Summary:
D30090760 (e182b459d9) was reverted by D30303292 because of a lint issue in `LibTorch-Lite-Nightly.podspec.template`. Resubmit the diff after fixing the issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63239
Test Plan: Imported from OSS
Reviewed By: xta0
Differential Revision: D30315690
Pulled By: hanton
fbshipit-source-id: f0fa719ffc3b8181ab28c123584ae5c1da8992c0
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
