Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54987
Based off of ezyang (https://github.com/pytorch/pytorch/pull/44799) and bdhirsh (https://github.com/pytorch/pytorch/pull/43702) 's prototype:
Here's a summary of the changes in this PR:
This PR adds a new dispatch key called Conjugate. This enables us to make conjugate operation a view and leverage the specialized library functions that fast path with the hermitian operation (conj + transpose).
1. Conjugate operation will now return a view with conj bit (1) for complex tensors and returns self for non-complex tensors as before. This also means `torch.view_as_real` will no longer be a view on conjugated complex tensors and is hence disabled. To fill the gap, we have added `torch.view_as_real_physical` which would return the real tensor agnostic of the conjugate bit on the input complex tensor. The information about conjugation on the old tensor can be obtained by calling `.is_conj()` on the new tensor.
2. NEW API:
a) `.conj()` -- now returning a view.
b) `.conj_physical()` -- does the physical conjugate operation. If the conj bit for input was set, you'd get `self.clone()`, else you'll get a new tensor with conjugated value in its memory.
c) `.conj_physical_()`, and `out=` variant
d) `.resolve_conj()` -- materializes the conjugation. returns self if the conj bit is unset, else returns a new tensor with conjugated values and conj bit set to 0.
e) `.resolve_conj_()` in-place version of (d)
f) `view_as_real_physical` -- as described in (1), it's functionally same as `view_as_real`, just that it doesn't error out on conjugated tensors.
g) `view_as_real` -- existing function, but now errors out on conjugated tensors.
3. Conjugate Fallback
a) Vast majority of PyTorch functions would currently use this fallback when they are called on a conjugated tensor.
b) This fallback is well equipped to handle the following cases:
- functional operation e.g., `torch.sin(input)`
- Mutable inputs and in-place operations e.g., `tensor.add_(2)`
- out-of-place operation e.g., `torch.sin(input, out=out)`
- Tensorlist input args
- NOTE: Meta tensors don't work with conjugate fallback.
4. Autograd
a) `resolve_conj()` is an identity function w.r.t. autograd
b) Everything else works as expected.
5. Testing:
a) All method_tests run with conjugate view tensors.
b) OpInfo tests that run with conjugate views
- test_variant_consistency_eager/jit
- gradcheck, gradgradcheck
- test_conj_views (that only run for `torch.cfloat` dtype)
NOTE: functions like `empty_like`, `zero_like`, `randn_like`, `clone` don't propagate the conjugate bit.
Follow up work:
1. conjugate view RFC
2. Add neg bit to re-enable view operation on conjugated tensors
3. Update linalg functions to call into specialized functions that fast path with the hermitian operation.
Test Plan: Imported from OSS
Reviewed By: VitalyFedyunin
Differential Revision: D28227315
Pulled By: anjali411
fbshipit-source-id: acab9402b9d6a970c6d512809b627a290c8def5f
Summary:
Adds `is_inference` as a native function w/ manual cpp bindings.
Also changes instances of `is_inference_tensor` to `is_inference` to be consistent with other properties such as `is_complex`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58729
Reviewed By: mruberry
Differential Revision: D28874507
Pulled By: soulitzer
fbshipit-source-id: 0fa6bcdc72a4ae444705e2e0f3c416c1b28dadc7
Summary:
The `UninitializedBuffer` class was previously left out of `nn.rst`, so it was not included in the generated documentation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59021
Reviewed By: anjali411
Differential Revision: D28723044
Pulled By: jbschlosser
fbshipit-source-id: 71e15b0c7fabaf57e8fbdf7fbd09ef2adbdb36ad
Summary:
Adds a note explaining the difference between several often conflated mechanisms in the autograd note
Also adds a link to this note from the docs in `grad_mode` and `nn.module`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58513
Reviewed By: gchanan
Differential Revision: D28651129
Pulled By: soulitzer
fbshipit-source-id: af9eb1749b641fc1b632815634eea36bf7979156
Summary:
This PR introduces a helper function named `torch.nn.utils.skip_init()` that accepts a module class object + `args` / `kwargs` and instantiates the module while skipping initialization of parameter / buffer values. See discussion at https://github.com/pytorch/pytorch/issues/29523 for more context. Example usage:
```python
import torch
m = torch.nn.utils.skip_init(torch.nn.Linear, 5, 1)
print(m.weight)
m2 = torch.nn.utils.skip_init(torch.nn.Linear, 5, 1, device='cuda')
print(m2.weight)
m3 = torch.nn.utils.skip_init(torch.nn.Linear, in_features=5, out_features=1)
print(m3.weight)
```
```
Parameter containing:
tensor([[-3.3011e+28, 4.5915e-41, -3.3009e+28, 4.5915e-41, 0.0000e+00]],
requires_grad=True)
Parameter containing:
tensor([[-2.5339e+27, 4.5915e-41, -2.5367e+27, 4.5915e-41, 0.0000e+00]],
device='cuda:0', requires_grad=True)
Parameter containing:
tensor([[1.4013e-45, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00]],
requires_grad=True)
```
Bikeshedding on the name / namespace is welcome, as well as comments on the design itself - just wanted to get something out there for discussion.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57555
Reviewed By: zou3519
Differential Revision: D28640613
Pulled By: jbschlosser
fbshipit-source-id: 5654f2e5af5530425ab7a9e357b6ba0d807e967f
Summary:
Will not land before the release, but would be good to have this function documented in master for its use in distributed debugability.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58322
Reviewed By: SciPioneer
Differential Revision: D28595405
Pulled By: rohan-varma
fbshipit-source-id: fb00fa22fbe97a38c396eae98a904d1c4fb636fa
Summary:
Temporary fix for https://github.com/pytorch/pytorch/issues/42218.
Numerically, grid_sampler should be fine in fp32 or fp16. So grid_sampler really belongs on the promote list. But performancewise, native grid_sampler backward kernels use gpuAtomicAdd, which is notoriously slow in fp16. So the simplest functionality fix is to put grid_sampler on the fp32 list.
In https://github.com/pytorch/pytorch/pull/58618 I implement the right long-term fix (refactoring kernels to use fp16-friendly fastAtomicAdd and moving grid_sampler to the promote list). But that's more invasive, and for 1.9 ngimel says this simple temporary fix is preferred.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58679
Reviewed By: soulitzer
Differential Revision: D28576559
Pulled By: ngimel
fbshipit-source-id: d653003f37eaedcbb3eaac8d7fec26c343acbc07
Summary:
* Fix lots of links.
* Minor improvements for consistency, clarity or grammar.
* Update jit_python_reference to note the limitations on __exit__.
(Related to https://github.com/pytorch/pytorch/issues/41420).
* Fix a comment in exit_transforms.cpp: removed the word "not" which
made the comment say the opposite of the truth.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57991
Reviewed By: malfet
Differential Revision: D28522247
Pulled By: SplitInfinity
fbshipit-source-id: fc63a59d19ea6c89f957c9f7d451be17d1c5fc91
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58056
This PR addresses an action item in #3428: disabling search engine
indexing of master documentation. This is desireable because we want to
direct users to our stable documentation (instead of master
documentation) because they are more likely to have a stable version of
PyTorch installed.
Test Plan:
1. run `make html`, check that the noindex tags are there
2. run `make html-stable, check that the noindex tags aren't there
Reviewed By: bdhirsh
Differential Revision: D28490504
Pulled By: zou3519
fbshipit-source-id: 695c944c4962b2bd484dd7a5e298914a37abe787
Summary:
Added a simple section indicating distributed profiling is expected to work similar to other torch operators, and is supported for all communication backends out of the box.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58286
Reviewed By: bdhirsh
Differential Revision: D28436489
Pulled By: rohan-varma
fbshipit-source-id: ce1905a987c0ede8011e8086a2c30edc777b4a38
Summary:
Adds a new file under `torch/nn/utils/parametrizations.py` which should contain all the parametrization implementations
For spectral_norm we add the `SpectralNorm` module which can be registered using `torch.nn.utils.parametrize.register_parametrization` or using a wrapper: `spectral_norm`, the same API the old implementation provided.
Most of the logic is borrowed from the old implementation:
- Just like the old implementation, there should be cases when retrieving the weight should perform another power iteration (thus updating the weight) and cases where it shouldn't. For example in eval mode `self.training=True`, we do not perform power iteration.
