Summary:
Bumps [addressable](https://github.com/sporkmonger/addressable) from 2.7.0 to 2.8.0.
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/sporkmonger/addressable/blob/main/CHANGELOG.md">addressable's changelog</a>.</em></p>
<blockquote>
<h1>Addressable 2.8.0</h1>
<ul>
<li>fixes ReDoS vulnerability in Addressable::Template#match</li>
<li>no longer replaces <code>+</code> with spaces in queries for non-http(s) schemes</li>
<li>fixed encoding ipv6 literals</li>
<li>the <code>:compacted</code> flag for <code>normalized_query</code> now dedupes parameters</li>
<li>fix broken <code>escape_component</code> alias</li>
<li>dropping support for Ruby 2.0 and 2.1</li>
<li>adding Ruby 3.0 compatibility for development tasks</li>
<li>drop support for <code>rack-mount</code> and remove Addressable::Template#generate</li>
<li>performance improvements</li>
<li>switch CI/CD to GitHub Actions</li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="6469a232c0"><code>6469a23</code></a> Updating gemspec again</li>
<li><a href="24336385de"><code>2433638</code></a> Merge branch 'main' of github.com:sporkmonger/addressable into main</li>
<li><a href="e9c76b8897"><code>e9c76b8</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/sporkmonger/addressable/issues/378">https://github.com/pytorch/pytorch/issues/378</a> from ashmaroli/flat-map</li>
<li><a href="56c5cf7ece"><code>56c5cf7</code></a> Update the gemspec</li>
<li><a href="c1fed1ca0a"><code>c1fed1c</code></a> Require a non-vulnerable rake</li>
<li><a href="0d8a3127e3"><code>0d8a312</code></a> Adding note about ReDoS vulnerability</li>
<li><a href="89c76130ce"><code>89c7613</code></a> Merge branch 'template-regexp' into main</li>
<li><a href="cf8884f815"><code>cf8884f</code></a> Note about alias fix</li>
<li><a href="bb03f7112e"><code>bb03f71</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/sporkmonger/addressable/issues/371">https://github.com/pytorch/pytorch/issues/371</a> from charleystran/add_missing_encode_component_doc_entry</li>
<li><a href="6d1d8094a6"><code>6d1d809</code></a> Adding note about :compacted normalization</li>
<li>Additional commits viewable in <a href="https://github.com/sporkmonger/addressable/compare/addressable-2.7.0...addressable-2.8.0">compare view</a></li>
</ul>
</details>
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Pull Request resolved: https://github.com/pytorch/pytorch/pull/61573
Reviewed By: xta0
Differential Revision: D29685329
Pulled By: seemethere
fbshipit-source-id: a43008155144a358950dc3ed1934fcc470b73c02
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59841
As lite interpreter moves to beta, it's recommended to let users start using it.
ghstack-source-id: 131766778
Test Plan: CI
Reviewed By: husthyc
Differential Revision: D29048350
fbshipit-source-id: 54d2ad09b4e9475304522c80b358647bcea79b14
Summary:
Two changes:
1. Build lite interpreter as default for iOS
2. Switch the previous lite interpreter test to full jit build test
Test Plan: Imported from OSS
Differential Revision: D27698039
Reviewed By: xta0
Pulled By: cccclai
fbshipit-source-id: 022b554f4997ae577681f2b79a9ebe9236ca4f7d
Summary:
*Context:* https://github.com/pytorch/pytorch/issues/53406 added a lint for trailing whitespace at the ends of lines. However, in order to pass FB-internal lints, that PR also had to normalize the trailing newlines in four of the files it touched. This PR adds an OSS lint to normalize trailing newlines.
