Joel Schlosser [Fri, 27 Aug 2021 18:28:03 +0000 (11:28 -0700)]
Modules note v2 (#63963)
Summary:
This PR expands the [note on modules](https://pytorch.org/docs/stable/notes/modules.html) with additional info for 1.10.
It adds the following:
* Examples of using hooks
* Examples of using apply()
* Examples for ParameterList / ParameterDict
* register_parameter() / register_buffer() usage
* Discussion of train() / eval() modes
* Distributed training overview / links
* TorchScript overview / links
* Quantization overview / links
* FX overview / links
* Parametrization overview / link to tutorial
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63963
Reviewed By: albanD
Differential Revision:
D30606604
Pulled By: jbschlosser
fbshipit-source-id:
c1030b19162bcb5fe7364bcdc981a2eb6d6e89b4
Tugsbayasgalan (Tugsuu) Manlaibaatar [Fri, 27 Aug 2021 18:18:52 +0000 (11:18 -0700)]
Detect out argument in the schema (#62755)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62755
After this change, out argument can be checked by calling is_out()
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision:
D30415256
Pulled By: tugsbayasgalan
fbshipit-source-id:
b2e1fa46bab7c813aaede1f44149081ef2df566d
Don Jang [Fri, 27 Aug 2021 17:42:50 +0000 (10:42 -0700)]
[Static Runtime] Add out variant of quantized::embedding_bag_byte_prepack (#64081)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64081
This change add an out variant of `quantized::embedding_bag_byte_prepack`.
Test Plan:
- Added `ShapeInferenceTest.QEmbeddingBagByteUnpack`.
- Observed
```
V0824 13:38:49.723708 1322143 impl.cpp:1394] Switch to out variant for node: %2 : Tensor = quantized::embedding_bag_byte_prepack(%input)
```
Reviewed By: hlu1
Differential Revision:
D30504216
fbshipit-source-id:
1d9d428e77a15bcc7da373d65e7ffabaf9c6caf2
BBuf [Fri, 27 Aug 2021 17:42:24 +0000 (10:42 -0700)]
fix resize bug (#61166)
Summary:
I think the original intention here is to only take effect in the case of align_corners (because output_size = 1 and the divisor will be 0), but it affects non-align_corners too. For example:
```python
input = torch.tensor(
np.arange(1, 5, dtype=np.int32).reshape((1, 1, 2, 2)) )
m = torch.nn.Upsample(scale_factor=0.5, mode="bilinear")
of_out = m(input)
```
The result we expect should be [[[[2.5]]]]
but pytorch get [[[[1.0]]]] which is different from OpenCV and PIL, this pr try to fixed it。
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61166
Reviewed By: malfet
Differential Revision:
D30543178
Pulled By: heitorschueroff
fbshipit-source-id:
21a4035483981986b0ae4a401ef0efbc565ccaf1
Pierluigi Taddei [Fri, 27 Aug 2021 17:36:08 +0000 (10:36 -0700)]
[caffe2] fixes to allow stricter compilation flag (#64016)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64016
In order to increase the strictness of the compilation for some target depending on caffe2 we need to fix some errors uncovered when rising such flags.
This change introduces the required override tokens for virtual destructors
Test Plan: CI. Moreover targets depending on caffe2 using clang strict warnings now compile
Reviewed By: kalman5
Differential Revision:
D30541714
fbshipit-source-id:
564af31b4a9df3536d7d6f43ad29e1d0c7040551
Heitor Schueroff [Fri, 27 Aug 2021 17:16:02 +0000 (10:16 -0700)]
Added reference tests to ReductionOpInfo (#62900)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/62900
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision:
D30408815
Pulled By: heitorschueroff
fbshipit-source-id:
6a1f82ac281920ff7405a42f46ccd796e60af9d6
Mike Iovine [Fri, 27 Aug 2021 17:10:48 +0000 (10:10 -0700)]
[JIT] Add aten::slice optimization (#63049)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63049
Given a graph produced from a function like this:
```
def foo():
li = [1, 2, 3, 4, 5, 6]
return li[0:2]
```
This pass produces a graph like this:
```
def foo():
li = [1, 2]
return li
```
These changes are mostly adapted from https://github.com/pytorch/pytorch/pull/62297/
Test Plan: `buck test //caffe2/test:jit -- TestPeephole`
Reviewed By: eellison
Differential Revision:
D30231044
fbshipit-source-id:
d12ee39f68289a574f533041a5adb38b2f000dd5
Jonathan Chang [Fri, 27 Aug 2021 16:49:39 +0000 (09:49 -0700)]
Add doc for nn.MultiMarginLoss (shape, example) (#63760)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/63747
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63760
Reviewed By: malfet
Differential Revision:
D30541581
Pulled By: jbschlosser
fbshipit-source-id:
99560641e614296645eb0e51999513f57dfcfa98
Peter Bell [Fri, 27 Aug 2021 16:37:10 +0000 (09:37 -0700)]
Refactor structured set_output in Register{DispatchKey}.cpp (#62188)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62188
These parts of the `set_output` code are identical for all operators in the
kernel registration files. So, this moves them from being copied into every
class to two helper functions at the top of the file.
