock.unlock(); + std::pair<double, float> v = log_softmax(n_vocab, logits + size_t(i)*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld); + kld_values[i] = (float)v.first; + p_diff_values[i] = v.second; + } + }; + for (auto & w : workers) { + w = std::thread(compute); + } + compute(); + for (auto & w : workers) { + w.join(); + } +} + +static results_perplexity perplexity_v2(llama_context * ctx, const common_params & params) { + // Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip + // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` + // Output: `perplexity: 13.5106 [114/114]` + // BOS tokens will be added for each chunk before eval + + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const bool add_bos = llama_vocab_get_add_bos(vocab); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); + + LOG_INF("%s: tokenizing the input ..\n", __func__); + + std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true); + + const int n_ctx = llama_n_ctx(ctx); + + if (int(tokens.size()) < 2*n_ctx) { + LOG_ERR("%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*n_ctx, + n_ctx); + LOG_ERR("%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size()); + return {std::move(tokens), 0., {}, {}}; + } + + std::vector<float> logit_history; + std::vector<float> prob_history; + + logit_history.resize(tokens.size()); + prob_history.resize(tokens.size()); + + if (params.ppl_stride <= 0) { + LOG_ERR("%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride); + return {tokens, -1, logit_history, prob_history}; + } + + const int calc_chunk = n_ctx; + + LOG_INF("%s: have %zu tokens. Calculation chunk = %d\n", __func__, tokens.size(), calc_chunk); + + if (int(tokens.size()) <= calc_chunk) { + LOG_ERR("%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__, + tokens.size(), n_ctx, params.ppl_stride); + return {tokens, -1, logit_history, prob_history}; + } + + const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1) / params.ppl_stride; + + const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); + const int n_batch = params.n_batch; + + const int n_vocab = llama_vocab_n_tokens(vocab); + + int count = 0; + double nll = 0.0; + + LOG_INF("%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch); + + for (int i = 0; i < n_chunk; ++i) { + const int start = i * params.ppl_stride; + const int end = start + calc_chunk; + + const int num_batches = (calc_chunk + n_batch - 1) / n_batch; + //LOG_DBG("%s: evaluating %d...%d using %d batches\n", __func__, start, end, num_batches); + + std::vector<float> logits; + + const auto t_start = std::chrono::high_resolution_clock::now(); + + // clear the KV cache + llama_memory_clear(llama_get_memory(ctx), true); + + llama_batch batch = llama_batch_init(n_batch, 0, 1); + + for (int j = 0; j < num_batches; ++j) { + const int batch_start = start + j * n_batch; + const int batch_size = std::min(end - batch_start, n_batch); + + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + + //LOG_DBG(" Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch); + if (llama_decode(ctx, batch)) { + //LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); + return {tokens, -1, logit_history, prob_history}; + } + + // save original token and restore it after eval + const auto token_org = tokens[batch_start]; + + // add BOS token for the first batch of each chunk + if (add_bos && j == 0) { + tokens[batch_start] = llama_vocab_bos(vocab); + } + + const auto * batch_logits = llama_get_logits(ctx); + logits.insert(logits.end(), batch_logits, batch_logits + size_t(batch_size) * n_vocab); + + if (j == 0) { + tokens[batch_start] = token_org; + } + } + + llama_batch_free(batch); + + const auto t_end = std::chrono::high_resolution_clock::now(); + + if (i == 0) { + const float t_total = std::chrono::duration<float>(t_end - t_start).count(); + LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total); + int total_seconds = (int)(t_total * n_chunk); + if (total_seconds >= 60*60) { + LOG("%d hours ", total_seconds / (60*60)); + total_seconds = total_seconds % (60*60); + } + LOG("%.2f minutes\n", total_seconds / 60.0); + } + + //LOG_DBG("%s: using tokens %d...%d\n",__func__,params.n_ctx - params.ppl_stride + start, params.n_ctx + start); + for (int j = n_ctx - params.ppl_stride - 1; j < n_ctx - 1; ++j) { + // Calculate probability of next token, given the previous ones. + const std::vector<float> tok_logits( + logits.begin() + size_t(j + 0) * n_vocab, + logits.begin() + size_t(j + 1) * n_vocab); + + const float prob = softmax(tok_logits)[tokens[start + j + 1]]; + logit_history[start + j + 1] = tok_logits[tokens[start + j + 1]]; + prob_history[start + j + 1] = prob; + + nll += -std::log(prob); + ++count; + } + // perplexity is e^(average negative log-likelihood) + if (params.ppl_output_type == 0) { + LOG("[%d]%.4lf,", i + 1, std::exp(nll / count)); + } else { + LOG("%8d %.4lf\n", i*params.ppl_stride, std::exp(nll / count)); + } + } + LOG("\n"); + + return {tokens, std::exp(nll / count), logit_history, prob_history}; +} + +static results_perplexity perplexity(llama_context * ctx, const common_params & params, const int32_t n_ctx) { + if (params.ppl_stride > 0) { + return perplexity_v2(ctx, params); + } + + // Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip + // Run `./llama-perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` + // Output: `perplexity: 13.5106 [114/114]` + // BOS tokens will be added for each chunk before eval + + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + const bool add_bos = llama_vocab_get_add_bos(vocab); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); + + std::ofstream logits_stream; + if (!params.logits_file.empty()) { + logits_stream.open(params.logits_file.c_str(), std::ios::binary); + if (!logits_stream.is_open()) { + LOG_ERR("%s: failed to open %s for writing\n", __func__, params.logits_file.c_str()); + return {}; + } + LOG_INF("%s: saving all logits to %s\n", __func__, params.logits_file.c_str()); + logits_stream.write("_logits_", 8); + logits_stream.write(reinterpret_cast<const char *>(&n_ctx), sizeof(n_ctx)); + } + + auto tim1 = std::chrono::high_resolution_clock::now(); + LOG_INF("%s: tokenizing the input ..