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Merge branch 'master' into compilade/convert-hf-refactor

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Francis Couture-Harpin 2024-05-02 20:02:46 -04:00
commit 6a54973d82
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2
.github/workflows/close-issue.yml vendored
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@ -12,7 +12,7 @@ jobs:
steps: steps:
- uses: actions/stale@v5 - uses: actions/stale@v5
with: with:
exempt-issue-labels: "refactor,help wanted,good first issue,research" exempt-issue-labels: "refactor,help wanted,good first issue,research,bug"
days-before-issue-stale: 30 days-before-issue-stale: 30
days-before-issue-close: 14 days-before-issue-close: 14
stale-issue-label: "stale" stale-issue-label: "stale"

2
common/common.h
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@ -135,7 +135,7 @@ struct gpt_params {
bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
bool kl_divergence = false; // compute KL-divergence bool kl_divergence = false; // compute KL divergence
bool random_prompt = false; // do not randomize prompt if none provided bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs bool use_color = false; // use color to distinguish generations and inputs

4
common/log.h
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@ -234,7 +234,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
// INTERNAL, DO NOT USE // INTERNAL, DO NOT USE
// USE LOG() INSTEAD // USE LOG() INSTEAD
// //
#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) #if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) || defined(__clang__)
#define LOG_IMPL(str, ...) \ #define LOG_IMPL(str, ...) \
do { \ do { \
if (LOG_TARGET != nullptr) \ if (LOG_TARGET != nullptr) \
@ -257,7 +257,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
// INTERNAL, DO NOT USE // INTERNAL, DO NOT USE
// USE LOG_TEE() INSTEAD // USE LOG_TEE() INSTEAD
// //
#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) #if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) || defined(__clang__)
#define LOG_TEE_IMPL(str, ...) \ #define LOG_TEE_IMPL(str, ...) \
do { \ do { \
if (LOG_TARGET != nullptr) \ if (LOG_TARGET != nullptr) \

2
examples/main/main.cpp
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@ -544,7 +544,7 @@ int main(int argc, char ** argv) {
// if we run out of context: // if we run out of context:
// - take the n_keep first tokens from the original prompt (via n_past) // - take the n_keep first tokens from the original prompt (via n_past)
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) { if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) >= n_ctx) {
if (params.n_predict == -2) { if (params.n_predict == -2) {
LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
break; break;

