Are there any plan for optimize the new offical support graphical card 7900xtx

[sdli1995]

It's very excited to hear that Rocm support 7900xtx offically. In my use case, i test serveral net works in difference precision and batchsize ,this card are not perform well against it's theroic performace in current rocm version 5.7.1 .
According the techup table ([https://www.techpowerup.com/gpu-specs/radeon-rx-7900-xtx.c3941]) 7900xtx has 61t fp32 flops and 120t fp16&bf16 flops and 960.0 GB/s memory bandwidth also have 96mb infinit cache, and we can see the rtx3090 which has 39t fp32 flops and 80t tensorcore fp16&bf16 flops and 936.0 GB/s memory bandwidth ,when i test lists network ,it performance not good
here is my several network test both in 3090 and 7900xtx

ref rtx3090

running benchmark for frameworks ['pytorch']
cuda version= 12.1
cudnn version= 8902
pytorch's batchsize at 16 vgg16 eval at fp32: 20.5ms avg
pytorch's batchsize at 16 vgg16 train at fp32: 58.7ms avg
pytorch's batchsize at 16 resnet152 eval at fp32: 27.9ms avg
pytorch's batchsize at 16 resnet152 train at fp32: 85.1ms avg
pytorch's batchsize at 16 densenet161 eval at fp32: 27.6ms avg
pytorch's batchsize at 16 densenet161 train at fp32: 87.4ms avg
pytorch's batchsize at 16 convnext_large eval at fp32: 57.3ms avg
pytorch's batchsize at 16 convnext_large train at fp32: 250.6ms avg
pytorch's batchsize at 16 swinbig eval at fp32: 39.9ms avg
pytorch's batchsize at 16 swinbig train at fp32: 124.2ms avg
pytorch's batchsize at 16 vgg16 eval at fp16: 12.4ms avg
pytorch's batchsize at 16 vgg16 train at fp16: 37.0ms avg
pytorch's batchsize at 16 resnet152 eval at fp16: 16.9ms avg
pytorch's batchsize at 16 resnet152 train at fp16: 63.1ms avg
pytorch's batchsize at 16 densenet161 eval at fp16: 20.4ms avg
pytorch's batchsize at 16 densenet161 train at fp16: 75.0ms avg
pytorch's batchsize at 16 convnext_large eval at fp16: 30.1ms avg
pytorch's batchsize at 16 convnext_large train at fp16: 87.6ms avg
pytorch's batchsize at 16 swinbig eval at fp16: 22.0ms avg
pytorch's batchsize at 16 swinbig train at fp16: 86.3ms avg

running benchmark for frameworks ['pytorch']
cuda version= None
cudnn version= 2020000
pytorch's batchsize at 16 vgg16 eval at fp32: 14.6ms avg
pytorch's batchsize at 16 vgg16 train at fp32: 71.2ms avg
pytorch's batchsize at 16 resnet152 eval at fp32: 23.2ms avg
pytorch's batchsize at 16 resnet152 train at fp32: 95.4ms avg
pytorch's batchsize at 16 densenet161 eval at fp32: 25.9ms avg
pytorch's batchsize at 16 densenet161 train at fp32: 100.3ms avg
pytorch's batchsize at 16 convnext_large eval at fp32: 108.3ms avg
pytorch's batchsize at 16 convnext_large train at fp32: 293.9ms avg
pytorch's batchsize at 16 swinbig eval at fp32: 43.3ms avg
pytorch's batchsize at 16 swinbig train at fp32: 142.4ms avg
pytorch's batchsize at 16 vgg16 eval at fp16: 8.8ms avg
pytorch's batchsize at 16 vgg16 train at fp16: 43.2ms avg
pytorch's batchsize at 16 resnet152 eval at fp16: 19.9ms avg
pytorch's batchsize at 16 resnet152 train at fp16: 72.5ms avg
pytorch's batchsize at 16 densenet161 eval at fp16: 22.2ms avg
pytorch's batchsize at 16 densenet161 train at fp16: 81.4ms avg
pytorch's batchsize at 16 convnext_large eval at fp16: 64.5ms avg
pytorch's batchsize at 16 convnext_large train at fp16: 168.2ms avg
pytorch's batchsize at 16 swinbig eval at fp16: 25.6ms avg
pytorch's batchsize at 16 swinbig train at fp16: 81.7ms avg

[briansp2020]

@sdli1995
Can you post the source for the benchmark? I'd like to run it on my setup and compare also.
Thanks!

[sdli1995]

https://github.com/sdli1995/deep-learning-benchmark/tree/master here

[johnnynunez]

https://pypi.org/project/new-ai-benchmark/

[xvel]

Any news on that?

[veb-101]

Any updates?