\n", __func__); + + std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true); + + auto tim2 = std::chrono::high_resolution_clock::now(); + LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count()); + + if (int(tokens.size()) < 2*n_ctx) { + LOG_ERR("%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*n_ctx, + n_ctx); + LOG_ERR("%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size()); + return {std::move(tokens), 0., {}, {}}; + } + + std::vector<float> logit_history; + logit_history.resize(tokens.size()); + + std::vector<float> prob_history; + prob_history.resize(tokens.size()); + + const int n_chunk_max = tokens.size() / n_ctx; + + const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); + const int n_batch = params.n_batch; + + const int n_vocab = llama_vocab_n_tokens(vocab); + + int count = 0; + double nll = 0.0; + double nll2 = 0.0; + + const int num_batches = (n_ctx + n_batch - 1) / n_batch; + const int n_seq = std::max(1, n_batch / n_ctx); + + GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0); + GGML_ASSERT(params.n_ctx == n_seq * n_ctx); + + llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1); + + std::vector<float> logits; + if (num_batches > 1) { + logits.reserve(size_t(n_ctx) * n_vocab); + } + + LOG_INF("%s: calculating perplexity over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq); + + std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1); + + std::vector<uint16_t> log_probs; + if (!params.logits_file.empty()) { + logits_stream.write((const char *)&n_vocab, sizeof(n_vocab)); + logits_stream.write((const char *)&n_chunk, sizeof(n_chunk)); + logits_stream.write((const char *)tokens.data(), n_chunk*n_ctx*sizeof(tokens[0])); + const int nv = 2*((n_vocab + 1)/2) + 4; + log_probs.resize(n_ctx * nv); + } + + // We get the logits for all the tokens in the context window (params.n_ctx) + // from llama_decode below. Now, based on https://huggingface.co/docs/transformers/perplexity, + // calculate the perplexity over the last half of the window (so the model always has + // some context to predict the token). + // + // We rely on the fact that attention in the forward pass only looks at previous + // tokens here, so the logits returned for each token are an accurate representation + // of what the model would have predicted at that point. + // + // Example, we have a context window of 512, we will compute perplexity for each of the + // last 256 tokens. Then, we split the input up into context window size chunks to + // process the entire prompt. + const int first = n_ctx/2; + + for (int i = 0; i < n_chunk; i += n_seq) { + const int start = i * n_ctx; + const int end = start + n_ctx; + + const int n_seq_batch = std::min(n_seq, n_chunk - i); + + const auto t_start = std::chrono::high_resolution_clock::now(); + + // clear the KV cache + llama_memory_clear(llama_get_memory(ctx), true); + + for (int j = 0; j < num_batches; ++j) { + const int batch_start = start + j * n_batch; + const int batch_size = std::min(end - batch_start, n_batch); + + int n_outputs = 0; + + batch.n_tokens = 0; + for (int seq = 0; seq < n_seq_batch; seq++) { + int seq_start = batch_start + seq*n_ctx; + + // save original token and restore it after decode + const auto token_org = tokens[seq_start]; + + // add BOS token for the first batch of each chunk + if (add_bos && j == 0) { + tokens[seq_start] = llama_vocab_bos(vocab); + } + + for (int k = 0; k < batch_size; ++k) { + const int idx = seq*n_ctx + k; + batch.token [idx] = tokens[seq_start + k]; + batch.pos [idx] = j*n_batch + k; + batch.n_seq_id[idx] = 1; + batch.seq_id [idx][0] = seq; + batch.logits [idx] = batch.pos[idx] >= first ? 1 : 0; + + n_outputs += batch.logits[idx] != 0; + } + batch.n_tokens += batch_size; + + // restore the original token in case it was set to BOS + tokens[seq_start] = token_org; + } + + if (llama_decode(ctx, batch)) { + LOG_INF("%s : failed to decode\n", __func__); + return {tokens, -1, logit_history, prob_history}; + } + + if (num_batches > 1 && n_outputs > 0) { + const auto * batch_logits = llama_get_logits(ctx); + logits.insert(logits.end(), batch_logits, batch_logits + size_t(n_outputs) * n_vocab); + } + } + + + if (i == 0) { + llama_synchronize(ctx); + const auto t_end = std::chrono::high_resolution_clock::now(); + const float t_total = std::chrono::duration<float>(t_end - t_start).count(); + LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total); + int total_seconds = (int)(t_total*n_chunk/n_seq); + if (total_seconds >= 60*60) { + LOG("%d hours ", total_seconds / (60*60)); + total_seconds = total_seconds % (60*60); + } + LOG("%.2f minutes\n", total_seconds / 60.0); + } + + for (int seq = 0; seq < n_seq_batch; seq++) { + const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx + first); + + llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first; + if (!params.logits_file.empty()) { + process_logits(logits_stream, n_vocab, all_logits, + tokens_data, n_ctx - 1 - first, + workers, log_probs, nll, nll2); + } else { + process_logits(n_vocab, all_logits, + tokens_data, n_ctx - 1 - first, + workers, nll, nll2, + logit_history.data() + start + seq*n_ctx + first, + prob_history.data() + start + seq*n_ctx + first); + } + count += n_ctx - first - 1; + + // perplexity is e^(average negative log-likelihood) + if (params.ppl_output_type == 0) { + LOG("[%d]%.4lf,", i + seq + 1, std::exp(nll / count)); + } else { + double av = nll/count; + double av2 = nll2/count - av*av; + if (av2 > 0) { + av2 = sqrt(av2/(count-1)); + } + LOG("%8d %.4lf %4lf %4lf\n", i*n_ctx, std::exp(nll / count), av, av2); + } + } + + logits.clear(); + } + LOG("\n"); + + nll2 /= count; + nll /= count; + const double ppl = exp(nll); + nll2 -= nll * nll; + if (nll2 > 0) { + nll2 = sqrt(nll2/(count-1)); + LOG_INF("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl); + } else { + LOG_ERR("Unexpected negative standard deviation of log(prob)\n"); + } + + llama_batch_free(batch); + + return {tokens, ppl, logit_history, prob_history}; +} + +static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<float> & batch_logits, int n_batch, int n_vocab) { + int prev_outputs = 0; + for (int i = 0; i < (int) batch.n_tokens; i += n_batch) { + const int n_tokens = std::min<int>(n_batch, batch.n_tokens - i); + + llama_batch batch_view = { + n_tokens, + batch.token + i, + nullptr, + batch.pos + i, + batch.n_seq_id + i, + batch.seq_id + i, + batch.logits + i, + }; + + const int ret = llama_decode(ctx, batch_view); + if (ret != 0) { + LOG_ERR("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret); + return false; + } + + int n_outputs = 0; + for (int i = 0; i < n_tokens; ++i) { + n_outputs += batch_view.logits[i] != 0; + } + + memcpy(batch_logits.data() + size_t(prev_outputs)*n_vocab, llama_get_logits(ctx), size_t(n_outputs)*n_vocab*sizeof(float)); + + prev_outputs += n_outputs; + } + + return true; +} + +#define K_TOKEN_CHUNK 4 + +static void compute_logprobs(const float * batch_logits, int n_vocab, std::vector<std::thread>& workers, + const std::vector<std::pair<size_t, llama_token>>& eval_pairs, std::vector<float>& eval_results) { + if (eval_results.size() != eval_pairs.size()) { + eval_results.resize(eval_pairs.size()); + } + if (eval_pairs.empty()) { + return; + } + + size_t max_threads = std::min((eval_pairs.size() + K_TOKEN_CHUNK - 1)/K_TOKEN_CHUNK, workers.size()); + + std::atomic<int> counter(0); + auto compute = [&counter, &eval_pairs, &eval_results, batch_logits, n_vocab] () { + float local_logprobs[K_TOKEN_CHUNK]; + while (true) { + const size_t first = counter.fetch_add(K_TOKEN_CHUNK, std::memory_order_relaxed); + if (first >= eval_results.size()) { + break; + } + const size_t last = std::min(first + K_TOKEN_CHUNK, eval_results.size()); + for (size_t i = first; i < last; ++i) { + const auto * logits = batch_logits + eval_pairs[i].first * n_vocab; + float max_logit = logits[0]; + for (int j = 1; j < n_vocab; ++j) { + max_logit = std::max(max_logit, logits[j]); + } + float sum_p = 0.f; + for (int j = 0; j < n_vocab; ++j) { + sum_p += expf(logits[j] - max_logit); + } + local_logprobs[i - first] = logits[eval_pairs[i].second] - max_logit - std::log(sum_p); + } + std::memcpy(eval_results.data() + first, local_logprobs, (last - first)*sizeof(float)); + } + }; + + for (size_t it = 0; it < max_threads; ++it) { + workers[it] = std::thread(compute); + } + for (size_t it = 0; it < max_threads; ++it) { + workers[it].join(); + } +} + +static void hellaswag_score(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + // 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); + std::string line; + + while (std::getline(strstream,line,'\n')) { + prompt_lines.push_back(line); + } + + if (prompt_lines.size() % 6 != 0) { + LOG_ERR("%s : number of lines in prompt not a multiple of 6.