118
examples/perplexity/README.md
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@ -1,8 +1,118 @@
# perplexity # Perplexity
TODO The `perplexity` example can be used to calculate the so-called perplexity value of a language model over a given text corpus.
Perplexity measures how well the model can predict the next token with lower values being better.
Note that perplexity is **not** directly comparable between models, especially if they use different tokenizers.
Also note that finetunes typically result in a higher perplexity value even though the human-rated quality of outputs increases.
Within llama.cpp the perplexity of base models is used primarily to judge the quality loss from e.g. quantized models vs. FP16.
The convention among contributors is to use the Wikitext-2 test set for testing unless noted otherwise (can be obtained with `scripts/get-wikitext-2.sh`).
By default only the mean perplexity value and the corresponding uncertainty is calculated.
The uncertainty is determined empirically by assuming a Gaussian distribution of the "correct" logits per and then applying error propagation.
More statistics can be obtained by recording the logits from the FP16 version of a model.
To do this, supply `perplexity` with `--kl-divergence-base path/to/logit/binary/file.kld`.
The program will then record all logits and save them to the provided path in binary format.
**The logit file will be very large, 11 GiB for LLaMA 2 or 37 GiB for LLaMA 3 when using the Wikitext-2 test set.**
Once you have the file, supply `perplexity` with the quantized model, the logits file via `--kl-divergence-base`,
and finally the `--kl-divergence` argument to indicate that the program should calculate the so-called Kullback-Leibler divergence.
This is a measure of how similar the FP16 and the quantized logit distributions are with a value of 0 indicating that the distribution are the same.
The uncertainty on the mean KL divergence is calculated by assuming the KL divergence per token follows a Gaussian distribution.
In addition to the KL divergence the following statistics are calculated with `--kl-divergence`:
* Ratio of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated. The logarithm of this metric is also calculated and printed, it is 0 if the logit distributions are the same.
* Difference of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated.
* Mean change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse.
* Pearson correlation coefficient of the "correct" token probabilites between models.
* Percentiles of change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse. Can be used to judge noise vs. quality loss from quantization. If the percentiles are symmetric then the quantization is essentially just adding noise. If the negative values are significantly larger than the positive values then this indicates that the model is actually becoming worse from the quantization.
* The root mean square of the change in token probabilities. If you were to assume that the quantization simply causes Gaussian noise on the token probabilities then this would be the standard deviation of said noise. The uncertainty on the value is calculated that the change in token probabilities follows a Gaussian distribution. Related discussion: https://github.com/ggerganov/llama.cpp/discussions/2875 .
* Same top p: Percentage of how often the token was assigned the highest probabilites by both models. The uncertainty is calculated from the Gaussian approximation of the binomial distribution.
## LLaMA 3 8b Scoreboard
Results are sorted by Kullback-Leibler divergence relative to FP16.
The "WT" importance matrices were created using varying numbers of Wikitext tokens and can be found [here](https://huggingface.co/JohannesGaessler/llama.cpp_importance_matrices/blob/main/imatrix-llama_3-8b-f16-2.7m_tokens.dat).
| Quantization | imatrix | Model size [GiB] | PPL | ΔPPL | KLD | Mean Δp | RMS Δp |
|--------------|---------|------------------|------------------------|------------------------|-----------------------|-------------------|------------------|
| f16 | None | 14.97 | 6.233160 ± 0.037828 | - | - | - | - |
| q8_0 | None | 7.96 | 6.234284 ± 0.037878 | 0.002650 ± 0.001006 | 0.001355 ± 0.000006 | -0.019 ± 0.003 % | 1.198 ± 0.007 % |
| q6_K | None | 6.14 | 6.253382 ± 0.038078 | 0.021748 ± 0.001852 | 0.005452 ± 0.000035 | -0.007 ± 0.006 % | 2.295 ± 0.019 % |
| q5_K_M | None | 5.33 | 6.288607 ± 0.038338 | 0.056974 ± 0.002598 | 0.010762 ± 0.000079 | -0.114 ± 0.008 % | 3.160 ± 0.031 % |
| q5_K_S | None | 5.21 | 6.336598 ± 0.038755 | 0.104964 ± 0.003331 | 0.016595 ± 0.000122 | -0.223 ± 0.010 % | 3.918 ± 0.036 % |
| q5_1 | None | 5.65 | 6.337857 ± 0.038677 | 0.106223 ± 0.003476 | 0.018045 ± 0.000139 | -0.287 ± 0.011 % | 4.123 ± 0.039 % |
| q5_0 | None | 5.21 | 6.363224 ± 0.038861 | 0.131591 ± 0.003894 | 0.022239 ± 0.000166 | -0.416 ± 0.012 % | 4.634 ± 0.043 % |
| q4_K_M | WT 10m | 4.58 | 6.382937 ± 0.039055 | 0.151303 ± 0.004429 | 0.028152 ± 0.000240 | -0.389 ± 0.014 % | 5.251 ± 0.049 % |
| q4_K_M | None | 4.58 | 6.407115 ± 0.039119 | 0.175482 ± 0.004620 | 0.031273 ± 0.000238 | -0.596 ± 0.014 % | 5.519 ± 0.050 % |
| q4_K_S | WT 10m | 4.37 | 6.409697 ± 0.039189 | 0.178064 ± 0.004744 | 0.031951 ± 0.000259 | -0.531 ± 0.015 % | 5.645 ± 0.051 % |
| iq4_NL | WT 10m | 4.35 | 6.455593 ± 0.039630 | 0.223959 ± 0.005201 | 0.035742 ± 0.000288 | -0.590 ± 0.016 % | 5.998 ± 0.054 % |
| iq4_XS | WT 10m | 4.14 | 6.459705 ± 0.039595 | 0.228071 ± 0.005207 | 0.036334 ± 0.000284 | -0.668 ± 0.016 % | 6.044 ± 0.054 % |
| q4_K_S | None | 4.37 | 6.500529 ± 0.039778 | 0.268895 ± 0.005638 | 0.043136 ± 0.000314 | -0.927 ± 0.017 % | 6.562 ± 0.055 % |
| q4_1 | None | 4.78 | 6.682737 ± 0.041285 | 0.451103 ± 0.008030 | 0.071683 ± 0.000505 | -0.927 ± 0.017 % | 8.512 ± 0.063 % |
| q4_0 | None | 4.34 | 6.700147 ± 0.041226 | 0.468514 ± 0.007951 | 0.071940 ± 0.000491 | -1.588 ± 0.022 % | 8.434 ± 0.061 % |
| q3_K_L | WT 10m | 4.03 | 6.671223 ± 0.041427 | 0.439590 ± 0.008154 | 0.073077 ± 0.000529 | -0.940 ± 0.023 % | 8.662 ± 0.064 % |
| q3_K_M | WT 10m | 3.74 | 6.734255 ± 0.041838 | 0.502622 ± 0.008901 | 0.084358 ± 0.000588 | -1.198 ± 0.024 % | 9.292 ± 0.065 % |
| q3_K_L | None | 4.03 | 6.787876 ± 0.042104 | 0.556242 ± 0.009171 | 0.087176 ± 0.000614 | -1.532 ± 0.025 % | 9.432 ± 0.067 % |
| q3_K_M | None | 3.74 | 6.888498 ± 0.042669 | 0.656864 ± 0.010071 | 0.101913 ± 0.000677 | -1.990 ± 0.026 % | 10.203 ± 0.068 % |
| iq3_M | WT 10m | 3.53 | 6.898327 ± 0.041643 | 0.666694 ± 0.009449 | 0.102534 ± 0.000663 | -3.178 ± 0.026 % | 10.513 ± 0.066 % |
