Is there workaround for 3090

[momo1986]

The machine is in 3090 platform.

Looks like that the pytorch version would be limited.

Is there any workaround for this version?

Thanks & Regards!

[dabensongbing]

A100 GPU detected, using flash attention if input tensor is on cuda
D:\denoising-diffusion-pytorch-main\denoising_diffusion_pytorch\attend.py:88: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\transformers\cuda\sdp_utils.cpp:263.)
out = F.scaled_dot_product_attention(
D:\denoising-diffusion-pytorch-main\denoising_diffusion_pytorch\attend.py:88: UserWarning: Memory efficient kernel not used because: (Triggered internally at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\transformers\cuda\sdp_utils.cpp:415.)
out = F.scaled_dot_product_attention(
D:\denoising-diffusion-pytorch-main\denoising_diffusion_pytorch\attend.py:88: UserWarning: Memory Efficient attention has been runtime disabled. (Triggered internally at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen/native/transformers/sdp_utils_cpp.h:456.)
out = F.scaled_dot_product_attention(
D:\denoising-diffusion-pytorch-main\denoising_diffusion_pytorch\attend.py:88: UserWarning: Flash attention kernel not used because: (Triggered internally at C:\actions-runner_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\transformers\cuda\sdp_utils.cpp:417.)
out = F.scaled_dot_product_attention(
0%| | 0/700000 [02:43<?, ?it/s]
Traceback (most recent call last):
File "D:\denoising-diffusion-pytorch-main\test.py", line 32, in
trainer.train()
File "D:\denoising-diffusion-pytorch-main\denoising_diffusion_pytorch\denoising_diffusion_pytorch.py", line 1058, in train
loss = self.model(data)
File "D:\condaa312\envs\ddp\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "D:\condaa312\envs\ddp\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "D:\condaa312\envs\ddp\lib\site-packages\accelerate\utils\operations.py", line 820, in forward
return model_forward(*args, **kwargs)
File "D:\condaa312\envs\ddp\lib\site-packages\accelerate\utils\operations.py", line 808, in call
return convert_to_fp32(self.model_forward(*args, **kwargs))
File "D:\condaa312\envs\ddp\lib\site-packages\torch\amp\autocast_mode.py", line 16, in decorate_autocast
return func(*args, **kwargs)
File "D:\denoising-diffusion-pytorch-main\denoising_diffusion_pytorch\denoising_diffusion_pytorch.py", line 841, in forward
return self.p_losses(img, t, *args, **kwargs)
File "D:\denoising-diffusion-pytorch-main\denoising_diffusion_pytorch\denoising_diffusion_pytorch.py", line 817, in p_losses
model_out = self.model(x, t, x_self_cond)
File "D:\condaa312\envs\ddp\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "D:\condaa312\envs\ddp\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "D:\denoising-diffusion-pytorch-main\denoising_diffusion_pytorch\denoising_diffusion_pytorch.py", line 411, in forward
x = attn(x) + x
File "D:\condaa312\envs\ddp\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "D:\condaa312\envs\ddp\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "D:\denoising-diffusion-pytorch-main\denoising_diffusion_pytorch\denoising_diffusion_pytorch.py", line 269, in forward
out = self.attend(q, k, v)
File "D:\condaa312\envs\ddp\lib\site-packages\torch\nn\modules\module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "D:\condaa312\envs\ddp\lib\site-packages\torch\nn\modules\module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "D:\denoising-diffusion-pytorch-main\denoising_diffusion_pytorch\attend.py", line 107, in forward
return self.flash_attn(q, k, v)
File "D:\denoising-diffusion-pytorch-main\denoising_diffusion_pytorch\attend.py", line 88, in flash_attn
out = F.scaled_dot_product_attention(
RuntimeError: No available kernel. Aborting execution.
我遇到了这个问题，不知道应该如何解决