\n", __func__); + return; + } + + size_t hs_task_count = prompt_lines.size()/6; + LOG_INF("%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count); + + const bool is_spm = llama_vocab_type(vocab) == LLAMA_VOCAB_TYPE_SPM; + LOG_INF("================================= is_spm = %d\n", is_spm); + + // The tasks should be randomized so the score stabilizes quickly. + bool randomize_tasks = true; + + // Number of tasks to use when computing the score + if (params.hellaswag_tasks < hs_task_count) { + hs_task_count = params.hellaswag_tasks; + } + + // 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]; + + size_t i_logits; // starting index of logits in the llama_batch + size_t common_prefix; // max number of initial tokens that are the same in all sentences + size_t required_tokens; // needed number of tokens to evaluate all 4 endings + std::vector<llama_token> seq_tokens[4]; + }; + + LOG_INF("%s : selecting %zu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first") ); + + // Select and read data from prompt lines + std::vector<hs_data_t> hs_data(hs_task_count); + for (size_t i = 0; i < hs_task_count; i++) { + size_t idx = i; + + auto & hs_cur = hs_data[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_cur.context = prompt_lines[idx*6]; + hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] ); + for (size_t j = 0; j < 4; j++) { + hs_cur.ending[j] = prompt_lines[idx*6+2+j]; + hs_cur.seq_tokens[j] = common_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true); + } + + // determine the common prefix of the endings + hs_cur.common_prefix = 0; + for (size_t k = 0; k < hs_cur.seq_tokens[0].size(); k++) { + if (hs_cur.seq_tokens[0][k] != hs_cur.seq_tokens[1][k] || + hs_cur.seq_tokens[0][k] != hs_cur.seq_tokens[2][k] || + hs_cur.seq_tokens[0][k] != hs_cur.seq_tokens[3][k]) { + break; + } + hs_cur.common_prefix++; + } + hs_cur.required_tokens = hs_cur.common_prefix + + hs_cur.seq_tokens[0].size() - hs_cur.common_prefix + + hs_cur.seq_tokens[1].size() - hs_cur.common_prefix + + hs_cur.seq_tokens[2].size() - hs_cur.common_prefix + + hs_cur.seq_tokens[3].size() - hs_cur.common_prefix; + + //GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, true).size()); + + // 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) ); + } + } + + LOG_INF("%s : calculating hellaswag score over selected tasks.\n", __func__); + + LOG("\ntask\tacc_norm\t95%% confidence interval\n"); + + double acc = 0.0f; + + const int n_ctx = llama_n_ctx(ctx); + const int n_batch = params.n_batch; + + const int n_vocab = llama_vocab_n_tokens(vocab); + + const int max_tasks_per_batch = 32; + const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx)); + + llama_batch batch = llama_batch_init(n_ctx, 0, 4); + + std::vector<float> tok_logits(n_vocab); + // TODO: this could be made smaller; it's currently the worst-case size + std::vector<float> batch_logits(size_t(n_ctx)*n_vocab); + + std::vector<std::pair<size_t, llama_token>> eval_pairs; + std::vector<float> eval_results; + std::vector<std::thread> workers(std::thread::hardware_concurrency()); + + for (size_t i0 = 0; i0 < hs_task_count; i0++) { + int n_cur = 0; + + size_t i1 = i0; + size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch + + common_batch_clear(batch); + + // batch as much tasks as possible into the available context + // each task has 4 unique sequence ids - one for each ending + // the common prefix is shared among the 4 sequences to save tokens + // we extract logits only from the last common token and from all ending tokens of each sequence + while (n_cur + (int) hs_data[i1].required_tokens <= n_ctx) { + auto & hs_cur = hs_data[i1]; + int n_logits = 0; + + const int s0 = 4*(i1 - i0); + if (s0 + 4 > max_seq) { + break; + } + + for (size_t i = 0; i < hs_cur.common_prefix; ++i) { + common_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false); + } + batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix + n_logits += 1; + + for (int s = 0; s < 4; ++s) { + const size_t seq_tokens_size = hs_cur.seq_tokens[s].size(); + // TODO: don't evaluate the last token of each sequence + for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) { + const bool needs_logits = i < seq_tokens_size - 1; + common_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits); + n_logits += needs_logits; + } + } + + hs_cur.i_logits = i_logits; + i_logits += n_logits; + + n_cur += hs_data[i1].required_tokens; + if (++i1 == hs_task_count) { + break; + } + } + + if (i0 == i1) { + LOG_ERR("%s : task %zu does not fit in the context window (requires %lu tokens)\n", __func__, i0, hs_data[i0].required_tokens); + return; + } + + llama_memory_clear(llama_get_memory(ctx), true); + + // decode all tasks [i0, i1) + if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) { + LOG_ERR("%s: llama_decode() failed\n", __func__); + return; + } + + // Compute log-probs in parallel + // First we collect all tasks + eval_pairs.clear(); + for (size_t i = i0; i < i1; ++i) { + auto & hs_cur = hs_data[i]; + size_t li = 1; // skip the last logit of the common prefix (computed separately below) + for (int s = 0; s < 4; ++s) { + for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) { + eval_pairs.emplace_back(hs_cur.i_logits + li++, hs_cur.seq_tokens[s][j + 1]); + } + } + } + // Then we do the actual calculation + compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results); + + size_t ir = 0; + + // compute the logprobs for each ending of the decoded tasks + for (size_t i = i0; i < i1; ++i) { + auto & hs_cur = hs_data[i]; + + // get the logits of the last token of the common prefix + std::memcpy(tok_logits.data(), batch_logits.data() + hs_cur.i_logits*n_vocab, n_vocab*sizeof(float)); + + const auto first_probs = softmax(tok_logits); + + for (int s = 0; s < 4; ++s) { + hs_cur.ending_logprob_count[s] = 1; + hs_cur.ending_logprob[s] = std::log(first_probs[hs_cur.seq_tokens[s][hs_cur.common_prefix]]); + for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) { + hs_cur.ending_logprob[s] += eval_results[ir++]; + hs_cur.ending_logprob_count[s]++; + } + hs_cur.ending_logprob[s] /= hs_cur.ending_logprob_count[s]; + } + + // Find the ending with maximum logprob + size_t ending_logprob_max_idx = 0; + double ending_logprob_max_val = hs_cur.ending_logprob[0]; + for (size_t s = 1; s < 4; s++) { + if (hs_cur.ending_logprob[s] > ending_logprob_max_val) { + ending_logprob_max_idx = s; + ending_logprob_max_val = hs_cur.ending_logprob[s]; + } + } + + //LOG("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_cur.gold_ending_idx); + + // If the gold ending got the maximum logprobe add one