| iq3_S | WT 10m | 3.42 | 6.965501 ± 0.042406 | 0.733867 ± 0.010245 | 0.111278 ± 0.000710 | -3.066 ± 0.027 % | 10.845 ± 0.068 % |
| iq3_XS | WT 10m | 3.28 | 7.163043 ± 0.043772 | 0.931409 ± 0.012084 | 0.138693 ± 0.000857 | -3.667 ± 0.031 % | 12.148 ± 0.070 % |
| iq3_XXS | WT 10m | 3.05 | 7.458436 ± 0.046404 | 1.226803 ± 0.015234 | 0.183625 ± 0.001042 | -3.918 ± 0.035 % | 13.836 ± 0.074 % |
| q3_K_S | WT 10m | 3.41 | 7.602878 ± 0.046848 | 1.371244 ± 0.015688 | 0.199821 ± 0.001008 | -5.046 ± 0.037 % | 14.980 ± 0.070 % |
| q3_K_S | None | 3.41 | 7.863786 ± 0.048885 | 1.632152 ± 0.017733 | 0.228217 ± 0.001079 | -5.604 ± 0.038 % | 15.541 ± 0.070 % |
| iq2_M | WT 10m | 2.74 | 8.600799 ± 0.055124 | 2.369166 ± 0.025244 | 0.325989 ± 0.00160 | -6.463 ± 0.046 % | 18.519 ± 0.080 % |
| q2_K | WT 10k | 2.96 | 8.652290 ± 0.055572 | 2.420657 ± 0.025587 | 0.331393 ± 0.001562 | -6.606 ± 0.046 % | 18.790 ± 0.078 % |
| q2_K | WT 100k | 2.96 | 8.641993 ± 0.055406 | 2.410359 ± 0.025495 | 0.331672 ± 0.001569 | -6.628 ± 0.047 % | 18.856 ± 0.078 % |
| q2_K | WT 10m | 2.96 | 8.647825 ± 0.055610 | 2.416191 ± 0.025683 | 0.332223 ± 0.001572 | -6.500 ± 0.047 % | 18.881 ± 0.078 % |
| q2_K | WT 1m | 2.96 | 8.674365 ± 0.055743 | 2.442732 ± 0.025843 | 0.335308 ± 0.001576 | -6.634 ± 0.047 % | 19.009 ± 0.079 % |
| q2_K | WT 1k | 2.96 | 8.682605 ± 0.055916 | 2.450972 ± 0.026069 | 0.337093 ± 0.001596 | -6.596 ± 0.047 % | 18.977 ± 0.079 % |
| q2_K_S | WT 10m | 2.96 | 9.323778 ± 0.061551 | 3.092145 ± 0.031914 | 0.403360 ± 0.001787 | -7.131 ± 0.049 % | 20.050 ± 0.081 % |
| q2_K_S | WT 1m | 2.96 | 9.329321 ± 0.061378 | 3.097688 ± 0.031816 | 0.403590 ± 0.001797 | -7.289 ± 0.049 % | 20.123 ± 0.081 % |
| q2_K_S | WT 100k | 2.96 | 9.362973 ± 0.061740 | 3.131339 ± 0.032169 | 0.408367 ± 0.001802 | -7.198 ± 0.050 % | 20.132 ± 0.081 % |
| q2_K_S | WT 10k | 2.96 | 9.376479 ± 0.062045 | 3.144846 ± 0.032464 | 0.408662 ± 0.001819 | -7.141 ± 0.050 % | 20.120 ± 0.081 % |
| q2_K_S | WT 1k | 2.96 | 9.415200 ± 0.062475 | 3.183567 ± 0.032993 | 0.415865 ± 0.001846 | -7.153 ± 0.050 % | 20.311 ± 0.082 % |
| iq2_S | WT 10m | 2.56 | 9.650781 ± 0.063209 | 3.419148 ± 0.034017 | 0.439197 ± 0.001976 | -8.319 ± 0.052 % | 21.491 ± 0.083 % |
| q2_K | None | 2.96 | 9.751568 ± 0.063312 | 3.519934 ± 0.033863 | 0.445132 ± 0.001835 | -9.123 ± 0.051 % | 21.421 ± 0.079 % |
| iq2_XS | WT 10m | 2.43 | 10.761424 ± 0.071056 | 4.529791 ± 0.042229 | 0.546290 ± 0.002133 | -10.576 ± 0.056 % | 23.872 ± 0.082 % |
| iq2_XXS | WT 10m | 2.24 | 14.091782 ± 0.098396 | 7.860148 ± 0.070752 | 0.812022 ± 0.002741 | -14.363 ± 0.065 % | 28.576 ± 0.084 % |
| iq1_M | WT 10m | 2.01 | 25.493722 ± 0.177903 | 19.262089 ± 0.152396 | 1.393084 ± 0.003529 | -24.672 ± 0.077 % | 38.287 ± 0.084 % |
| iq1_S | WT 1m | 1.88 | 58.097760 ± 0.438604 | 51.866126 ± 0.416604 | 2.211278 ± 0.004688 | -32.471 ± 0.087 % | 46.418 ± 0.085 % |
| iq1_S | WT 1k | 1.88 | 58.267851 ± 0.446208 | 52.036218 ± 0.424373 | 2.214858 ± 0.004778 | -31.880 ± 0.089 % | 46.330 ± 0.086 % |
| iq1_S | WT 100k | 1.88 | 58.581498 ± 0.453145 | 52.349864 ± 0.431360 | 2.220834 ± 0.004818 | -32.261 ± 0.089 % | 46.002 ± 0.086 % |
| iq1_S | WT 10m | 1.88 | 60.694593 ± 0.471290 | 54.462959 ± 0.449644 | 2.254554 ± 0.004868 | -31.973 ± 0.088 % | 46.271 ± 0.086 % |
| iq1_S | WT 10k | 1.88 | 63.221324 ± 0.493077 | 56.989691 ± 0.471423 | 2.293527 ± 0.004885 | -32.261 ± 0.089 % | 46.562 ± 0.086 % |
There seems to be no consistent improvement from using more Wikitext tokens for the importance matrix.
K-quants score better on mean Δp than the legacy quants than e.g. KL divergence would suggest.
## LLaMA 2 vs. LLaMA 3 Quantization comparison
| Metric | L2 7b q2_K | L3 8b q2_K | L2 7b q4_K_M | L3 8b q4_K_M | L2 7b q6_K | L3 8b q6_K | L2 7b q8_0 | L3 8b q8_0 |
|-----------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|
| Mean PPL | 5.794552 ± 0.032298 | 9.751568 ± 0.063312 | 5.877078 ± 0.032781 | 6.407115 ± 0.039119 | 5.808494 ± 0.032425 | 6.253382 ± 0.038078 | 5.798542 ± 0.032366 | 6.234284 ± 0.037878 |
| Mean PPL ratio | 1.107955 ± 0.001427 | 1.564849 ± 0.004525 | 1.014242 ± 0.000432 | 1.028160 ± 0.000723 | 1.002406 ± 0.000191 | 1.003490 ± 0.000296 | 1.000689 ± 0.000107 | 1.000425 ± 0.000161 |
| Mean ΔPPL | 0.625552 ± 0.008725 | 3.519934 ± 0.033863 | 0.082526 ± 0.002530 | 0.175482 ± 0.004620 | 0.013941 ± 0.001110 | 0.021748 ± 0.001852 | 0.003990 ± 0.000624 | 0.002650 ± 0.001006 |
| PPL correlation | 97.36% | 89.62% | 99.71% | 99.34% | 99.94% | 99.88% | 99.98% | 99.96% |
| Mean KLD | 0.108903 ± 0.000645 | 0.445132 ± 0.001835 | 0.012686 ± 0.000079 | 0.031273 ± 0.000238 | 0.002098 ± 0.000014 | 0.005452 ± 0.000035 | 0.000369 ± 0.000007 | 0.001355 ± 0.000006 |
| Mean Δp | -2.710 ± 0.023 % | -9.123 ± 0.051 % | -0.416 ± 0.008 % | -0.596 ± 0.014 % | -0.035 ± 0.003 % | -0.007 ± 0.006 % | -0.005 ± 0.002 % | -0.019 ± 0.003 % |
| Maximum Δp | 85.136% | 94.268% | 45.209% | 95.054% | 23.593% | 53.601% | 43.925% | 28.734% |
| 99.9% Δp | 37.184% | 50.003% | 17.461% | 27.084% | 7.798% | 13.613% | 3.387% | 6.402% |
| 99.0% Δp | 18.131% | 25.875% | 7.798% | 12.084% | 3.838% | 6.407% | 1.867% | 3.544% |
| Median Δp | -0.391% | -2.476% | -0.026% | -0.024% | -0.001% | 0.000% | -0.000% | -0.000% |
| 1.0% Δp | -39.762% | -87.173% | -11.433% | -19.567% | -4.222% | -6.767% | -1.862% | -3.698% |
| 0.1% Δp | -79.002% | -98.897% | -26.433% | -56.054% | -9.091% | -16.584% | -3.252% | -6.579% |
| Minimum Δp | -99.915% | -99.965% | -83.383% | -98.699% | -43.142% | -68.487% | -9.343% | -24.301% |
| RMS Δp | 9.762 ± 0.053 % | 21.421 ± 0.079 % | 3.252 ± 0.024 % | 5.519 ± 0.050 % | 1.339 ± 0.010 % | 2.295 ± 0.019 % | 0.618 ± 0.011 % | 1.198 ± 0.007 % |
| Same top p | 85.584 ± 0.086 % | 71.138 ± 0.119 % | 94.665 ± 0.055 % | 91.901 ± 0.072 % | 97.520 ± 0.038 % | 96.031 ± 0.051 % | 98.846 ± 0.026 % | 97.674 ± 0.040 % |
## Old Numbers
<details>
<summary>Llama 2 70B Scoreboard</summary>
## Llama 2 70B Scorechart
| Quantization | Model size (GiB) | Perplexity | Delta to fp16 | | Quantization | Model size (GiB) | Perplexity | Delta to fp16 |
|--------------|------------------|------------|---------------| |--------------|------------------|------------|---------------|
| Q4_0 | 36.20 | 3.5550 | 3.61% | | Q4_0 | 36.20 | 3.5550 | 3.61% |
@ -18,3 +128,5 @@ TODO
| Q5_K_M | 45.41 | 3.4451 | 0.40% | | Q5_K_M | 45.41 | 3.4451 | 0.40% |
| Q6_K | 52.70 | 3.4367 | 0.16% | | Q6_K | 52.70 | 3.4367 | 0.16% |
| fp16 | 128.5 | 3.4313 | - | | fp16 | 128.5 | 3.4313 | - |
</details>

