性能结果

这里提供了Bow Pod 平台的初步性能结果，以及MLPerf Training v2.0 提交结果，以及我们自己针对训练和推理的更广泛模型的基准测试结果。

Bow平台-训练

这里提供了 Bow Pod 平台的初步训练性能结果。此处吞吐量定义为模型每秒处理的输入数据点（序列、图像或行）的数量。

以下结果详细说明了在指定配置中，每个引用模型的吞吐量。

Model	Variant	Platform	SDK Version	Framework	Dataset	Batch Size	Precision	Throughput (items/sec)
BERT Large	Ph1 Pre-Training (SL128) - Packed	Bow Pod16	SDK 3.1	PopART	Wikipedia	54,784	16.16	6044
BERT Large	Ph1 Pre-Training (SL128)	Bow Pod16	SDK 3.3	TensorFlow2	Wikipedia	65,280	16.16	4527
BERT Large	Ph1 Pre-Training (SL128) - Packed	Bow Pod16	SDK 3.3	PyTorch	Wikipedia	56,064	16.16	5600
BERT Large	Ph1 Pre-Training (SL128) - Packed	Bow Pod64	SDK 3.1	PopART	Wikipedia	54,784	16.16	22759
BERT Large	Ph1 Pre-Training (SL128)	Bow Pod64	SDK 3.0	TensorFlow2	Wikipedia	66,560	16.16	18199
BERT Large	Ph1 Pre-Training (SL128) - Packed	Bow Pod64	SDK 3.2	PyTorch	Wikipedia	56,064	16.16	18442
BERT Large	Ph2 Pre-Training (SL512) - Packed	Bow Pod16	SDK 3.1	PopART	Wikipedia	9,600	16.16	2126
BERT Large	Ph2 Pre-Training (SL512) - Packed	Bow Pod16	SDK 3.3	PyTorch	Wikipedia	8,192	16.16	1973
BERT Large	Ph2 Pre-Training (SL512) - Packed	Bow Pod64	SDK 3.1	PopART	Wikipedia	9,600	16.16	7789
BERT Large	Ph2 Pre-Training (SL512) - Packed	Bow Pod64	SDK 3.2	PyTorch	Wikipedia	8,192	16.16	6543
BERT Large	Fine-Tuning (SL384 - SQuAD)	Bow Pod16	SDK 3.1	PopART	SQuAD	256	16.16	1183
BERT Large	Fine-Tuning (SL384 - SQuAD)	Bow Pod16	SDK 3.3	PyTorch	SQuAD	256	16.16	1009
BERT Base	Ph1 Pre-Training (SL128)	Bow Pod16	SDK 3.1	PopART	Wikipedia	65,536	16.16	16391
BERT Base	Ph1 Pre-Training (SL128)	Bow Pod16	SDK 3.3	TensorFlow2	Wikipedia	65,280	16.16	15173
BERT Base	Ph1 Pre-Training (SL128)	Bow Pod16	SDK 3.3	PyTorch	Wikipedia	65,536	16.16	15911
BERT Base	Ph2 Pre-Training (SL512)	Bow Pod16	SDK 3.1	PopART	Wikipedia	16,384	16.16	3656
BERT Base	Ph2 Pre-Training (SL384)	Bow Pod16	SDK 3.3	TensorFlow2	Wikipedia	16,320	16.16	4387
BERT Base	Ph2 Pre-Training (SL512)	Bow Pod16	SDK 3.3	PyTorch	Wikipedia	16,384	16.16	3527
Group BERT Base	Ph1 Pre-Training (SL128)	Bow Pod16	SDK 3.0	TensorFlow1	Wikipedia	65,520	16.16	7187
Group BERT Base	Ph2 Pre-Training (SL384)	Bow Pod16	SDK 3.0	TensorFlow1	Wikipedia	32,800	16.16	2288
Group BERT Base	Ph1 Pre-Training (SL128)	Bow Pod64	SDK 3.0	TensorFlow1	Wikipedia	64,800	16.16	26425
Group BERT Base	Ph2 Pre-Training (SL384)	Bow Pod64	SDK 3.0	TensorFlow1	Wikipedia	32,640	16.16	7572
BERT Base - HuggingFace	Fine-Tuning (SL384 - SQuAD)	Bow Pod16	SDK 3.0	TensorFlow2	SQuAD	320	16.16	1014
GPT2	GPT2-Large (SL512)	Bow Pod16	SDK 3.3	PyTorch	Wikipedia	8,192	16.16	414
GPT2	GPT2-Large (SL512)	Bow Pod64	SDK 3.3	PyTorch	Wikipedia	8,192	16.16	1590
GPT2	GPT2-Large (SL1024)	Bow Pod16	SDK 3.3	PyTorch	Wikipedia	8,192	16.16	178
GPT2	GPT2-Medium (SL1024)	Bow Pod16	SDK 3.3	PyTorch	Wikipedia	8,192	16.16	337
GPT2	GPT2-Medium (SL1024)	Bow Pod64	SDK 3.3	PyTorch	Wikipedia	8,192	16.16	1320