accuracy point + if (ending_logprob_max_idx == hs_cur.gold_ending_idx) { + acc += 1.0; + } + + double freq = acc / double(i + 1); + + const double za = 1.95996398454; + + // // Wald normal approx + // double conf =za*sqrt(freq*(1-freq)/double(i + 1)); + // LOG("%zu\t%.8lf +/- %.8lf\n", i + 1, freq*100.0, conf*100.0); + + // Wilson score interval, more accurate + double z = za * za / double(i + 1); + double cnf = z * sqrt(double(i + 1) * (4.0 * freq * (1 - freq) + z)) / (za + za); + double a = (freq + z * 0.5 - cnf) / (1.0 + z); + double b = (freq + z * 0.5 + cnf) / (1.0 + z); + + // Print the accumulated accuracy mean x 100 and confidence interval + LOG("%zu\t%3.8lf%%\t[%3.4lf%%, %3.4lf%%]\n", i + 1, freq * 100.0, a * 100.0, b * 100.0); + } + + i0 = i1 - 1; + } + + llama_batch_free(batch); + + LOG("\n"); +} + +struct winogrande_entry { + std::string first; + std::string second; + std::array<std::string, 2> choices; + int answer; + + size_t i_logits; + size_t common_prefix; + size_t required_tokens; + size_t n_base1; // number of tokens for context + choice 1 + size_t n_base2; // number of tokens for context + choice 2 + std::vector<llama_token> seq_tokens[2]; +}; + +static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string & prompt) { + std::vector<winogrande_entry> result; + std::istringstream in(prompt); + std::string line; + std::array<int, 4> comma_pos; + while (true) { + std::getline(in, line); + if (in.fail() || in.eof()) break; + int ipos = 0; + bool quote_open = false; + for (int i = 0; i < int(line.size()); ++i) { + if (!quote_open) { + if (line[i] == ',') { + comma_pos[ipos++] = i; + if (ipos == 4) break; + } + else if (line[i] == '"') { + quote_open = true; + } + } + else { + if (line[i] == '"') { + quote_open = false; + } + } + } + if (ipos != 4) { + LOG_ERR("%s: failed to find comma separators in <%s>\n", __func__, line.c_str()); + continue; + } + auto sentence = line[comma_pos[0]+1] == '"' ? line.substr(comma_pos[0]+2, comma_pos[1] - comma_pos[0] - 3) + : line.substr(comma_pos[0]+1, comma_pos[1] - comma_pos[0] - 1); + auto choice1 = line.substr(comma_pos[1]+1, comma_pos[2] - comma_pos[1] - 1); + auto choice2 = line.substr(comma_pos[2]+1, comma_pos[3] - comma_pos[2] - 1); + auto answer = line.substr(comma_pos[3]+1, line.size() - comma_pos[3] - 1); + auto index = line.substr(0, comma_pos[0]); + int where = 0; + for ( ; where < int(sentence.size()); ++where) { + if (sentence[where] == '_') break; + } + if (where == int(sentence.size())) { + LOG_ERR("%s: no _ in <%s>\n", __func__, sentence.c_str()); + continue; + } + std::istringstream stream(answer.c_str()); + int i_answer; stream >> i_answer; + if (stream.fail() || i_answer < 1 || i_answer > 2) { + LOG_ERR("%s: failed to parse answer <%s>\n", __func__, answer.c_str()); + continue; + } + result.emplace_back(); + auto& wg = result.back(); + wg.first = sentence.substr(0, where); + wg.second = sentence.substr(where + 1, sentence.size() - where - 1); + wg.choices[0] = std::move(choice1); + wg.choices[1] = std::move(choice2); + wg.answer = i_answer; + } + return result; +} + +/* + * Evaluates the Winogrande score. + * Uses a CSV containing task index, dentence, choice 1, choice 2, answer (1 or 2) + * You can get one such dataset from e.g. https://huggingface.co/datasets/ikawrakow/winogrande-eval-for-llama.cpp + * As an example, the 1st row in the above dataset is + * + * 0,Sarah was a much better surgeon than Maria so _ always got the easier cases.,Sarah,Maria,2 + * + */ +static void winogrande_score(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + constexpr int k_min_trailing_ctx = 3; + + auto data = load_winogrande_from_csv(params.prompt); + if (data.empty()) { + LOG_ERR("%s: no tasks\n", __func__); + return; + } + + LOG_INF("%s : loaded %zu tasks from prompt.\n", __func__, data.size()); + + if (params.winogrande_tasks > 0 && params.winogrande_tasks < data.size()) { + LOG_INF("%s : selecting %zu random tasks\n", __func__, params.winogrande_tasks); + std::mt19937 rng(1); + std::vector<int> aux(data.size()); + for (int i = 0; i < int(data.size()); ++i) { + aux[i] = i; + } + float scale = 1/(1.f + (float)rng.max()); + std::vector<winogrande_entry> selected; + selected.resize(params.winogrande_tasks); + for (int i = 0; i < int(params.winogrande_tasks); ++i) { + int j = int(scale*rng()*aux.size()); + selected[i] = std::move(data[aux[j]]); + aux[j] = aux.back(); + aux.pop_back(); + } + data = std::move(selected); + } + + LOG_INF("%s : tokenizing selected tasks\n", __func__); + + for (auto & task : data) { + task.seq_tokens[0] = common_tokenize(ctx, task.first + task.choices[0] + task.second, true); + task.seq_tokens[1] = common_tokenize(ctx, task.first + task.choices[1] + task.second, true); + + task.common_prefix = 0; + for (size_t k = 0; k < task.seq_tokens[0].size(); k++) { + if (task.seq_tokens[0][k] != task.seq_tokens[1][k]) { + break; + } + task.common_prefix++; + } + + // TODO: the last token of each of the sequences don't need to be evaluated + task.required_tokens = task.common_prefix + + task.seq_tokens[0].size() - task.common_prefix + + task.seq_tokens[1].size() - task.common_prefix; + + task.n_base1 = common_tokenize(ctx, task.first + task.choices[0], true).size(); + task.n_base2 = common_tokenize(ctx, task.first + task.choices[1], true).size(); + } + + LOG_INF("%s : calculating winogrande score over selected tasks.\n", __func__); + + const int n_ctx = llama_n_ctx(ctx); + const int n_batch = params.n_batch; + + const int n_vocab = llama_vocab_n_tokens(vocab); + + const int max_tasks_per_batch = 128; + const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx)); + + llama_batch batch = llama_batch_init(n_ctx, 0, 2); + + std::vector<float> tok_logits(n_vocab); + // TODO: this could be made smaller; it's currently the worst-case size + std::vector<float> batch_logits(size_t(n_ctx)*n_vocab); + + std::vector<std::pair<size_t, llama_token>> eval_pairs; + std::vector<float> eval_results; + std::vector<std::thread> workers(std::thread::hardware_concurrency()); + + int n_correct = 0; + int n_done = 0; + + for (size_t i0 = 0; i0 < data.size(); i0++) { + int n_cur = 0; + + size_t i1 = i0; + size_t i_logits = 0; + + common_batch_clear(batch); + + while (n_cur + (int) data[i1].required_tokens <= n_ctx) { + int n_logits = 0; + const int s0 = 2*(i1 - i0); + if (s0 + 2 > max_seq) { + break; + } + + for (size_t i = 0; i < data[i1].common_prefix; ++i) { + common_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false); + } + batch.logits[batch.n_tokens - 1] = true; + n_logits += 1; + + for (int s = 0; s < 2; ++s) { + // TODO: end before the last token, no need to predict past the end of the sequences + for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) { + common_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true); + n_logits += 1; + } + } + + data[i1].i_logits = i_logits; + i_logits += n_logits; + + n_cur += data[i1].required_tokens; + if (++i1 == data.size()) { + break; + } + } + + if (i0 == i1) { + LOG_ERR("%s : task %zu does not fit in the context window (requires %lu tokens)\n", __func__, i0, data[i0].required_tokens); + return; + } + + llama_memory_clear(llama_get_memory(ctx), true); + + // decode