212
examples/perplexity/perplexity.cpp
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@ -216,17 +216,22 @@ static void process_logits(std::ostream& out, int n_vocab, const float * logits,
} }
struct kl_divergence_result { struct kl_divergence_result {
double sum_nll = 0; double sum_nll = 0.0;
double sum_nll2 = 0; double sum_nll2 = 0.0;
double sum_kld = 0; double sum_nll_base = 0.0;
double sum_kld2 = 0; double sum_nll_base2 = 0.0;
double sum_nll_diff = 0; double sum_nll_nll_base = 0.0;
double sum_nll_diff2 = 0; double sum_kld = 0.0;
size_t n_same_top = 0; double sum_kld2 = 0.0;
size_t count = 0; double sum_p_diff = 0.0;
double sum_p_diff2 = 0.0;
double sum_p_diff4 = 0.0;
float max_p_diff = 0.0f;
size_t n_same_top = 0.0;
size_t count = 0.0;
}; };
static double log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) { static std::pair<double, float> log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) {
float max_logit = logits[0]; float max_logit = logits[0];
int imax = 0; int imax = 0;
for (int i = 1; i < n_vocab; ++i) { for (int i = 1; i < n_vocab; ++i) {
@ -244,12 +249,17 @@ static double log_softmax(int n_vocab, const float * logits, const uint16_t * ba
const float scale = d[0]; const float scale = d[0];
const float min_log_prob = d[1]; const float min_log_prob = d[1];
base_log_prob += 4; base_log_prob += 4;
float nll = max_logit + log_sum_exp - logits[tok];
const float nll = max_logit + log_sum_exp - logits[tok];
kld.sum_nll += nll; kld.sum_nll += nll;
kld.sum_nll2 += nll*nll; kld.sum_nll2 += nll*nll;
nll += (scale*base_log_prob[tok] + min_log_prob);
kld.sum_nll_diff += nll; const float nll_base = -(scale*base_log_prob[tok] + min_log_prob);
kld.sum_nll_diff2 += nll*nll; kld.sum_nll_base += nll_base;
kld.sum_nll_base2 += nll_base*nll_base;
kld.sum_nll_nll_base += nll*nll_base;
max_logit += log_sum_exp; max_logit += log_sum_exp;
double sum = 0; double sum = 0;
int imax_base = -1; int imax_base = -1;
@ -269,16 +279,26 @@ static double log_softmax(int n_vocab, const float * logits, const uint16_t * ba
kld.sum_kld2 += sum*sum; kld.sum_kld2 += sum*sum;
++kld.count; ++kld.count;
if (imax == imax_base) ++kld.n_same_top; if (imax == imax_base) ++kld.n_same_top;
return sum;
const float p_base = expf(-nll_base);
const float p = expf(-nll);
const float p_diff = p - p_base;
kld.sum_p_diff += p_diff;
const double p_diff2 = p_diff*p_diff;
kld.sum_p_diff2 += p_diff2;
kld.sum_p_diff4 += p_diff2*p_diff2;
kld.max_p_diff = std::max(kld.max_p_diff, std::fabs(p_diff));
return std::make_pair(sum, p_diff);
} }
static void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, static void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token,
std::vector<std::thread> & workers, const std::vector<uint16_t> & base_log_probs, kl_divergence_result & kld, std::vector<std::thread> & workers, const std::vector<uint16_t> & base_log_probs, kl_divergence_result & kld,
float * kld_values) { float * kld_values, float * p_diff_values) {
std::mutex mutex; std::mutex mutex;
const int nv = 2*((n_vocab + 1)/2) + 4; const int nv = 2*((n_vocab + 1)/2) + 4;
int counter = 0; int counter = 0;
auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values] () { auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values, p_diff_values] () {
kl_divergence_result local_kld; kl_divergence_result local_kld;
while (true) { while (true) {
std::unique_lock<std::mutex> lock(mutex); std::unique_lock<std::mutex> lock(mutex);
@ -286,17 +306,23 @@ static void process_logits(int n_vocab, const float * logits, const int * tokens
if (i >= n_token) { if (i >= n_token) {
kld.sum_nll += local_kld.sum_nll; kld.sum_nll += local_kld.sum_nll;
kld.sum_nll2 += local_kld.sum_nll2; kld.sum_nll2 += local_kld.sum_nll2;
kld.sum_nll_base += local_kld.sum_nll_base;
kld.sum_nll_base2 += local_kld.sum_nll_base2;
kld.sum_nll_nll_base += local_kld.sum_nll_nll_base;
kld.sum_kld += local_kld.sum_kld; kld.sum_kld += local_kld.sum_kld;
kld.sum_kld2 += local_kld.sum_kld2; kld.sum_kld2 += local_kld.sum_kld2;
kld.sum_nll_diff += local_kld.sum_nll_diff; kld.sum_p_diff += local_kld.sum_p_diff;
kld.sum_nll_diff2 += local_kld.sum_nll_diff2; kld.sum_p_diff2 += local_kld.sum_p_diff2;
kld.sum_p_diff4 += local_kld.sum_p_diff4;
kld.n_same_top += local_kld.n_same_top; kld.n_same_top += local_kld.n_same_top;
kld.max_p_diff = std::max(kld.max_p_diff, local_kld.max_p_diff);
kld.count += local_kld.count; kld.count += local_kld.count;
break; break;
} }
lock.unlock(); lock.unlock();
double v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld); std::pair<double, float> v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
kld_values[i] = (float)v; kld_values[i] = (float)v.first;
p_diff_values[i] = v.second;
} }
}; };
for (auto & w : workers) { for (auto & w : workers) {
@ -1712,6 +1738,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv); std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv);
std::vector<float> kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); std::vector<float> kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
std::vector<float> p_diff_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
std::vector<float> logits; std::vector<float> logits;
if (num_batches > 1) { if (num_batches > 1) {
logits.reserve(n_ctx * n_vocab); logits.reserve(n_ctx * n_vocab);
@ -1728,9 +1755,18 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
df = df > 0 && count > 10 ? sqrt(df/(count-1)) : 0.; df = df > 0 && count > 10 ? sqrt(df/(count-1)) : 0.;
return std::make_pair(f, df); return std::make_pair(f, df);
}; };
auto covariance = [] (double suma, double sumb, double sumab, size_t count) {
if (count < 10) {
return 0.0;
}
double var = sumab/count - (suma/count)*(sumb/count);
var /= count - 1;
return var;
};
kl_divergence_result kld; kl_divergence_result kld;
auto kld_ptr = kld_values.data(); auto kld_ptr = kld_values.data();
auto p_diff_ptr = p_diff_values.data();
for (int i = 0; i < n_chunk; ++i) { for (int i = 0; i < n_chunk; ++i) {
const int start = i * n_ctx; const int start = i * n_ctx;
@ -1785,24 +1821,42 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
} }
fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0); fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
printf("\nchunk PPL ln(PPL(Q)/PPL(base)) KL-Divergence Same top\n"); printf("\nchunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p\n");
} }
const int first = n_ctx/2; const int first = n_ctx/2;
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx); const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
workers, log_probs_uint16, kld, kld_ptr); workers, log_probs_uint16, kld, kld_ptr, p_diff_ptr);
p_diff_ptr += n_ctx - 1 - first;
kld_ptr += n_ctx - 1 - first; kld_ptr += n_ctx - 1 - first;
auto ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count); printf("%4d", i+1);
auto log_ppl_ratio = mean_and_uncertainty(kld.sum_nll_diff, kld.sum_nll_diff2, kld.count);
auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
auto p_top = 1.*kld.n_same_top/kld.count;
auto d_p_top = sqrt(p_top*(1 - p_top)/(kld.count - 1));
printf("%4d %10.4lf %10.5lf ± %10.5f %10.5f ± %10.5lf %.5f ± %.5f\n", i+1, exp(ppl.first), auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
log_ppl_ratio.first, log_ppl_ratio.second, kl_div.first, kl_div.second, const double ppl_val = exp(log_ppl.first);
