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google / gemma_pytorch

The official PyTorch implementation of Google's Gemma models
https://ai.google.dev/gemma
Apache License 2.0
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RuntimeError: at::cuda::blas::gemm: not implemented for struct c10::BFloat16 #3

Closed dhchenx closed 7 months ago

dhchenx commented 7 months ago

Hi,

I am running the example in Gemma in Pytorch and encountered the following issue:

Part of the example code

image

The error is: RuntimeError: at::cuda::blas::gemm: not implemented for struct c10::BFloat16

image

My environment: Windows 11 pytorch-1.8.1, cuda 10.2, python 3.9

Any idea how to solve the issue?

pengchongjin commented 7 months ago

What's your GPU type? It might be the case that you are running it on a GPU that does not support bfloat16 type.

As a work-around, you can try to change here to

dtype: str = 'float16' # or 'float32'

and test to see if that works.

dhchenx commented 7 months ago

What's your GPU type? It might be the case that you are running it on a GPU that does not support bfloat16 type.

As a work-around, you can try to change here to

dtype: str = 'float16' # or 'float32'

and test to see if that works.

Thanks, I used NVIDIA Quadro P2200 (5 GB) .

I first tried to change dtype:str='float16', the error is below:

image

Then, I tried to change dtype:str='float32', the another error is shown below:

image

I wonder if the example code can run on low computing capacity computers. Any solution? Thanks!

pengchongjin commented 7 months ago

Which model variant are you trying? 2B or 7B or 7B-quant? In any case, 5GB might be insufficient.

dhchenx commented 7 months ago

Which model variant are you trying? 2B or 7B or 7B-quant?

I used 2B-it

pengchongjin commented 7 months ago

Yeah, 2B f16 may still be too large to fit 5GB card. Maybe try CPU first.

dhchenx commented 7 months ago

Yeah, 2B f16 may still be too large to fit 5GB card. Maybe try CPU first.

Thanks. I try CPU (32GB) and it works well.

pengchongjin commented 7 months ago

OK, yeah, to fit 5GB card, we probably would need a int8 quantized 2B variant, which is not included in the release.

You can try the free Colab T4 runtime using this Colab, and it should work. https://ai.google.dev/gemma/docs/pytorch_gemma

dhchenx commented 7 months ago

OK, yeah, to fit 5GB card, we probably would need a int8 quantized 2B variant, which is not included in the release.

You can try the free Colab T4 runtime using this Colab, and it should work. https://ai.google.dev/gemma/docs/pytorch_gemma

Thanks!

pengchongjin commented 7 months ago

Closing the issue for now.