zig-cache/
ppl-*.txt
+qnt-*.txt
examples/jeopardy/results.txt
| 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.
- [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
// TODO: not great allocating this every time
std::vector<llama_token> 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<llama_token> res(text.size() + (int)add_bos);
- int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
+ std::vector<llama_token> 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);
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());
}
// 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++) {
// 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 < n_chunk; ++i) {
+ const int start = i * params.n_ctx;
+ const int end = start + params.n_ctx;
- for (int i = 0; i < seq_count; ++i) {
- int start = i * params.n_ctx;
- int end = start + params.n_ctx;
+ const int num_batches = (params.n_ctx + n_batch - 1) / n_batch;
std::vector<float> 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<float>(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<float>(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
// 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<float> tok_logits(
- logits.begin() + j * n_vocab,
+ const std::vector<float> 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;
}
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;
}
if (bos) {
- output.push_back(1);
+ output.push_back(llama_token_bos());
}
tokenizer.tokenize(text, output);
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;
}
--- /dev/null
+#!/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
overview / pkg / ggml / sources / llama.cpp / commitdiff