Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71658
This adds the beginnings of a TensorboardEventHandler which will log stats to Tensorboard.
Test Plan: buck test //caffe2/test:monitor
Reviewed By: edward-io
Differential Revision: D33719954
fbshipit-source-id: e9847c1319255ce0d9cf2d85d8b54b7a3c681bd2
(cherry picked from commit 5c8520a6ba)
Fix the wiki URL.
Also minor reorganization in onnx.rst.
[ONNX] restore documentation of public functions (#69623)
The build-docs check requires all public functions to be documented.
These should really not be public, but we'll fix that later.'
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71609
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69567
This exposes torch.monitor events and stats via pybind11 to the underlying C++ implementation.
* The registration interface is a tad different since it takes a lambda function in Python where as in C++ it's a full class.
* This has a small amount of changes to the counter interfaces since there's no way to create an initializer list at runtime so they now also take a vector.
* Only double based stats are provided in Python since it's intended more for high level stats where float imprecision shouldn't be an issue. This can be changed down the line if need arises.
```
events = []
def handler(event):
events.append(event)
handle = register_event_handler(handler)
log_event(Event(type="torch.monitor.TestEvent", timestamp=datetime.now(), metadata={"foo": 1.0}))
```
D32969391 is now included in this diff.
This cleans up the naming for events. type is now name, message is gone, and metadata is renamed data.
Test Plan: buck test //caffe2/test:monitor //caffe2/test/cpp/monitor:monitor
Reviewed By: kiukchung
Differential Revision: D32924141
fbshipit-source-id: 563304c2e3261a4754e40cca39fc64c5a04b43e8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66933
This PR exposes `torch.lu` as `torch.linalg.lu_factor` and
`torch.linalg.lu_factor_ex`.
This PR also adds support for matrices with zero elements both in
the size of the matrix and the batch. Note that this function simply
returns empty tensors of the correct size in this case.
We add a test and an OpInfo for the new function.
This PR also adds documentation for this new function in line of
the documentation in the rest of `torch.linalg`.
Fixes https://github.com/pytorch/pytorch/issues/56590
Fixes https://github.com/pytorch/pytorch/issues/64014
cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano
Test Plan: Imported from OSS
Reviewed By: gchanan
Differential Revision: D32834069
Pulled By: mruberry
fbshipit-source-id: 51ef12535fa91d292f419acf83b800b86ee9c7eb
Summary:
Fixes https://github.com/pytorch/pytorch/issues/70185
The extraheader block in docs/source/_templates/layout.html overrides the one from the pytorch theme. The theme block adds Google Analytics, so they were missing from the `master` documentation. This came up in PR pytorch/pytorch.github.io#899.
brianjo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70187
Reviewed By: bdhirsh
Differential Revision: D33248466
Pulled By: malfet
fbshipit-source-id: b314916a3f0789b6617cf9ba6bd938bf5ca27242
Summary:
Refactor torch.profiler.profile by separate it into one low level class and one high level wrapper.
The PR include the following change:
1. separate class torch.profiler.profile into two separated class: kineto_profiler and torch.profiler.profile.
2. The former class has the low-level functionality exposed in C++ level like: prepare_profiler, start_profiler, stop_profiler.
3. The original logics in torch.profiler.profile including export_chrome_trace, export_stacks, key_averages, events, add_metadata are all moved into kineto_profiler since they are all exposed by the torch.autograd.profiler.
4. The new torch.profiler.profile is fully back-compatible with original class since it inherit from torch.profiler.kineto_profiler. Its only responsibility in new implementation is the maintenance of the finite state machine of ProfilerAction.
With the refactoring, the responsibility boundary is clear and the new logic is simple to understand.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63302
Reviewed By: albanD
Differential Revision: D33006442
Pulled By: robieta
fbshipit-source-id: 30d7c9f5c101638703f1243fb2fcc6ced47fb690
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65993
This PR attempts to port `index_add` to structured kernels, but does more than that:
* Adds an `out=` variant to `index_add`
* Revises `native_functions.yaml` registrations, to not have multiple entries and instead pass default value to `alpha`.
* Changes in `derivatives.yaml` file for autograd functioning
* Revises error messages, please see: https://github.com/pytorch/pytorch/pull/65993#issuecomment-945441615
Follow-up PRs in near future will attempt to refactor the OpInfo test, and will give another look at tests in `test/test_torch.py` for this function. (hence the use of ghstack for this)
~This is WIP because there are tests failing for `Dimname` variant on mobile/android builds, and I'm working on fixing them.~
Issue tracker: https://github.com/pytorch/pytorch/issues/55070
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D32646426
fbshipit-source-id: b035ecf843a9a27d4d1e18b202b035adc2a49ab5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69789
Add details on how to save and load quantized models without hitting errors
Test Plan:
CI autogenerated docs
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D33030991
fbshipit-source-id: 8ec4610ae6d5bcbdd3c5e3bb725f2b06af960d52
Summary:
Also fixes the documentation failing to appear and adds a test to validate that op works with multiple devices properly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69640
Reviewed By: ngimel
Differential Revision: D32965391
Pulled By: mruberry
fbshipit-source-id: 4fe502809b353464da8edf62d92ca9863804f08e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69104
Add nvidia-smi memory and utilization as native Python API
Test Plan:
testing the function returns the appropriate value.
Unit tests to come.
Reviewed By: malfet
Differential Revision: D32711562
fbshipit-source-id: 01e676203299f8fde4f3ed4065f68b497e62a789
Summary:
Per title.
