pytorch

bcec8cc3 - Add amax/amin (#43092)

Add amax/amin (#43092) Summary: Add a max/min operator that only return values. ## Some important decision to discuss | **Question** | **Current State** | |---------------------------------------|-------------------| | Expose torch.max_values to python? | No | | Remove max_values and only keep amax? | Yes | | Should amax support named tensors? | Not in this PR | ## Numpy compatibility Reference: https://numpy.org/doc/stable/reference/generated/numpy.amax.html | Parameter | PyTorch Behavior | |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------| | `axis`: None or int or tuple of ints, optional. Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before. | Named `dim`, behavior same as `torch.sum` (https://github.com/pytorch/pytorch/issues/29137) | | `out`: ndarray, optional. Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. | Same | | `keepdims`: bool, optional. If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. | implemented as `keepdim` | | `initial`: scalar, optional. The minimum value of an output element. Must be present to allow computation on empty slice. | Not implemented in this PR. Better to implement for all reductions in the future. | | `where`: array_like of bool, optional. Elements to compare for the maximum. | Not implemented in this PR. Better to implement for all reductions in the future. | **Note from numpy:** > NaN values are propagated, that is if at least one item is NaN, the corresponding max value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmax. PyTorch has the same behavior Pull Request resolved: https://github.com/pytorch/pytorch/pull/43092 Reviewed By: ngimel Differential Revision: D23360705 Pulled By: mruberry fbshipit-source-id: 5bdeb08a2465836764a5a6fc1a6cc370ae1ec09d