Summary:
Adds two more "missing" NumPy aliases: arctanh and arcsinh, and simplifies the dispatch of other arc* aliases.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43762
Reviewed By: ngimel
Differential Revision: D23396370
Pulled By: mruberry
fbshipit-source-id: 43eb0c62536615fed221d460c1dec289526fb23c
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
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42708
Add rowwise prune pytorch op.
This operator introduces sparsity to the 'weights' matrix with the help
of the importance indicator 'mask'.
A row is considered important and not pruned if the mask value for that
particular row is 1(True) and not important otherwise.
Test Plan:
buck test caffe2/torch/fb/sparsenn:test -- rowwise_prune
buck test caffe2/test:pruning
Reviewed By: supriyar
Differential Revision: D22849432
fbshipit-source-id: 456f4f77c04158cdc3830b2e69de541c7272a46d
Summary:
Related to https://github.com/pytorch/pytorch/issues/38349
Implement NumPy-like functions `maximum` and `minimum`.
The `maximum` and `minimum` functions compute input tensors element-wise, returning a new array with the element-wise maxima/minima.
If one of the elements being compared is a NaN, then that element is returned, both `maximum` and `minimum` functions do not support complex inputs.
This PR also promotes the overloaded versions of torch.max and torch.min, by re-dispatching binary `torch.max` and `torch.min` to `torch.maximum` and `torch.minimum`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42579
Reviewed By: mrshenli
Differential Revision: D23153081
Pulled By: mruberry
fbshipit-source-id: 803506c912440326d06faa1b71964ec06775eac1
Summary:
This adds the torch.arccosh alias and updates alias testing to validate the consistency of the aliased and original operations. The alias testing is also updated to run on CPU and CUDA, which revealed a memory leak when tracing (see https://github.com/pytorch/pytorch/issues/43119).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43107
Reviewed By: ngimel
Differential Revision: D23156472
Pulled By: mruberry
fbshipit-source-id: 6155fac7954fcc49b95e7c72ed917c85e0eabfcd
Summary:
This PR:
- updates test_op_normalization.py, which verifies that aliases are correctly translated in the JIT
- adds torch.linalg.det as an alias for torch.det
- moves the torch.linalg.outer alias to torch.outer (to be consistent with NumPy)
The torch.linalg.outer alias was put the linalg namespace erroneously as a placeholder since it's a "linear algebra op" according to NumPy but is actually still in the main NumPy namespace.
The updates to test_op_normalization are necessary. Previously it was using method_tests to generate tests, and method_tests assumes test suites using it also use the device generic framework, which test_op_normalization did not. For example, some ops require decorators like `skipCPUIfNoLapack`, which only works in device generic test classes. Moving test_op_normalization to the device generic framework also lets these tests run on CPU and CUDA.
Continued reliance on method_tests() is excessive since the test suite is only interested in testing aliasing, and a simpler and more readable `AliasInfo` class is used for the required information. An example impedance mismatch between method_tests and the new tests, for example, was how to handle ops in namespaces like torch.linalg.det. In the future this information will likely be folded into a common 'OpInfo' registry in the test suite.
The actual tests performed are similar to what they were previously: a scripted and traced version of the op is run and the test verifies that both graphs do not contain the alias name and do contain the aliased name.
The guidance for adding an alias has been updated accordingly.
cc mattip
Note:
ngimel suggests:
- deprecating and then removing the `torch.ger` name
- reviewing the implementation of `torch.outer`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42802
Reviewed By: zou3519
Differential Revision: D23059883
Pulled By: mruberry
fbshipit-source-id: 11321c2a7fb283a6e7c0d8899849ad7476be42d1
Summary:
Per title. Also updates our guidance for adding aliases to clarify interned_string and method_test requirements. The alias is tested by extending test_clamp to also test clip.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42770
Reviewed By: ngimel
Differential Revision: D23020655
Pulled By: mruberry
fbshipit-source-id: f1d8e751de9ac5f21a4f95d241b193730f07b5dc
Summary:
According to pytorch/rfcs#3
From the goals in the RFC:
1. Support subclassing `torch.Tensor` in Python (done here)
2. Preserve `torch.Tensor` subclasses when calling `torch` functions on them (done here)
3. Use the PyTorch API with `torch.Tensor`-like objects that are _not_ `torch.Tensor`
subclasses (done in https://github.com/pytorch/pytorch/issues/30730)
4. Preserve `torch.Tensor` subclasses when calling `torch.Tensor` methods. (done here)
5. Propagating subclass instances correctly also with operators, using
views/slices/indexing/etc. (done here)
6. Preserve subclass attributes when using methods or views/slices/indexing. (done here)
7. A way to insert code that operates on both functions and methods uniformly
(so we can write a single function that overrides all operators). (done here)
8. The ability to give external libraries a way to also define
functions/methods that follow the `__torch_function__` protocol. (will be addressed in a separate PR)
This PR makes the following changes:
1. Adds the `self` argument to the arg parser.
2. Dispatches on `self` as well if `self` is not `nullptr`.
3. Adds a `torch._C.DisableTorchFunction` context manager to disable `__torch_function__`.
4. Adds a `torch::torch_function_enabled()` and `torch._C._torch_function_enabled()` to check the state of `__torch_function__`.
5. Dispatches all `torch._C.TensorBase` and `torch.Tensor` methods via `__torch_function__`.
TODO:
- [x] Sequence Methods
- [x] Docs
- [x] Tests
Closes https://github.com/pytorch/pytorch/issues/28361
Benchmarks in https://github.com/pytorch/pytorch/pull/37091#issuecomment-633657778
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37091
Reviewed By: ngimel
Differential Revision: D22765678
Pulled By: ezyang
fbshipit-source-id: 53f8aa17ddb8b1108c0997f6a7aa13cb5be73de0
