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Optimize inference using torch.compile()

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Optimize inference using torch.compile()

このガイドは、torch.compile() を使用した推論速度の向上に関するベンチマークを提供することを目的としています。これは、🤗 Transformers のコンピュータビジョンモデル向けのものです。

Benefits of torch.compile

torch.compile()の利点モデルとGPUによっては、torch.compile()は推論時に最大30%の高速化を実現します。 torch.compile()を使用するには、バージョン2.0以上のtorchをインストールするだけです。

モデルのコンパイルには時間がかかるため、毎回推論するのではなく、モデルを1度だけコンパイルする場合に役立ちます。任意のコンピュータビジョンモデルをコンパイルするには、以下のようにモデルにtorch.compile()を呼び出します：

from transformers import AutoModelForImageClassification

model = AutoModelForImageClassification.from_pretrained(MODEL_ID, device_map="auto")
+ model = torch.compile(model)

compile() は、コンパイルに関する異なるモードを備えており、基本的にはコンパイル時間と推論のオーバーヘッドが異なります。max-autotune は reduce-overhead よりも時間がかかりますが、推論速度が速くなります。デフォルトモードはコンパイルにおいては最速ですが、推論時間においては reduce-overhead に比べて効率が良くありません。このガイドでは、デフォルトモードを使用しました。詳細については、こちらを参照してください。

torch バージョン 2.0.1 で異なるコンピュータビジョンモデル、タスク、ハードウェアの種類、およびバッチサイズを使用して torch.compile をベンチマークしました。

Benchmarking code

以下に、各タスクのベンチマークコードを示します。推論前にGPUをウォームアップし、毎回同じ画像を使用して300回の推論の平均時間を取得します。

Image Classification with ViT

from PIL import Image
import requests
import numpy as np
from transformers import AutoImageProcessor, AutoModelForImageClassification

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224", device_map="auto")
model = torch.compile(model)

processed_input = processor(image, return_tensors='pt').to(model.device)

with torch.no_grad():
    _ = model(**processed_input)

Object Detection with DETR

from transformers import AutoImageProcessor, AutoModelForObjectDetection

processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50", device_map="auto")
model = torch.compile(model)

texts = ["a photo of a cat", "a photo of a dog"]
inputs = processor(text=texts, images=image, return_tensors="pt").to(model.device)

with torch.no_grad():
    _ = model(**inputs)

Image Segmentation with Segformer

from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation

processor = SegformerImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512", device_map="auto")
model = torch.compile(model)
seg_inputs = processor(images=image, return_tensors="pt").to(model.device)

with torch.no_grad():
    _ = model(**seg_inputs)

以下は、私たちがベンチマークを行ったモデルのリストです。

Image Classification

Image Segmentation

Object Detection

以下は、torch.compile()を使用した場合と使用しない場合の推論時間の可視化と、異なるハードウェアとバッチサイズの各モデルに対するパフォーマンス向上の割合です。

Duration Comparison on V100 with Batch Size of 1

Percentage Improvement on T4 with Batch Size of 4

下記は、各モデルについてcompile()を使用した場合と使用しなかった場合の推論時間（ミリ秒単位）です。なお、OwlViTは大きなバッチサイズでの使用時にメモリ不足（OOM）が発生することに注意してください。

A100 (batch size: 1)

Task/Model	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/ViT	9.325	7.584
Image Segmentation/Segformer	11.759	10.500
Object Detection/OwlViT	24.978	18.420
Image Classification/BeiT	11.282	8.448
Object Detection/DETR	34.619	19.040
Image Classification/ConvNeXT	10.410	10.208
Image Classification/ResNet	6.531	4.124
Image Segmentation/Mask2former	60.188	49.117
Image Segmentation/Maskformer	75.764	59.487
Image Segmentation/MobileNet	8.583	3.974
Object Detection/Resnet-101	36.276	18.197
Object Detection/Conditional-DETR	31.219	17.993

A100 (batch size: 4)

Task/Model	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/ViT	14.832	14.499
Image Segmentation/Segformer	18.838	16.476
Image Classification/BeiT	13.205	13.048
Object Detection/DETR	48.657	32.418
Image Classification/ConvNeXT	22.940	21.631
Image Classification/ResNet	6.657	4.268
Image Segmentation/Mask2former	74.277	61.781
Image Segmentation/Maskformer	180.700	159.116
Image Segmentation/MobileNet	14.174	8.515
Object Detection/Resnet-101	68.101	44.998
Object Detection/Conditional-DETR	56.470	35.552

A100 (batch size: 16)

Task/Model	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/ViT	40.944	40.010
Image Segmentation/Segformer	37.005	31.144
Image Classification/BeiT	41.854	41.048
Object Detection/DETR	164.382	161.902
Image Classification/ConvNeXT	82.258	75.561
Image Classification/ResNet	7.018	5.024
Image Segmentation/Mask2former	178.945	154.814
Image Segmentation/Maskformer	638.570	579.826
Image Segmentation/MobileNet	51.693	30.310
Object Detection/Resnet-101	232.887	155.021
Object Detection/Conditional-DETR	180.491	124.032

V100 (batch size: 1)

Task/Model	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/ViT	10.495	6.00
Image Segmentation/Segformer	13.321	5.862
Object Detection/OwlViT	25.769	22.395
Image Classification/BeiT	11.347	7.234
Object Detection/DETR	33.951	19.388
Image Classification/ConvNeXT	11.623	10.412
Image Classification/ResNet	6.484	3.820
Image Segmentation/Mask2former	64.640	49.873
Image Segmentation/Maskformer	95.532	72.207
Image Segmentation/MobileNet	9.217	4.753
Object Detection/Resnet-101	52.818	28.367
Object Detection/Conditional-DETR	39.512	20.816