The changes to the following files (made in 54847d0adb9be71be4979cead3d9d4c02160e4cd) are the only manually-written parts of this PR:
- `.github/workflows/lint.yml`
- `mypy-strict.ini`
- `tools/README.md`
- `tools/test/test_trailing_newlines.py`
- `tools/trailing_newlines.py`
I would have liked to make this just a shell one-liner like the other three similar lints, but nothing I could find quite fit the bill. Specifically, all the answers I tried from the following Stack Overflow questions were far too slow (at least a minute and a half to run on this entire repository):
- [How to detect file ends in newline?](https://stackoverflow.com/q/38746)
- [How do I find files that do not end with a newline/linefeed?](https://stackoverflow.com/q/4631068)
- [How to list all files in the Git index without newline at end of file](https://stackoverflow.com/q/27624800)
- [Linux - check if there is an empty line at the end of a file [duplicate]](https://stackoverflow.com/q/34943632)
- [git ensure newline at end of each file](https://stackoverflow.com/q/57770972)
To avoid giving false positives during the few days after this PR is merged, we should probably only merge it after https://github.com/pytorch/pytorch/issues/54967.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54737
Test Plan:
Running the shell script from the "Ensure correct trailing newlines" step in the `quick-checks` job of `.github/workflows/lint.yml` should print no output and exit in a fraction of a second with a status of 0. That was not the case prior to this PR, as shown by this failing GHA workflow run on an earlier draft of this PR:
- https://github.com/pytorch/pytorch/runs/2197446987?check_suite_focus=true
In contrast, this run (after correcting the trailing newlines in this PR) succeeded:
- https://github.com/pytorch/pytorch/pull/54737/checks?check_run_id=2197553241
To unit-test `tools/trailing_newlines.py` itself (this is run as part of our "Test tools" GitHub Actions workflow):
```
python tools/test/test_trailing_newlines.py
```
Reviewed By: malfet
Differential Revision: D27409736
Pulled By: samestep
fbshipit-source-id: 46f565227046b39f68349bbd5633105b2d2e9b19
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857
These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
- `GLOSSARY.md`
- `aten/src/ATen/core/op_registration/README.md`
- `scripts/README.md`
- `torch/csrc/jit/codegen/fuser/README.md`
The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```
I looked over the auto-generated changes and didn't see anything that looked problematic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406
Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377
This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348
Reviewed By: walterddr, seemethere
Differential Revision: D26856620
Pulled By: samestep
fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49176
Bump up the cocoapods version
ghstack-source-id: 118305636
Test Plan: CI
Reviewed By: xta0
Differential Revision: D25466321
fbshipit-source-id: 916adc514c5edc8971445da893362a160cfc092b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46935
Bump up the cocoapods version
ghstack-source-id: 115283786
(Note: this ignores all push blocking failures!)
Test Plan: CI
Reviewed By: xta0
Differential Revision: D24572715
fbshipit-source-id: 41ffcd43512dc7d4e94af887fb5dfeab703d7602
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45862
Bump up the cocoapods version
ghstack-source-id: 113585513
(Note: this ignores all push blocking failures!)
Test Plan: CI
Reviewed By: xta0
Differential Revision: D24119158
fbshipit-source-id: e689b69628dcf802084e67c5ea627220cafcc575
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41895
### Summary
The iOS binary for 1.6.0 has been uploaded to AWS. This PR bumps up the version for cocoapods.
### Test Plan
- Check CI
Test Plan: Imported from OSS
Reviewed By: husthyc
Differential Revision: D22683787
Pulled By: xta0
fbshipit-source-id: bb95b670a7945d823d55e9c65b357765753f295a
Summary:
This re-applies D21232894 (b9d3869df3) and D22162524, plus updates jni_deps in a few places
to avoid breaking host JNI tests.
Test Plan: `buck test @//fbandroid/mode/server //fbandroid/instrumentation_tests/com/facebook/caffe2:host-test`
Reviewed By: xcheng16
Differential Revision: D22199952
fbshipit-source-id: df13eef39c01738637ae8cf7f581d6ccc88d37d5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37243
*** Why ***
As it stands, we have two thread pool solutions concurrently in use in PyTorch mobile: (1) the open source pthreadpool library under third_party, and (2) Caffe2's implementation of pthreadpool under caffe2/utils/threadpool. Since the primary use-case of the latter has been to act as a drop-in replacement for the third party version so as to enable integration and usage from within NNPACK and QNNPACK, Caffe2's implementation is intentionally written to the exact same interface as the third party version.
The original argument in favor of C2's implementation has been improved performance as a result of using spin locks, as opposed to relinquishing the thread's time slot and putting it to sleep - a less expensive operation up to a point. That seems to have given C2's implementation the upper hand in performance, hence justifying the added maintenance complexity, until the third party version improved in parallel surpassing the efficiency of C2's implementation as I have verified in benchmarks. With that advantage gone, there is no reason to continue using C2's implementation in PyTorch mobile either from the perspective of performance or code hygiene. As a matter of fact, there is considerable performance benefit to be had as a result of using the third party version as it currently stands.
This is a tricky change though, mainly because in order to avoid potential performance regressions, of which I have witnessed none but just in abundance of caution, we have decided to continue using the internal C2's implementation whenever building for Caffe2. Again, this is mainly to avoid potential performance regressions in production C2 use cases even if doing so results in reduced performance as far as I can tell.
So to summarize, today, and as it currently stands, we are using C2's implementation for (1) NNPACK, (2) PyTorch QNNPACK, and (3) ATen parallel_for on mobile builds, while using the third party version of pthreadpool for XNNPACK as XNNPACK does not provide any build options to link against an external implementation unlike NNPACK and QNNPACK do.
The goal of this PR then, is to unify all usage on mobile to the third party implementation both for improved performance and better code hygiene. This applies to PyTorch's use of NNPACK, QNNPACK, XNNPACK, and mobile's implementation of ATen parallel_for, all getting routed to the
exact same third party implementation in this PR.
Considering that NNPACK, QNNPACK, and XNNPACK are not mobile specific, these benefits carry over to non-mobile builds of PyTorch (but not Caffe2) as well. The implementation of ATen parallel_for on non-mobile builds remains unchanged.
*** How ***
This is where things get tricky.
