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This PR follows https://github.com/pytorch/pytorch/pull/129374#pullrequestreview-2136555775 cc @malfet: > Lots of formatting changes unrelated to PR goal, please keep them as part of separate PR (and please add lint rule if you want to enforce those, or at least cite one) `usort` allows empty lines within import segments. For example, `usort` do not change the following code: ```python import torch.aaa import torch.bbb import torch.ccc x = ... # some code ``` ```python import torch.aaa import torch.bbb import torch.ccc x = ... # some code ``` ```python import torch.aaa import torch.bbb import torch.ccc x = ... # some code ``` This PR first sort imports via `isort`, then re-sort the file using `ufmt` (`usort` + `black`). This enforces the following import style: 1. no empty lines within segments. 2. single empty line between segments. 3. two spaces after import statements. All the code snippets above will be formatted to: ```python import torch.aaa import torch.bbb import torch.ccc x = ... # some code ``` which produces a consistent code style. Pull Request resolved: https://github.com/pytorch/pytorch/pull/129751 Approved by: https://github.com/malfet
NumPy <> PyTorch Compat Layer
This folder contains an implementation of (most of) the NumPy public API using PyTorch tensors. Note that this folder does not depend on NumPy in any way. This is a standalone implementation.
This implementation is used by Dynamo to through NumPy code and lower it into PyTorch code.
To see design decisions that went into this implementation, please see the rfc.
Structure of the code
This folder exports a drop-in replacement for the NumPy namespace and its modules linalg, fft and random via its __init__.py.
The implementation is split into files that work with PyTorch objects (PyTorch Tensors, dtypes, etc) and files that
use these PyTorch-only files and convert them into functions/objects that can process all the types that the NumPy functions
accept. In particular, they accept torch._numpy.dtypes or torch._numpy.ndarrays.
The PyTorch-only files are the *_impl.py files, while the wrapper files are those that do not have an *_impl.py. This creates a
hierarchy, wherein, for example, _dtypes.py will import _dtypes_impl.py, but not the other way around. In particular, *_impl.py
will only depend on other *_impl.py files.
As discussed in the rfc, we use types as tags in our PyTorch implementations. We then use
a decorator called normalizer that will inspect these types and preprocess the inputs before sending them to the function. This
preprocessing is the one in charge of mapping array-like objects into Tensors, dtype-like objects into PyTorch dtypes, implement
the out= behaviour and so on.
In the files _funcs.py and _ufuncs.py we use register the normalizer decorator to all the *_impl.py functions.
In the file _ndarray.py we define the ndarray class, which is just a thin wrapper around a PyTorch tensor. We use the free functions
and a bit of metaprogramming to implement many of the methods.
Adding a new function
You just need to add a function in the relevant *_impl.py file. You will need to tag the inputs with the relevant Types. After that, you
can assume that the inputs are all PyTorch objects. Your function should return PyTorch tensors. The normalizer will make sure that you
always get PyTorch objects. If in doubt, you can see the implementation of the normalization attached to each type annotation in the file
_normalizations.py.
Debugging
It may be useful to figure out whether a given bug is caused by dynamo or the compatibility layer. You may use the compat layer in eager mode
simply by changing import numpy as np by import torch._numpy as np in your program, without having to call torch.compile at all.
Note that torch._numpy will be quite slow when used in eager mode, and it is in no way a replacement or an alternative to the regular PyTorch API.
This should only be used as a debugging tool.