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https://github.com/zebrajr/pytorch.git
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e7c1e6a8e3
Here's the command I used to invoke autopep8 (in parallel!):
git ls-files | grep '\.py$' | xargs -n1 -P`nproc` autopep8 -i
Several rules are ignored in setup.cfg. The goal is to let autopep8
handle everything which it can handle safely, and to disable any rules
which are tricky or controversial to address. We may want to come back
and re-enable some of these rules later, but I'm trying to make this
patch as safe as possible.
Also configures flake8 to match pep8's behavior.
Also configures TravisCI to check the whole project for lint.
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
import torch.cuda
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import torch.cuda.comm as comm
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from torch.autograd import Function
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class Broadcast(Function):
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def __init__(self, target_gpus):
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super(Broadcast, self).__init__()
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self.target_gpus = target_gpus
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def forward(self, input):
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assert input.is_cuda, "Broadcast function not implemented for CPU tensors"
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self.input_device = input.get_device()
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return comm.broadcast(input, self.target_gpus)
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def backward(self, *grad_output):
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return comm.reduce_add(grad_output, self.input_device)
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class Gather(Function):
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def __init__(self, target_device, dim=0):
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super(Gather, self).__init__()
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self.target_device = target_device
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self.dim = dim
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def forward(self, *inputs):
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assert all(map(lambda i: i.is_cuda, inputs))
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self.input_gpus = tuple(map(lambda i: i.get_device(), inputs))
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self.input_sizes = tuple(map(lambda i: i.size(self.dim), inputs))
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return comm.gather(inputs, self.dim, self.target_device)
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def backward(self, grad_output):
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return comm.scatter(grad_output, self.input_gpus, self.input_sizes,
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self.dim)
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class Scatter(Function):
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def __init__(self, target_gpus, chunk_sizes=None, dim=0):
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super(Scatter, self).__init__()
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self.target_gpus = target_gpus
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self.chunk_sizes = chunk_sizes
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self.dim = dim
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def forward(self, input):
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self.input_device = input.get_device() if input.is_cuda else -1
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return comm.scatter(input, self.target_gpus, self.chunk_sizes, self.dim)
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def backward(self, *grad_output):
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return comm.gather(grad_output, self.dim, self.input_device)
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