1c641e6aac
* `main`/`server`: rename to `llama` / `llama-server` for consistency w/ homebrew
* server: update refs -> llama-server
gitignore llama-server
* server: simplify nix package
* main: update refs -> llama
fix examples/main ref
* main/server: fix targets
* update more names
* Update build.yml
* rm accidentally checked in bins
* update straggling refs
* Update .gitignore
* Update server-llm.sh
* main: target name -> llama-cli
* Prefix all example bins w/ llama-
* fix main refs
* rename {main->llama}-cmake-pkg binary
* prefix more cmake targets w/ llama-
* add/fix gbnf-validator subfolder to cmake
* sort cmake example subdirs
* rm bin files
* fix llama-lookup-* Makefile rules
* gitignore /llama-*
* rename Dockerfiles
* rename llama|main -> llama-cli; consistent RPM bin prefixes
* fix some missing -cli suffixes
* rename dockerfile w/ llama-cli
* rename(make): llama-baby-llama
* update dockerfile refs
* more llama-cli(.exe)
* fix test-eval-callback
* rename: llama-cli-cmake-pkg(.exe)
* address gbnf-validator unused fread warning (switched to C++ / ifstream)
* add two missing llama- prefixes
* Updating docs for eval-callback binary to use new `llama-` prefix.
* Updating a few lingering doc references for rename of main to llama-cli
* Updating `run-with-preset.py` to use new binary names.
Updating docs around `perplexity` binary rename.
* Updating documentation references for lookup-merge and export-lora
* Updating two small `main` references missed earlier in the finetune docs.
* Update apps.nix
* update grammar/README.md w/ new llama-* names
* update llama-rpc-server bin name + doc
* Revert "update llama-rpc-server bin name + doc"
This reverts commit e474ef1df4.
* add hot topic notice to README.md
* Update README.md
* Update README.md
* rename gguf-split & quantize bins refs in **/tests.sh
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Co-authored-by: HanClinto <hanclinto@gmail.com>
2.2 KiB
Token generation performance troubleshooting
Verifying that the model is running on the GPU with CUDA
Make sure you compiled llama with the correct env variables according to this guide, so that llama accepts the -ngl N (or --n-gpu-layers N) flag. When running llama, you may configure N to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
./llama-cli -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some "
When running llama, before it starts the inference work, it will output diagnostic information that shows whether cuBLAS is offloading work to the GPU. Look for these lines:
llama_model_load_internal: [cublas] offloading 60 layers to GPU
llama_model_load_internal: [cublas] offloading output layer to GPU
llama_model_load_internal: [cublas] total VRAM used: 17223 MB
... rest of inference
If you see these lines, then the GPU is being used.
Verifying that the CPU is not oversaturated
llama accepts a -t N (or --threads N) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physical CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down.
Example of runtime flags effect on inference speed benchmark
These runs were tested on the following machine: GPU: A6000 (48GB VRAM) CPU: 7 physical cores RAM: 32GB
Model: TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.q4_0.gguf (30B parameters, 4bit quantization, GGML)
Run command: ./llama-cli -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]
Result:
| command | tokens/second (higher is better) |
|---|---|
| -ngl 2000000 | N/A (less than 0.1) |
| -t 7 | 1.7 |
| -t 1 -ngl 2000000 | 5.5 |
| -t 7 -ngl 2000000 | 8.7 |
| -t 4 -ngl 2000000 | 9.1 |