This PR introduces a global flag that lets pytorch prefer one of the many backend implementations while calling linear algebra functions on GPU.
Usage:
```python
torch.backends.cuda.preferred_linalg_library('cusolver')
```
Available options (str): `'default'`, `'cusolver'`, `'magma'`.
Issue https://github.com/pytorch/pytorch/issues/63992 inspired me to write this PR. No heuristic is perfect on all devices, library versions, matrix shapes, workloads, etc. We can obtain better performance if we can conveniently switch linear algebra backends at runtime.
Performance of linear algebra operators after this PR should be no worse than before. The flag is set to **`'default'`** by default, which makes everything the same as before this PR.
The implementation of this PR is basically following that of https://github.com/pytorch/pytorch/pull/67790.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67980
Reviewed By: mruberry
Differential Revision: D32849457
Pulled By: ngimel
fbshipit-source-id: 679fee7744a03af057995aef06316306073010a6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69251
This adds some actual documentation for deploy, which is probably useful
since we told everyone it was experimentally available so they will
probably be looking at what the heck it is.
It also wires up various compoenents of the OSS build to actually work
when used from an external project.
Differential Revision:
D32783312
D32783312
Test Plan: Imported from OSS
Reviewed By: wconstab
Pulled By: suo
fbshipit-source-id: c5c0a1e3f80fa273b5a70c13ba81733cb8d2c8f8
Summary:
These APIs are not yet officially released and are still under discussion. Hence, this commit removes those APIs from docs and will add them back when ready.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69011
Reviewed By: fduwjj
Differential Revision: D32703124
Pulled By: mrshenli
fbshipit-source-id: ea049fc7ab6b0015d38cc40c5b5daf47803b7ea0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66933
This PR exposes `torch.lu` as `torch.linalg.lu_factor` and
`torch.linalg.lu_factor_ex`.
This PR also adds support for matrices with zero elements both in
the size of the matrix and the batch. Note that this function simply
returns empty tensors of the correct size in this case.
We add a test and an OpInfo for the new function.
This PR also adds documentation for this new function in line of
the documentation in the rest of `torch.linalg`.
Fixes https://github.com/pytorch/pytorch/issues/56590
Fixes https://github.com/pytorch/pytorch/issues/64014
cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D32521980
Pulled By: mruberry
fbshipit-source-id: 26a49ebd87f8a41472f8cd4e9de4ddfb7f5581fb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63568
This PR adds the first solver with structure to `linalg`. This solver
has an API compatible with that of `linalg.solve` preparing these for a
possible future merge of the APIs. The new API:
- Just returns the solution, rather than the solution and a copy of `A`
- Removes the confusing `transpose` argument and replaces it by a
correct handling of conj and strides within the call
- Adds a `left=True` kwarg. This can be achieved via transposes of the
inputs and the result, but it's exposed for convenience.
This PR also implements a dataflow that minimises the number of copies
needed before calling LAPACK / MAGMA / cuBLAS and takes advantage of the
conjugate and neg bits.
This algorithm is implemented for `solve_triangular` (which, for this, is
the most complex of all the solvers due to the `upper` parameters).
Once more solvers are added, we will factor out this calling algorithm,
so that all of them can take advantage of it.
Given the complexity of this algorithm, we implement some thorough
testing. We also added tests for all the backends, which was not done
before.
We also add forward AD support for `linalg.solve_triangular` and improve the
docs of `linalg.solve_triangular`. We also fix a few issues with those of
`torch.triangular_solve`.
Resolves https://github.com/pytorch/pytorch/issues/54258
Resolves https://github.com/pytorch/pytorch/issues/56327
Resolves https://github.com/pytorch/pytorch/issues/45734
cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano
Test Plan: Imported from OSS
Reviewed By: jbschlosser
Differential Revision: D32588230
Pulled By: mruberry
fbshipit-source-id: 69e484849deb9ad7bb992cc97905df29c8915910
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63568
This PR adds the first solver with structure to `linalg`. This solver
has an API compatible with that of `linalg.solve` preparing these for a
possible future merge of the APIs. The new API:
- Just returns the solution, rather than the solution and a copy of `A`
- Removes the confusing `transpose` argument and replaces it by a
correct handling of conj and strides within the call
- Adds a `left=True` kwarg. This can be achieved via transposes of the
inputs and the result, but it's exposed for convenience.
This PR also implements a dataflow that minimises the number of copies
needed before calling LAPACK / MAGMA / cuBLAS and takes advantage of the
conjugate and neg bits.
This algorithm is implemented for `solve_triangular` (which, for this, is
the most complex of all the solvers due to the `upper` parameters).
Once more solvers are added, we will factor out this calling algorithm,
so that all of them can take advantage of it.
Given the complexity of this algorithm, we implement some thorough
testing. We also added tests for all the backends, which was not done
before.
We also add forward AD support for `linalg.solve_triangular` and improve the
docs of `linalg.solve_triangular`. We also fix a few issues with those of
`torch.triangular_solve`.
Resolves https://github.com/pytorch/pytorch/issues/54258
Resolves https://github.com/pytorch/pytorch/issues/56327
Resolves https://github.com/pytorch/pytorch/issues/45734
cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano
Test Plan: Imported from OSS
Reviewed By: zou3519, JacobSzwejbka
Differential Revision: D32283178
Pulled By: mruberry
fbshipit-source-id: deb672e6e52f58b76536ab4158073927a35e43a8