Test Plan: Imported from OSS
Reviewed By: soulitzer
Differential Revision:
D29962045
Pulled By: albanD
fbshipit-source-id:
753b8aac755f3c91b77ffa2c30a89ac91a84b7c4
Sergei Vorobev [Fri, 27 Aug 2021 16:31:36 +0000 (09:31 -0700)]
[bazel] GPU-support: add @local_config_cuda and @cuda (#63604)
Summary:
## Context
We take the first step at tackling the GPU-bazel support by adding bazel external workspaces `local_config_cuda` and `cuda`, where the first one has some hardcoded values and lists of files, and the second one provides a nicer, high-level wrapper that maps into the already expected by pytorch bazel targets that are guarded with `if_cuda` macro.
The prefix `local_config_` signifies the fact that we are breaking the bazel hermeticity philosophy by explicitly relaying on the CUDA installation that is present on the machine.
## Testing
Notice an important scenario that is unlocked by this change: compilation of cpp code that depends on cuda libraries (i.e. cuda.h and so on).
Before:
```
sergei.vorobev@cs-sv7xn77uoy-gpu-
1628706590:~/src/pytorch4$ bazelisk build --define=cuda=true //:c10
ERROR: /home/sergei.vorobev/src/pytorch4/tools/config/BUILD:12:1: no such package 'tools/toolchain': BUILD file not found in any of the following directories. Add a BUILD file to a directory to mark it as a package.
- /home/sergei.vorobev/src/pytorch4/tools/toolchain and referenced by '//tools/config:cuda_enabled_and_capable'
ERROR: While resolving configuration keys for //:c10: Analysis failed
ERROR: Analysis of target '//:c10' failed; build aborted: Analysis failed
INFO: Elapsed time: 0.259s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (2 packages loaded, 2 targets configured)
```
After:
```
sergei.vorobev@cs-sv7xn77uoy-gpu-
1628706590:~/src/pytorch4$ bazelisk build --define=cuda=true //:c10
INFO: Analyzed target //:c10 (6 packages loaded, 246 targets configured).
INFO: Found 1 target...
Target //:c10 up-to-date:
bazel-bin/libc10.lo
bazel-bin/libc10.so
INFO: Elapsed time: 0.617s, Critical Path: 0.04s
INFO: 0 processes.
INFO: Build completed successfully, 1 total action
```
The `//:c10` target is a good testing one for this, because it has such cases where the [glob is different](https://github.com/pytorch/pytorch/blob/
075024b9a34904ec3ecdab3704c3bcaa329bdfea/BUILD.bazel#L76-L81), based on do we compile for CUDA or not.
## What is out of scope of this PR
This PR is a first in a series of providing the comprehensive GPU bazel build support. Namely, we don't tackle the [cu_library](https://github.com/pytorch/pytorch/blob/
11a40ad915d4d3d8551588e303204810887fcf8d/tools/rules/cu.bzl#L2) implementation here. This would be a separate large chunk of work.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63604
Reviewed By: soulitzer
Differential Revision:
D30442083
Pulled By: malfet
fbshipit-source-id:
b2a8e4f7e5a25a69b960a82d9e36ba568eb64595
Hanton Yang [Fri, 27 Aug 2021 16:23:45 +0000 (09:23 -0700)]
[OSS] Enable Metal in PyTorch MacOS nightly builds (#63718)
Summary:
Build on https://github.com/pytorch/pytorch/pull/63825
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63718
Test Plan:
1.Add `ci/binaries` label to PR, so the CI will build those nightly builds
2.Make sure the following CI jobs build with `USE_PYTORCH_METAL_EXPORT` option is `ON`:
```
ci/circleci: binary_macos_arm64_conda_3_8_cpu_nightly_build
ci/circleci: binary_macos_arm64_conda_3_9_cpu_nightly_build
ci/circleci: binary_macos_arm64_wheel_3_8_cpu_nightly_build
ci/circleci: binary_macos_arm64_wheel_3_9_cpu_nightly_build
ci/circleci: binary_macos_conda_3_6_cpu_nightly_build
ci/circleci: binary_macos_conda_3_7_cpu_nightly_build
ci/circleci: binary_macos_conda_3_8_cpu_nightly_build
ci/circleci: binary_macos_conda_3_9_cpu_nightly_build
ci/circleci: binary_macos_libtorch_3_7_cpu_nightly_build
ci/circleci: binary_macos_wheel_3_6_cpu_nightly_build
ci/circleci: binary_macos_wheel_3_7_cpu_nightly_build
ci/circleci: binary_macos_wheel_3_8_cpu_nightly_build
ci/circleci: binary_macos_wheel_3_9_cpu_nightly_build
```
3.Test `conda` and `wheel` builds locally on [HelloWorld-Metal](https://github.com/pytorch/ios-demo-app/tree/master/HelloWorld-Metal) demo with [(Prototype) Use iOS GPU in PyTorch](https://pytorch.org/tutorials/prototype/ios_gpu_workflow.html)
(1) conda
```
conda install https://
15667941-
65600975-gh.circle-artifacts.com/0/Users/distiller/project/final_pkgs/pytorch-1.10.0.dev20210826-py3.8_0.tar.bz2
```
(2) wheel
```
pip3 install https://
15598647-
65600975-gh.circle-artifacts.com/0/Users/distiller/project/final_pkgs/torch-1.10.0.dev20210824-cp38-none-macosx_10_9_x86_64.whl
```
Reviewed By: xta0
Differential Revision:
D30593167
Pulled By: hanton
fbshipit-source-id:
471da204e94b29c11301c857c50501307a5f0785
Aswin Murali [Fri, 27 Aug 2021 16:02:22 +0000 (09:02 -0700)]
Adds return type annotation for fork_rng function (#63724)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/63723
Since it's a generator function the type annotation shall be `Generator`.