GPT2	GPT2-Small (SL1024)	Bow Pod16	SDK 3.3	PyTorch	Wikipedia	8,192	16.16	1065
GPT2	GPT2-Small (SL1024)	Bow Pod64	SDK 3.3	PyTorch	Wikipedia	8,192	16.16	4094
Conformer-Medium	WeNet-Conformer-Medium	Bow Pod16	SDK 3.3	PyTorch	AiShell1	288	16.16	1167
RNN-T	Transformer Transducer	Bow Pod16	SDK 3.1	PopART	Generated	32	32.32	1442
DeepVoice3		Bow-2000	SDK 3.1	PopART	VCTK Corpus	128	32.32	9653
FastSpeech2		Bow Pod16	SDK 3.1	TensorFlow2	LJ Speech	64	16.16	1653
FastPitch	frames/s	Bow Pod16	SDK 3.3	PyTorch	Generated	128	32.32	1489341
TGN	Temporal Graph Network	1x Bow IPU	SDK 3.3	Pytorch Geometric				31617
Cluster-GCN		Bow-2000	SDK 3.2	TensorFlow2	PPI		16.16	684439
Cluster-GCN		Bow-2000	SDK 3.2	TensorFlow2	ArXiv		16.16	3521863
Cluster-GCN		Bow-2000	SDK 3.2	TensorFlow2	Reddit		16.16	1959184
Cluster-GCN		Bow-2000	SDK 3.2	TensorFlow2	Products		16.16	3412474
Cluster-GCN		Bow-2000	SDK 3.2	TensorFlow2	ogbn-mag		16.16	2586673
MPNN-GIN	MP Graph Isomorphism n/w	Bow-2000	SDK 3.3	TensorFlow2	Generated	1,024	16.16	473608
ResNet-50 v1.5		Bow Pod16	SDK 3.0	TensorFlow1	ImageNet2012	3,520	16.16	44059
ResNet-50 v1.5		Bow Pod16	SDK 3.3	PyTorch	ImageNet2012	16,384	16.16	38036
ResNet-50 v1.5		Bow Pod64	SDK 3.0	TensorFlow1	ImageNet2012	5,120	16.16	153205
ResNet-50 v1.5		Bow Pod64	SDK 3.2	PyTorch	ImageNet2012	16,384	16.16	109232
EfficientNet-B4	G16-EfficientNet	Bow Pod16	SDK 3.0	TensorFlow1	ImageNet2012	6,144	16.16	9000
EfficientNet-B4	G16-EfficientNet	Bow Pod16	SDK 3.3	PyTorch	ImageNet2012	1,024	16.32	8223
EfficientNet-B4	G16-EfficientNet	Bow Pod64	SDK 3.0	TensorFlow1	ImageNet2012	6,144	16.16	34140
ResNeXt101		Bow Pod16	SDK 3.0	TensorFlow1	ImageNet2012	768	16.16	12277
ViT	Pre-Training	Bow Pod16	SDK 3.3	PyTorch	ImageNet1k	65,536	16.16	7608
ViT	Pre-Training	Bow Pod64	SDK 3.3	PyTorch	ImageNet1k	65,536	16.16	26185
ViT	Fine-Tuning	Bow Pod16	SDK 3.3	PyTorch	ImageNet1k	2,040	16.16	8148
DINO	Vision Transformer	Bow Pod16	SDK 3.3	PyTorch	ImageNet1k	3,200	16.16	696
DINO	Vision Transformer	Bow Pod64	SDK 3.2	PyTorch	ImageNet1k	3,200	16.16	3437
Swin-Base (224)	Vision Transformer - Pre-Training	Bow Pod16	SDK 3.3	PyTorch	ImageNet1k	512	32.32	1442
Swin-Tiny (224)	Vision Transformer - Pre-Training	Bow Pod16	SDK 3.3	PyTorch	ImageNet1k	1,024	32.32	3687
Swin-Large (224)	Vision Transformer - Fine-Tuning	Bow Pod16	SDK 3.3	PyTorch	ImageNet1k	8,196	16.16	3283
UNet (Medical)		Bow-2000	SDK 3.3	TensorFlow2	EM segmentation	24	16.16	152
Mini DALL-E		Bow Pod16	SDK 3.3	PyTorch	COCO 2017	6,144	16.16	1843
Mini DALL-E		Bow Pod64	SDK 3.3	PyTorch	COCO 2017	24,576	16.16	6787
MAE	Masked Autoencoder for visual representation learning	Bow Pod16	SDK 3.1	PyTorch	ImageNet	4,128	16.16	7111
Frozen In Time	Multimodal - Pre-Training (1 frame)	Bow Pod8	SDK 3.3	PyTorch	webvid	240	16.16	447
CLIP	Multimodel (language/vision)	Bow Pod8	SDK 3.3	PyTorch	c3m	795	16.16	2498