V100 (batch size: 4)

Task/Model	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/ViT	15.181	14.501
Image Segmentation/Segformer	16.787	16.188
Image Classification/BeiT	15.171	14.753
Object Detection/DETR	88.529	64.195
Image Classification/ConvNeXT	29.574	27.085
Image Classification/ResNet	6.109	4.731
Image Segmentation/Mask2former	90.402	76.926
Image Segmentation/Maskformer	234.261	205.456
Image Segmentation/MobileNet	24.623	14.816
Object Detection/Resnet-101	134.672	101.304
Object Detection/Conditional-DETR	97.464	69.739

V100 (batch size: 16)

Task/Model	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/ViT	52.209	51.633
Image Segmentation/Segformer	61.013	55.499
Image Classification/BeiT	53.938	53.581
Object Detection/DETR	OOM	OOM
Image Classification/ConvNeXT	109.682	100.771
Image Classification/ResNet	14.857	12.089
Image Segmentation/Mask2former	249.605	222.801
Image Segmentation/Maskformer	831.142	743.645
Image Segmentation/MobileNet	93.129	55.365
Object Detection/Resnet-101	482.425	361.843
Object Detection/Conditional-DETR	344.661	255.298

T4 (batch size: 1)

Task/Model	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/ViT	16.520	15.786
Image Segmentation/Segformer	16.116	14.205
Object Detection/OwlViT	53.634	51.105
Image Classification/BeiT	16.464	15.710
Object Detection/DETR	73.100	53.99
Image Classification/ConvNeXT	32.932	30.845
Image Classification/ResNet	6.031	4.321
Image Segmentation/Mask2former	79.192	66.815
Image Segmentation/Maskformer	200.026	188.268
Image Segmentation/MobileNet	18.908	11.997
Object Detection/Resnet-101	106.622	82.566
Object Detection/Conditional-DETR	77.594	56.984

T4 (batch size: 4)

Task/Model	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/ViT	43.653	43.626
Image Segmentation/Segformer	45.327	42.445
Image Classification/BeiT	52.007	51.354
Object Detection/DETR	277.850	268.003
Image Classification/ConvNeXT	119.259	105.580
Image Classification/ResNet	13.039	11.388
Image Segmentation/Mask2former	201.540	184.670
Image Segmentation/Maskformer	764.052	711.280
Image Segmentation/MobileNet	74.289	48.677
Object Detection/Resnet-101	421.859	357.614
Object Detection/Conditional-DETR	289.002	226.945

T4 (batch size: 16)

Task/Model	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/ViT	163.914	160.907
Image Segmentation/Segformer	192.412	163.620
Image Classification/BeiT	188.978	187.976
Object Detection/DETR	OOM	OOM
Image Classification/ConvNeXT	422.886	388.078
Image Classification/ResNet	44.114	37.604
Image Segmentation/Mask2former	756.337	695.291
Image Segmentation/Maskformer	2842.940	2656.88
Image Segmentation/MobileNet	299.003	201.942
Object Detection/Resnet-101	1619.505	1262.758
Object Detection/Conditional-DETR	1137.513	897.390

PyTorch Nightly

また、PyTorchのナイトリーバージョン（2.1.0dev）でのベンチマークを行い、コンパイルされていないモデルとコンパイル済みモデルの両方でレイテンシーの向上を観察しました。ホイールはこちらから入手できます。

A100

Task/Model	Batch Size	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/BeiT	Unbatched	12.462	6.954
Image Classification/BeiT	4	14.109	12.851
Image Classification/BeiT	16	42.179	42.147
Object Detection/DETR	Unbatched	30.484	15.221
Object Detection/DETR	4	46.816	30.942
Object Detection/DETR	16	163.749	163.706

T4

Task/Model	Batch Size	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/BeiT	Unbatched	14.408	14.052
Image Classification/BeiT	4	47.381	46.604
Image Classification/BeiT	16	42.179	42.147
Object Detection/DETR	Unbatched	68.382	53.481
Object Detection/DETR	4	269.615	204.785
Object Detection/DETR	16	OOM	OOM

V100

Task/Model	Batch Size	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/BeiT	Unbatched	13.477	7.926
Image Classification/BeiT	4	15.103	14.378
Image Classification/BeiT	16	52.517	51.691
Object Detection/DETR	Unbatched	28.706	19.077
Object Detection/DETR	4	88.402	62.949
Object Detection/DETR	16	OOM	OOM

Reduce Overhead

NightlyビルドでA100およびT4向けの reduce-overhead コンパイルモードをベンチマークしました。

A100

Task/Model	Batch Size	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/ConvNeXT	Unbatched	11.758	7.335
Image Classification/ConvNeXT	4	23.171	21.490
Image Classification/ResNet	Unbatched	7.435	3.801
Image Classification/ResNet	4	7.261	2.187
Object Detection/Conditional-DETR	Unbatched	32.823	11.627
Object Detection/Conditional-DETR	4	50.622	33.831
Image Segmentation/MobileNet	Unbatched	9.869	4.244
Image Segmentation/MobileNet	4	14.385	7.946

T4

Task/Model	Batch Size	torch 2.0 - no compile	torch 2.0 - compile
Image Classification/ConvNeXT	Unbatched	32.137	31.84
Image Classification/ConvNeXT	4	120.944	110.209
Image Classification/ResNet	Unbatched	9.761	7.698
Image Classification/ResNet	4	15.215	13.871
Object Detection/Conditional-DETR	Unbatched	72.150	57.660
Object Detection/Conditional-DETR	4	301.494	247.543
Image Segmentation/MobileNet	Unbatched	22.266	19.339
Image Segmentation/MobileNet	4	78.311	50.983

Update on GitHub

←大きなモデルのインスタンス化 🤗 Transformersにモデルを追加する方法→