![image](https://user-images.githubusercontent.com/
47299190/
130318830-
29ef9529-0daa-463c-90b2-
1b11f63ade8a.png)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63724
Reviewed By: iramazanli
Differential Revision:
D30543098
Pulled By: heitorschueroff
fbshipit-source-id:
ebdd34749defe1e26c899146786a0357ab4b4b9b
gmagogsfm [Fri, 27 Aug 2021 15:49:54 +0000 (08:49 -0700)]
More robust check of whether a class is defined in torch (#64083)
Summary:
This would prevent bugs for classes that
1) Is defined in a module that happens to start with `torch`, say `torchvision`
2) Is defined in torch but with an import alias like `import torch as th`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64083
Reviewed By: soulitzer
Differential Revision:
D30598369
Pulled By: gmagogsfm
fbshipit-source-id:
9d3a7135737b2339c9bd32195e4e69a9c07549d4
Harut Movsisyan [Fri, 27 Aug 2021 10:03:32 +0000 (03:03 -0700)]
[Static Runtime] Out version for fmod (#64046)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/64046
Test Plan:
Confirm out variant is used:
```
> //caffe2/benchmarks/static_runtime:static_runtime_cpptest -- --v=1
V0826 23:31:30.321382 193428 impl.cpp:1395] Switch to out variant for node: %4 : Tensor = aten::fmod(%a.1, %b.1)
```
Reviewed By: mikeiovine
Differential Revision:
D30581228
fbshipit-source-id:
dfab9a16ff8afd40b29338037769f938f154bf74
Don Jang [Fri, 27 Aug 2021 09:43:22 +0000 (02:43 -0700)]
[Static Runtime] Manage temporary Tensors for aten::layer_norm (#64078)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64078
This change converts `aten::layer_norm -> output Tensor` to `static_runtime::layer_norm -> (output Tensor, temp1 Tensor, tmp2 Tensor)` to manage `tmp1` and `tmp2` Tensors by the static runtime.
Currently the out-variant of `aten::layer_norm` creates two temporary Tensors inside it:
```
at::Tensor mean = create_empty_from({M}, *X);
at::Tensor rstd = create_empty_from({M}, *X);
```
that the static runtime misses an opportunity to manage.
This change puts them into (unused) output Tensors of a new placeholder op `static_runtime::layer_norm` so that the static runtime can mange them since the static runtime as of now chooses to manage only output tensors.
Test Plan:
- Enhanced `StaticRuntime.LayerNorm` to ensure that `static_runtime::layer_norm` gets activated.
- Confirmed that the new op gets activated during testing:
```
V0825 12:51:50.017890 2265227 impl.cpp:1396] Switch to out variant for node: %8 : Tensor, %9 : Tensor, %10 : Tensor = static_runtime::layer_norm(%input.1, %normalized_shape.1, %4, %4, %5, %3)
```
Reviewed By: hlu1
Differential Revision:
D30486475
fbshipit-source-id:
5121c44ab58c2d8a954aa0bbd9dfeb7468347a2d
Hao Lu [Fri, 27 Aug 2021 08:39:14 +0000 (01:39 -0700)]
[Static Runtime] Use F14FastMap/F14FastSet (#63999)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63999
Use folly::F14FastMap/F14FastSet instead of std::unordered_map/unordered_set in the Static Runtime code base. folly::F14FastMap/F14FastSet implements the same APIs as std::unordered_map/unordered_set but faster. For details see https://github.com/facebook/folly/blob/master/folly/container/F14.md
Reviewed By: d1jang
Differential Revision:
D30566149
fbshipit-source-id:
20a7fa2519e4dde96fb3fc61ef6c92bf6d759383
Ansha Yu [Fri, 27 Aug 2021 06:17:42 +0000 (23:17 -0700)]
[static runtime] port c2 argmin kernel (#63632)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63632
Local benchmarking with 1 input repeated 10k iter on 290331537_4 local net. Reduces argmin runtime by about 80% and and local net execution by about ~0.71-0.77ms.
