mirror of
https://github.com/ggml-org/llama.cpp.git
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cf658adc83
* llama : refactor GGUF constants into static maps * llama : check if model architecture is known * llama : refactor llama_model_load_internal() * gguf : add KV constant maps * llm : read arch-specific KVs * convert : add dummy scores + types * falcon : load tensor data (CPU only) * llama : fix loading progress bar * llama : add arch member to llama_model * falcon : CPU inference working * falcon : support non-40B models * falcon : minor * llama : minor updates ggml-ci * convert-falcon-hf-to-gguf.py : fix special token mapping * llama.cpp : llama default UNK token = id 0 * llama.cpp : fix bpe tokenizer * llama.cpp : fix the fix of bpe tokenizer * ggml : pass eps to ggml_norm * metal : implement RoPE (mode = 2) + avoid ggml_repeat * ggml : ggml_repeat always creates new tensor * falcon : copy-paste self-attention from LLaMA * metal : print extra compute pipeline info * falcon : minor changes (still chasing the Metal problem) * llama.cpp : fix linefeed token * metal : fix GELU kernel numerical stability by using precise::tanh * metal : temporary workaround for the concurrency optimization bug * falcon : add CUDA offloading (#2739) * llama : better model naming and size reporting * llama : prep new tokenizer support * llama : advanced BPE tokenizer based on ggllm.cpp imlpementation * llama : remove oboslete comment ggml-ci * common : remove obsolete BPE API + disable test-tokenizer-1 * llama : revert BPE special-case in llama_byte_to_token() * cuda : add TODOs for RoPE NeoX implementation * llama : default special tokens based on vocab type * perplexity : add log for start of tokenization --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> Co-authored-by: slaren <slarengh@gmail.com>
558 lines
21 KiB
C++
558 lines
21 KiB
C++
#include "common.h"
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#include "llama.h"
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#include "build-info.h"
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#include <cmath>
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#include <ctime>
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#include <sstream>
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#include <cstring>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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std::vector<float> softmax(const std::vector<float>& logits) {
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std::vector<float> probs(logits.size());
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float max_logit = logits[0];
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for (float v : logits) max_logit = std::max(max_logit, v);
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double sum_exp = 0.0;
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for (size_t i = 0; i < logits.size(); i++) {
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// Subtract the maximum logit value from the current logit value for numerical stability
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const float logit = logits[i] - max_logit;
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const float exp_logit = expf(logit);
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sum_exp += exp_logit;
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probs[i] = exp_logit;
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}
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for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;
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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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const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
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const bool add_bos = is_spm;
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fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
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auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
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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 (add_bos && 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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// BOS tokens will be added for each chunk before eval
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const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
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const bool add_bos = is_spm;
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fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
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auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
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const int n_chunk_max = tokens.size() / params.n_ctx;
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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.n_ctx;
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const int end = start + params.n_ctx;
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const int num_batches = (params.n_ctx + n_batch - 1) / n_batch;
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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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// 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 (add_bos && j == 0) {
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tokens[batch_start] = llama_token_bos(ctx);
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}
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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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// restore the original token in case it was set to BOS
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tokens[batch_start] = token_org;
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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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}
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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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// We get the logits for all the tokens in the context window (params.n_ctx)
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// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,
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// calculate the perplexity over the last half of the window (so the model always has
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// some context to predict the token).
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//
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// We rely on the fact that attention in the forward pass only looks at previous
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// tokens here, so the logits returned for each token are an accurate representation
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// of what the model would have predicted at that point.
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//
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// Example, we have a context window of 512, we will compute perplexity for each of the
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// last 256 tokens. Then, we split the input up into context window size chunks to
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// process the entire prompt.
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for (int j = std::min(512, params.n_ctx / 2); 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.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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}
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std::vector<float> hellaswag_evaluate_tokens(llama_context * ctx, const std::vector<int>& tokens, int n_past, int n_batch,
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int n_vocab, int n_thread) {
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std::vector<float> result;
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result.reserve(tokens.size() * n_vocab);
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size_t n_chunk = (tokens.size() + n_batch - 1)/n_batch;
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for (size_t i_chunk = 0; i_chunk < n_chunk; ++i_chunk) {
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size_t n_tokens = tokens.size() - i_chunk * n_batch;
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n_tokens = std::min(n_tokens, size_t(n_batch));
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if (llama_eval(ctx, tokens.data() + i_chunk * n_batch, n_tokens, n_past, n_thread)) {
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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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const auto logits = llama_get_logits(ctx);
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result.insert(result.end(), logits, logits + n_tokens * n_vocab);
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n_past += n_tokens;
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}
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return result;
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}
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void hellaswag_score(llama_context * ctx, const gpt_params & params) {
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// Calculates hellaswag score (acc_norm) from prompt
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//
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// Data extracted from the HellaSwag validation dataset (MIT license) https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
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// All used data fields are preprocessed as in https://github.com/EleutherAI/lm-evaluation-harness/blob/df3da98c5405deafd519c2ddca52bb7c3fe36bef/lm_eval/tasks/hellaswag.py#L62-L68
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//
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// All 10042 tasks should be extracted to keep the results standardized like other implementations.