A good deal of the build system complexity in this PR arises from our desire to maintain C2's implementation intact for C2's use.
pthreadpool is a C library with no concept of namespaces, which means two copies of the library cannot exist in the same binary or symbol collision will occur violating ODR. This means that somehow, and based on some condition, we must decide on the choice of a pthreadpool implementation. In practice, this has become more complicated as a result of all the possible combinations that USE_NNPACK, USE_QNNPACK, USE_PYTORCH_QNNPACK, USE_XNNPACK, USE_SYSTEM_XNNPACK, USE_SYSTEM_PTHREADPOOL and other variables can result in. Having said that, I have done my best in this PR to surgically cut through this complexity in a way that minimizes the side effects, considering the significance of the performance we are leaving on the table, yet, as a result of this combinatorial explosion explained above I cannot guarantee that every single combination will work as expected on the first try. I am heavily relying on CI to find any issues as local testing can only go that far.
Having said that, this PR provides a simple non mobile-specific C++ thread pool implementation on top of pthreadpool, namely caffe2::PThreadPool that automatically routes to C2's implementation or the third party version depending on the build configuration. This simplifies the logic at the cost of pushing the complexity to the build scripts. From there on, this thread pool is used in aten parallel_for, and NNPACK and family, again, routing all usage of threading to C2 or third party pthreadpool depending on the build configuration.
When it is all said or done, the layering will look like this:
a) aten::parallel_for, uses
b) caffe2::PThreadPool, which uses
c) pthreadpool C API, which delegates to
c-1) third_party implementation of pthreadpool if that's what the build has requested, and the rabbit hole ends here.
c-2) C2's implementation of pthreadpool if that's what the build has requested, which itself delegates to
c-2-1) caffe2::ThreadPool, and the rabbit hole ends here.
NNPACK, and (PyTorch) QNNPACK directly hook into (c). They never go through (b).
Differential Revision: D21232894
Test Plan: Imported from OSS
Reviewed By: dreiss
Pulled By: AshkanAliabadi
fbshipit-source-id: 8b3de86247fbc3a327e811983e082f9d40081354
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39868
### Summary
why disable NNPACK on iOS
- To stay consistency with our internal version
- It's currently blocking some external users due to its lack support of x86 architecture
- https://github.com/pytorch/pytorch/issues/32040
- https://discuss.pytorch.org/t/undefined-symbols-for-architecture-x86-64-for-libtorch-in-swift-unit-test/84552/6
- NNPACK uses fast convolution algorithms (FFT, winograd) to reduce the computational complexity of convolutions with large kernel size. The algorithmic speedup is limited to specific conv params which are unlikely to appear in mobile networks.
- Since XNNPACK has been enabled, it performs much better than NNPACK on depthwise-separable convolutions which is the algorithm being used by most of mobile computer vision networks.
### Test Plan
- CI Checks
Test Plan: Imported from OSS
Differential Revision: D22087365
Pulled By: xta0
fbshipit-source-id: 89a959b0736c1f8703eff10723a8fbd02357fd4a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39743
### Summary
Still need this RAII guard for full JIT
### Test Plan
- CI checks
Test Plan: Imported from OSS
Differential Revision: D21968256
Pulled By: xta0
fbshipit-source-id: 8ea63c699fed4e2a01390232a58f039110391844
Summary:
Since we've done the branch cut for 1.5.0 we should bump nightlies to 1.6.0
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35495
Differential Revision: D20697043
Pulled By: seemethere
fbshipit-source-id: 3646187a5e729994138bf2c68625f25f11430b3a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34515
Once upon a time we thought this was necessary. In reality it is not, so
removing it.
For backcompat, our public interface (defined in `api/`) still has
typedefs to the old `script::` names.
There was only one collision: `Pass` as a `Stmt` and `Pass` as a graph
transform. I renamed one of them.
Test Plan: Imported from OSS
Differential Revision: D20353503
Pulled By: suo
fbshipit-source-id: 48bb911ce75120a8c9e0c6fb65262ef775dfba93
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34556
According to
https://github.com/pytorch/pytorch/pull/34012#discussion_r388581548,
this `at::globalContext().setQEngine(at::QEngine::QNNPACK);` call isn't
really necessary for mobile.
In Context.cpp it selects the last available QEngine if the engine isn't
set explicitly. For OSS mobile prebuild it should only include QNNPACK
engine so the default behavior should already be desired behavior.
It makes difference only when USE_FBGEMM is set - but it should be off
for both OSS mobile build and internal mobile build.
Test Plan: Imported from OSS
Differential Revision: D20374522
Pulled By: ljk53
fbshipit-source-id: d4e437a03c6d4f939edccb5c84f02609633a0698
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34203
Currently cmake and mobile build scripts still build libcaffe2 by
default. To build pytorch mobile users have to set environment variable
BUILD_PYTORCH_MOBILE=1 or set cmake option BUILD_CAFFE2_MOBILE=OFF.
PyTorch mobile has been released for a while. It's about time to change
CMake and build scripts to build libtorch by default.