Before:
```
I0826 17:25:53.972786 1104614 PyTorchPredictorBenchLib.cpp:313] PyTorch run finished. Milliseconds per iter: 7.37599. Iters per second: 135.57
```
```
Static runtime ms per iter: 8.22086. Iters per second: 121.642
Time per node type:
4.13527 ms. 50.9157%. fb::sigrid_transforms_torch_bind (1 nodes, out variant)
0.868506 ms. 10.6935%. aten::argmin (1 nodes, out variant)
...
```
After:
```
I0826 17:17:54.165174 1064079 PyTorchPredictorBenchLib.cpp:313] PyTorch run finished. Milliseconds per iter: 6.66724. Iters per second: 149.987
```
```
Static runtime ms per iter: 7.68172. Iters per second: 130.179
Time per node type:
4.1452 ms. 54.0612%. fb::sigrid_transforms_torch_bind (1 nodes, out variant)
0.656778 ms. 8.56562%. fb::quantized_linear (8 nodes)
0.488229 ms. 6.36741%. static_runtime::to_copy (827 nodes, out variant)
0.372678 ms. 4.86042%. aten::argmin (1 nodes, out variant)
...Time per node type:
3.39387 ms. 53.5467%. fb::sigrid_transforms_torch_bind (1 nodes, out variant)
0.636216 ms. 10.0379%. fb::quantized_linear (8 nodes, out variant)
0.410535 ms. 6.47721%. fb::clip_ranges_to_gather_to_offsets (304 nodes, out variant)
0.212721 ms. 3.3562%. fb::clip_ranges_gather_sigrid_hash_precompute_v3 (157 nodes, out variant)
0.173736 ms. 2.74111%. aten::matmul (1 nodes, out variant)
0.150514 ms. 2.37474%. aten::argmin (1 nodes, out variant)
```
P447422384
Test Plan:
Test with local replayer sending traffic to `ansha_perf_test_0819.test`, and compare outputs to jit interpreter.
Start compute tier:
```
RUN_UUID=ansha_perf_test_0819.test.storage JOB_EXPIRE_TIME=864000 MODEL_ID=290331537_4 PREDICTOR_TAG= PREDICTOR_VERSION=405 PREDICTOR_TYPE=CPU ADDITIONAL_FLAGS="--enable_disagg_file_split=true --enable_adx=false --load_remote_file_locally=true --pytorch_predictor_static_runtime_whitelist_by_id=
290331537" GFLAGS_CONFIG_PATH=sigrid/predictor/gflags/predictor_gflags_ads_perf_cpu_pyper SMC_TIER_NAME=sigrid.predictor.perf.ansha_per_test_0819.test.storage CLUSTER=tsp_rva ENTITLEMENT_NAME=ads_ranking_infra_test_t6 PREDICTOR_LOCAL_DIRECTORY= ICET_CONFIG_PATH= NNPI_COMPILATION_CONFIG_FILE= NUM_TASKS=1 NNPI_NUM_WORKERS=0 tw job start /data/users/ansha/fbsource/fbcode/tupperware/config/admarket/sigrid/predictor/predictor_perf_canary.tw
```
Start nnpi tier:
```
RUN_UUID=ansha_perf_test_0819.test JOB_EXPIRE_TIME=247200 MODEL_ID=290331537_4 PREDICTOR_TAG= PREDICTOR_VERSION=343 PREDICTOR_TYPE=NNPI_TWSHARED ADDITIONAL_FLAGS="--torch_glow_min_fusion_group_size=30 --pytorch_storage_tier_replayer_sr_connection_options=overall_timeout:1000000,processing_timeout:1000000 --predictor_storage_smc_tier=sigrid.predictor.perf.ansha_perf_test_0819.test.storage --pytorch_predictor_static_runtime_whitelist_by_id=
290331537" GFLAGS_CONFIG_PATH=sigrid/predictor/gflags/predictor_gflags_ads_perf_glow_nnpi_pyper_v1 SMC_TIER_NAME=sigrid.predictor.perf.ansha_perf_test_0819.test CLUSTER=tsp_rva ENTITLEMENT_NAME=ads_ranking_infra_test_t17 PREDICTOR_LOCAL_DIRECTORY= ICET_CONFIG_PATH= NNPI_COMPILATION_CONFIG_FILE= NUM_TASKS=1 NNPI_NUM_WORKERS=0 tw job start /data/users/ansha/fbsource/fbcode/tupperware/config/admarket/sigrid/predictor/predictor_perf_canary.tw
```
```buck test caffe2/benchmarks/static_runtime:static_runtime_cpptest -- StaticRuntime.IndividualOps_Argmin --print-passing-details```
Compared outputs to jit interpreter to check for no differences greater than 1e-3 (with nnc on) https://www.internalfb.com/intern/diff/view-version/
136824794/
Reviewed By: hlu1
Differential Revision:
D30445635
fbshipit-source-id:
048de8867ac72f764132295d1ebfa843cde2fa27
Supriya Rao [Fri, 27 Aug 2021 04:05:56 +0000 (21:05 -0700)]
[quant] Add support for linear_relu fusion for FP16 dynamic quant (#63826)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63826
Support the conversion of the intrinsic linearRelu module to the quantized dynamic LinearReLU module
Verify the support works for both linear module and functional linear fusion
Test Plan:
python test/test_quantization.py test_dynamic_with_fusion
Imported from OSS
Reviewed By: iramazanli
Differential Revision:
D30503513
fbshipit-source-id:
70446797e9670dfef7341cba2047183d6f88b70f
Supriya Rao [Fri, 27 Aug 2021 04:05:56 +0000 (21:05 -0700)]
[quant] Add op support for linear_relu_dynamic_fp16 (#63824)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63824
Add a fused operator implementation that will work with the quantization fusion APIs.