p_top, d_p_top); const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 )
printf(" %9.4lf ± %9.4lf", ppl_val, ppl_unc);
auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count);
const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count);
const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first;
const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov);
printf(" %10.5lf ± %10.5lf", log_ppl_ratio_val, log_ppl_ratio_unc);
auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
printf(" %10.5lf ± %10.5lf", kl_div.first, kl_div.second);
auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count);
const double p_diff_rms_val = sqrt(p_diff_mse.first);
const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second;
printf(" %6.3lf ± %6.3lf %%", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc);
double p_top_val = 1.*kld.n_same_top/kld.count;
double p_top_unc = sqrt(p_top_val*(1 - p_top_val)/(kld.count - 1));
printf(" %6.3lf ± %6.3lf %%", 100.0*p_top_val, 100.0*p_top_unc);
printf("\n");
fflush(stdout); fflush(stdout);
@ -1813,31 +1867,97 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
if (kld.count < 100) return; // we do not wish to do statistics on so few values if (kld.count < 100) return; // we do not wish to do statistics on so few values
std::sort(kld_values.begin(), kld_values.end()); std::sort(kld_values.begin(), kld_values.end());
std::sort(p_diff_values.begin(), p_diff_values.end());
printf("===== KL-divergence statistics\n"); printf("====== Perplexity statistics ======\n");
auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
const double ppl_val = exp(log_ppl.first);
const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 )
printf("Mean PPL(Q) : %10.6lf ± %10.6lf\n", ppl_val, ppl_unc);
auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count);
const double ppl_base_val = exp(log_ppl_base.first);
const double ppl_base_unc = ppl_base_val * log_ppl_base.second; // ppl_base_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_base.second ** 2 )
printf("Mean PPL(base) : %10.6lf ± %10.6lf\n", ppl_base_val, ppl_base_unc);
const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count);
// printf("Cov(ln(PPL(Q)), ln(PPL(base))): %10.6lf\n", log_ppl_cov);
const double log_ppl_cor = log_ppl_cov / (log_ppl.second*log_ppl_base.second);
printf("Cor(ln(PPL(Q)), ln(PPL(base))): %6.2lf%%\n", 100.0*log_ppl_cor);
const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first;
const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov);
printf("Mean ln(PPL(Q)/PPL(base)) : %10.6lf ± %10.6lf\n", log_ppl_ratio_val, log_ppl_ratio_unc);
const double ppl_ratio_val = exp(log_ppl_ratio_val);
const double ppl_ratio_unc = ppl_ratio_val * log_ppl_ratio_unc; // ppl_ratio_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_ratio.second ** 2 )
printf("Mean PPL(Q)/PPL(base) : %10.6lf ± %10.6lf\n", ppl_ratio_val, ppl_ratio_unc);
const double ppl_cov = ppl_val * ppl_base_val * log_ppl_cov;
const double ppl_diff_val = ppl_val - ppl_base_val;
const double ppl_diff_unc = sqrt(ppl_unc*ppl_unc + ppl_base_unc*ppl_base_unc - 2.0*ppl_cov);
printf("Mean PPL(Q)-PPL(base) : %10.6lf ± %10.6lf\n", ppl_diff_val, ppl_diff_unc);
printf("\n");
printf("====== KL divergence statistics ======\n");
auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count); auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
printf("Average: %10.6f ±%10.6lf\n", kl_div.first, kl_div.second); printf("Mean KLD: %10.6lf ± %10.6lf\n", kl_div.first, kl_div.second);
auto kld_median = kld_values.size()%2 == 0 ? 0.5f*(kld_values[kld_values.size()/2] + kld_values[kld_values.size()/2-1]) auto kld_median = kld_values.size()%2 == 0 ? 0.5f*(kld_values[kld_values.size()/2] + kld_values[kld_values.size()/2-1])
: kld_values[kld_values.size()/2]; : kld_values[kld_values.size()/2];
printf("Median : %10.6f\n", kld_median);
auto percentile = [&kld_values] (float fraction) { auto percentile = [] (std::vector<float> values, float fraction) {
if (fraction <= 0) return kld_values.front(); if (fraction <= 0) return values.front();
if (fraction >= 1) return kld_values.back(); if (fraction >= 1) return values.back();
float p = fraction*(kld_values.size() - 1); float p = fraction*(values.size() - 1);
size_t ip = size_t(p); p -= ip; size_t ip = size_t(p); p -= ip;
return (1 - p)*kld_values[ip] + p*kld_values[std::min(ip+1, kld_values.size()-1)]; return (1 - p)*values[ip] + p*values[std::min(ip+1, values.size()-1)];
}; };
printf("Maximum: %10.6f\n", kld_values.back()); printf("Maximum KLD: %10.6f\n", kld_values.back());
printf("KLD_99 : %10.6f\n", percentile(0.99f)); printf("99.9%% KLD: %10.6f\n", percentile(kld_values, 0.999f));
printf("KLD_95 : %10.6f\n", percentile(0.95f)); printf("99.0%% KLD: %10.6f\n", percentile(kld_values, 0.990f));
printf("KLD_90 : %10.6f\n", percentile(0.90f)); printf("99.0%% KLD: %10.6f\n", percentile(kld_values, 0.990f));
printf("Median KLD: %10.6f\n", kld_median);
printf("10.0%% KLD: %10.6f\n", percentile(kld_values, 0.100f));
printf(" 5.0%% KLD: %10.6f\n", percentile(kld_values, 0.050f));
printf(" 1.0%% KLD: %10.6f\n", percentile(kld_values, 0.010f));
printf("Minimum KLD: %10.6f\n", kld_values.front());
printf("Minimum: %10.6f\n", kld_values.front()); printf("\n");
printf("KLD_01 : %10.6f\n", percentile(0.01f));
printf("KLD_05 : %10.6f\n", percentile(0.05f)); printf("====== Token probability statistics ======\n");
printf("KLD_10 : %10.6f\n", percentile(0.10f));
auto p_diff = mean_and_uncertainty(kld.sum_p_diff, kld.sum_p_diff2, kld.count);
printf("Mean Δp: %6.3lf ± %5.3lf %%\n", 100.0*p_diff.first, 100.0*p_diff.second);
auto p_diff_median = p_diff_values.size()%2 == 0 ? 0.5f*(p_diff_values[p_diff_values.size()/2] + p_diff_values[p_diff_values.size()/2-1])
: p_diff_values[p_diff_values.size()/2];
printf("Maximum Δp: %6.3lf%%\n", 100.0*p_diff_values.back());
printf("99.9%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.999f));
printf("99.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.990f));
printf("95.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.950f));
printf("90.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.900f));
printf("75.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.750f));
printf("Median Δp: %6.3lf%%\n", 100.0*p_diff_median);
printf("25.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.250f));
printf("10.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.100f));
printf(" 5.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.050f));
printf(" 1.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.010f));
printf(" 0.1%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.001f));
printf("Minimum Δp: %6.3lf%%\n", 100.0*p_diff_values.front());
auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count);
// printf("MSE Δp : %10.6lf ± %10.6lf\n", p_diff_mse.first, p_diff_mse.second);
const double p_diff_rms_val = sqrt(p_diff_mse.first);
const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second;
printf("RMS Δp : %6.3lf ± %5.3lf %%\n", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc);
const double same_top_p = 1.0*kld.n_same_top/kld.count;
printf("Same top p: %6.3lf ± %5.3lf %%\n", 100.0*same_top_p, 100.0*sqrt(same_top_p*(1.0 - same_top_p)/(kld.count - 1)));
} }