Changed caffe2 CI job to build libcaffe2 by setting BUILD_CAFFE2_MOBILE=1
environment variable. Only found android CI for libcaffe2 - do we ever
have iOS CI for libcaffe2?
Test Plan: Imported from OSS
Differential Revision: D20267274
Pulled By: ljk53
fbshipit-source-id: 9d997032a599c874d62fbcfc4f5d4fbf8323a12e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33722
In order to improve CPU performance on floating-point models on mobile, this PR introduces a new CPU backend for mobile that implements the most common mobile operators with NHWC memory layout support through integration with XNNPACK.
XNNPACK itself, and this codepath, are currently only included in the build, but the actual integration is gated with USE_XNNPACK preprocessor guards. This preprocessor symbol is intentionally not passed on to the compiler, so as to enable this rollout in multiple stages in follow up PRs. This changeset will build XNNPACK as part of the build if the identically named USE_XNNPACK CMAKE variable, defaulted to ON, is enabled, but will not actually expose or enable this code path in any other way.
Furthermore, it is worth pointing out that in order to efficiently map models to these operators, some front-end method of exposing this backend to the user is needed. The less efficient implementation would be to hook these operators into their corresponding native implementations, granted that a series of XNNPACK-specific conditions are met, much like how NNPACK is integrated with PyTorch today for instance.
Having said that, while the above implementation is still expected to outperform NNPACK based on the benchmarks I ran, the above integration would be leave a considerable gap between the performance achieved and the maximum performance potential XNNPACK enables, as it does not provide a way to compute and factor out one-time operations out of the inner most forward() loop.
The more optimal solution, and one we will decide on soon, would involve either providing a JIT pass that maps nn operators onto these newly introduced operators, while allowing one-time calculations to be factored out, much like quantized mobile models. Alternatively, new eager-mode modules can also be introduced that would directly call into these implementations either through c10 or some other mechanism, also allowing for decoupling of op creation from op execution.
This PR does not include any of the front end changes mentioned above. Neither does it include the mobile threadpool unification present in the original https://github.com/pytorch/pytorch/issues/30644. Furthermore, this codepath seems to be faster than NNPACK in a good number of use cases, which can potentially allow us to remove NNPACK from aten to make the codebase a little simpler, granted that there is widespread support for such a move.
Regardless, these changes will be introduced gradually and in a more controlled way in subsequent PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32509
Test Plan:
Build: CI
Functionality: Not exposed
Reviewed By: dreiss
Differential Revision: D20069796
Pulled By: AshkanAliabadi
fbshipit-source-id: d46c1c91d4bea91979ea5bd46971ced5417d309c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32323
### Summary
Since we have released the custom build in 1.4.0, it's time to setup a CI for that. This PR adds a new iOS job to the iOS builds. To save time, It only runs the arm64 build.
### Test Plan
- Don't break any iOS jobs
- Custom Build works.
Test Plan: Imported from OSS
Differential Revision: D19451342
Pulled By: xta0
fbshipit-source-id: 9de305c004fc795710ecf01d436ef4792c07760c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32316
### Summary
Since the Custom Build has been released in 1.4.0, it's time setup CI. To do that, we need
1. Add a python script to generate the yaml file
2. Add new build scripts to circle CI (arm64 only).
### Test Plan
- Don't break the current iOS CIs
Test Plan: Imported from OSS
Differential Revision: D19437362
Pulled By: xta0
fbshipit-source-id: 395e27a582c43663af88d11b1ef974a4687e672c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32147
### Summary
Got some security warnings regarding the ruby dependencies. This diff updates the packages in Gemfile.
```
GitHub has detected that a package defined in the ios/TestApp/Gemfile.lock file of the pytorch/pytorch repository contains a security vulnerability.
Package name: excon
Affected versions: < 0.71.0
Fixed in version: 0.71.0
Severity: LOW
Identifier(s):
GHSA-q58g-455p-8vw9
CVE-2019-16779
```
### Test Plan
- Won't affect the existing iOS CI jobs
Test Plan: Imported from OSS
Differential Revision: D19400087
Pulled By: xta0
fbshipit-source-id: 34b548d136cfd6b68fcc53bf0b243461bd7afd64
Summary:
Done with:
```
❯ sed -i 's/1\.4\.0/1.5.0/g' $(find -type f -not -path "./third_party/*")
```
This was previously done in separate commits, but it would be beneficial to bump all included projects within this repository at the same time.
Old bumps for reference:
* [iOS]Update Cocoapods to 1.4.0: https://github.com/pytorch/pytorch/pull/30326
* [android] Change nightly builds version to 1.4.0-SNAPSHOT: https://github.com/pytorch/pytorch/pull/27381
* Roll master to 1.4.0: https://github.com/pytorch/pytorch/pull/27374
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31785
Differential Revision: D19277925
Pulled By: seemethere
fbshipit-source-id: f72ad082f0566004858c9374879f4b1bee169f9c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30315
The new structure is that libtorch_cpu contains the bulk of our
code, and libtorch depends on libtorch_cpu and libtorch_cuda.