all tasks [i0, i1) + if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) { + LOG_ERR("%s: llama_decode() failed\n", __func__); + return; + } + + eval_pairs.clear(); + for (size_t i = i0; i < i1; ++i) { + auto & task = data[i]; + + const bool skip_choice = + task.seq_tokens[0].size() - task.common_prefix > k_min_trailing_ctx && + task.seq_tokens[1].size() - task.common_prefix > k_min_trailing_ctx; + + const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix; + const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0; + size_t li = n_base1 - task.common_prefix; + for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) { + eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[0][j+1]); + } + const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix; + const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0; + // FIXME: this uses the wrong first logits when not skipping the choice word + li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - task.common_prefix; + for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) { + eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[1][j+1]); + } + } + compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results); + + size_t ir = 0; + for (size_t i = i0; i < i1; ++i) { + auto & task = data[i]; + + const bool skip_choice = + task.seq_tokens[0].size() - task.common_prefix > k_min_trailing_ctx && + task.seq_tokens[1].size() - task.common_prefix > k_min_trailing_ctx; + + float score_1st = 0; + const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix; + const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0; + for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) { + score_1st += eval_results[ir++]; + } + score_1st /= (task.seq_tokens[0].size() - n_base1 - last_1st); + + float score_2nd = 0; + const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix; + const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0; + for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) { + score_2nd += eval_results[ir++]; + } + score_2nd /= (task.seq_tokens[1].size() - n_base2 - last_2nd); + + int result = score_1st > score_2nd ? 1 : 2; + + if (result == task.answer) { + ++n_correct; + } + ++n_done; + + // print the accumulated accuracy mean x 100 + LOG("%zu\t%.4lf\t%10.6f %10.6f %d %d\n", i+1, 100.0 * n_correct/n_done, score_1st, score_2nd, result, task.answer); + } + + i0 = i1 - 1; + } + + LOG("\n"); + + if (n_done < 100) return; + + const float p = 1.f*n_correct/n_done; + const float sigma = 100.f*sqrt(p*(1-p)/(n_done-1)); + + LOG_INF("Final Winogrande score(%d tasks): %.4lf +/- %.4lf\n", n_done, 100*p, sigma); +} + +static bool deserialize_string(std::istream & in, std::string & str) { + uint32_t size; + if (!in.read((char *)&size, sizeof(size)).fail()) { + str.resize(size); + if (!in.read((char *)&str[0], size).fail()) return true; + } + return false; +} + +struct multiple_choice_answers { + std::vector<std::string> answers; + std::vector<int> labels; + bool deserialize(std::istream& in) { + uint32_t n; + in.read((char *)&n, sizeof(n)); + if (in.fail() || n > 100) return false; // 100 as max. number of answers should be good enough for any practical purpose + answers.resize(n); + labels.resize(n); + for (auto& a : answers) { + if (!deserialize_string(in, a)) return false; + } + in.read((char *)labels.data(), n*sizeof(int)); + return !in.fail(); + } +}; + +struct multiple_choice_task { + std::string question; // the question (or context that needs to be continued) + multiple_choice_answers mc1; // possible answers (continuations) with a single correct answer + multiple_choice_answers mc2; // possible answers (continuations) with multiple correct answers - not handled yet + bool deserialize(std::istream& in) { + if (!deserialize_string(in, question)) return false; + return mc1.deserialize(in) && mc2.deserialize(in); + } + + // For evaluation + size_t i_logits; // starting index of logits in the llama_batch + size_t common_prefix; // max number of initial tokens that are the same in all sentences + size_t required_tokens; // needed number of tokens to evaluate all answers + std::vector<std::vector<llama_token>> seq_tokens; + std::vector<float> log_probs; +}; + +static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choice_task& task, bool log_error) { + if (task.question.empty() || task.mc1.answers.empty()) { + if (log_error) { + LOG_ERR("%s: found bad task with empty question and/or answers\n", __func__); + } + return false; + } + task.seq_tokens.reserve(task.mc1.answers.size()); + for (auto& answer : task.mc1.answers) { + if (answer.empty()) { + if (log_error) { + LOG_ERR("%s: found empty answer\n", __func__); + } + return false; + } + task.seq_tokens.emplace_back(::common_tokenize(ctx, task.question + " " + answer, true)); + } + auto min_len = task.seq_tokens.front().size(); + for (auto& seq : task.seq_tokens) { + min_len = std::min(min_len, seq.size()); + } + task.common_prefix = 0; + for (size_t k = 0; k < min_len; ++k) { + auto token = task.seq_tokens[0][k]; + bool all_same = true; + for (size_t i = 1; i < task.seq_tokens.size(); ++i) { + if (task.seq_tokens[i][k] != token) { + all_same = false; + break; + } + } + if (!all_same) { + break; + } + ++task.common_prefix; + } + task.required_tokens = task.common_prefix; + for (auto& seq : task.seq_tokens) { + task.required_tokens += seq.size() - task.common_prefix; + } + return true; +} + +// +// Calculates score for multiple choice tasks with single correct answer from prompt. +// Commonly used LLM evaluation metrics of this type are +// * ARC +// * HellaSwag +// * MMLU +// * TruthfulQA +// +// Validation datasets for these 4 tests can be found at +// https://huggingface.co/datasets/ikawrakow/validation-datasets-for-llama.cpp +// The data for these datasets was extracted from +// git@hf.co:datasets/allenai/ai2_arc +// https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl +// git@hf.co:datasets/Stevross/mmlu +// https://huggingface.co/datasets/truthful_qa +// +static void multiple_choice_score(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + std::istringstream strstream(params.prompt); + uint32_t n_task; + strstream.read((char *)&n_task, sizeof(n_task)); + if (strstream.fail() || n_task == 0) { + LOG_ERR("%s: no tasks\n", __func__); + return; + } + LOG_INF("%s: there are %u tasks in prompt\n", __func__, n_task); + std::vector<uint32_t> task_pos(n_task); + strstream.read((char *)task_pos.data(), task_pos.size()*sizeof(uint32_t)); + if (strstream.fail()) { + LOG_ERR("%s: failed to read task positions from prompt\n", __func__); + return; + } + + std::vector<multiple_choice_task> tasks; + if (params.multiple_choice_tasks == 0 || params.multiple_choice_tasks >= (size_t)n_task) { + // Use all tasks + tasks.resize(n_task); + LOG_INF("%s: reading tasks", __func__); + int n_dot = std::max((int) n_task/100, 1); + int i = 0; + for (auto& task : tasks) { + ++i; + if (!task.deserialize(strstream)) { + LOG_ERR("%s: failed to read task %d of %u\n", __func__, i, n_task); + return; + } + if (i%n_dot == 0) LOG("."); + } + LOG("done\n"); + } + else { + LOG_INF("%s: selecting %zu random tasks from %u tasks