Comments (2)
Thanks for the report.
I could add a version check for torch<2.0:
from packaging.version import Version
class CheckerboardLatentCodec(LatentCodec):
def _y_ctx_zero(self, y: Tensor) -> Tensor:
if Version(torch.__version__) < Version("2.0.0"):
return self._mask(self.context_prediction(y).detach(), "all")
return y.new_zeros(self.context_prediction(y.to("meta")).shape)
...but perhaps simpler is just to revert:
class CheckerboardLatentCodec(LatentCodec):
def _y_ctx_zero(self, y: Tensor) -> Tensor:
return self._mask(self.context_prediction(y).detach(), "all")
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To be fair I don't actually know which is the earliest torch version that supports meta device tensors as I couldn't find any solid information.
Although I think the simpler fix is probably good enough. On my machine with a 14900K and a 3090 and an (unreasonably large) context size of (16, 192, 512, 512)
it takes 0.06ms to execute that line on GPU. It does take about 4 seconds on CPU, but with a more reasonable context size of (16, 192, 32, 32)
it takes roughly 80ms on CPU.
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