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//
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// Datafile layout:
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// ['??'] denotes json fields
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// 6 lines per task:
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// ['activity_label'] + ": " +['ctx'] - The first part of the query, the context
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// ['label'] - The index the best common sense ending aka gold ending
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// ['endings'][0] - Endings added to the first part of the query
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// ['endings'][1]
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// ['endings'][2]
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// ['endings'][3]
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std::vector<std::string> prompt_lines;
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std::istringstream strstream(params.prompt);
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std::string line;
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while (std::getline(strstream,line,'\n')) {
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prompt_lines.push_back(line);
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}
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if( prompt_lines.size() % 6 != 0) {
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fprintf(stderr, "%s : number of lines in prompt not a multiple of 6.\n", __func__);
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return;
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}
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size_t hs_task_count = prompt_lines.size()/6;
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fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);
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const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
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// This is needed as usual for LLaMA models
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const bool add_bos = is_spm;
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// Number of tasks to use when computing the score
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if ( params.hellaswag_tasks < hs_task_count ) {
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hs_task_count = params.hellaswag_tasks;
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}
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// The tasks should be randomized so the score stabilizes quickly.
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bool randomize_tasks = true;
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// The random seed should not impact the final result if the computation is done over enough tasks, so kept hardcoded for now
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std::mt19937 rng(1);
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// Dataholder for hellaswag tasks
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struct hs_data_t {
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std::string context;
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size_t gold_ending_idx;
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std::string ending[4];
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size_t ending_logprob_count[4];
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double ending_logprob[4];
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};
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fprintf(stderr, "%s : selecting %zu %s tasks.\n", __func__, hs_task_count, (randomize_tasks?"randomized":"the first") );
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// Select and read data from prompt lines
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hs_data_t *hs_data = new hs_data_t[hs_task_count];
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for (size_t i=0; i < hs_task_count; i++) {
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size_t idx = i;
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// Select a random example of those left in the prompt
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if (randomize_tasks) {
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std::uniform_int_distribution<size_t> dist(0, prompt_lines.size()/6-1 ) ;
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idx = dist(rng);
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}
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hs_data[i].context = prompt_lines[idx*6];
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hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
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for (size_t j=0; j < 4; j++) {
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hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j];
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}
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// Delete the selected random example from the prompt
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if (randomize_tasks) {
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prompt_lines.erase( std::next(prompt_lines.begin(),idx*6) , std::next(prompt_lines.begin(),idx*6+6) );
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}
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}
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fprintf(stderr, "%s : calculating hellaswag score over selected tasks.\n", __func__);
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printf("\ntask\tacc_norm\n");
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double acc = 0.0f;
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const int n_vocab = llama_n_vocab(ctx);
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std::vector<float> tok_logits(n_vocab);
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for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
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// Tokenize the context to count tokens
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std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, add_bos);
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size_t context_size = context_embd.size();
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// Do the 1st ending