available\n", __func__, params.multiple_choice_tasks, n_task); + std::mt19937 rng(1); + std::vector<int> aux(n_task); + for (uint32_t i = 0; i < n_task; ++i) aux[i] = i; + float scale = 1.f/(1.f + (float)std::mt19937::max()); + tasks.resize(params.multiple_choice_tasks); + for (auto& task : tasks) { + int j = (int)(scale * rng() * aux.size()); + int idx = aux[j]; + aux[j] = aux.back(); + aux.pop_back(); + strstream.seekg(task_pos[idx], std::ios::beg); + if (!task.deserialize(strstream)) { + LOG_ERR("%s: failed to read task %d at position %u\n", __func__, idx, task_pos[idx]); + return; + } + } + n_task = params.multiple_choice_tasks; + } + + LOG_INF("%s: preparing task data", __func__); + if (n_task > 500) { + LOG("..."); + std::atomic<int> counter(0); + std::atomic<int> n_bad(0); + auto prepare = [&counter, &n_bad, &tasks, ctx] () { + int num_tasks = tasks.size(); + int n_bad_local = 0; + while (true) { + int first = counter.fetch_add(K_TOKEN_CHUNK); + if (first >= num_tasks) { + if (n_bad_local > 0) n_bad += n_bad_local; + break; + } + int last = std::min(first + K_TOKEN_CHUNK, num_tasks); + for (int i = first; i < last; ++i) { + if (!multiple_choice_prepare_one_task(ctx, tasks[i], false)) ++n_bad_local; + } + } + }; + size_t max_thread = std::thread::hardware_concurrency(); + max_thread = std::min(max_thread, (tasks.size() + K_TOKEN_CHUNK - 1)/K_TOKEN_CHUNK); + std::vector<std::thread> workers(max_thread-1); + for (auto& w : workers) w = std::thread(prepare); + prepare(); + for (auto& w : workers) w.join(); + LOG("done\n"); + int nbad = n_bad; + if (nbad > 0) { + LOG_ERR("%s: found %d malformed tasks\n", __func__, nbad); + return; + } + } else { + int n_dot = std::max((int) n_task/100, 1); + int i_task = 0; + for (auto& task : tasks) { + ++i_task; + if (!multiple_choice_prepare_one_task(ctx, task, true)) { + return; + } + if (i_task%n_dot == 0) { + LOG("."); + } + } + LOG("done\n"); + } + + LOG_INF("%s : calculating TruthfulQA score over %zu tasks.\n", __func__, tasks.size()); + + LOG("\ntask\tacc_norm\n"); + + const int n_ctx = llama_n_ctx(ctx); + const int n_batch = params.n_batch; + + const int n_vocab = llama_vocab_n_tokens(vocab); + + const int max_tasks_per_batch = 32; + const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx)); + + llama_batch batch = llama_batch_init(n_ctx, 0, max_seq); + + std::vector<float> tok_logits(n_vocab); + std::vector<float> batch_logits(size_t(n_ctx)*n_vocab); + + std::vector<std::pair<size_t, llama_token>> eval_pairs; + std::vector<float> eval_results; + std::vector<std::thread> workers(std::thread::hardware_concurrency()); + std::vector<int> batch_indeces; + + int n_done = 0; + int n_correct = 0; + int n_tot_answers = 0; + + for (size_t i0 = 0; i0 < tasks.size(); i0++) { + int n_cur = 0; + + size_t i1 = i0; + size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch + + common_batch_clear(batch); + + // batch as much tasks as possible into the available context + // each task has 4 unique sequence ids - one for each ending + // the common prefix is shared among the 4 sequences to save tokens + // we extract logits only from the last common token and from all ending tokens of each sequence + int s0 = 0; + while (n_cur + (int) tasks[i1].required_tokens <= n_ctx) { + auto& cur_task = tasks[i1]; + int n_logits = 0; + + int num_answers = cur_task.seq_tokens.size(); + if (s0 + num_answers > max_seq) { + if (s0 == 0) { + LOG_ERR("%s : task %zu requires a higher -np|--parallel value (at least %d)\n", __func__, i0, num_answers); + return; + } + break; + } + + if (int(batch_indeces.size()) != num_answers) { + batch_indeces.resize(num_answers); + } + + for (int s = 0; s < num_answers; ++s) { + batch_indeces[s] = s0 + s; + } + + for (size_t i = 0; i < cur_task.common_prefix; ++i) { + //llama_batch_add(batch, cur_task.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false); + common_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false); + } + batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix + n_logits += 1; + + for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) { + const size_t seq_tokens_size = cur_task.seq_tokens[s].size(); + // TODO: don't evaluate the last token of each sequence + for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) { + const bool needs_logits = i < seq_tokens_size - 1; + common_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits); + n_logits += needs_logits; + } + } + + s0 += num_answers; + + cur_task.i_logits = i_logits; + i_logits += n_logits; + + n_cur += cur_task.required_tokens; + if (++i1 == tasks.size()) { + break; + } + } + + if (i0 == i1) { + LOG_ERR("%s : task %zu does not fit in the context window (requires %lu tokens)\n", __func__, i0, tasks[i0].required_tokens); + return; + } + + llama_memory_clear(llama_get_memory(ctx), true); + + // decode all tasks [i0, i1) + if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) { + LOG_ERR("%s: llama_decode() failed\n", __func__); + return; + } + + // Compute log-probs in parallel + // First we collect all tasks + eval_pairs.clear(); + for (size_t i = i0; i < i1; ++i) { + auto& cur_task = tasks[i]; + size_t li = 1; // skip the last logit of the common prefix (computed separately below) + for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) { + for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) { + eval_pairs.emplace_back(cur_task.i_logits + li++, cur_task.seq_tokens[s][j + 1]); + } + } + } + // Then we do the actual calculation + compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results); + + size_t ir = 0; + + // compute the logprobs for each ending of the decoded tasks + for (size_t i = i0; i < i1; ++i) { + auto & cur_task = tasks[i]; + //LOG("==== Evaluating <%s> with correct answer ", cur_task.question.c_str()); + //for (int j = 0; j < int(cur_task.mc1.labels.size()); ++j) { + // if (cur_task.mc1.labels[j] == 1) { + // LOG("%d", j+1); + // } + //} + //LOG("\n common_prefix: %zu\n", cur_task.common_prefix); + + // get the logits of the last token of the common prefix + std::memcpy(tok_logits.data(), batch_logits.data() + cur_task.i_logits*n_vocab, n_vocab*sizeof(float)); + + const auto first_probs = softmax(tok_logits); + + cur_task.log_probs.resize(cur_task.seq_tokens.size()); + for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) { + size_t count = 1; + float log_prob = std::log(first_probs[cur_task.seq_tokens[s][cur_task.common_prefix]]); + for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) { + //LOG(" %zu %g\n", ir, eval_results[ir]); + ++count; + log_prob += eval_results[ir++]; + } + cur_task.log_probs[s] = log_prob / count; + //LOG(" Final: %g\n", log_prob / count); + //LOG(" <%s> : %g\n", cur_task.mc1.answers[s].c_str(), log_prob/count); + } + + // Find the ending with maximum logprob + size_t logprob_max_idx = 0; + float logprob_max_val = cur_task.log_probs[0]; + for (size_t s = 1; s < cur_task.log_probs.size(); s++) { + if (cur_task.log_probs[s] > logprob_max_val) { + logprob_max_val = cur_task.log_probs[s]; + logprob_max_idx = s; + } + } + + n_tot_answers += cur_task.log_probs.size(); + if (cur_task.mc1.labels[logprob_max_idx] == 1) { + ++n_correct; + } + ++n_done; + + // Print the accumulated accuracy mean x 100 + LOG("%d\t%.8lf\n", n_done, 100.