This is a reland of https://github.com/pytorch/pytorch/pull/29731 but
I've extracted all of the prep work into separate PRs which can be
landed before this one.
Some things of note:
* torch/csrc/cuda/nccl.cpp was added to the wrong list of SRCS, now fixed (this didn't matter before because previously they were all in the same library)
* The dummy file for libtorch was brought back from the dead; it was previously deleted in #20774
In an initial version of the patch, I forgot to make torch_cuda explicitly depend on torch_cpu. This lead to some very odd errors, most notably "bin/blob_test: hidden symbol `_ZNK6google8protobuf5Arena17OnArenaAllocationEPKSt9type_infom' in lib/libprotobuf.a(arena.cc.o) is referenced by DSO"
* A number of places in Android/iOS builds have to add torch_cuda explicitly as a library, as they do not have transitive dependency calculation working correctly
* I had to torch_cpu/torch_cuda caffe2_interface_library so that they get whole-archived linked into torch when you statically link. And I had to do this in an *exported* fashion because torch needs to depend on torch_cpu_library. In the end I exported everything and removed the redefinition in the Caffe2Config.cmake. However, I am not too sure why the old code did it in this way in the first place; however, it doesn't seem to have broken anything to switch it this way.
* There's some uses of `__HIP_PLATFORM_HCC__` still in `torch_cpu` code, so I had to apply it to that library too (UGH). This manifests as a failer when trying to run the CUDA fuser. This doesn't really matter substantively right now because we still in-place HIPify, but it would be good to fix eventually. This was a bit difficult to debug because of an unrelated HIP bug, see https://github.com/ROCm-Developer-Tools/HIP/issues/1706Fixes#27215 (as our libraries are smaller), and executes on
part of the plan in #29235.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D18790941
Pulled By: ezyang
fbshipit-source-id: 01296f6089d3de5e8365251b490c51e694f2d6c7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30327
### Summary
Seems like starting from macOS 10.15, we can no longer get access to the `Downloads` folder in our macOS machines.
```
permissionError: [Errno 1] Operation not permitted: '/Users/distiller/Downloads'
```
The fix is to change the conda download directory to ${HOME}
### Test Plan
- iOS jobs are back to normal
- Don't break other jobs
Test Plan: Imported from OSS
Differential Revision: D18717380
Pulled By: xta0
fbshipit-source-id: cad754076bf4ae5035741aa57a310ad87c76726e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30326
Note that this PR won't trigger the cocoapods build. We'll push the binary and release the cocoapods after the branch cut.
Test Plan: Imported from OSS
Differential Revision: D18660308
Pulled By: xta0
fbshipit-source-id: 95dd97b7b67e70ecee3a65d8bbc125791872b7ca
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30133
### Summary
Recently we've found that the master branch was constantly broken due to some unwanted change being landed on mobile. The problem is that our CI was not able to detect the runtime errors.
### Previous work
- Add an unit test target to the iOS TestApp ( #29962 )
- Update Fastlane to run tests ( #29963 )
### What's been changed in CI
1. XCode version has been updated to 11.2.1
2. For iOS simulator build, we'll run some unit tests( currently only one) after the build test.
Test Plan: Imported from OSS
Differential Revision: D18641413
Pulled By: xta0
fbshipit-source-id: 12942206f1dee045b2addba3ae618760e992752c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29731
The new structure is that libtorch_cpu contains the bulk of our
code, and libtorch depends on libtorch_cpu and libtorch_cuda.
Some subtleties about the patch:
- There were a few functions that crossed CPU-CUDA boundary without API macros. I just added them, easy enough. An inverse situation was aten/src/THC/THCTensorRandom.cu where we weren't supposed to put API macros directly in a cpp file.
- DispatchStub wasn't getting all of its symbols related to static members on DispatchStub exported properly. I tried a few fixes but in the end I just moved everyone off using DispatchStub to dispatch CUDA/HIP (so they just use normal dispatch for those cases.) Additionally, there were some mistakes where people incorrectly were failing to actually import the declaration of the dispatch stub, so added includes for those cases.
- torch/csrc/cuda/nccl.cpp was added to the wrong list of SRCS, now fixed (this didn't matter before because previously they were all in the same library)
- The dummy file for libtorch was brought back from the dead; it was previously deleted in #20774
- In an initial version of the patch, I forgot to make torch_cuda explicitly depend on torch_cpu. This lead to some very odd errors, most notably "bin/blob_test: hidden symbol `_ZNK6google8protobuf5Arena17OnArenaAllocationEPKSt9type_infom' in lib/l
ibprotobuf.a(arena.cc.o) is referenced by DSO"
- A number of places in Android/iOS builds have to add torch_cuda explicitly as a library, as they do not have transitive dependency calculation working correctly. This situation also happens with custom C++ extensions.
- There's a ROCm compiler bug where extern "C" on functions is not respected. There's a little workaround to handle this.