Once FBGEMM FP16 kernel supports relu fusion natively we can remove the addition from the PT operator.
Test Plan:
python test/test_quantization.py
Imported from OSS
Reviewed By: heitorschueroff
Differential Revision:
D30503514
fbshipit-source-id:
6bf3bd53f47ffaa3f1d178eaad8cc980a7f5258a
Supriya Rao [Fri, 27 Aug 2021 04:05:56 +0000 (21:05 -0700)]
[quant] support linear_relu_dynamic for qnnpack backend (#63820)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63820
Adds support in the operator directly to call relu operator if relu fusion is enabled.
Once QNNPACK natively supports relu fusion in the linear_dynamic this can be removed
Test Plan:
python test/test_quantization.py TestDynamicQuantizedLinear.test_qlinear
Imported from OSS
Reviewed By: vkuzo
Differential Revision:
D30502813
fbshipit-source-id:
3352ee5f73e482b6d1941f389d720a461b84ba23
Supriya Rao [Fri, 27 Aug 2021 04:05:56 +0000 (21:05 -0700)]
[quant][fx] Add support for dynamic linear + relu fusion (INT8) (#63799)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63799
Add a new module that can be used for module swap with the nni.LinearReLU module in convert function.
Supports INT8 currently (since FP16 op doesn't have relu fusion yet).
Fixes #55393
Test Plan:
python test/test_quantization.py test_dynamic_fusion
Imported from OSS
Reviewed By: heitorschueroff
Differential Revision:
D30502812
fbshipit-source-id:
3668e4f001a0626d469e17ac323acf582ee28a51
Michael Suo [Fri, 27 Aug 2021 03:54:54 +0000 (20:54 -0700)]
[torch/deploy] add torch.distributed to build (#63918)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63918
Previously we were building with `USE_DISTRIBUTED` off, because c10d was built as a separately library for historical reasons. Since then, lw has merged the c10d build into libtorch, so this is fairly easy to turn on.
Differential Revision:
D30492442
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.intern.facebook.com/intern/diff/
D30492442/)!
D30492442
D30492442
Test Plan: added a unit test
Reviewed By: wconstab
Pulled By: suo
fbshipit-source-id:
843b8fcf349a72a7f6fcbd1fcc8961268690fb8c
Can Balioglu [Fri, 27 Aug 2021 03:16:10 +0000 (20:16 -0700)]
Introduce the torchrun entrypoint (#64049)
Summary:
This PR introduces a new `torchrun` entrypoint that simply "points" to `python -m torch.distributed.run`. It is shorter and less error-prone to type and gives a nicer syntax than a rather cryptic `python -m ...` command line. Along with the new entrypoint the documentation is also updated and places where `torch.distributed.run` are mentioned are replaced with `torchrun`.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse agolynski SciPioneer H-Huang mrzzd cbalioglu gcramer23
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64049
Reviewed By: cbalioglu
Differential Revision:
D30584041
Pulled By: kiukchung
fbshipit-source-id:
d99db3b5d12e7bf9676bab70e680d4b88031ae2d
nikithamalgi [Fri, 27 Aug 2021 01:54:51 +0000 (18:54 -0700)]
Merge script and _script_pdt API (#62420)
Summary:
Merge `torch.jit.script` and `torch.jit._script_pdt` API. This PR merges profile directed typing with script api
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62420
Reviewed By: iramazanli
Differential Revision:
D30579015
Pulled By: nikithamalgifb
fbshipit-source-id:
99ba6839d235d61b2dd0144b466b2063a53ccece
Maksim Levental [Fri, 27 Aug 2021 00:59:59 +0000 (17:59 -0700)]
port glu to use structured kernel approach (#61800)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61800
resubmitting because the [last one](https://github.com/pytorch/pytorch/pull/61433) was unrecoverable due to making changes incorrectly in the stack
Test Plan: Imported from OSS
Reviewed By: iramazanli
Differential Revision:
D29812492
Pulled By: makslevental
fbshipit-source-id:
c3dfeacd1e00a526e24fbaab02dad48069d690ef
Jane Xu [Fri, 27 Aug 2021 00:36:56 +0000 (17:36 -0700)]
Run through failures on trunk (#64063)
Summary:
This PR runs all the tests on trunk instead of stopping on first failure.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64063
Reviewed By: malfet, seemethere
Differential Revision:
D30592020
Pulled By: janeyx99
fbshipit-source-id:
318b225cdf918a98f73e752d1cc0227d9227f36c
Paul Johnson [Fri, 27 Aug 2021 00:28:35 +0000 (17:28 -0700)]
[pytorch] add per_sample_weights support for embedding_bag_4bit_rowwise_offsets (#63605)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/63605
Reviewed By: houseroad
Differential Revision:
D30434664
fbshipit-source-id:
eb4cbae3c705f9dec5c073a56f0f23daee353bc1
Michael Dagitses [Fri, 27 Aug 2021 00:26:52 +0000 (17:26 -0700)]
document that `torch.triangular_solve` has optional out= parameter (#63253)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63253
Fixes #57955
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision:
D30312134
Pulled By: dagitses
fbshipit-source-id:
4f484620f5754f4324a99bbac1ff783c64cee6b8
Jiewen Tan [Thu, 26 Aug 2021 23:49:13 +0000 (16:49 -0700)]
Enable test_api IMethodTest in OSS (#63345)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63345
This diff did the following few things to enable the tests:
1. Exposed IMethod as TORCH_API.
2. Linked torch_deploy to test_api if USE_DEPLOY == 1.
3. Generated torch::deploy examples when building torch_deploy library.
Test Plan: ./build/bin/test_api --gtest_filter=IMethodTest.*
Reviewed By: ngimel
Differential Revision:
D30346257
Pulled By: alanwaketan
fbshipit-source-id:
932ae7d45790dfb6e00c51893933a054a0fad86d
Don Jang [Thu, 26 Aug 2021 23:28:35 +0000 (16:28 -0700)]
[Static Runtime] Remove unnecessary fb::equally_split nodes (#64022)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/64022
Test Plan: - Added unittest `StaticRuntime.RemoveEquallySplitListUnpack`.
Reviewed By: hlu1
Differential Revision:
D30472189
fbshipit-source-id:
36040b0146f4be9d0d0fda293f7205f43aad0b87
Shijun Kong [Thu, 26 Aug 2021 23:06:17 +0000 (16:06 -0700)]
[pytorch][quant][oss] Support 2-bit embedding_bag op "embedding_bag_2bit_rowwise_offsets" (#63658)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63658
Support 2-bit embedding_bag op "embedding_bag_2bit_rowwise_offsets"
Reviewed By: jingsh, supriyar
Differential Revision:
D30454994
fbshipit-source-id:
7aa7bfe405c2ffff639d5658a35181036e162dc9
soulitzer [Thu, 26 Aug 2021 23:00:21 +0000 (16:00 -0700)]
Move variabletype functions around (#63330)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63330
- This is in preparation for templated/boxed autograd-not-implemented fallback
- Make sure VariableTypeUtils does not depend on generated code
- Lift `isFwGradDefined` into `autograd/functions/utils.cpp` so it's available to mobile builds
- Removes `using namespace at` from VariableTypeUtils, previously we needed this for Templated version, but now its not strictly necessary but still a good change to avoid name conflicts if this header is included elsewhere in the future.
Test Plan: Imported from OSS
Reviewed By: heitorschueroff
Differential Revision:
D30518573
Pulled By: soulitzer
fbshipit-source-id:
a0fb904baafc9713de609fffec4b813f6cfcc000
Bo Wang [Thu, 26 Aug 2021 23:00:16 +0000 (16:00 -0700)]
More sharded_tensor creation ops: harded_tensor.zeros, sharded_tensor.full, sharded_tensor.rand (#63732)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/63732
Test Plan:
$ python test/distributed/_sharded_tensor/test_sharded_tensor.py --v
$ python test/distributed/_sharded_tensor/test_sharded_tensor.py TestCreateTensorFromParams --v
$ python test/distributed/_sharded_tensor/test_sharded_tensor.py TestShardedTensorChunked --v
Imported from OSS
Differential Revision:
D30472621
D30472621
Reviewed By: pritamdamania87
Pulled By: bowangbj
fbshipit-source-id:
fd8ebf9b815fdc292ad1aad521f9f4f454163d0e
Jane Xu [Thu, 26 Aug 2021 22:42:00 +0000 (15:42 -0700)]
Add shard number to print_test_stats.py upload name (#64055)
Summary:
Now that the render test results job is gone, each shard on GHA is uploading a JSON test stats report. To ensure differentiation, this PR includes the shard number in the report name.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64055
Reviewed By: iramazanli
Differential Revision:
D30586869
Pulled By: janeyx99
fbshipit-source-id:
fd19f347131deec51486bb0795e4e13ac19bc71a
MengeTM [Thu, 26 Aug 2021 22:32:06 +0000 (15:32 -0700)]
Derivatives of relu (#63027) (#63089)
Summary:
Optimization of relu and leaky_relu derivatives for reduction of VRAM needed for the backward-passes
Fixes https://github.com/pytorch/pytorch/issues/63027
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63089
Reviewed By: iramazanli
Differential Revision:
D30582049
Pulled By: albanD
fbshipit-source-id:
a9481fe8c10cbfe2db485e28ce80cabfef501eb8
Facebook Community Bot [Thu, 26 Aug 2021 22:18:37 +0000 (15:18 -0700)]
Automated submodule update: FBGEMM (#62879)
Summary:
This is an automated pull request to update the first-party submodule for [pytorch/FBGEMM](https://github.com/pytorch/FBGEMM).