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// In this case we include the context when evaluating
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auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);
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auto query_size = query_embd.size();
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//printf("First query: %d\n",(int)query_size);
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// Stop if query wont fit the ctx window
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if (query_size > (size_t)params.n_ctx) {
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fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
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return;
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}
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// Speedup small evaluations by evaluating atleast 32 tokens
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if (query_size < 32) {
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query_embd.resize(32);
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}
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auto logits = hellaswag_evaluate_tokens(ctx, query_embd, 0, params.n_batch, n_vocab, params.n_threads);
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if (logits.empty()) {
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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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std::memcpy(tok_logits.data(), logits.data() + (context_size-1)*n_vocab, n_vocab*sizeof(float));
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const auto first_probs = softmax(tok_logits);
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hs_data[task_idx].ending_logprob_count[0] = 1;
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hs_data[task_idx].ending_logprob[0] = std::log(first_probs[query_embd[context_size]]);
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|
|
// Calculate the logprobs over the ending
|
|
for (size_t j = context_size; j < query_size - 1; j++) {
|
|
|
|
std::memcpy(tok_logits.data(), logits.data() + j*n_vocab, n_vocab*sizeof(float));
|
|
|
|
const float prob = softmax(tok_logits)[query_embd[j + 1]];
|
|
|
|
hs_data[task_idx].ending_logprob[0] += std::log(prob);
|
|
hs_data[task_idx].ending_logprob_count[0]++;
|
|
}
|
|
|
|
// Calculate the mean token logprob for acc_norm
|
|
hs_data[task_idx].ending_logprob[0] /= hs_data[task_idx].ending_logprob_count[0];
|
|
|
|
// Do the remaining endings
|
|
// For these, we use the bare ending with n_past = context_size
|
|
//
|
|
for (size_t ending_idx = 1; ending_idx < 4; ending_idx++) {
|
|
|
|
// Tokenize the query
|
|
query_embd = ::llama_tokenize(ctx, hs_data[task_idx].ending[ending_idx], false);
|
|
query_size = query_embd.size();
|
|
|
|
// Stop if query wont fit the ctx window
|
|
if (context_size + query_size > (size_t)params.n_ctx) {
|
|
fprintf(stderr, "%s : number of tokens in query %zu > n_ctxl\n", __func__, query_size);
|
|
return;
|
|
}
|
|
|
|
// Speedup small evaluations by evaluating atleast 32 tokens
|
|
// No, resizing to 32 is actually slightly slower (at least on CUDA)
|
|
//if (query_size < 32) {
|
|
// query_embd.resize(32);
|
|
//}
|
|
|
|
// Evaluate the query
|
|
logits = hellaswag_evaluate_tokens(ctx, query_embd, context_size, params.n_batch, n_vocab, params.n_threads);
|
|
if (logits.empty()) {
|
|
fprintf(stderr, "%s : failed to eval\n", __func__);
|
|
return;
|
|
}
|
|
|
|
hs_data[task_idx].ending_logprob_count[ending_idx] = 1;
|
|
hs_data[task_idx].ending_logprob[ending_idx] = std::log(first_probs[query_embd[0]]);
|
|
|
|
// Calculate the logprobs over the ending
|
|
for (size_t j = 0; j < query_size - 1; j++) {
|
|
std::memcpy(tok_logits.data(), logits.data() + j*n_vocab, n_vocab*sizeof(float));
|
|
|
|
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] );
|
|
}
|
|
|
|
// Find the ending with maximum logprob
|
|
size_t ending_logprob_max_idx = 0;
|
|
double ending_logprob_max_val = hs_data[task_idx].ending_logprob[0];
|
|
for (size_t j = 1; j < 4; j++) {
|
|
if (hs_data[task_idx].ending_logprob[j] > ending_logprob_max_val) {
|
|
ending_logprob_max_idx = j;
|
|
ending_logprob_max_val = hs_data[task_idx].ending_logprob[j];
|
|
}
|
|
}
|
|
|
|
// printf("max logprob ending idx %lu, gold ending idx %lu\n", ending_logprob_max_idx, hs_data[task_idx].gold_ending_idx);
|
|
|
|
// If the gold ending got the maximum logprobe add one accuracy point
|
|
if (ending_logprob_max_idx == hs_data[task_idx].gold_ending_idx) {
|
|
acc += 1.0;
|
|
}
|
|
|
|
// Print the accumulated accuracy mean x 100
|
|
printf("%zu\t%.8lf\n",task_idx+1, acc/double(task_idx+1)*100.0);
|
|
fflush(stdout);
|
|
}
|
|
|
|
delete [] hs_data;
|
|
|
|
printf("\n");
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
gpt_params params;
|
|
|
|
params.n_batch = 512;
|
|
if (gpt_params_parse(argc, argv, params) == false) {
|
|
return 1;
|
|
}
|
|
|
|
params.perplexity = true;
|
|
params.n_batch = std::min(params.n_batch, params.n_ctx);
|
|
|
|
if (params.ppl_stride > 0) {
|
|
fprintf(stderr, "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;
|
|
}
|
|
|
|
if (params.n_ctx > 2048) {
|
|
fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
|
|
"expect poor results\n", __func__, params.n_ctx);
|
|
}
|
|
|
|
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
|
|
|
|
if (params.seed == LLAMA_DEFAULT_SEED) {
|
|
params.seed = time(NULL);
|
|
}
|
|
|
|
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
|
|
|
|
std::mt19937 rng(params.seed);
|
|
if (params.random_prompt) {
|
|
params.prompt = gpt_random_prompt(rng);
|
|
}
|
|
|
|
llama_backend_init(params.numa);
|
|
|
|
llama_model * model;
|
|
llama_context * ctx;
|
|
|
|
// load the model and apply lora adapter, if any
|
|
std::tie(model, ctx) = llama_init_from_gpt_params(params);
|
|
if (model == NULL) {
|
|
fprintf(stderr, "%s: error: unable to load model\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
// print system information
|
|
{
|
|
fprintf(stderr, "\n");
|
|
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
|
|
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
|
|
}
|
|
|
|
if (params.hellaswag) {
|
|
hellaswag_score(ctx, params);
|
|
} else {
|
|
perplexity(ctx, params);
|
|
}
|
|
|
|
llama_print_timings(ctx);
|
|
llama_free(ctx);
|
|
llama_free_model(model);
|
|
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|