90
examples/server/tests/features/results.feature
View file

@ -7,44 +7,16 @@ Feature: Results
And a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models And a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models
And a model file test-model-00001-of-00003.gguf And a model file test-model-00001-of-00003.gguf
And 128 as batch size And 128 as batch size
And 256 KV cache size And 1024 KV cache size
And 128 max tokens to predict And 128 max tokens to predict
Scenario Outline: Multi users completion
Given <n_slots> slots
And continuous batching And continuous batching
Scenario Outline: consistent results with same seed
Given <n_slots> slots
Then the server is starting Then the server is starting
Then the server is healthy Then the server is healthy
Given 42 as seed Given 4 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 42
And a prompt:
"""
Write a very long story about AI.
"""
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given concurrent completion requests Given concurrent completion requests
Then the server is busy Then the server is busy
@ -55,3 +27,55 @@ Feature: Results
| n_slots | | n_slots |
| 1 | | 1 |
| 2 | | 2 |
Scenario Outline: different results with different seed
Given <n_slots> slots
Then the server is starting
Then the server is healthy
Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 42
Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 43
Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 44
Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 45
Given concurrent completion requests
Then the server is busy
Then the server is idle
And all slots are idle
Then all predictions are different
Examples:
| n_slots |
| 1 |
| 2 |
Scenario Outline: consistent results with same seed and varying batch size
Given 4 slots
And <temp> temperature
# And 0 as draft
Then the server is starting
Then the server is healthy
Given 1 prompts "Write a very long story about AI." with seed 42
And concurrent completion requests
# Then the server is busy # Not all slots will be utilized.
Then the server is idle
And all slots are idle
Given <n_parallel> prompts "Write a very long story about AI." with seed 42
And concurrent completion requests
# Then the server is busy # Not all slots will be utilized.
Then the server is idle
And all slots are idle
Then all predictions are equal
Examples:
| n_parallel | temp |
| 1 | 0.0 |
| 2 | 0.0 |
| 4 | 0.0 |
| 1 | 1.0 |
# FIXME: These tests fail on master. The problem seems to be the unified KV cache.
# See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227
# and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 .
# | 2 | 1.0 |
# | 4 | 1.0 |

