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llama : require first token to be BOS (#1303)
* llama : require first token to be BOS * scripts : add ppl-run-all.sh * perplexity : add BOS for each chunk * readme : update perplexity values after BOS fix * perplexity : add clarifying comments
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Georgi Gerganov
GitHub
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@ -43,5 +43,6 @@ zig-out/
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zig-cache/
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ppl-*.txt
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qnt-*.txt
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examples/jeopardy/results.txt
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32
README.md
32
README.md
@ -298,17 +298,25 @@ Several quantization methods are supported. They differ in the resulting model d
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| Model | Measure | F16 | Q4_0 | Q4_1 | Q4_2 | Q5_0 | Q5_1 | Q8_0 |
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|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|-------:|
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| 7B | perplexity | 5.9565 | 6.2103 | 6.1286 | 6.1698 | 6.0139 | 5.9934 | 5.9571 |
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| 7B | perplexity | 5.9066 | 6.1620 | 6.0910 | 6.1466 | 5.9862 | 5.9481 | 5.9069 |
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| 7B | file size | 13.0G | 4.0G | 4.8G | 4.0G | 4.4G | 4.8G | 7.1G |
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| 7B | ms/tok @ 4th | 128 | 56 | 61 | 84 | 91 | 95 | 75 |
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| 7B | ms/tok @ 8th | 128 | 47 | 55 | 48 | 53 | 59 | 75 |
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| 7B | bits/weight | 16.0 | 5.0 | 6.0 | 5.0 | 5.5 | 6.0 | 9.0 |
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| 13B | perplexity | 5.2455 | 5.3748 | 5.3471 | 5.3433 | 5.2768 | 5.2582 | 5.2458 |
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| 13B | perplexity | 5.2543 | 5.3863 | 5.3607 | 5.3513 | 5.2856 | 5.2706 | 5.2548 |
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| 13B | file size | 25.0G | 7.6G | 9.1G | 7.6G | 8.4G | 9.1G | 14G |
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| 13B | ms/tok @ 4th | 239 | 104 | 113 | 160 | 176 | 185 | 141 |
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| 13B | ms/tok @ 8th | 240 | 85 | 99 | 97 | 108 | 117 | 147 |
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| 13B | bits/weight | 16.0 | 5.0 | 6.0 | 5.0 | 5.5 | 6.0 | 9.0 |
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### Perplexity (measuring model quality)
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You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better).
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For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity).
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The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512.
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The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 threads.
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### Interactive mode
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If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.
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@ -407,26 +415,6 @@ If your issue is with model generation quality, then please at least scan the fo
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- [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
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- [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
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### Perplexity (measuring model quality)
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You can use the `perplexity` example to measure perplexity over the given prompt. For more background, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity). However, in general, lower perplexity is better for LLMs.
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#### Latest measurements
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The latest perplexity scores for the various model sizes and quantizations are being tracked in [discussion #406](https://github.com/ggerganov/llama.cpp/discussions/406). `llama.cpp` is measuring very well compared to the baseline implementations. Quantization has a small negative impact on quality, but, as you can see, running
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13B at q4_0 beats the 7B f16 model by a significant amount.
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All measurements are done against the wikitext2 test dataset (https://paperswithcode.com/dataset/wikitext-2), with default options (512 length context).
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Note that changing the context length will have a significant impact on perplexity (longer context = better perplexity).
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```
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Perplexity - model options
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5.5985 - 13B, q4_0
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5.9565 - 7B, f16
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6.3001 - 7B, q4_1
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6.5949 - 7B, q4_0
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6.5995 - 7B, q4_0, --memory_f16
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```
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#### How to run
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1. Download/extract: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
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@ -438,8 +438,8 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
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// TODO: not great allocating this every time
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std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
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// initialize to prompt numer of chars, since n_tokens <= n_prompt_chars
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std::vector<llama_token> res(text.size() + (int)add_bos);
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int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
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std::vector<llama_token> res(text.size() + (int) add_bos);
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const int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
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assert(n >= 0);
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res.resize(n);
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@ -313,7 +313,8 @@ int main(int argc, char ** argv) {
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if (n_past + (int) embd.size() > n_ctx) {
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const int n_left = n_past - params.n_keep;
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n_past = params.n_keep;
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// always keep the first token - BOS
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n_past = std::max(1, params.n_keep);
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// insert n_left/2 tokens at the start of embd from last_n_tokens
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embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size());
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@ -331,7 +332,6 @@ int main(int argc, char ** argv) {
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}
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// try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
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// REVIEW
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if (n_session_consumed < (int) session_tokens.size()) {
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size_t i = 0;
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for ( ; i < embd.size(); i++) {
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@ -25,46 +25,68 @@ void perplexity(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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auto tokens = ::llama_tokenize(ctx, params.prompt, true);
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int count = 0;
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int seq_count = tokens.size() / params.n_ctx;
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int n_vocab = llama_n_vocab(ctx);
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int count = 0;
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const int n_chunk = tokens.size() / params.n_ctx;
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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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double nll = 0.0;
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fprintf(stderr, "%s : calculating perplexity over %d chunks, batch_size=%d\n", __func__, seq_count, params.n_batch);
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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 < seq_count; ++i) {
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int start = i * params.n_ctx;
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int end = start + params.n_ctx;
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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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int num_batches = (params.n_ctx + params.n_batch - 1) / params.n_batch;
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auto start_t = std::chrono::high_resolution_clock::now();
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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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int batch_start = start + j * params.n_batch;