There are also some differences/difficulties with the new implementation:
- Using new parametrization functionality as-is there doesn't seem to be a good way to tell whether a 'forward' call was the result of parametrizations are unregistered (and leave_parametrizations=True) or when the injected property's getter was invoked. The issue is that we want perform power iteration in the latter case but not the former, but we don't have this control as-is. So, in this PR I modified the parametrization functionality to change the module to eval mode before triggering their forward call
- Updates the vectors based on weight on initialization to fix https://github.com/pytorch/pytorch/issues/51800 (this avoids silently update weights in eval mode). This also means that we perform twice any many power iterations by the first forward.
- right_inverse is just the identity for now, but maybe it should assert that the passed value already satisfies the constraints
- So far, all the old spectral_norm tests have been cloned, but maybe we don't need so much testing now that the core functionality is already well tested
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57784
Reviewed By: ejguan
Differential Revision: D28413201
Pulled By: soulitzer
fbshipit-source-id: e8f1140f7924ca43ae4244c98b152c3c554668f2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58039
The new function has the following signature
`inv_ex(Tensor inpit, *, bool check_errors=False) -> (Tensor inverse, Tensor info)`.
When `check_errors=True`, an error is thrown if the matrix is not invertible; `check_errors=False` - responsibility for checking the result is on the user.
`linalg_inv` is implemented using calls to `linalg_inv_ex` now.
Resolves https://github.com/pytorch/pytorch/issues/25095
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D28405148
Pulled By: mruberry
fbshipit-source-id: b8563a6c59048cb81e206932eb2f6cf489fd8531
Summary:
Fixes https://github.com/pytorch/pytorch/issues/56608
- Adds binding to the `c10::InferenceMode` RAII class in `torch._C._autograd.InferenceMode` through pybind. Also binds the `torch.is_inference_mode` function.
- Adds context manager `torch.inference_mode` to manage an instance of `c10::InferenceMode` (global). Implemented in `torch.autograd.grad_mode.py` to reuse the `_DecoratorContextManager` class.
- Adds some tests based on those linked in the issue + several more for just the context manager
Issues/todos (not necessarily for this PR):
- Improve short inference mode description
- Small example
- Improved testing since there is no direct way of checking TLS/dispatch keys
-
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58045
Reviewed By: agolynski
Differential Revision: D28390595
Pulled By: soulitzer
fbshipit-source-id: ae98fa036c6a2cf7f56e0fd4c352ff804904752c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58160
This PR updates the Torch Distributed Elastic documentation with references to the new `c10d` backend.
ghstack-source-id: 128783809
Test Plan: Visually verified the correct
Reviewed By: tierex
Differential Revision: D28384996
fbshipit-source-id: a40b0c37989ce67963322565368403e2be5d2592
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57341
Require that users be explicit about what they are going to be
interning. There are a lot of changes that are enabled by this. The new
overall scheme is:
PackageExporter maintains a dependency graph. Users can add to it,
either explicitly (by issuing a `save_*` call) or explicitly (through
dependency resolution). Users can also specify what action to take when
PackageExporter encounters a module (deny, intern, mock, extern).
Nothing (except pickles, tho that can be changed with a small amount
of work) is written to the zip archive until we are finalizing the
package. At that point, we consult the dependency graph and write out
the package exactly as it tells us to.
This accomplishes two things:
1. We can gather up *all* packaging errors instead of showing them one at a time.
2. We require that users be explicit about what's going in packages, which is a common request.
Differential Revision: D28114185
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Pulled By: suo
fbshipit-source-id: fa1abf1c26be42b14c7e7cf3403ecf336ad4fc12
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58170
Now comm hook can be supported on MPI and GLOO backends besides NCCL. No longer need these warnings and check.
ghstack-source-id: 128799123
Test Plan: N/A
Reviewed By: agolynski
Differential Revision: D28388861
fbshipit-source-id: f56a7b9f42bfae1e904f58cdeccf7ceefcbb0850
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58023
Clearly state that some features of RPC aren't yet compatible with CUDA.
ghstack-source-id: 128688856
Test Plan: None
Reviewed By: agolynski
Differential Revision: D28347605
fbshipit-source-id: e8df9a4696c61a1a05f7d2147be84d41aeeb3b48
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50048
To reflect the many changes introduced recently.