Bow平台-推理

此处模型推理是指在输入数据上运行经过训练的模型来推断输出。实际商业应用中的推理性能通常根据两个指标来衡量：吞吐量（如前所述）和时延，在此上下文中是指为模型在给定输入的情况下提供输出所花费的时间。

以下为 Bow-2000 平台上指定批大小下的吞吐量和延迟结果。

Model	Variant	Platform	SDK Version	Framework	Dataset	Batch Size	Precision	Throughput (items/sec)	Latency (ms)
GPT2	GPT2-Small	Bow Pod16	SDK 3.3	PyTorch	Synthetic (host-generated)	4	16.16	1361	5.51
GPT2	GPT2-Medium	Bow Pod16	SDK 3.3	PyTorch	Synthetic (host-generated)	2	16.16	337	11.75
GPT2	GPT2-Large	Bow Pod16	SDK 3.3	PyTorch	Synthetic (host-generated)	2	16.16	97	20.57
BERT-Large	SL128	Bow-2000	SDK 3.1	PopART	SQuAD	4	16.16	2908	1.36
BERT-Large	SL128	Bow-2000	SDK 3.1	PopART	SQuAD	8	16.16	4096	1.94
BERT-Large	SL128	Bow-2000	SDK 3.1	PopART	SQuAD	12	16.16	4655	2.56
BERT-Large	SL128	Bow-2000	SDK 3.1	PopART	SQuAD	16	16.16	5292	3.01
BERT-Base	SL128	Bow-2000	SDK 3.1	PopART	SQuAD	4	16.16	6508	0.6
BERT-Base	SL128	Bow-2000	SDK 3.1	PopART	SQuAD	320	16.16	28069	11.41
ResNet-50v1.5	lowest latency config	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	4	16.16	9297	0.58
ResNet-50v1.5	higher throughput config	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	4	16.16	12282	1.66
ResNet-50v1.5		Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	256	16.16	46174	26.16
EfficientNet-B0	lowest latency config	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	4	16.16	10866	0.39
EfficientNet-B0	higher throughput config	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	4	16.16	14789	1.29
EfficientNet-B0		Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	192	16.16	46107	19.97
EfficientNet-B4	lowest latency config	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	4	16.16	4419	1.26
EfficientNet-B4	higher throughput config	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	4	16.16	5646	3.53
EfficientNet-B4		Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	48	16.16	15458	14.35
Yolo v4	image 896, bps 5, max det 200	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	4	16.16	924	6.49
Yolo v4	image 896, bps 10, max det 300	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	4	16.16	988	6.76
Yolo v4	image 640, bps 5, max det 200	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	8	16.16	1854	6.61
Yolo v4	image 640, bps 10, max det 300	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	8	16.16	1948	6.92
Yolo v4	image 512, bps 5, max det 200	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	8	16.16	2477	4.81
Yolo v4	image 512, bps 10, max det 300	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	8	16.16	2663	4.93
Yolo v4	image 416, bps 5, max det 200	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	8	16.16	3202	3.7
Yolo v4	image 416, bps 10, max det 100	Bow-2000	SDK 3.3	PyTorch	Synthetic (host-generated)	16	16.16	4268	6.28
EfficientDet-D0		Bow-2000	SDK 3.3	TF2 w/Keras	Synthetic (host-generated)	16	16.16	5171	0.77
EfficientDet-D1		Bow-2000	SDK 3.3	TF2 w/Keras	Synthetic (host-generated)	12	16.16	2875	1.4
EfficientDet-D2		Bow-2000	SDK 3.3	TF2 w/Keras	Synthetic (host-generated)	8	16.16	1869	2.14
EfficientDet-D3		Bow-2000	SDK 3.3	TF2 w/Keras	Synthetic (host-generated)	4	16.16	925	4.33
EfficientDet-D4		Bow-2000	SDK 3.3	TF2 w/Keras	Synthetic (host-generated)	4	16.16	664	6.03
Unet (Medical)		Bow-2000	SDK 3.3	TensorFlow2	Synthetic (host-generated)	4	16.16	1920
Unet (Medical)		Bow-2000	SDK 3.3	TensorFlow2	Synthetic (host-generated)	8	16.16	2081
FastSpeech2		Bow-2000	SDK 3.1	TensorFlow2	Synthetic (host-generated)	4	16.16	2610	1.53
FastSpeech2		Bow-2000	SDK 3.1	TensorFlow2	Synthetic (host-generated)	16	16.16	4354	0.92
FastSpeech2		Bow-2000	SDK 3.1	TensorFlow2	Synthetic (host-generated)	32	16.16	4949	0.81
FastSpeech2		Bow-2000	SDK 3.1	TensorFlow2	Synthetic (host-generated)	60	16.16	5201	0.77