*n_correct/n_done); + } + + i0 = i1 - 1; + } + + llama_batch_free(batch); + + if (n_done < 100 && (params.multiple_choice_tasks != 0 && params.multiple_choice_tasks < (size_t)n_task)) return; + + float p = 1.f*n_correct/n_done; + float sigma = sqrt(p*(1-p)/(n_done-1)); + LOG("\n"); + LOG_INF("Final result: %.4f +/- %.4f\n", 100.f*p, 100.f*sigma); + p = 1.f*n_done/n_tot_answers; + sigma = sqrt(p*(1-p)/(n_done-1)); + LOG_INF("Random chance: %.4f +/- %.4f\n", 100.f*p, 100.f*sigma); + + LOG_INF("\n"); +} + +static void kl_divergence(llama_context * ctx, const common_params & params) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + if (params.logits_file.empty()) { + LOG_ERR("%s: you must provide a name of a file containing the log probabilities of the base model\n", __func__); + return; + } + std::ifstream in(params.logits_file.c_str(), std::ios::binary); + if (!in) { + LOG_ERR("%s: failed to open %s\n", __func__, params.logits_file.c_str()); + return; + } + { + char check[9]; check[8] = 0; + in.read(check, 8); + if (in.fail() || strncmp("_logits_", check, 8) != 0) { + LOG_ERR("%s: %s does not look like a file containing log-probabilities\n", __func__, params.logits_file.c_str()); + return; + } + } + + uint32_t n_ctx; + in.read((char *)&n_ctx, sizeof(n_ctx)); + if (n_ctx > llama_n_ctx(ctx)) { + LOG_ERR("%s: %s has been computed with %u, while the current context is %d. Increase it with -c and retry\n", + __func__, params.logits_file.c_str(), n_ctx, params.n_ctx); + } + + int n_vocab; + int n_chunk; + in.read((char *)&n_vocab, sizeof(n_vocab)); + in.read((char *)&n_chunk, sizeof(n_chunk)); + if (in.fail()) { + LOG_ERR("%s: failed reading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str()); + return; + } + if (n_vocab != llama_vocab_n_tokens(vocab)) { + LOG_ERR("%s: inconsistent vocabulary (%d vs %d)\n", __func__, n_vocab, llama_vocab_n_tokens(vocab)); + } + + std::vector<llama_token> tokens(size_t(n_ctx) * n_chunk); + if (in.read((char *)tokens.data(), tokens.size()*sizeof(tokens[0])).fail()) { + LOG_ERR("%s: failed reading evaluation tokens from %s\n", __func__, params.logits_file.c_str()); + return; + } + + const int n_batch = params.n_batch; + const int num_batches = (n_ctx + n_batch - 1)/n_batch; + const int nv = 2*((n_vocab + 1)/2) + 4; + const bool add_bos = llama_vocab_get_add_bos(vocab); + GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); + + std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv); + std::vector<float> kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); + std::vector<float> p_diff_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); + std::vector<float> logits; + if (num_batches > 1) { + logits.reserve(size_t(n_ctx) * n_vocab); + } + + std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1); + + auto mean_and_uncertainty = [] (double sum, double sum2, size_t count) { + if (count < 1) { + return std::make_pair(0., 0.); + } + double f = sum/count; + double df = sum2/count - f*f; + df = df > 0 && count > 10 ? sqrt(df/(count-1)) : 0.; + return std::make_pair(f, df); + }; + auto covariance = [] (double suma, double sumb, double sumab, size_t count) { + if (count < 10) { + return 0.0; + } + double var = sumab/count - (suma/count)*(sumb/count); + var /= count - 1; + return var; + }; + + kl_divergence_result kld; + auto kld_ptr = kld_values.data(); + auto p_diff_ptr = p_diff_values.data(); + + for (int i = 0; i < n_chunk; ++i) { + const int start = i * n_ctx; + const int end = start + n_ctx; + + const auto t_start = std::chrono::high_resolution_clock::now(); + + if (in.read((char *)log_probs_uint16.data(), log_probs_uint16.size()*sizeof(uint16_t)).fail()) { + LOG_ERR("%s: failed reading log-probs for chunk %d\n", __func__, i); + return; + } + + // clear the KV cache + llama_memory_clear(llama_get_memory(ctx), true); + + llama_batch batch = llama_batch_init(n_batch, 0, 1); + + for (int j = 0; j < num_batches; ++j) { + const int batch_start = start + j * n_batch; + const int batch_size = std::min(end - batch_start, n_batch); + + // save original token and restore it after eval + const auto token_org = tokens[batch_start]; + + // add BOS token for the first batch of each chunk + if (add_bos && j == 0) { + tokens[batch_start] = llama_vocab_bos(vocab); + } + + common_batch_clear(batch); + for (int i = 0; i < batch_size; i++) { + common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + } + + if (llama_decode(ctx, batch)) { + LOG_ERR("%s : failed to eval\n", __func__); + llama_batch_free(batch); + return; + } + + // restore the original token in case it was set to BOS + tokens[batch_start] = token_org; + + if (num_batches > 1) { + const auto * batch_logits = llama_get_logits(ctx); + logits.insert(logits.end(), batch_logits, batch_logits + size_t(batch_size) * n_vocab); + } + } + + llama_batch_free(batch); + + const auto t_end = std::chrono::high_resolution_clock::now(); + + if (i == 0) { + const float t_total = std::chrono::duration<float>(t_end - t_start).count(); + LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total); + int total_seconds = (int)(t_total * n_chunk); + if (total_seconds >= 60*60) { + LOG("%d hours ", total_seconds / (60*60)); + total_seconds = total_seconds % (60*60); + } + LOG("%.2f minutes\n", total_seconds / 60.0); + } + LOG("\n"); + LOG("chunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p\n"); + + const int first = n_ctx/2; + const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx); + process_logits(n_vocab, all_logits + size_t(first)*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, + workers, log_probs_uint16, kld, kld_ptr, p_diff_ptr); + p_diff_ptr += n_ctx - 1 - first; + kld_ptr += n_ctx - 1 - first; + + LOG("%4d", i+1); + + auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count); + const double ppl_val = exp(log_ppl.first); + const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 ) + LOG(" %9.4lf ± %9.4lf", ppl_val, ppl_unc); + + auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count); + const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count); + const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first; + const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov); + LOG(" %10.5lf ± %10.5lf", log_ppl_ratio_val, log_ppl_ratio_unc); + + auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count); + LOG(" %10.5lf ± %10.5lf", kl_div.first, kl_div.second); + + auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count); + const double p_diff_rms_val = sqrt(p_diff_mse.first); + const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second; + LOG(" %6.3lf ± %6.3lf %%", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc); + + double p_top_val = 1.