Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44224
The purpose of this file is to help developers on PT distributed get
upto speed on the code structure and layout for PT Distributed.
ghstack-source-id: 111644842
Test Plan: waitforbuildbot
Reviewed By: rohan-varma
Differential Revision: D23548377
fbshipit-source-id: 561d5b8e257642de172def8fdcc1311fae20690b
Summary:
This PR adds the following aliaes:
- not_equal for torch.ne
- greater for torch.gt
- greater_equal for torch.ge
- less for torch.lt
- less_equal for torch.le
This aliases are consistent with NumPy's naming for these functions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43870
Reviewed By: zou3519
Differential Revision: D23498975
Pulled By: mruberry
fbshipit-source-id: 78560df98c9f7747e804a420c1e53fd1dd225002
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43887
As part of addressing #23232, this PR adds support for `broadcast_object_list` which is an API to broadcast arbitrary picklable objects to all the other ranks. This has been a long-requested feature, so would be good for Pytorch to natively support this.
The implementation approach follows a similar approach as https://github.com/pytorch/pytorch/pull/42189. The input is a list of objects to be broadcasted and it is in place, meaning all ranks part of the group will have their input list modified to contain the broadcasted objects from the src rank.
Note that the API is designed to match the tensor-based collectives other than supporting async_op. For now, it is a blocking call. If we see demand to support async_op, we will have to make more progress on merging work/future to support this.
ghstack-source-id: 111180436
Reviewed By: mrshenli
Differential Revision: D23422577
fbshipit-source-id: fa700abb86eff7128dc29129a0823e83caf4ab0e
Summary:
This PR needs discussion as it changes the behavior of `DataLoader`. It can be closed if its not considered a good practice.
Currently, the `DataLoader` spawns a new `_BaseDataLoaderIter` object every epoch,
In the case of the multiprocess DataLoader, every epoch the worker processes are re-created and they make a copy of the original `Dataset` object.
If users want to cache data or do some tracking on their datasets, all their data will be wiped out every epoch. Notice that this doesn't happen when the number of workers is 0. giving some inconsistencies with the multiprocess and serial data loaders.
This PR keeps the `_BaseDataLoaderIter` object alive and just resets it within epochs, so the workers remain active and so their own `Dataset` objects. People seem to file issues about this often.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35795
Reviewed By: ailzhang
Differential Revision: D23426612
Pulled By: VitalyFedyunin
fbshipit-source-id: e16950036bae35548cd0cfa78faa06b6c232a2ea
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:
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 PR:
- Adds a method variant to movedim
- Fixes the movedim docs so it will actually appear in the documentation
- Fixes three view doc links which were broken
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43122
Reviewed By: ngimel
Differential Revision: D23166222
Pulled By: mruberry
fbshipit-source-id: 14971585072bbc04b5366d4cc146574839e79cdb
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42563
Moved logic for non-named unflatten from python nn module to aten/native to be reused by the nn module later. Fixed some inconsistencies with doc and code logic.
Test Plan: Imported from OSS
Reviewed By: zou3519
Differential Revision: D23030301
Pulled By: heitorschueroff
fbshipit-source-id: 7c804ed0baa5fca960a990211b8994b3efa7c415
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:
Added a new option in AutogradContext to tell autograd to not materialize output grad tensors, that is, don't expand undefined/None tensors into tensors full of zeros before passing them as input to the backward function.
This PR is the second part that closes https://github.com/pytorch/pytorch/issues/41359. The first PR is https://github.com/pytorch/pytorch/pull/41490.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41821
Reviewed By: albanD
Differential Revision: D22693163
Pulled By: heitorschueroff
fbshipit-source-id: a8d060405a17ab1280a8506a06a2bbd85cb86461
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42740
Adds a pass to hoist conv packed params to root module.
The benefit is that if there is nothing else in the conv module,
subsequent passes will delete it, which will reduce module size.
For context, freezing does not handle this because conv packed
params is a custom object.
Test Plan:
```
PYTORCH_JIT_LOG_LEVEL=">hoist_conv_packed_params.cpp" python test/test_mobile_optimizer.py TestOptimizer.test_hoist_conv_packed_params
```
Imported from OSS
Reviewed By: kimishpatel
Differential Revision: D23005961
fbshipit-source-id: 31ab1f5c42a627cb74629566483cdc91f3770a94
Summary:
This PR adds the `torch.linalg` namespace as part of our continued effort to be more compatible with NumPy. The namespace is tested by adding a single function, `torch.linalg.outer`, and testing it in a new test suite, test_linalg.py. It follows the same pattern that https://github.com/pytorch/pytorch/pull/41911, which added the `torch.fft` namespace, did.
Future PRs will likely:
- add more functions to torch.linalg
- expand the testing done in test_linalg.py, including legacy functions, like torch.ger
- deprecate existing linalg functions outside of `torch.linalg` in preference to the new namespace
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42664
Reviewed By: ngimel
Differential Revision: D22991019
Pulled By: mruberry
fbshipit-source-id: 39258d9b116a916817b3588f160b141f956e5d0b
Summary:
This PR creates a new namespace, torch.fft (torch::fft) and puts a single function, fft, in it. This function is analogous to is a simplified version of NumPy's [numpy.fft.fft](https://numpy.org/doc/1.18/reference/generated/numpy.fft.fft.html?highlight=fft#numpy.fft.fft) that accepts no optional arguments. It is intended to demonstrate how to add and document functions in the namespace, and is not intended to deprecate the existing torch.fft function.
Adding this namespace was complicated by the existence of the torch.fft function in Python. Creating a torch.fft Python module makes this name ambiguous: does it refer to a function or module? If the JIT didn't exist, a solution to this problem would have been to make torch.fft refer to a callable class that mimicked both the function and module. The JIT, however, cannot understand this pattern. As a workaround it's required to explicitly `import torch.fft` to access the torch.fft.fft function in Python:
```
import torch.fft
t = torch.randn(128, dtype=torch.cdouble)
torch.fft.fft(t)
```
See https://github.com/pytorch/pytorch/issues/42175 for future work. Another possible future PR is to get the JIT to understand torch.fft as a callable class so it need not be imported explicitly to be used.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41911
Reviewed By: glaringlee
Differential Revision: D22941894
Pulled By: mruberry
fbshipit-source-id: c8e0b44cbe90d21e998ca3832cf3a533f28dbe8d
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