MLPerf训练2.0版本表现

对于我们向 MLPerf 培训 2.0 版提交的内容，我们选择提交图像分类 (ResNet-50) 和自然语言处理 (BERT) 的流行应用程序基准类别，以及一个新条目作为 RNN-T 语音转录类别中的公开提交。

提交有两个分区（Division)。封闭分区（Closed Division）要求提交者使用完全相同的模型和优化器实施，包括定义超参数状态和训练时期。还有一个开放分区（Open Division)，通过支持更适合不同处理器功能的不同模型实现来促进和支持创新，但确保达到与封闭分区完全相同的模型准确性和质量。

Division	Model	MLPerf Quality Target	Platform	SDK Version	Framework	MLPerf ID	Dataset	Precision	Time to Train (mins)
Closed	ResNet50 v1.5	75.90% classification	Bow Pod16	SDK 2.5.1	TensorFlow	2.0-2047	ImageNet2012	16.16	19.64
Closed	ResNet50 v1.5	75.90% classification	Bow Pod64	SDK 2.5.1	TensorFlow	2.0-2050	ImageNet2012	16.16	6.30
Closed	ResNet50 v1.5	75.90% classification	Bow Pod128	SDK 2.5.1	TensorFlow	2.0-2052	ImageNet2012	16.16	4.19
Closed	ResNet50 v1.5	75.90% classification	Bow Pod256	SDK 2.5.1	TensorFlow	2.0-2054	ImageNet2012	16.16	2.67
Closed	BERT	0.72 Mask-LM accuracy	Bow Pod16	SDK 2.5.1	PopART	2.0-2045	Wikipedia	16.16	20.66
Closed	BERT	0.72 Mask-LM accuracy	Bow Pod16	SDK 2.5.1	PaddlePaddle	2.0-2046	Wikipedia	16.16	20.75
Closed	BERT	0.72 Mask-LM accuracy	Bow Pod64	SDK 2.5.1	PopART	2.0-2049	Wikipedia	16.16	6.70
Closed	BERT	0.72 Mask-LM accuracy	Bow Pod64	SDK 2.5.1	PaddlePaddle	2.0-2048	Wikipedia	16.16	6.77
Closed	BERT	0.72 Mask-LM accuracy	Bow Pod128	SDK 2.5.1	PopART	2.0-2051	Wikipedia	16.16	4.42
Closed	BERT	0.72 Mask-LM accuracy	Bow Pod256	SDK 2.5.1	PopART	2.0-2053	Wikipedia	16.16	3.19
Open	RNN-T	-	Bow Pod64	SDK 2.5.1	PopART	2.0-2125	Customer dataset	16.16	109.36