New submodule commit: https://github.com/pytorch/FBGEMM/commit/
ce5470385723b0262b47250d6af05f1b734e4509
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62879
Test Plan: Ensure that CI jobs succeed on GitHub before landing.
Reviewed By: jspark1105
Differential Revision:
D30154801
fbshipit-source-id:
b2ce185da6f6cadf5128f82b15097d9e13e9e6a0
Mike Iovine [Thu, 26 Aug 2021 21:09:10 +0000 (14:09 -0700)]
[JIT] UseVariadicOp takes list_idx parameter (#63915)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63915
Previously, this function only worked for variadic op substitutions of the form `op(list, args) -> variadic_op(list_1, ..., list_n, args)`. This change allows for transformations of the form `op(args_0, list, args_1) -> variadic_op(args_0, list_1, ..., list_n, args_1)`.
Test Plan:
`buck test caffe2/test/cpp/jit:jit -- Stack Concat`
(tests exercising `list_idx != 0` will be added further up in this diff stack)
Reviewed By: navahgar
Differential Revision:
D30529729
fbshipit-source-id:
568080679c3b40bdaedee56bef2e8a5ce7985d2f
Can Balioglu [Thu, 26 Aug 2021 20:55:08 +0000 (13:55 -0700)]
[torch/elastic] Pretty print the failure message captured by @record (#64036)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64036
This PR slightly revises the implementation of the internal `_format_failure()` method in order to pretty print the error message captured in a subprocess by the `record` annotation.
With this PR a failure log is formatted as below:
```
Root Cause:
[0]:
time: 2021-08-26_17:12:07
rank: 0 (local_rank: 0)
exitcode: 1 (pid: 8045)
error_file: /tmp/torchelastic_6cj9eppm/
6d9d844a-6ce4-4838-93ed-1639a9525b00_rec9kuv3/attempt_0/0/error.json
msg:
{
"message": "ValueError: Test",
"extraInfo": {
"py_callstack": [
" File \"/data/home/balioglu/fail.py\", line 7, in <module>\n main()\n",
" File \"/fsx/users/balioglu/repos/pytorch/torch/distributed/elastic/multiprocessing/errors/__init__.py\", line 373, in wrapper\n error_handler.record_exception(e)\n",
" File \"/fsx/users/balioglu/repos/pytorch/torch/distributed/elastic/multiprocessing/errors/error_handler.py\", line 86, in record_exception\n _write_error(e, self._get_error_file_path())\n",
" File \"/fsx/users/balioglu/repos/pytorch/torch/distributed/elastic/multiprocessing/errors/error_handler.py\", line 26, in _write_error\n \"py_callstack\": traceback.format_stack(),\n"
],
"timestamp": "
1629997927"
}
}
```
in contrast to the old formatting:
```
Root Cause:
[0]:
time: 2021-08-26_17:15:50
rank: 0 (local_rank: 0)
exitcode: 1 (pid: 9417)
error_file: /tmp/torchelastic_22pwarnq/
19f22638-848c-4b8f-8379-677f34fc44e7_u43o9vs7/attempt_0/0/error.json
msg: "{'message': 'ValueError: Test', 'extraInfo': {'py_callstack': 'Traceback (most recent call last):\n File "/fsx/users/balioglu/repos/pytorch/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 351, in wrapper\n return f(*args, **kwargs)\n File "/data/home/balioglu/fail.py", line 5, in main\n raise ValueError("BALIOGLU")\nValueError: BALIOGLU\n', 'timestamp': '
1629998150'}}"
```
ghstack-source-id:
136761768
Test Plan: Run the existing unit tests.
Reviewed By: kiukchung
Differential Revision:
D30579025
fbshipit-source-id:
37df0b7c7ec9b620355766122986c2c77e8495ae
Ilqar Ramazanli [Thu, 26 Aug 2021 20:29:03 +0000 (13:29 -0700)]
To add Chained Scheduler to the list of PyTorch schedulers. (#63491)
Summary:
In this PR we are introducing ChainedScheduler which initially proposed in the discussion https://github.com/pytorch/pytorch/pull/26423#discussion_r329976246 .
The idea is to provide a user friendly chaining method for schedulers, especially for the cases many of them are involved and we want to have a clean and easy to read interface for schedulers. This method will be even more crucial once CompositeSchedulers and Schedulers for different type of parameters are involved.
The immediate application of Chained Scheduler is expected to happen in TorchVision Library to combine WarmUpLR and MultiStepLR https://github.com/pytorch/vision/blob/master/references/video_classification/scheduler.py#L5 . However, it can be expected that in many other use cases also this method could be applied.