- Because I was too lazy to check if HIPify was converting TORCH_CUDA_API into TORCH_HIP_API, I just made it so HIP build also triggers the TORCH_CUDA_API macro. Eventually, we should translate and keep the nature of TORCH_CUDA_API constant in all cases.
Fixes#27215 (as our libraries are smaller), and executes on
part of the plan in #29235.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D18632773
Pulled By: ezyang
fbshipit-source-id: ea717c81e0d7554ede1dc404108603455a81da82
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29963
### Summary
To run unit tests via Fastlane, simply run `fastlane scan`. Under the hood, it uses `xcodebuild` to run the unit tests. The Scanfile serves as a config file for Fastlane where you can specify parameters you want to pass to `xcodebuild`. More about Scan - https://docs.fastlane.tools/actions/scan/
### Test Plan
- `fastlane scan` is able to run on CI machines.
Test Plan: Imported from OSS
Differential Revision: D18606098
Pulled By: xta0
fbshipit-source-id: b4727d964fa56076b2ff383b40d1b13607721394
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29962
### Summary
Recently we've found that the master branch was constantly broken due to some unwanted change being landed on mobile. The problem is that our CI was not able to detect the runtime errors. Starting from this PR, we'll add some unit tests to the iOS Simulator build. As follows:
1. Add an unit test target to XCode (this PR)
2. Use Fastlane to run the tests on CI
3. Modify the CI scripts to trigger tests
### Test Plan
- Don't break the existing CI jobs unless they are flaky.
Test Plan: Imported from OSS
Differential Revision: D18582908
Pulled By: xta0
fbshipit-source-id: f960c47d3bbda79e754a0513e8711867fd3588d2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29247
### Summary
If you run the TestApp using Cocoapods, you'll likely run into an error due to the lack of `config.json` in the main bundle. This PR fixes this crash and updates the README as well.
### Test Plan
- Don't break CIs
Test Plan: Imported from OSS
Differential Revision: D18339047
Pulled By: xta0
fbshipit-source-id: 244cf1ca8729c7ac918258d4eff14d34363e8389
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29240
### Summary
The 1.3.1 binary has been uploaded to AWS - https://ossci-ios.s3.amazonaws.com/libtorch_ios_1.3.1.zip. This PR updates the cocoapods version to 1.3.1
### Test Plan
- The 1.3.1 binary works well
Test Plan: Imported from OSS
Differential Revision: D18333750
Pulled By: xta0
fbshipit-source-id: fe6e42c51f3902ad42cab33f473dffb0f6f33333
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28996
### Summary
It'd be frustrated to realize the device is not connected after waiting for the build finishes. This PR checks the device connection status before xcodebuild.
### Test Plan
- Don't break `bootstrap.sh`
Test Plan: Imported from OSS
Differential Revision: D18258348
Pulled By: xta0
fbshipit-source-id: dda90e7194114e99b2774a3b64ed41f78221f827
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28809
### Summary
This PR adds the interactive mode to `bootstrap.sh`. Instead of passing the credential information from command parameters(`-t`,`-p`), we're going to ask the user enter that information and save it to a config file, such that next time you don't have to enter again. So all you need now, is one line command
```shell
./bootstrap
```
### Test Plan
- TestApp.ipa can be installed on any devices
- Don't break CI jobs
Test Plan: Imported from OSS
Differential Revision: D18194032
Pulled By: xta0
fbshipit-source-id: a416ef7f13fa565e2c10bb55f94a8ce994b4e869
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28622
### Summary
As discussed in #28405 , this is the third PR. The`bootstrap.sh` script is mainly for those who want to do perf on iOS but don't want to touch XCode or any iOS code. But it does require you have valid iOS dev credentials installed on your machine. (You can easily acquire those stuff from any experienced iOS developers. Takes only 5 mins to setup )
All you need to do is run
```shell
./bootstrap -t ${TEAM_ID} -p ${PROFILE}
```
The testing app will be automatically installed on your device. The log of the benchmark function will be displayed on the screen.
### Test plan
Don't break any CI jobs unless they're flaky.
Test Plan: Imported from OSS
Differential Revision: D18156178
Pulled By: xta0
fbshipit-source-id: cd7ba8d87bf26db885262888b9d6a5fd072309d1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28469
### Summary
As described [here](https://github.com/pytorch/pytorch/pull/28405), This PR is the second one that contains scripts for setting up the benchmark projects.
### Test Plan
Don't break CI jobs unless they are flaky.
Test Plan: Imported from OSS
Differential Revision: D18097248
Pulled By: xta0
fbshipit-source-id: 6f9d1275a07aecae21afd81d5e90a89a75d0270f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28405
### Summary
As discussed with AshkanAliabadi and ljk53, the iOS TestApp will share the same benchmark code with Android's speed_benchmark_torch.cpp. This PR is the first part which contains the Objective-C++ code.
The second PR will include the scripts to setup and run the benchmark project. The third PR will include scripts that can automate the whole "build - test - install" process.