MLPerf的名称和徽标是MLCommons联盟（MLCommons Association）在美国和其他国家的商标。
版权所有，严禁未经授权使用。有关更多信息，请访问 www.mlperf.org

IPU-POD经典款-训练

训练机器学习模型涉及在输入数据集（训练数据）上运行算法，直到模型收敛，收敛意味着它已经学会以指定的准确性产生所需的输出。在此语境中，吞吐量被定义为模型每秒处理的输入数据点（序列、图像或行）的数量。吞吐量通常用作衡量硬件性能的指标，因为它与将模型训练达到指定准确性的时间直接相关。

下面提供的结果详细说明了在指定配置中每个参考模型获得的吞吐量值。在真实数据上运行的所有配置都针对收敛进行了验证。

Model	Variant	Platform	SDK Version	Framework	Dataset	Batch Size	Precision	Throughput (items/sec)
BERT Large	Ph1 Pre-Training (SL128)	IPU-POD16	SDK 2.4.0	PopART	Wikipedia	65,536	16.16	3738
BERT Large	Ph1 Pre-Training (SL128)	IPU-POD16	SDK 2.4.0	TensorFlow1	Wikipedia	65,600	16.16	3704
BERT Large	Ph1 Pre-Training (SL128)	IPU-POD16	SDK 2.4.0	PyTorch	Wikipedia	65,536	16.16	3582
BERT Large	Ph1 Pre-Training (SL128)	IPU-POD64	SDK 2.4.0	PopART	Wikipedia	65,536	16.16	14189
BERT Large	Ph1 Pre-Training (SL128)	IPU-POD64	SDK 2.4.0	TensorFlow1	Wikipedia	66,560	16.16	13917
BERT Large	Ph1 Pre-Training (SL128)	IPU-POD64	SDK 2.4.0	PyTorch	Wikipedia	65,536	16.16	12251
BERT Large	Ph1 Pre-Training (SL128)	IPU-POD128	SDK 2.4.0	PopART	Wikipedia	65,536	16.16	24424
BERT Large	Ph1 Pre-Training (SL128)	IPU-POD128	SDK 2.4.0	TensorFlow1	Wikipedia	66,560	16.16	24900
BERT Large	Ph1 Pre-Training (SL128)	IPU-POD128	SDK 2.4.0	PyTorch	Wikipedia	65,536	16.16	22402
BERT Large	Ph2 Pre-Training (SL384)	IPU-POD16	SDK 2.4.0	PopART	Wikipedia	16,384	16.16	1063
BERT Large	Ph2 Pre-Training (SL384)	IPU-POD16	SDK 2.4.0	TensorFlow1	Wikipedia	16,400	16.16	1025
BERT Large	Ph2 Pre-Training (SL384)	IPU-POD16	SDK 2.4.0	PyTorch	Wikipedia	16,384	16.16	1012
BERT Large	Ph2 Pre-Training (SL384)	IPU-POD64	SDK 2.4.0	PopART	Wikipedia	16,384	16.16	4003
BERT Large	Ph2 Pre-Training (SL384)	IPU-POD64	SDK 2.4.0	TensorFlow1	Wikipedia	16,640	16.16	3938
BERT Large	Ph2 Pre-Training (SL384)	IPU-POD64	SDK 2.4.0	PyTorch	Wikipedia	16,384	16.16	3611
BERT Large	Ph2 Pre-Training (SL384)	IPU-POD128	SDK 2.4.0	PopART	Wikipedia	16,384	16.16	7127
BERT Large	Ph2 Pre-Training (SL384)	IPU-POD128	SDK 2.4.0	TensorFlow1	Wikipedia	16,640	16.16	7292
BERT Large	Ph2 Pre-Training (SL384)	IPU-POD128	SDK 2.4.0	PyTorch	Wikipedia	16,384	16.16	6500
BERT Large	Fine-Tuning (SL384 - SQuAD)	IPU-POD16	SDK 2.4.0	PopART	SQuAD	256	16.16	884
BERT Large	Fine-Tuning (SL384 - SQuAD)	IPU-POD16	SDK 2.4.0	PyTorch	SQuAD	256	16.16	744
BERT Base	Ph1 Pre-Training (SL128)	IPU-POD16	SDK 2.4.0	PopART	Wikipedia	65,536	16.16	11991
BERT Base	Ph1 Pre-Training (SL128)	IPU-POD16	SDK 2.4.0	TensorFlow1	Wikipedia	65,280	16.16	11647
BERT Base	Ph1 Pre-Training (SL128)	IPU-POD16	SDK 2.4.0	TensorFlow2	Wikipedia	65,280	16.16	11035
BERT Base	Ph1 Pre-Training (SL128)	IPU-POD16	SDK 2.4.0	PyTorch	Wikipedia	65,536	16.16	11184
BERT Base	Ph2 Pre-Training (SL384)	IPU-POD16	SDK 2.4.0	PopART	Wikipedia	16,384	16.16	3545
BERT Base	Ph2 Pre-Training (SL384)	IPU-POD16	SDK 2.4.0	TensorFlow1	Wikipedia	16,320	16.16	3288