128
examples/server/tests/features/steps/steps.py
View file

@ -65,6 +65,7 @@ def step_server_config(context, server_fqdn, server_port):
context.server_seed = None context.server_seed = None
context.user_api_key = None context.user_api_key = None
context.response_format = None context.response_format = None
context.temperature = None
context.tasks_result = [] context.tasks_result = []
context.concurrent_tasks = [] context.concurrent_tasks = []
@ -232,15 +233,17 @@ async def step_all_slots_status(context, expected_slot_status_string):
@async_run_until_complete @async_run_until_complete
async def step_request_completion(context, api_error): async def step_request_completion(context, api_error):
expect_api_error = api_error == 'raised' expect_api_error = api_error == 'raised'
seeds = await completions_seed(context, num_seeds=1)
completion = await request_completion(context.prompts.pop(), completion = await request_completion(context.prompts.pop(),
seeds[0] if seeds is not None else seeds,
context.base_url, context.base_url,
debug=context.debug, debug=context.debug,
n_predict=context.n_predict, n_predict=context.n_predict,
cache_prompt=context.cache_prompt, cache_prompt=context.cache_prompt,
id_slot=context.id_slot, id_slot=context.id_slot,
seed=await completions_seed(context),
expect_api_error=expect_api_error, expect_api_error=expect_api_error,
user_api_key=context.user_api_key) user_api_key=context.user_api_key,
temperature=context.temperature)
context.tasks_result.append(completion) context.tasks_result.append(completion)
if context.debug: if context.debug:
print(f"Completion response: {completion}") print(f"Completion response: {completion}")
@ -269,6 +272,15 @@ async def step_predictions_equal(context):
context.tasks_result = [] context.tasks_result = []
@step('all predictions are different')
@async_run_until_complete
async def step_predictions_equal(context):
n_completions = await gather_tasks_results(context)
assert n_completions >= 2, "need at least 2 completions"
assert_all_predictions_different(context.tasks_result)
context.tasks_result = []
@step('the completion is truncated') @step('the completion is truncated')
def step_assert_completion_truncated(context): def step_assert_completion_truncated(context):
step_assert_completion_truncated(context, '') step_assert_completion_truncated(context, '')
@ -311,6 +323,11 @@ def step_response_format(context, response_format):
context.response_format = json.loads(response_format) context.response_format = json.loads(response_format)
@step('{temperature:f} temperature')
def step_temperature(context, temperature):
context.temperature = temperature
@step('streaming is {enable_streaming}') @step('streaming is {enable_streaming}')
def step_streaming(context, enable_streaming): def step_streaming(context, enable_streaming):
context.enable_streaming = enable_streaming == 'enabled' context.enable_streaming = enable_streaming == 'enabled'
@ -353,7 +370,10 @@ def step_n_ubatch(context, n_ubatch):
@step('{seed:d} as seed') @step('{seed:d} as seed')
def step_seed(context, seed): def step_seed(context, seed):
context.seed = seed if context.seed is None:
context.seed = [seed]
else:
context.seed.append(seed)
@step('a prefix prompt') @step('a prefix prompt')
@ -413,7 +433,9 @@ async def step_oai_chat_completions(context, api_error):
if context.debug: if context.debug:
print(f"Submitting OAI compatible completions request...") print(f"Submitting OAI compatible completions request...")
expect_api_error = api_error == 'raised' expect_api_error = api_error == 'raised'
seeds = await completions_seed(context, num_seeds=1),
completion = await oai_chat_completions(context.prompts.pop(), completion = await oai_chat_completions(context.prompts.pop(),
seeds[0] if seeds is not None else seeds,
context.system_prompt, context.system_prompt,
context.base_url, context.base_url,
'/v1/chat', '/v1/chat',
@ -429,8 +451,6 @@ async def step_oai_chat_completions(context, api_error):
response_format=context.response_format response_format=context.response_format
if hasattr(context, 'response_format') else None, if hasattr(context, 'response_format') else None,
seed=await completions_seed(context),
user_api_key=context.user_api_key user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None, if hasattr(context, 'user_api_key') else None,
@ -457,10 +477,21 @@ def step_a_prompt_prompt(context, prompt):
context.n_prompts = len(context.prompts) context.n_prompts = len(context.prompts)
@step('{num_prompts:d} prompts {prompt} with seed {seed:d}')
def step_many_prompts(context, num_prompts, prompt, seed):
if context.seed is None:
context.seed = []
for _ in range(num_prompts):
context.seed.append(seed)
context.prompts.append(prompt)
context.n_prompts = len(context.prompts)
@step('concurrent completion requests') @step('concurrent completion requests')
@async_run_until_complete() @async_run_until_complete()
async def step_concurrent_completion_requests(context): async def step_concurrent_completion_requests(context):
await concurrent_requests(context, await concurrent_requests(
context,
request_completion, request_completion,
# prompt is inserted automatically # prompt is inserted automatically
context.base_url, context.base_url,
@ -468,9 +499,9 @@ async def step_concurrent_completion_requests(context):
prompt_prefix=context.prompt_prefix, prompt_prefix=context.prompt_prefix,
prompt_suffix=context.prompt_suffix, prompt_suffix=context.prompt_suffix,
n_predict=context.n_predict if hasattr(context, 'n_predict') else None, n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
seed=await completions_seed(context), user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None,
user_api_key=context.user_api_key if hasattr(context, temperature=context.temperature,
'user_api_key') else None) )
@step('concurrent OAI completions requests') @step('concurrent OAI completions requests')
@ -490,7 +521,6 @@ async def step_oai_chat_completions(context):
if hasattr(context, 'enable_streaming') else None, if hasattr(context, 'enable_streaming') else None,
response_format=context.response_format response_format=context.response_format
if hasattr(context, 'response_format') else None, if hasattr(context, 'response_format') else None,
seed=await completions_seed(context),
user_api_key=context.user_api_key user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None) if hasattr(context, 'user_api_key') else None)
@ -512,10 +542,6 @@ async def step_oai_chat_completions(context):
if hasattr(context, 'enable_streaming') else None, if hasattr(context, 'enable_streaming') else None,
response_format=context.response_format response_format=context.response_format
if hasattr(context, 'response_format') else None, if hasattr(context, 'response_format') else None,
seed=context.seed
if hasattr(context, 'seed') else
context.server_seed
if hasattr(context, 'server_seed') else None,
user_api_key=context.user_api_key user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None) if hasattr(context, 'user_api_key') else None)
@ -544,7 +570,7 @@ async def all_prompts_are_predicted(context, expected_predicted_n=None):
@async_run_until_complete @async_run_until_complete
async def step_compute_embedding(context): async def step_compute_embedding(context):
context.n_prompts = 1 context.n_prompts = 1
context.embeddings = await request_embedding(context_text(context), base_url=context.base_url) context.embeddings = await request_embedding(context_text(context), None, base_url=context.base_url)
@step('all embeddings are the same') @step('all embeddings are the same')
@ -585,7 +611,7 @@ def step_assert_embeddings(context):
@async_run_until_complete @async_run_until_complete
async def step_oai_compute_embeddings(context): async def step_oai_compute_embeddings(context):
context.n_prompts = 1 context.n_prompts = 1
context.embeddings = await request_oai_embeddings(context_text(context), context.embeddings = await request_oai_embeddings(context_text(context), None,
base_url=context.base_url, base_url=context.base_url,
user_api_key=context.user_api_key, user_api_key=context.user_api_key,
model=context.model) model=context.model)
@ -594,7 +620,7 @@ async def step_oai_compute_embeddings(context):
@step('an OAI compatible embeddings computation request for multiple inputs') @step('an OAI compatible embeddings computation request for multiple inputs')
@async_run_until_complete @async_run_until_complete
async def step_oai_compute_embeddings_multiple_inputs(context): async def step_oai_compute_embeddings_multiple_inputs(context):
context.embeddings = await request_oai_embeddings(context.prompts, context.embeddings = await request_oai_embeddings(context.prompts, None,
base_url=context.base_url, base_url=context.base_url,
user_api_key=context.user_api_key, user_api_key=context.user_api_key,
model=context.model) model=context.model)
@ -740,8 +766,9 @@ async def concurrent_requests(context, f_completion, *args, **kwargs):
if context.debug: if context.debug:
print(f"starting {context.n_prompts} concurrent completion requests...") print(f"starting {context.n_prompts} concurrent completion requests...")
assert context.n_prompts > 0 assert context.n_prompts > 0
seeds = await completions_seed(context)
for prompt_no in range(context.n_prompts): for prompt_no in range(context.n_prompts):
shifted_args = [context.prompts.pop(), *args] shifted_args = [context.prompts.pop(), seeds[prompt_no], *args]
context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs))) context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
await asyncio.sleep(0.1) await asyncio.sleep(0.1)
@ -781,6 +808,7 @@ def step_server_responds_with_status_code(context, status_code):
async def request_completion(prompt, async def request_completion(prompt,
seed,
base_url, base_url,
debug=False, debug=False,
prompt_prefix=None, prompt_prefix=None,
@ -788,9 +816,9 @@ async def request_completion(prompt,
n_predict=None, n_predict=None,
cache_prompt=False, cache_prompt=False,
id_slot=None, id_slot=None,
seed=None,
expect_api_error=None, expect_api_error=None,
user_api_key=None): user_api_key=None,
temperature=None):
if debug: if debug:
print(f"Sending completion request: {prompt}") print(f"Sending completion request: {prompt}")
origin = "my.super.domain" origin = "my.super.domain"
@ -811,7 +839,8 @@ async def request_completion(prompt,
"n_predict": n_predict if n_predict is not None else -1, "n_predict": n_predict if n_predict is not None else -1,
"cache_prompt": cache_prompt, "cache_prompt": cache_prompt,
"id_slot": id_slot, "id_slot": id_slot,
"seed": seed if seed is not None else 42 "seed": seed if seed is not None else 42,
"temperature": temperature if temperature is not None else "0.8f",
}, },
headers=headers, headers=headers,
timeout=3600) as response: timeout=3600) as response:
@ -824,6 +853,7 @@ async def request_completion(prompt,
async def oai_chat_completions(user_prompt, async def oai_chat_completions(user_prompt,
seed,
system_prompt, system_prompt,
base_url, base_url,
base_path, base_path,
@ -833,7 +863,6 @@ async def oai_chat_completions(user_prompt,
n_predict=None, n_predict=None,
enable_streaming=None, enable_streaming=None,
response_format=None, response_format=None,
seed=None,
user_api_key=None, user_api_key=None,
expect_api_error=None): expect_api_error=None):
if debug: if debug:
@ -952,7 +981,7 @@ async def oai_chat_completions(user_prompt,
return completion_response return completion_response
async def request_embedding(content, base_url=None): async def request_embedding(content, seed, base_url=None):
async with aiohttp.ClientSession() as session: async with aiohttp.ClientSession() as session:
async with session.post(f'{base_url}/embedding', async with session.post(f'{base_url}/embedding',
json={ json={
@ -963,7 +992,7 @@ async def request_embedding(content, base_url=None):
return [response_json['embedding']] return [response_json['embedding']]
async def request_oai_embeddings(input, async def request_oai_embeddings(input, seed,
base_url=None, user_api_key=None, base_url=None, user_api_key=None,
model=None, async_client=False): model=None, async_client=False):
# openai client always expects an api_key # openai client always expects an api_key
@ -1036,21 +1065,31 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
f' {n_predicted} <> {expected_predicted_n}') f' {n_predicted} <> {expected_predicted_n}')
def assert_all_predictions_equal(completion_responses): def assert_all_predictions_equal(completion_responses):
content_0 = completion_responses[0]['content']
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
print(f"content 0: {content_0}") for i, response_i in enumerate(completion_responses):
content_i = response_i['content']
print(f"content {i}: {content_i}")
for i, response_i in enumerate(completion_responses):
content_i = response_i['content']
for j, response_j in enumerate(completion_responses):
if i == j:
continue
content_j = response_j['content']
assert content_i == content_j, "contents not equal"
i = 1
for response in completion_responses[1:]:
content = response['content']
def assert_all_predictions_different(completion_responses):
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
print(f"content {i}: {content}") for i, response_i in enumerate(completion_responses):
content_i = response_i['content']
assert content == content_0, "contents not equal" print(f"content {i}: {content_i}")
for i, response_i in enumerate(completion_responses):
i += 1 content_i = response_i['content']
for j, response_j in enumerate(completion_responses):
if i == j:
continue
content_j = response_j['content']
assert content_i != content_j, "contents not different"
async def gather_tasks_results(context): async def gather_tasks_results(context):
@ -1145,9 +1184,22 @@ def assert_slots_status(slots, expected_slots):
f" = {expected[key]} != {slot[key]}") f" = {expected[key]} != {slot[key]}")
async def completions_seed(context): async def completions_seed(context, num_seeds=None):
return context.seed if hasattr(context, 'seed') and context.seed is not None \ if hasattr(context, "seed") and context.seed is not None:
else context.server_seed if hasattr(context, 'server_seed') else None assert len(context.seed) == context.n_prompts
if num_seeds is None:
num_seeds = context.n_prompts
assert num_seeds <= context.n_prompts
seeds = context.seed[:num_seeds]
context.seed = context.seed[num_seeds:] if num_seeds < context.n_prompts else None
return seeds
if hasattr(context, "server_seed") and context.server_seed is not None:
if num_seeds is None:
return [context.server_seed] * context.n_prompts
else:
return [context.server_seed] * num_seeds
return None
def context_text(context): def context_text(context):