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int batch_size = std::min(end - batch_start, params.n_batch);
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if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * params.n_batch, params.n_threads)) {
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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 (j == 0) {
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tokens[batch_start] = llama_token_bos();
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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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auto batch_logits = llama_get_logits(ctx);
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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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auto end_t = std::chrono::high_resolution_clock::now();
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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 seconds = std::chrono::duration<float>(end_t - start_t).count();
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printf("%.2f seconds per pass - ETA ", seconds);
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int total_seconds = (int)(seconds * seq_count);
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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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printf("%d hours ", 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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printf("%d minutes\n", total_seconds / 60);
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fprintf(stderr, "%d minutes\n", total_seconds / 60);
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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 the window (so the model always has
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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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@ -76,10 +98,12 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
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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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std::vector<float> tok_logits(
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logits.begin() + j * n_vocab,
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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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float prob = softmax(tok_logits)[tokens[start + j + 1]];
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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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12
llama.cpp
12
llama.cpp
@ -1052,6 +1052,13 @@ static bool llama_eval_internal(
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const int n_tokens,
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const int n_past,
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const int n_threads) {
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// enforce that the first token is BOS
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if (n_past == 0 && tokens[0] != llama_token_bos()) {
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fprintf(stderr, "%s: first token must be BOS\n", __func__);
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return false;
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}
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const int64_t t_start_us = ggml_time_us();
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const int N = n_tokens;
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@ -1482,7 +1489,7 @@ static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, co
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}
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if (bos) {
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output.push_back(1);
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output.push_back(llama_token_bos());
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}
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tokenizer.tokenize(text, output);
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@ -2727,11 +2734,14 @@ int llama_eval(
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fprintf(stderr, "%s: failed to eval\n", __func__);
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return 1;
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}
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// get a more accurate load time, upon first eval
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// TODO: fix this
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if (!ctx->has_evaluated_once) {
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ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
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ctx->has_evaluated_once = true;
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}
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return 0;
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}
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43
scripts/ppl-run-all.sh
Executable file
43
scripts/ppl-run-all.sh
Executable file
@ -0,0 +1,43 @@
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#!/bin/bash
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#
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# quantize
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#
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# 7B
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time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q4_0.bin q4_0 2>&1 | tee ../qnt-7b-q4_0.txt
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time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q4_1.bin q4_1 2>&1 | tee ../qnt-7b-q4_1.txt
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time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q4_2.bin q4_2 2>&1 | tee ../qnt-7b-q4_2.txt
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time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q5_0.bin q5_0 2>&1 | tee ../qnt-7b-q5_0.txt
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time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q5_1.bin q5_1 2>&1 | tee ../qnt-7b-q5_1.txt
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time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q8_0.bin q8_0 2>&1 | tee ../qnt-7b-q8_0.txt
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# 13B
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time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q4_0.bin q4_0 2>&1 | tee ../qnt-13b-q4_0.txt
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time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q4_1.bin q4_1 2>&1 | tee ../qnt-13b-q4_1.txt
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time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q4_2.bin q4_2 2>&1 | tee ../qnt-13b-q4_2.txt
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time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q5_0.bin q5_0 2>&1 | tee ../qnt-13b-q5_0.txt
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time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q5_1.bin q5_1 2>&1 | tee ../qnt-13b-q5_1.txt
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time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q8_0.bin q8_0 2>&1 | tee ../qnt-13b-q8_0.txt
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#
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# perplexity
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#
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# 7B
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time ./bin/perplexity -m ../models/7B/ggml-model-f16.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-f16.txt
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time ./bin/perplexity -m ../models/7B/ggml-model-q4_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q4_0.txt
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time ./bin/perplexity -m ../models/7B/ggml-model-q4_1.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q4_1.txt
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time ./bin/perplexity -m ../models/7B/ggml-model-q4_2.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q4_2.txt
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time ./bin/perplexity -m ../models/7B/ggml-model-q5_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q5_0.txt
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time ./bin/perplexity -m ../models/7B/ggml-model-q5_1.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q5_1.txt
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time ./bin/perplexity -m ../models/7B/ggml-model-q8_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q8_0.txt
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# 13B
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time ./bin/perplexity -m ../models/13B/ggml-model-f16.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-f16.txt
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time ./bin/perplexity -m ../models/13B/ggml-model-q4_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q4_0.txt
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time ./bin/perplexity -m ../models/13B/ggml-model-q4_1.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q4_1.txt
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time ./bin/perplexity -m ../models/13B/ggml-model-q4_2.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q4_2.txt
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time ./bin/perplexity -m ../models/13B/ggml-model-q5_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q5_0.txt
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time ./bin/perplexity -m ../models/13B/ggml-model-q5_1.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q5_1.txt
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time ./bin/perplexity -m ../models/13B/ggml-model-q8_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q8_0.txt
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