pytorch

f99bc714 - Migrate lt and lt_ from the TH to Aten (#25998)

Migrate lt and lt_ from the TH to Aten (#25998) Summary: https://github.com/pytorch/pytorch/issues/24593 https://github.com/pytorch/pytorch/issues/24727 **torch.lt(Tensor a, Tensor b)** will compute common dtype (highest) based on inputs and then compare values. The result will be Bool tensor ``` >>> x = torch.tensor([0], dtype=torch.int) >>> y = torch.tensor([0.5], dtype=torch.double) >>> x < y tensor([True]) ``` Previously it was impossible to make comparison of two tensors with different dtype. **torch.lt(Tensor a, Tensor b, out=c)** will compute common dtype (highest) based on inputs and then compare values. The result can be populated only to Bool tensor ``` >>> x = torch.tensor([0], dtype=torch.int) >>> y = torch.tensor([0.5], dtype=torch.double) >>> z = torch.empty([1], dtype=torch.bool) >>> torch.lt(x, y, out=z) tensor([True]) ``` Previously it was impossible to make comparison of two tensors with different dtype. Also previously the result dtype could be Bool and Byte(deprecated). Currently it will accept only Bool result. **a.lt_(Tensor b)** Expects that a and b has same dtype, otherwise it's possible to get an overflow(Example: 'a' is uint8, 'b' is float32. 'a' will be promoted to float32 and the result will be also float32. Then it will be casted back to uint8 so potential for overflow). Will not compute common dtype. Result will have type of a. ``` >>> x = torch.tensor([0], dtype=torch.double) >>> y = torch.tensor([0.5], dtype=torch.double) >>> x < y tensor([True]) ``` Works similar to previous implementation. **torch.lt(Tensor a, Scalar b)** will check if there is no overflow when converting b to the same type as a. Then will compute common dtype and compare. ``` >>> x = torch.tensor([0], dtype=torch.double) >>> x < 0.5 tensor([True]) >>> x = torch.tensor([0], dtype=torch.int) >>> x < 0.5 tensor([True]) ``` Fix https://github.com/pytorch/pytorch/issues/22301. **torch.lt(Tensor a, Scalar b, out=c)** will check if there is no overflow when converting b to the same type as a. Then will compute common dtype and compare. The result can be populated only to Bool tensor ``` >>> x = torch.tensor([0], dtype=torch.double) >>> torch.lt(x, 0.5, out=z) tensor([True]) ``` Previously the result dtype could be Bool and Byte(deprecated). Currently it will accept only Bool result. The rest works similar to previous implementation. **torch.lt_(Tensor a, Scalar b)** will check if there is no overflow when converting b to the same type as a. Then will compute common dtype and compare. Result will have type of a. ``` >>> x = torch.tensor([0], dtype=torch.int) >>> x.lt_(1) tensor([1], dtype=torch.int32) >>> x = torch.tensor([0], dtype=torch.int) >>> x.lt_(1.0) tensor([1], dtype=torch.int32) ``` Works similar to previous implementation. Pull Request resolved: https://github.com/pytorch/pytorch/pull/25998 Differential Revision: D17431853 Pulled By: ifedan fbshipit-source-id: b5effc6a5d9b32da379395b32abc628b604faaf7