There are many ways to run the benchmark project. The easiest way is to use cocoapods. Simply run `pod install`. However, that will pull the 1.3 binary which is not what we want, but we can still use this approach to test the benchmark code. The second PR will contain scripts to run custom builds that we can tweak.
### Test Plan
- Don't break any existing CI jobs (except for those flaky ones)
Test Plan: Imported from OSS
Differential Revision: D18064187
Pulled By: xta0
fbshipit-source-id: 4cfbb83c045803d8b24bf6d2c110a55871d22962
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28356
### Summary
I'm working on setting up a benchmark test project for iOS, which will reuse this Fastlane file. This PR removes the "cert install" code from "before_all" to a standalone lane target.
### Test Plan
- don't break any existing CI jobs
Test Plan: Imported from OSS
Differential Revision: D18053675
Pulled By: xta0
fbshipit-source-id: e4760a8494916c410af19ca43f040fc463551d11
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27594
## Summary
Since the nightly jobs are lack of testing phases, we don't really have a way to test the binary before uploading it to AWS. To make the work more solid, we need to figure out a way to verify the binary.
Fortunately, the XCode tool chain offers a way to build your app without XCode app, which is the [xcodebuild](https://developer.apple.com/library/archive/technotes/tn2339/_index.html) command. Now we can link our binary to a testing app and run `xcodebuild` to to see if there is any linking error. The PRs below have already done some of the preparation jobs
- [#26261](https://github.com/pytorch/pytorch/pull/26261)
- [#26632](https://github.com/pytorch/pytorch/pull/26632)
The challenge comes when testing the arm64 build as we don't have a way to code-sign our TestApp. Circle CI has a [tutorial](https://circleci.com/docs/2.0/ios-codesigning/) but is too complicated to implement. Anyway, I figured out an easier way to do it
1. Disable automatically code sign in XCode
2. Export the encoded developer certificate and provisioning profile to org-context in Circle CI (done)
3. Install the developer certificate to the key chain store on CI machines via Fastlane.
4. Add the testing code to PR jobs and verify the result.
5. Add the testing code to nightly jobs and verify the result.
## Test Plan
- Both PR jobs and nightly jobs can finish successfully.
- `xcodebuild` can finish successfully
Test Plan: Imported from OSS
Differential Revision: D17850703
Pulled By: xta0
fbshipit-source-id: ab220061c6e2ec75cae23684ad999c4f9c276820
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27593
## Summary
Since the nightly jobs are lack of testing phases, we don't really have a way to test the binary before uploading it to AWS. To make the work more solid, we need to figure out a way to verify the binary.
Fortunately, the XCode tool chain offers a way to build your app without XCode app, which is the [xcodebuild](https://developer.apple.com/library/archive/technotes/tn2339/_index.html) command. Now we can link our binary to a testing app and run `xcodebuild` to to see if there is any linking error. The PRs below have already done some of the preparation jobs
- [#26261](https://github.com/pytorch/pytorch/pull/26261)
- [#26632](https://github.com/pytorch/pytorch/pull/26632)
The challenge comes when testing the arm64 build as we don't have a way to code-sign our TestApp. Circle CI has a [tutorial](https://circleci.com/docs/2.0/ios-codesigning/) but is too complicated to implement. Anyway, I figured out an easier way to do it
1. Disable automatically code sign in XCode
2. Export the encoded developer certificate and provisioning profile to org-context in Circle CI (done)
3. Install the developer certificate to the key chain store on CI machines via Fastlane.
4. Add the testing code to PR jobs and verify the result.
5. Add the testing code to nightly jobs and verify the result.
## Test Plan
- Both PR jobs and nightly jobs can finish successfully.
- `xcodebuild` can finish successfully
Test Plan: Imported from OSS
Differential Revision: D17848814
Pulled By: xta0
fbshipit-source-id: 48353f001c38e61eed13a43943253cae30d8831a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27591
## Summary
Since the nightly jobs are lack of testing phases, we don't really have a way to test the binary before uploading it to AWS. To make the work more solid, we need to figure out a way to verify the binary.
Fortunately, the XCode tool chain offers a way to build your app without XCode app, which is the [xcodebuild](https://developer.apple.com/library/archive/technotes/tn2339/_index.html) command. Now we can link our binary to a testing app and run `xcodebuild` to to see if there is any linking error. The PRs below have already done some of the preparation jobs
- [#26261](https://github.com/pytorch/pytorch/pull/26261)
- [#26632](https://github.com/pytorch/pytorch/pull/26632)
The challenge comes when testing the arm64 build as we don't have a way to code-sign our TestApp. Circle CI has a [tutorial](https://circleci.com/docs/2.0/ios-codesigning/) but is too complicated to implement. Anyway, I figured out an easier way to do it
1. Disable automatically code sign in XCode
2. Export the encoded developer certificate and provisioning profile to org-context in Circle CI (done)
3. Install the developer certificate to the key chain store on CI machines via Fastlane.
4. Add the testing code to PR jobs and verify the result.
5. Add the testing code to nightly jobs and verify the result.
## Test Plan
- Both PR jobs and nightly jobs can finish successfully.
