Summary:
These could use some autograd tests, which are coming in a later PR, but using them in autograd is probably pretty rare.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9947
Reviewed By: ezyang
Differential Revision: D9032778
Pulled By: gchanan
fbshipit-source-id: fa5a6509d3bac31ea4fae25143e82de62daabfbd
Summary:
This PR implements and tests N-dimensional empty tensors for indexing, factories, and reductions if compiled with -DUSE_TH_SIZE_ZERO_DIM.
Still remaining to add:
1) TensorShape functions
2) Simple linear algebra functions (matrix multiply variants)
3) Other functions that operate over a dimension (but don't reduce).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9209
Reviewed By: ezyang
Differential Revision: D8751257
Pulled By: gchanan
fbshipit-source-id: 2113374dc7af6caf31a99bf67b3893f130a29e23
Summary:
Booleaning indexing was special cased to handle a single boolean value, but didn't generally work given multiple booleans.
This PR unifies the behavior with slicing. Note that only 'True' and torch.tensor(True) behave like NumPy due to the lack of n-dimensional empty tensors.
The corresponding tests for false values have been added, but are guarded behind a flag until we add n-dimensional empty tensors.
Closes https://github.com/pytorch/pytorch/pull/8920
Reviewed By: ezyang
Differential Revision: D8661876
Pulled By: gchanan
fbshipit-source-id: 0dc8a45a303aa41f729d04ab8908cfaf2e3ce3d7
* Fix performance regression on simple cases of indexing
Dispatches to the old kernels
* Adapt JIT test
The test was expected to fail, but due to the change in the previous diff, it would now dispatch to index_select, which succeeds. I modified the function to go through the advanced indexing codepath
* Only do checks once, properly AutoNoGil, AutoGPU.
* Codemod to update our codebase to 0.4 standard
* Update some of the test scri[ts
* remove Variable in test_clip_grad_value
* fix _symbolic_override_wrapper_maker
* Add numpy.array-like type inference to torch.tensor.
* Temporary fix for int/double types.
* Treat python floats as the default (scalar) dtype.
* Also make 0-length sequences the default scalar type and add more tests.
* Add type inference to sparse_coo_tensor.
* Fix sparse test.
* Remove allow_variables.
* Check numpy platform bits.
* Address review comments.
* Make suggested changes to constraints.
* More checking windows builds.
* Fix test for windows.
This replaces the torch.Tensor constructors with factories that produce
Variables. Similarly, functions on the torch module (e.g. torch.randn)
now return Variables.
To keep the PR to a reasonable size, I've left most of the unused tensor
code. Subsequent PRs will remove the dead code, clean-up calls to
torch.autograd.Variable, and rename Variable to Tensor everywhere.
There are some breaking changes because Variable and Tensors had
slightly different semantics. There's a list of those changes here:
https://github.com/pytorch/pytorch/wiki/Breaking-Changes-from-Variable-and-Tensor-merge
This better maintains backwards compatibility when Tensors and Variables
are merged. For example:
>>> loss = var.sum().data[0]
Currently, `var.sum().data` is 1-dim so indexing. Once scalars are
enabled and Variable and Tensor are merged it will be zero-dim. This
change allows that expression to continue working (with a warning). In
the future, the canonical way to compute that expression will be:
>>> loss = float(var.sum())
Or an equivalent alternative:
>>> loss = var.sum().item()
Also fixes a few error cases.
1) Have 0-dim byte tensors behave like Py_TRUE, Py_FALSE
1) Py_TRUE now properly returns a copy from getitem
3) setitem now properly shapes the LHS consistent with the RHS (this doesn't really matter outside of error messages having the proper shape)
4) setitem supports numpy-style copy_to broadcasting (cuts off prefix 1s from src), so e.g. you can setitem (1,1,2,3) to (2,3) even though
that doesn't follow the normal inplace broadcasting rules.
Implements basic and advanced indexing using ATen tensors/variables.
Basic indexing is translated at the Python-binding level
(python_variable_indexing.cpp) to slice/squeeze/unsqueeze/select calls.
Advanced indexing is implemented in ATen in terms of take() and put()
calls.