BERT Base	Ph2 Pre-Training (SL384)	IPU-POD16	SDK 2.4.0	TensorFlow2	Wikipedia	16,320	16.16	3155
BERT Base	Ph2 Pre-Training (SL384)	IPU-POD16	SDK 2.4.0	PyTorch	Wikipedia	16,384	16.16	3334
BERT Base - HuggingFace	Fine-Tuning (SL384 - SQuAD)	IPU-POD16	SDK 2.4.0	TensorFlow2	SQuAD	320	16.16	375
GPT2	GPT2-medium	IPU-POD16	SDK 2.3.0	PyTorch	Wikipedia	65,536	16.16	2540
GPT2	GPT2-medium	IPU-POD64	SDK 2.3.0	PyTorch	Wikipedia	65,536	16.16	9870
GPT2	GPT2-medium	IPU-POD128	SDK 2.3.0	PyTorch	Wikipedia	65,536	16.16	18842
GPT2	GPT2-medium	IPU-POD256	SDK 2.3.0	PyTorch	Wikipedia	65,536	16.16	31025
ResNet-50 v1.5		IPU-M2000	SDK 2.4.0	TensorFlow1	ImageNet2012	1,920	16.16	7864
ResNet-50 v1.5		IPU-M2000	SDK 2.4.0	PyTorch	ImageNet2012	16,384	16.16	7303
ResNet-50 v1.5		IPU-POD16	SDK 2.4.0	TensorFlow1	ImageNet2012	1,920	16.16	30690
ResNet-50 v1.5		IPU-POD16	SDK 2.4.0	PyTorch	ImageNet2012	16,384	16.16	25534
ResNet-50 v1.5		IPU-POD64	SDK 2.4.0	TensorFlow1	ImageNet2012	2,560	16.16	108566
ResNet-50 v1.5		IPU-POD128	SDK 2.4.0	TensorFlow1	ImageNet2012	5,120	16.16	205006
ResNet-50 v1.5		IPU-POD256	SDK 2.4.0	TensorFlow1	ImageNet2012	10,240	16.16	365040
ResNeXt101		IPU-M2000	SDK 2.4.0	TensorFlow1	ImageNet2012	768	16.16	2514
ResNeXt101		IPU-POD16	SDK 2.4.0	TensorFlow1	ImageNet2012	768	16.16	9023
EfficientNet-B4	G16-EfficientNet	IPU-M2000	SDK 2.4.0	TensorFlow1	ImageNet2012	800	16.16	1618
EfficientNet-B4	G16-EfficientNet	IPU-M2000	SDK 2.4.0	PyTorch	ImageNet2012	1,024	16.32	1400
EfficientNet-B4	G16-EfficientNet	IPU-POD16	SDK 2.4.0	TensorFlow1	ImageNet2012	6,144	16.16	6379
EfficientNet-B4	G16-EfficientNet	IPU-POD16	SDK 2.4.0	PyTorch	ImageNet2012	1,024	16.32	4311
EfficientNet-B4	G16-EfficientNet	IPU-POD64	SDK 2.4.0	TensorFlow1	ImageNet2012	6,144	16.16	24946
EfficientNet-B4	G16-EfficientNet	IPU-POD128	SDK 2.4.0	TensorFlow1	ImageNet2012	6,144	16.16	48015
EfficientNet-B4	G16-EfficientNet	IPU-POD256	SDK 2.4.0	TensorFlow1	ImageNet2012	6,144	16.16	87968
ViT	Vision Transformer	IPU-POD16	SDK 2.3.0	PyTorch	ImageNet1k	65,536	16.16	6535
ViT	Vision Transformer	IPU-POD64	SDK 2.3.0	PyTorch	ImageNet1k	65,536	16.16	25080
ViT	Vision Transformer	IPU-POD128	SDK 2.3.0	PyTorch	ImageNet1k	65,536	16.16	46320
ViT	Vision Transformer	IPU-POD256	SDK 2.3.0	PyTorch	ImageNet1k	65,536	16.16	68800
UNet (Medical)		IPU-M2000	SDK 2.4.0	TensorFlow2	EM segmentation	24	16.16	139
Mini DALL-E		IPU-M2000	SDK 2.4.0	PyTorch	COCO 2017	1,536	16.16	319
Mini DALL-E		IPU-POD16	SDK 2.4.0	PyTorch	COCO 2017	6,144	16.16	815
DeepVoice3		IPU-M2000	SDK 2.4.0	PopART	VCTK Corpus	128	32.32	8496
FastSpeech2		IPU-M2000	SDK 2.4.0	TensorFlow2	LJ Speech	32	16.16	406
FastSpeech2		IPU-POD16	SDK 2.4.0	TensorFlow2	LJ Speech	64	16.16	1141
Conformer		IPU-M2000	SDK 2.4.0	PyTorch	AiShell1	96	16.16	1030
Conformer		IPU-POD16	SDK 2.4.0	PyTorch	AiShell1	96	16.16	3395
TGN	Temporal Graph Network	GC200 IPU	SDK 2.4.0	TensorFlow1	JODIE Wikipedia	200	16.32	190472

