From 06dab189cb517cabe81503caa755849d99c23b97 Mon Sep 17 00:00:00 2001
From: Ilyas Moutawwakil <57442720+IlyasMoutawwakil@users.noreply.github.com>
Date: Mon, 19 Feb 2024 09:42:13 +0100
Subject: [PATCH] moved complex examples (#127)
---
.gitignore | 5 +-
README.md | 6 +-
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examples/fast-mteb/report.py | 222 -------
examples/fast-mteb/script.sh | 7 -
examples/openvino_diffusion.yaml | 3 +-
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delete mode 100644 examples/whisper/scripts/benchmark.sh
delete mode 100644 examples/whisper/scripts/report.sh
diff --git a/.gitignore b/.gitignore
index a8e86c83..ff31100b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -167,8 +167,7 @@ sweeps/
data/
version.txt
+.engine/
actions-runner/
experiments/
-examples/
-.engine/
-amdsmi
\ No newline at end of file
+amdsmi/
diff --git a/README.md b/README.md
index 49889327..8b5e7368 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
-All benchmarks are wrong, some will cost you less than the others.
+All benchmarks are wrong, some will cost you less than others.
Optimum-Benchmark 🏋️
-Optimum-Benchmark is a unified [multi-backend & multi-device](#backends--devices-) utility for benchmarking [Transformers](https://github.com/huggingface/transformers), [Diffusers](https://github.com/huggingface/diffusers), [PEFT](https://github.com/huggingface/peft), [TIMM](https://github.com/huggingface/pytorch-image-models) and [Optimum](https://github.com/huggingface/optimum) flavors, along with all their supported [optimizations & quantization schemes](#backend-features-), for [inference & training](#benchmark-features-%EF%B8%8F), in [distributed & non-distributed settings](#backend-features-), in the most correct and scalable way possible (no need to even download model weights).
+Optimum-Benchmark is a unified [multi-backend & multi-device](#backends--devices-) utility for benchmarking [Transformers](https://github.com/huggingface/transformers), [Diffusers](https://github.com/huggingface/diffusers), [PEFT](https://github.com/huggingface/peft), [TIMM](https://github.com/huggingface/pytorch-image-models) and [Optimum](https://github.com/huggingface/optimum) flavors, along with all their supported [optimizations & quantization schemes](#backend-features-), for [inference & training](#benchmark-features-%EF%B8%8F), in [distributed & non-distributed settings](#backend-features-), in the most correct, efficient and scalable way possible (you don't even need to download the weights).
*News* 📰
- PYPI release soon.
@@ -128,7 +128,7 @@ optimum-benchmark --config-dir examples --config-name pytorch_bert -m backend.de
### Configurations structure 📁
-You can create custom configuration files following the [examples here]([examples](https://github.com/IlyasMoutawwakil/optimum-benchmark-examples)).
+You can create custom and more complex configuration files following these [examples]([examples](https://github.com/IlyasMoutawwakil/optimum-benchmark-examples)).
## Features 🎨
diff --git a/examples/api_launch.py b/examples/api_launch.py
index 987ec8c9..734dfd3c 100644
--- a/examples/api_launch.py
+++ b/examples/api_launch.py
@@ -4,7 +4,6 @@
from optimum_benchmark.launchers.torchrun.config import TorchrunConfig
from optimum_benchmark.logging_utils import setup_logging
-
if __name__ == "__main__":
setup_logging(level="INFO")
launcher_config = TorchrunConfig(nproc_per_node=2)
diff --git a/examples/fast-mteb/README.md b/examples/fast-mteb/README.md
deleted file mode 100644
index fee12ad6..00000000
--- a/examples/fast-mteb/README.md
+++ /dev/null
@@ -1,44 +0,0 @@
-# Optimum-Benchmark x MTEB
-
-A set of performance benchmarks using [`BAAI/bge-base-en-v1.5`](https://huggingface.co/BAAI/bge-base-en-v1.5), the number one embedding model on the [`Massive Text Embedding Benchmark (MTEB) Leaderboard`](https://huggingface.co/spaces/mteb/leaderboard).
-
-For these benchmarks in particular, we recommend using a docker image with TensorRT intalled, for example [`nvcr.io/nvidia/tensorrt:22.12-py3`](../../docker/gpu.dockerfile) or removing the `TensorrtExecutionProvider` experiments from `configs/`.
-
-Just run `script.sh` from this directory:
-
-```bash
-sh script.sh
-```
-
-This script will run sweeps over batch sizes and sequence lengths for each backend using the config files in `configs/`. The results of the sweeps will be saved in `experiments/`. Then run the reporting script `report.py`:
-
-```bash
-python report.py -e experiments/
-```
-
-Which will generate the plots and csv files in `artifact/`.
-
-## Results
-
-### Latency
-
-For latency, we consider the case of a server processing requests one at a time (i.e. batch size of 1).
-We achieve 1 to 2 milliseconds latency for the forward pass of the embedding model using either `CUDAExecutionProvider` with `O4` optimization level or `TensorrtExecutionProvider` with `fp16` precision. This can be seen as a 5x to 7x speedup over the baseline pytorch model.
-
-
- 
-
-
-### Throughput
-
-For throughput, we consider the case of a server processing requests of average length (i.e. sequence length of 256).
-We achieve a throughput of +2000 samples per second for the forward pass of the embedding model at an optimal batch size of 128 using either `CUDAExecutionProvider` with `O4` optimization level or `TensorrtExecutionProvider` with `fp16` precision. This can be seen as a 7.5x increase over the baseline pytorch model.
-
-
- 
-
-
-### Notes
-
-- The `TensorrtExecutionProvider` requires engine building, which can take a few minutes during model loading and the first forward pass. It also rebuilds the engine every time the sequence length changes. This is why I think that `CUDAExecutionProvider` with `O4` optimization level is the sweetspot for GPU inference.
-- Some other cases, such as processing big batches of short sequences, can demonstrate even higher speedups (~15x). We don't study them here.
diff --git a/examples/fast-mteb/artifacts/forward_latency_plot.png b/examples/fast-mteb/artifacts/forward_latency_plot.png
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