43
ggml-cuda/common.cuh
View file

@ -138,6 +138,7 @@
#define WARP_SIZE 32 #define WARP_SIZE 32
#define CUDART_HMAX 11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed) #define CUDART_HMAX 11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed)
#define CUDART_HMASK 12000 // CUDA 12.0, min. ver. for half2 -> uint mask comparisons
#define CC_PASCAL 600 #define CC_PASCAL 600
#define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
@ -293,20 +294,54 @@ static __device__ __forceinline__ float warp_reduce_max(float x) {
return x; return x;
} }
static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) {
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
#if CUDART_VERSION >= CUDART_HMAX
return __hmax(a, b);
#else
return __half2float(a) > __half2float(b) ? a : b;
#endif // CUDART_VERSION >= CUDART_HMAX
#else
GGML_UNUSED(a);
GGML_UNUSED(b);
NO_DEVICE_CODE;
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX
}
static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const half2 b) {
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
#if CUDART_VERSION >= CUDART_HMAX
return __hmax2(a, b);
#else
half2 ret;
reinterpret_cast<half&>(ret.x) = __low2float(a) > __low2float(b) ? __low2half(a) : __low2half(b);
reinterpret_cast<half&>(ret.y) = __high2float(a) > __high2float(b) ? __high2half(a) : __high2half(b);
return ret;
#endif // CUDART_VERSION >= CUDART_HMAX
#else
GGML_UNUSED(a);
GGML_UNUSED(b);
NO_DEVICE_CODE;
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX
}
static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
#pragma unroll #pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) { for (int mask = 16; mask > 0; mask >>= 1) {
x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
} }
return x; return x;
#else #else
GGML_UNUSED(x); GGML_UNUSED(x);
NO_DEVICE_CODE; NO_DEVICE_CODE;
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
} }
#if CUDART_VERSION < 12000 #if CUDART_VERSION < CUDART_HMASK
static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half2 b) { static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half2 b) {
const uint32_t mask_low = 0x0000FFFF * (float( __low2half(a)) > float( __low2half(b))); const uint32_t mask_low = 0x0000FFFF * (float( __low2half(a)) > float( __low2half(b)));
const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b))); const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b)));

6
ggml-cuda/fattn.cu
View file

@ -116,7 +116,7 @@ static __global__ void flash_attn_vec_ext_f16(
sum2 = warp_reduce_sum(sum2); sum2 = warp_reduce_sum(sum2);
half sum = __low2half(sum2) + __high2half(sum2); half sum = __low2half(sum2) + __high2half(sum2);
sum += mask ? maskh[k_VKQ_0 + i_KQ] : __float2half(0.0f); sum += mask ? maskh[k_VKQ_0 + i_KQ] : __float2half(0.0f);
kqmax_new = __hmax(kqmax_new, sum); kqmax_new = ggml_cuda_hmax(kqmax_new, sum);
if (threadIdx.x == 0) { if (threadIdx.x == 0) {
KQ[i_KQ] = sum; KQ[i_KQ] = sum;
} }
@ -416,9 +416,9 @@ static __global__ void flash_attn_ext_f16(
const int k = k0 + threadIdx.x; const int k = k0 + threadIdx.x;
KQ2_tmp[k0/WARP_SIZE] += mask ? mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); KQ2_tmp[k0/WARP_SIZE] += mask ? mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
KQ_max_new = __hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]); KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
} }
KQ_max_new = __half2half2(warp_reduce_max(__hmax(__low2half(KQ_max_new), __high2half(KQ_max_new)))); KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new; const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
KQ_max_scale_h2[j0/nwarps] = h2exp(diff); KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));

2
llama.cpp
View file

@ -2359,7 +2359,7 @@ static bool llama_kv_cache_init(
cache.recurrent = model.arch == LLM_ARCH_MAMBA; cache.recurrent = model.arch == LLM_ARCH_MAMBA;
cache.v_trans = !cparams.flash_attn; cache.v_trans = !cparams.flash_attn;
// TODO: support mixed reccurent Transformer architectues // TODO: support mixed recurrent Transformer architectures
// NOTE: (!a || b) is a logical implication (a -> b) // NOTE: (!a || b) is a logical implication (a -> b)
GGML_ASSERT(!cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_s()); GGML_ASSERT(!cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_s());
GGML_ASSERT(!cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_s()); GGML_ASSERT(!cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_s());