IPU-POD经典款（Time to Result）

Model	Variant	Platform	SDK Version	Framework	Dataset	Batch Size	Precision	Time To Result (secs)
MCMC TFP		IPU-M2000	SDK 2.4.0	TensorFlow1	Proprietary		32.32	49

IPU-POD经典款- 推理

此语境中的模型推理是指在输入数据上运行模型以推断输出。生产设置中的推理性能通常通过两个指标来衡量：吞吐量（如前所述）和时延，后者被定义为执行推理所需的时间。

Model	Variant	Platform	SDK Version	Framework	Dataset	Batch Size	Precision	Throughput (items/sec)	Latency (ms)
BERT-Large	SL128	IPU-M2000	SDK 2.4.0	PopART	SQuAD	4	16.16	2071	1.92
BERT-Large	SL128	IPU-M2000	SDK 2.4.0	PopART	SQuAD	8	16.16	2911	2.73
BERT-Large	SL128	IPU-M2000	SDK 2.4.0	PopART	SQuAD	12	16.16	3303	3.62
BERT-Base	SL128	IPU-M2000	SDK 2.4.0	PopART	SQuAD	4	16.16	4580	0.86
BERT-Base	SL128	IPU-M2000	SDK 2.4.0	PopART	SQuAD	8	16.16	7069	1.11
BERT-Base	SL128	IPU-M2000	SDK 2.4.0	PopART	SQuAD	16	16.16	9687	1.65
BERT-Base	SL128	IPU-M2000	SDK 2.4.0	PopART	SQuAD	32	16.16	12584	2.53
BERT-Base	SL128	IPU-M2000	SDK 2.4.0	PopART	SQuAD	64	16.16	15346	4.16
BERT-Base	SL128	IPU-M2000	SDK 2.4.0	PopART	SQuAD	128	16.16	17972	7.11
BERT-Base	SL128	IPU-M2000	SDK 2.4.0	PopART	SQuAD	256	16.16	19484	13.11
BERT-Base	SL128	IPU-M2000	SDK 2.4.0	PopART	SQuAD	320	16.16	20803	15.36
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	4	16.16	7152	1.66
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	8	16.16	10515	2.27
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	16	16.16	16207	2.95
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	32	16.16	22544	4.24
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	64	16.16	28762	6.66
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	128	16.16	35155	10.91
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	256	16.16	40085	19.14
ResNet-50v1.5	lowest latency config	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	4	16.16	7397	0.52
ResNet-50v1.5	higher throughput config	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	4	16.16	9404	2.04
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	16	16.16	14321	2.69
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	32	16.16	20927	3.7
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	64	16.16	36193	8.62
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	128	16.16	43472	14.38
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	256	16.16	49816	25.13
ResNet-50v1.5		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	360	16.16	50883	30.68
ResNeXt101		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	4	16.16	4483	2.66
ResNeXt101		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	8	16.16	6435	3.71
ResNeXt101		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	16	16.16	9705	4.93
ResNeXt101		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	32	16.16	13693	6.99
ResNeXt101		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	64	16.16	17176	11.16
ResNeXt101		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	4	16.16	3395	1.14
ResNeXt101		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	8	16.16	4840	1.62
ResNeXt101		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	16	16.16	6483	2.43
ResNeXt101		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	64	16.16	11320	27.83
EfficientNet-B0	lowest latency config	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	4	16.16	8686	0.44
EfficientNet-B0	higher throughput config	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	4	16.16	10907	1.69
EfficientNet-B0		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	32	16.16	50510	3.05
