Accelerazione GPU su M1

L’accelerazione GPU per PyTorch è ora disponibile su Apple Silicon. Ho voluto documentare come usare l’accelerazione GPU nei framework che utilizzo.

Pytorch

Alla data del 2022-05-20.

Installare PyTorch 1.12. Funziona solo con la nightly build.

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import torch
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import torchvision.models as models
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from torchsummary import summary
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print(torch.__version__)
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mps_device = torch.device("mps")
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print(mps_device)
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# Create a Tensor directly on the mps device
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x = torch.ones((1, 3, 224, 224), device=mps_device)
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print(x.shape)
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# Move your model to mps just like any other device
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model = models.resnet18()
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summary(model, (3, 244, 244))
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model.to(mps_device)
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# Now every call runs on the GPU
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pred = model(x)
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print(pred, pred.shape)

HuggingFace

Non si installa tramite pip o conda, quindi bisogna compilare dai sorgenti. Ho usato il tokenizer in rust.

# install rust on arm terminal
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh


# intsall tokenizer
git clone https://github.com/huggingface/tokenizers
cd tokenizers/bindings/python
pip install setuptools_rust
python setup.py install

# install transformers
pip install git+https://github.com/huggingface/transformers

# install datasets
pip install git+https://github.com/huggingface/datasets

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from transformers import AutoTokenizer, BertModel
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device = "mps"
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sentence  = 'Hello World!'
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tokenizer = AutoTokenizer.from_pretrained('bert-large-uncased', use_fast=True)
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model     = BertModel.from_pretrained('bert-large-uncased')
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inputs    = tokenizer(sentence, return_tensors="pt").to(device)
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model     = model.to(device)
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outputs   = model(**inputs)
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print(outputs)

Accelerazione GPU su M1

Pytorch

HuggingFace

Ref