2. Hardware acceleration¶
Since Norse is using PyTorch primitives, it is worth familiarising yourself with the use of GPUs in PyTorch.
Here is a tutorial describing how to use GPUs with the
to method in PyTorch:
2.1. Accelerating Norse models with
To accelerate neuron models in Norse, one simply has to call the
.to method on models and data:
from norse.torch.module.lif import LIFCell LIFCell(10, 20).to('cuda')
import torch torch.randn(100, 20).to('cuda')
2.2. Accelerating Norse nested models¶
It might be necessary to sometimes build your own nested torch.nn modules. We recommend that you adhere to the PyTorch torch.nn.Module idiosyncrasy, where you register possible nested tensors as either Parameters or Buffers .
If things are setup correctly, it should be simple to move models and tensors to the GPU (the example is a simplified version of our MNIST task example):
class LIFConvNet(torch.nn.Module): def __init__( self, input_features, seq_length, model="super", ): super(LIFConvNet, self).__init__() self.constant_current_encoder = ConstantCurrentLIFEncoder(seq_length=seq_length) self.input_features = input_features self.rsnn = ConvNet4(method=model) self.seq_length = seq_length def forward(self, x): batch_size = x.shape x = self.constant_current_encoder( x.view(-1, self.input_features) ) x = x.reshape(self.seq_length, batch_size, 1, 28, 28) voltages = self.rsnn(x) m, _ = torch.max(voltages, 0) log_p_y = torch.nn.functional.log_softmax(m, dim=1) return log_p_y
We can now initialise the model and move it to the correct device:
def main(args): device = ... input_features = ... seq_length = ... model = LIFConvNet(input_features, seq_length).to(device) ...