*kld.n_same_top/kld.count; + double p_top_unc = sqrt(p_top_val*(1 - p_top_val)/(kld.count - 1)); + LOG(" %6.3lf ± %6.3lf %%", 100.0*p_top_val, 100.0*p_top_unc); + + LOG("\n"); + + logits.clear(); + } + LOG("\n"); + + if (kld.count < 100) return; // we do not wish to do statistics on so few values + + std::sort(kld_values.begin(), kld_values.end()); + std::sort(p_diff_values.begin(), p_diff_values.end()); + + LOG("====== Perplexity statistics ======\n"); + + auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count); + const double ppl_val = exp(log_ppl.first); + const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 ) + LOG("Mean PPL(Q) : %10.6lf ± %10.6lf\n", ppl_val, ppl_unc); + + auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count); + const double ppl_base_val = exp(log_ppl_base.first); + const double ppl_base_unc = ppl_base_val * log_ppl_base.second; // ppl_base_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_base.second ** 2 ) + LOG("Mean PPL(base) : %10.6lf ± %10.6lf\n", ppl_base_val, ppl_base_unc); + + const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count); + // LOG("Cov(ln(PPL(Q)), ln(PPL(base))): %10.6lf\n", log_ppl_cov); + const double log_ppl_cor = log_ppl_cov / (log_ppl.second*log_ppl_base.second); + LOG("Cor(ln(PPL(Q)), ln(PPL(base))): %6.2lf%%\n", 100.0*log_ppl_cor); + + const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first; + const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov); + LOG("Mean ln(PPL(Q)/PPL(base)) : %10.6lf ± %10.6lf\n", log_ppl_ratio_val, log_ppl_ratio_unc); + + const double ppl_ratio_val = exp(log_ppl_ratio_val); + const double ppl_ratio_unc = ppl_ratio_val * log_ppl_ratio_unc; // ppl_ratio_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_ratio.second ** 2 ) + LOG("Mean PPL(Q)/PPL(base) : %10.6lf ± %10.6lf\n", ppl_ratio_val, ppl_ratio_unc); + + const double ppl_cov = ppl_val * ppl_base_val * log_ppl_cov; + const double ppl_diff_val = ppl_val - ppl_base_val; + const double ppl_diff_unc = sqrt(ppl_unc*ppl_unc + ppl_base_unc*ppl_base_unc - 2.0*ppl_cov); + LOG("Mean PPL(Q)-PPL(base) : %10.6lf ± %10.6lf\n", ppl_diff_val, ppl_diff_unc); + + LOG("\n"); + + LOG("====== KL divergence statistics ======\n"); + auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count); + LOG("Mean KLD: %10.6lf ± %10.6lf\n", kl_div.first, kl_div.second); + auto kld_median = kld_values.size()%2 == 0 ? 0.5f*(kld_values[kld_values.size()/2] + kld_values[kld_values.size()/2-1]) + : kld_values[kld_values.size()/2]; + + auto percentile = [] (std::vector<float> values, float fraction) { + if (fraction <= 0) return values.front(); + if (fraction >= 1) return values.back(); + float p = fraction*(values.size() - 1); + size_t ip = size_t(p); p -= ip; + return (1 - p)*values[ip] + p*values[std::min(ip+1, values.size()-1)]; + }; + + LOG("Maximum KLD: %10.6f\n", kld_values.back()); + LOG("99.9%% KLD: %10.6f\n", percentile(kld_values, 0.999f)); + LOG("99.0%% KLD: %10.6f\n", percentile(kld_values, 0.990f)); + LOG("95.0%% KLD: %10.6f\n", percentile(kld_values, 0.950f)); + LOG("90.0%% KLD: %10.6f\n", percentile(kld_values, 0.900f)); + LOG("Median KLD: %10.6f\n", kld_median); + LOG("10.0%% KLD: %10.6f\n", percentile(kld_values, 0.100f)); + LOG(" 5.0%% KLD: %10.6f\n", percentile(kld_values, 0.050f)); + LOG(" 1.0%% KLD: %10.6f\n", percentile(kld_values, 0.010f)); + LOG(" 0.1%% KLD: %10.6f\n", percentile(kld_values, 0.001f)); + LOG("Minimum KLD: %10.6f\n", kld_values.front()); + + LOG("\n"); + + LOG("====== Token probability statistics ======\n"); + + auto p_diff = mean_and_uncertainty(kld.sum_p_diff, kld.sum_p_diff2, kld.count); + LOG("Mean Δp: %6.3lf ± %5.3lf %%\n", 100.0*p_diff.first, 100.0*p_diff.second); + + auto p_diff_median = p_diff_values.size()%2 == 0 ? 0.5f*(p_diff_values[p_diff_values.size()/2] + p_diff_values[p_diff_values.size()/2-1]) + : p_diff_values[p_diff_values.size()/2]; + + LOG("Maximum Δp: %6.3lf%%\n", 100.0*p_diff_values.back()); + LOG("99.9%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.999f)); + LOG("99.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.990f)); + LOG("95.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.950f)); + LOG("90.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.900f)); + LOG("75.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.750f)); + LOG("Median Δp: %6.3lf%%\n", 100.0*p_diff_median); + LOG("25.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.250f)); + LOG("10.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.100f)); + LOG(" 5.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.050f)); + LOG(" 1.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.010f)); + LOG(" 0.1%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.001f)); + LOG("Minimum Δp: %6.3lf%%\n", 100.0*p_diff_values.front()); + + auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count); + // LOG("MSE Δp : %10.6lf ± %10.6lf\n", p_diff_mse.first, p_diff_mse.second); + + const double p_diff_rms_val = sqrt(p_diff_mse.first); + const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second; + LOG("RMS Δp : %6.3lf ± %5.3lf %%\n", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc); + + const double same_top_p = 1.0*kld.n_same_top/kld.count; + LOG("Same top p: %6.3lf ± %5.3lf %%\n", 100.0*same_top_p, 100.0*sqrt(same_top_p*(1.0 - same_top_p)/(kld.count - 1))); +} + +int main(int argc, char ** argv) { + common_params params; + + params.n_ctx = 512; + params.escape = false; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) { + return 1; + } + + common_init(); + + const int32_t n_ctx = params.n_ctx; + + if (n_ctx <= 0) { + LOG_ERR("%s: perplexity tool requires '--ctx-size' > 0\n", __func__); + return 1; + } + + const bool ppl = !params.hellaswag && !params.winogrande && !params.multiple_choice && !params.kl_divergence; + + if (ppl) { + const int32_t n_seq = std::max(1, params.n_batch / n_ctx); + const int32_t n_kv = n_seq * n_ctx; + + params.n_parallel = n_seq; + params.n_ctx = n_kv; + + params.n_batch = std::min(params.n_batch, n_kv); + } else { + params.n_batch = std::min(params.n_batch, params.n_ctx); + if (params.kl_divergence) { + params.n_parallel = 1; + } else { + // ensure there's at least enough seq_ids for HellaSwag + params.n_parallel = std::max(4, params.n_parallel); + } + } + + if (params.ppl_stride > 0) { + LOG_INF("Will perform strided perplexity calculation -> adjusting context size from %d to %d\n", + params.n_ctx, params.n_ctx + params.ppl_stride/2); + params.n_ctx += params.ppl_stride/2; + } + + llama_backend_init(); + llama_numa_init(params.numa); + + // load the model and apply lora adapter, if any + auto llama_init = common_init_from_params(params); + + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); + + if (model == NULL) { + LOG_ERR("%s: unable to load model\n", __func__); + return 1; + } + + const int n_ctx_train = llama_model_n_ctx_train(model); + + if (params.n_ctx > n_ctx_train) { + LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", + __func__, n_ctx_train, params.n_ctx); + } + + // print system information + { + LOG_INF("\n"); + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); + } + + struct results_perplexity results; + if (params.hellaswag) { + hellaswag_score(ctx, params); + } else if (params.winogrande) { + winogrande_score(ctx, params); + } else if (params.multiple_choice) { + multiple_choice_score(ctx, params); + } else if (params.kl_divergence) { + kl_divergence(ctx, params); + } else { + results = perplexity(ctx, params, n_ctx); + } + + LOG("\n"); + llama_perf_context_print(ctx); + llama_memory_breakdown_print(ctx); + + llama_backend_free(); + + return 0; +} |