Performs Tensor dtype and/or device conversion. A torch.dtype and torch.device are inferred from the arguments of self.to(*args, **kwargs).
Note
If self requires gradients (requires_grad=True) but the target dtype specified is an integer type, the returned tensor will implicitly set requires_grad=False. This is because only tensors with floating-point or complex dtypes can require gradients.
Note
According to C++ type conversion rules, converting floating point value to integer type will truncate the fractional part. If the truncated value cannot fit into the target type (e.g., casting torch.inf to torch.long), the behavior is undefined and the result may vary across platforms.
>>> tensor = torch.randn(2, 2) # Initially dtype=float32, device=cpu
>>> tensor.to(torch.float64)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], dtype=torch.float64)
>>> cuda0 = torch.device('cuda:0')
>>> tensor.to(cuda0)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], device='cuda:0')
>>> tensor.to(cuda0, dtype=torch.float64)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')
>>> other = torch.randn((), dtype=torch.float64, device=cuda0)
>>> tensor.to(other, non_blocking=True)
tensor([[-0.5044, 0.0005],
[ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')
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