EfficientNet-B0		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	64	16.16	71839	4.26
EfficientNet-B0		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	128	16.16	86986	6.77
EfficientNet-B0		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	144	16.16	69852	9.15
EfficientNet-B0		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	196	16.16	61714	13.38
EfficientNet-B0		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	4	16.16	8289	1.43
EfficientNet-B0		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	8	16.16	13056	1.82
EfficientNet-B0		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	16	16.16	22217	2.15
EfficientNet-B0		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	32	16.16	34448	2.77
EfficientNet-B0		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	64	16.16	43351	4.41
EfficientNet-B0		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	128	16.16	53256	7.19
EfficientNet-B0		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	160	16.16	55169	8.68
EfficientNet-B4	lowest latency config	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	4	16.16	3539	1.09
EfficientNet-B4	higher throughput config	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	4	16.16	4081	1.85
EfficientNet-B4		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	16	16.16	8299	3.5
EfficientNet-B4		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	24	16.16	9874	4.37
EfficientNet-B4		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	32	16.16	10753	5.3
EfficientNet-B4		IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	40	16.16	11578	6.22
EfficientNet-B4		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	4	16.16	3718	3.21
EfficientNet-B4		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	8	16.16	5514	4.34
EfficientNet-B4		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	16	16.16	7959	6.01
EfficientNet-B4		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	20	16.16	8958	6.68
EfficientNet-B7		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	4	16.16	1407	8.52
EfficientNet-B7		IPU-M2000	SDK 2.4.0	TensorFlow1	Synthetic (host-generated)	8	16.16	1869	12.82
Yolo v4	image 896, bps 5, max det 200	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	4	16.16	690	9.4
Yolo v4	image 896, bps 10, max det 300	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	4	16.16	722	9.74
Yolo v4	image 640, bps 5, max det 200	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	8	16.16	1306	10.03
Yolo v4	image 640, bps 10, max det 300	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	8	16.16	1364	10.39
Yolo v4	image 512, bps 5, max det 200	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	8	16.16	1772	7.25
Yolo v4	image 512, bps 10, max det 300	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	8	16.16	1915	7.31
Yolo v4	image 416, bps 5, max det 200	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	4	16.16	2195	5.88
Yolo v4	image 416, bps 10, max det 100	IPU-M2000	SDK 2.4.0	PyTorch	Synthetic (host-generated)	4	16.16	2994	9.42
Unet (Medical)		IPU-M2000	SDK 2.4.0	TensorFlow2	Synthetic (host-generated)	4	16.16	1144
Unet (Medical)		IPU-M2000	SDK 2.4.0	TensorFlow2	Synthetic (host-generated)	8	16.16	1190

精度术语：X.Y定义如下：X是存储激活和梯度的精度，Y是存储权重的精度。在16.16权重中训练时，我们可能仍将FP32用于其他变量（例如规范或动量），并包括随机舍入。基准测试是使用我们在 Graphcore GitHub 上的示例生成的。

本页最近更新日期为2023年4月18

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