diff --git a/.gitignore b/.gitignore index 6f275fea4..a5fef3277 100644 --- a/.gitignore +++ b/.gitignore @@ -43,5 +43,6 @@ zig-out/ zig-cache/ ppl-*.txt +qnt-*.txt examples/jeopardy/results.txt diff --git a/README.md b/README.md index 6cbdcbf83..438748a91 100644 --- a/README.md +++ b/README.md @@ -298,17 +298,25 @@ Several quantization methods are supported. They differ in the resulting model d | Model | Measure | F16 | Q4_0 | Q4_1 | Q4_2 | Q5_0 | Q5_1 | Q8_0 | |------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|-------:| -| 7B | perplexity | 5.9565 | 6.2103 | 6.1286 | 6.1698 | 6.0139 | 5.9934 | 5.9571 | +| 7B | perplexity | 5.9066 | 6.1620 | 6.0910 | 6.1466 | 5.9862 | 5.9481 | 5.9069 | | 7B | file size | 13.0G | 4.0G | 4.8G | 4.0G | 4.4G | 4.8G | 7.1G | | 7B | ms/tok @ 4th | 128 | 56 | 61 | 84 | 91 | 95 | 75 | | 7B | ms/tok @ 8th | 128 | 47 | 55 | 48 | 53 | 59 | 75 | | 7B | bits/weight | 16.0 | 5.0 | 6.0 | 5.0 | 5.5 | 6.0 | 9.0 | -| 13B | perplexity | 5.2455 | 5.3748 | 5.3471 | 5.3433 | 5.2768 | 5.2582 | 5.2458 | +| 13B | perplexity | 5.2543 | 5.3863 | 5.3607 | 5.3513 | 5.2856 | 5.2706 | 5.2548 | | 13B | file size | 25.0G | 7.6G | 9.1G | 7.6G | 8.4G | 9.1G | 14G | | 13B | ms/tok @ 4th | 239 | 104 | 113 | 160 | 176 | 185 | 141 | | 13B | ms/tok @ 8th | 240 | 85 | 99 | 97 | 108 | 117 | 147 | | 13B | bits/weight | 16.0 | 5.0 | 6.0 | 5.0 | 5.5 | 6.0 | 9.0 | +### Perplexity (measuring model quality) + +You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better). +For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity). + +The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512. +The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 threads. + ### Interactive mode If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter. @@ -407,26 +415,6 @@ If your issue is with model generation quality, then please at least scan the fo - [Aligning language models to follow instructions](https://openai.com/research/instruction-following) - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155) -### Perplexity (measuring model quality) - -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. - -#### Latest measurements - -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 -13B at q4_0 beats the 7B f16 model by a significant amount. - -All measurements are done against the wikitext2 test dataset (https://paperswithcode.com/dataset/wikitext-2), with default options (512 length context). -Note that changing the context length will have a significant impact on perplexity (longer context = better perplexity). -``` -Perplexity - model options -5.5985 - 13B, q4_0 -5.9565 - 7B, f16 -6.3001 - 7B, q4_1 -6.5949 - 7B, q4_0 -6.5995 - 7B, q4_0, --memory_f16 -``` - #### How to run 1. Download/extract: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research diff --git a/examples/common.cpp b/examples/common.cpp index f1c3bae13..6af440272 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -438,8 +438,8 @@ std::string gpt_random_prompt(std::mt19937 & rng) { // TODO: not great allocating this every time std::vector llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) { // initialize to prompt numer of chars, since n_tokens <= n_prompt_chars - std::vector res(text.size() + (int)add_bos); - int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos); + std::vector res(text.size() + (int) add_bos); + const int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos); assert(n >= 0); res.resize(n); diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 5ac151e14..045093c72 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -313,7 +313,8 @@ int main(int argc, char ** argv) { if (n_past + (int) embd.size() > n_ctx) { const int n_left = n_past - params.n_keep; - n_past = params.n_keep; + // always keep the first token - BOS + n_past = std::max(1, params.n_keep); // insert n_left/2 tokens at the start of embd from last_n_tokens embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size()); @@ -331,7 +332,6 @@ int main(int argc, char ** argv) { } // try to reuse a matching prefix from the loaded session instead of re-eval (via n_past) - // REVIEW if (n_session_consumed < (int) session_tokens.size()) { size_t i = 0; for ( ; i < embd.size(); i++) { diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 299a19999..9212dee5c 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -25,46 +25,68 @@ void perplexity(llama_context * ctx, const gpt_params & params) { // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research // 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 auto tokens = ::llama_tokenize(ctx, params.prompt, true); - int count = 0; - int seq_count = tokens.size() / params.n_ctx; - int n_vocab = llama_n_vocab(ctx); + int count = 0; + + const int n_chunk = tokens.size() / params.n_ctx; + const int n_vocab = llama_n_vocab(ctx); + const int n_batch = params.n_batch; double nll = 0.0; - fprintf(stderr, "%s : calculating perplexity over %d chunks, batch_size=%d\n", __func__, seq_count, params.n_batch); + fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch); - for (int i = 0; i < seq_count; ++i) { - int start = i * params.n_ctx; - int end = start + params.n_ctx; + for (int i = 0; i < n_chunk; ++i) { + const int start = i * params.n_ctx; + const int end = start + params.n_ctx; + + const int num_batches = (params.n_ctx + n_batch - 1) / n_batch; std::vector logits; - int num_batches = (params.n_ctx + params.n_batch - 1) / params.n_batch; - auto start_t = std::chrono::high_resolution_clock::now(); + + const auto t_start = std::chrono::high_resolution_clock::now(); + for (int j = 0; j < num_batches; ++j) { - int batch_start = start + j * params.n_batch; - int batch_size = std::min(end - batch_start, params.n_batch); - if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * params.n_batch, params.n_threads)) { + 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 (j == 0) { + tokens[batch_start] = llama_token_bos(); + } + + if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) { fprintf(stderr, "%s : failed to eval\n", __func__); return; } - auto batch_logits = llama_get_logits(ctx); + + // restore the original token in case it was set to BOS + tokens[batch_start] = token_org; + + const auto batch_logits = llama_get_logits(ctx); logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); } - auto end_t = std::chrono::high_resolution_clock::now(); + + const auto t_end = std::chrono::high_resolution_clock::now(); + if (i == 0) { - const float seconds = std::chrono::duration(end_t - start_t).count(); - printf("%.2f seconds per pass - ETA ", seconds); - int total_seconds = (int)(seconds * seq_count); + const float t_total = std::chrono::duration(t_end - t_start).count(); + fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total); + int total_seconds = (int)(t_total * n_chunk); if (total_seconds >= 60*60) { - printf("%d hours ", total_seconds / (60*60)); + fprintf(stderr, "%d hours ", total_seconds / (60*60)); total_seconds = total_seconds % (60*60); } - printf("%d minutes\n", total_seconds / 60); + fprintf(stderr, "%d minutes\n", total_seconds / 60); } + // We get the logits for all the tokens in the context window (params.n_ctx) // from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity, - // calculate the perplexity over the last half the window (so the model always has + // 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 @@ -76,10 +98,12 @@ void perplexity(llama_context * ctx, const gpt_params & params) { // process the entire prompt. for (int j = std::min(512, params.n_ctx / 2); j < params.n_ctx - 1; ++j) { // Calculate probability of next token, given the previous ones. - std::vector tok_logits( - logits.begin() + j * n_vocab, + const std::vector tok_logits( + logits.begin() + (j + 0) * n_vocab, logits.begin() + (j + 1) * n_vocab); - float prob = softmax(tok_logits)[tokens[start + j + 1]]; + + const float prob = softmax(tok_logits)[tokens[start + j + 1]]; + nll += -std::log(prob); ++count; } diff --git a/llama.cpp b/llama.cpp index c36c6ced6..d54fa502c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1052,6 +1052,13 @@ static bool llama_eval_internal( const int n_tokens, const int n_past, const int n_threads) { + + // enforce that the first token is BOS + if (n_past == 0 && tokens[0] != llama_token_bos()) { + fprintf(stderr, "%s: first token must be BOS\n", __func__); + return false; + } + const int64_t t_start_us = ggml_time_us(); const int N = n_tokens; @@ -1482,7 +1489,7 @@ static std::vector llama_tokenize(const llama_vocab & vocab, co } if (bos) { - output.push_back(1); + output.push_back(llama_token_bos()); } tokenizer.tokenize(text, output); @@ -2727,11 +2734,14 @@ int llama_eval( fprintf(stderr, "%s: failed to eval\n", __func__); return 1; } + // get a more accurate load time, upon first eval + // TODO: fix this if (!ctx->has_evaluated_once) { ctx->t_load_us = ggml_time_us() - ctx->t_start_us; ctx->has_evaluated_once = true; } + return 0; } diff --git a/scripts/ppl-run-all.sh b/scripts/ppl-run-all.sh new file mode 100755 index 000000000..28f31ca71 --- /dev/null +++ b/scripts/ppl-run-all.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +# +# quantize +# + +# 7B +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 +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 +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 +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 +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 +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 + +# 13B +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 +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 +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 +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 +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 +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 + +# +# perplexity +# + +# 7B +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 +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 +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 +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 +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 +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 +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 + +# 13B +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 +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 +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 +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 +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 +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 +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