- `xcodebuild` can finish successfully
Test Plan: Imported from OSS
Differential Revision: D17844036
Pulled By: xta0
fbshipit-source-id: 741f0442a718c9bda706107a2c4c3baed4c37137
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26751
### Summary
We're going to use the AWS s3 bucket - `s3://ossci-ios` to store the release binary. To release the cocoapods, we can follow the steps below:
1. Open a fake PR to trigger the CI job that pulls the code from the 1.3.0 tag branch and does the building and uploading.
2. Verify the binary locally - Run tests on both arm64 and simulator
3. Publish the cocoapods officially
### Test plan
- podspec lint command succeeds
- `pod spec lint --verbose --allow-warnings --no-clean --use-libraries --skip-import-validation`
Test Plan: Imported from OSS
Differential Revision: D17577131
Pulled By: xta0
fbshipit-source-id: 55fee918ecc5c4e0b6d714488a12351b4370afac
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26632
### Summary
This script builds the TestApp (located in ios folder) to generate an iOS x86 executable via the `xcodebuild` toolchain on macOS. The goal is to provide a quick way to test the generated static libraries to see if there are any linking errors. The script can also be used by the iOS CI jobs. To run the script, simply see description below:
```shell
$ruby scripts/xcode_ios_x86_build.rb --help
-i, --install path to the cmake install folder
-x, --xcodeproj path to the XCode project file
```
### Note
The script mainly deals with the iOS simulator build. For the arm64 build, I haven't found a way to disable the Code Sign using the `xcodebuiild` tool chain (XCode 10). If anyone knows how to do that, please feel free to leave a comment below.
### Test Plan
- The script can build the TestApp and link the generated static libraries successfully
- Don't break any CI job
Test Plan: Imported from OSS
Differential Revision: D17530990
Pulled By: xta0
fbshipit-source-id: f50bef7127ff8c11e884c99889cecff82617212b
Summary:
ignore the folder and its children ios/TestApp
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26399
Differential Revision: D17451239
Pulled By: houseroad
fbshipit-source-id: d6ba666bf955454eca4a10c00784ee5947a70f59
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26261
### Summary
Previously we have enabled the CI jobs for Pull Requests and nightly build
- **#25840 [iOS][Circle CI] Add PR jobs for iOS builds**
- **#26074 [IOS][CIRCLE CI] Nightly jobs for iOS builds**
The testing phase is missing in the nightly build process. Although we are able to generate the build and upload it to the AWS, there is no way to know whether the binary is valid or not (there could be a linking error). To add the test phase to the process, we need
1. Put a dummy test App in the repo.
2. After the build jobs finishes, manually link the static libs to the dummy app to produce an executable using the xcode tool chain.
3. If there is no linking error, then upload the binaris to AWS. If there is an error, then stops the following process and reports an error in CI.
The second and third steps depends on the first step which needs to be landed first.
### Test Plan
- Don't break any existing CI jobs
Test Plan: Imported from OSS
Differential Revision: D17408929
Pulled By: xta0
fbshipit-source-id: e391da242639943005453d1318795f981034cc72
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26222
### Summary
The last generation of armv7s device is Phone 5C. As discussed with David offline, we decided not to support iOS armv7s devices.
### Test plan
- CI finishes successfully
- Builds can be run only on X86_64 and arm64 devices
Test Plan: Imported from OSS
Differential Revision: D17385308
Pulled By: xta0
fbshipit-source-id: f883999aed18224ea3386b1f016964a33270fa34
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26089
### Summary
A couple of changes
1. Replace the source link with the newly nightly build address
2. Remove module support for Swift and Objective-C
3. Expose all static libraries instead of archiving them into one single library. This is because those static libraries might contain object files that have the same name, e.g. `init.c.o` in both `libcupinfo.a` and `libqnnpack.a`. If we archive them into one using this `libtool -static` command, by default, it only picks one object file and discards the others, which could result in undefined symbols when linking the executable. The change here is to expose all the static libraries and let the linker decide which one to use.
### Test Plan
- pod spec lint succeed
- `pod spec lint --verbose --allow-warnings --no-clean --use-libraries --skip-import-validation`
Test Plan: Imported from OSS
Differential Revision: D17363037
Pulled By: xta0
fbshipit-source-id: ba77b0001b58e6e2353d8379d932db598166d37d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25847
### Summary
The Podspec file for iOS OSS release. This podspec contains the C++ header files and a static library that supports three architectures.
Please ignore the link for `s.source` for now, as I'm still working on the CI nightly build. This is a temporary link for testing purpose.
### Note
Previously I have a cocoapods release proposal - https://github.com/pytorch/pytorch/pull/25543 which contains two podspec files. However, for the time being, we haven't decided whether we want to release the Objective-C API wrapper or not. Please review and refer to this one if you have questions.
Test Plan: Imported from OSS
Differential Revision: D17262459
Pulled By: xta0
fbshipit-source-id: 4cc60787a41beab14cf9b1c0e9ab62b8b14603c5