In my mind, CUDAFuture should be considered a "private" subclass, which in practice should always be returned as a downcast pointer to an ivalue::Future. Hence, we should document the CUDA behavior in the superclass, even if it's CUDA-agnostic, since that's the interface the users will see also for CUDA-enabled futures.
ghstack-source-id: 128640983
Test Plan: Built locally and looked at them.
Reviewed By: mrshenli
Differential Revision: D25757474
fbshipit-source-id: c6f66ba88fa6c4fc33601f31136422d6cf147203
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57965
The bold effect does not work under quotes, so move it out.
ghstack-source-id: 128570357
Test Plan:
locally view
{F614715259}
Reviewed By: rohan-varma
Differential Revision: D28329694
fbshipit-source-id: 299b427f4c0701ba70c84148f65203a6e2d6ac61
Summary:
This PR is focused on the API for `linalg.matrix_norm` and delegates computations to `linalg.norm` for the moment.
The main difference between the norms is when `dim=None`. In this case
- `linalg.norm` will compute a vector norm on the flattened input if `ord=None`, otherwise it requires the input to be either 1D or 2D in order to disambiguate between vector and matrix norm
- `linalg.vector_norm` will flatten the input
- `linalg.matrix_norm` will compute the norm over the last two dimensions, treating the input as batch of matrices
In future PRs, the computations will be moved to `torch.linalg.matrix_norm` and `torch.norm` and `torch.linalg.norm` will delegate computations to either `linalg.vector_norm` or `linalg.matrix_norm` based on the arguments provided.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57127
Reviewed By: mrshenli
Differential Revision: D28186736
Pulled By: mruberry
fbshipit-source-id: 99ce2da9d1c4df3d9dd82c0a312c9570da5caf25
Summary:
Redo of https://github.com/pytorch/pytorch/issues/56373 out of stack.
---
To reviewers: **please be nitpicky**. I've read this so often that I probably missed some typos and inconsistencies.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57247
Reviewed By: albanD
Differential Revision: D28247402
Pulled By: mruberry
fbshipit-source-id: 71142678ee5c82cc8c0ecc1dad6a0b2b9236d3e6
Summary:
Pull Request resolved: https://github.com/pytorch/elastic/pull/148
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56811
Moves docs sphinx `*.rst` files from the torchelastic repository to torch. Note: only moves the rst files the next step is to link it to the main pytorch `index.rst` and write new `examples.rst`
Reviewed By: H-Huang
Differential Revision: D27974751
fbshipit-source-id: 8ff9f242aa32e0326c37da3916ea0633aa068fc5
Summary:
Fixes https://github.com/pytorch/pytorch/issues/54555
It has been discussed in the issue https://github.com/pytorch/pytorch/issues/54555 that {h,v,d}split methods unexpectedly matches argument of single int[] when it is expected to match single argument of int. The same unexpected behavior can happen in other functions/methods which can take both int[] and int? as single argument signatures.
In this PR we solve this problem by giving higher priority to int/int? arguments over int[] while sorting signatures.
We also add methods of {h,v,d}split methods here, which helped us to discover this unexpected behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57346
Reviewed By: ezyang
Differential Revision: D28121234
Pulled By: iramazanli
fbshipit-source-id: 851cf40b370707be89298177b51ceb4527f4b2d6
Summary:
The new function has the following signature `cholesky_ex(Tensor input, *, bool check_errors=False) -> (Tensor L, Tensor infos)`. When `check_errors=True`, an error is thrown if the decomposition fails; `check_errors=False` - responsibility for checking the decomposition is on the user.
When `check_errors=False`, we don't have host-device memory transfers for checking the values of the `info` tensor.
Rewrote the internal code for `torch.linalg.cholesky`. Added `cholesky_stub` dispatch. `linalg_cholesky` is implemented using calls to `linalg_cholesky_ex` now.
Resolves https://github.com/pytorch/pytorch/issues/57032.
Ref. https://github.com/pytorch/pytorch/issues/34272, https://github.com/pytorch/pytorch/issues/47608, https://github.com/pytorch/pytorch/issues/47953
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56724
Reviewed By: ngimel
Differential Revision: D27960176
Pulled By: mruberry
fbshipit-source-id: f05f3d5d9b4aa444e41c4eec48ad9a9b6fd5dfa5
