mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 10:24:35 +00:00
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
This commit is contained in:
Georgi Gerganov
GitHub
parent
95078cc554
commit
f9a6364912
1
.gitignore
vendored
1
.gitignore
vendored
@ -43,5 +43,6 @@ zig-out/
|
||||
zig-cache/
|
||||
|
||||
ppl-*.txt
|
||||
qnt-*.txt
|
||||
|
||||
examples/jeopardy/results.txt
|
||||
|
||||
32
README.md
32
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
|
||||
|
||||
@ -439,7 +439,7 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
|
||||
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);
|
||||
const int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
|
||||
assert(n >= 0);
|
||||
res.resize(n);
|
||||
|
||||
|
||||
@ -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++) {
|
||||
|
||||
@ -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);
|
||||
|
||||
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<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
|
||||
@ -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<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;
|
||||
}
|
||||
|
||||
12
llama.cpp
12
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_vocab::id> 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;
|
||||
}
|
||||
|
||||
|
||||
43
scripts/ppl-run-all.sh
Executable file
43
scripts/ppl-run-all.sh
Executable file
@ -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
|
||||
Loading…
Reference in New Issue
Block a user