### Example
The usage is as simple as below:
```python
sched=ChainedScheduler([ExponentialLR(self.opt, gamma=0.9),
WarmUpLR(self.opt, warmup_factor=0.2, warmup_iters=4, warmup_method="constant"),
StepLR(self.opt, gamma=0.1, step_size=3)])
```
Then calling
```python
sched.step()
```
would trigger step function for all three schedulers consecutively
Partially resolves https://github.com/pytorch/vision/issues/4281
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63491
Reviewed By: datumbox, mruberry
Differential Revision:
D30576180
Pulled By: iramazanli
fbshipit-source-id:
b43f0749f55faab25079641b7d91c21a891a87e4
Shiyan Deng [Thu, 26 Aug 2021 20:06:46 +0000 (13:06 -0700)]
[fx_acc] [fx2trt] add acc op mapper for argmin and converter for topk (#63823)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63823
Add mapper for `torch.argmin` which maps it to `acc_ops.flatten` (optional) + `acc_ops.topk` + `acc_ops.getitem` + `acc_ops.squeeze` (optional). This diff doesn't allow mapping if `dim=None && keepdim=True` in `torch.argmin`.
Add fx2trt converter for `acc_ops.topk`.
Test Plan:
buck test mode/opt glow/fb/fx/oss_acc_tracer:test_acc_tracer -- test_argmin
buck run mode/opt caffe2/torch/fb/fx2trt:test_topk
Reviewed By: jfix71
Differential Revision:
D30501771
fbshipit-source-id:
0babc45e69bac5e61ff0b9b4dfb98940398e3e57
Don Jang [Thu, 26 Aug 2021 19:58:05 +0000 (12:58 -0700)]
[Static Runtime] Add native op for aten::expand_as (#64024)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64024
`aten::expand_as` creates a view of the input tensor. This change adds its native op implementation for the static runtime.
Test Plan: - Added `StaticRuntime.IndividualOps_ExpandAs`
Reviewed By: hlu1
Differential Revision:
D30546851
fbshipit-source-id:
e53483048af890bc41b6192a1ab0c5ba0ee2bdc0
Meghan Lele [Thu, 26 Aug 2021 19:48:01 +0000 (12:48 -0700)]
Back out "[ONNX] Fix an issue that optimizations might adjust graph inputs unexpectedly. (#61280)" (#64004)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64004
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63904
Fixes T98808160
Test Plan: T98808160
Reviewed By: msaroufim
Differential Revision:
D30527450
fbshipit-source-id:
6262901a78ca929cecda1cf740893139aa26f1b4
Ansley Ussery [Thu, 26 Aug 2021 19:14:32 +0000 (12:14 -0700)]
Allow uncompiled strings as input to `checkScriptRaisesRegex` (#63901)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63901
cc gmagogsfm
Test Plan: Imported from OSS
Reviewed By: gmagogsfm
Differential Revision:
D30579472
Pulled By: ansley
fbshipit-source-id:
59ee09c1f25278d4f6e51f626588251bd095c6ea
Luca Wehrstedt [Thu, 26 Aug 2021 19:08:00 +0000 (12:08 -0700)]
Leverage TensorPipe's automatic SHM address selection (#63028)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63028
TensorPipe until now required PyTorch to come up and provide a unique identifier to use as address for the UNIX domain socket used in the SHM transport. However the Linux kernel can automatically assign an available address (like it does with IP ports), and TensorPipe now supports it, so we can remove that useless PyTorch logic.
Test Plan: CI
Reviewed By: mrshenli
Differential Revision:
D30220352
fbshipit-source-id:
78e8a6ef5916b2a72df26cdc9cd367b9d083e821
Erjia Guan [Thu, 26 Aug 2021 17:21:48 +0000 (10:21 -0700)]
Rename IterableAsDataPipe to IterableWrapper (#63981)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63981
Rename `IterableAsDataPipe` to `IterableWrapper` based on our naming convention `Op-er`
Test Plan: Imported from OSS
Reviewed By: VitalyFedyunin
Differential Revision:
D30554197
Pulled By: ejguan
fbshipit-source-id:
c2eacb20df5645d83ca165d6a1591f7e4791990f
Cheng Chang [Thu, 26 Aug 2021 16:52:42 +0000 (09:52 -0700)]
[NNC] Add C++ codegen backend to NNC (#62869)
Summary:
Adds a C++ codegen backend to NNC to generate C++ for CPU instead of generating LLVM IR.
Tensors are represented as blobs of float. Vector operations are devectorized/unrolled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62869
Test Plan:
https://github.com/pytorch/pytorch/tree/mvz-nnc-aot-prototype makes it able to AOT compile the whole MobileNetV3 model into binary code through LLVM codegen in NNC.
I forked that branch to https://github.com/cheng-chang/pytorch/tree/cc-aot-cpp, merged this PR into it, and modified `fancy_compile` to compile MobileNetV3 into C++ through
```
import torch
m = torch.jit.load('mobnet.pt')
m.eval()
f = torch.jit.freeze(m)
torch._C._fancy_compile(f.graph, [1, 3, 224, 224])
```
The generated C++ file `mobnet.cc` can be found at https://gist.github.com/cheng-chang/
e2830cc6920b39204ebf368035b2bcec.
I manually compiled the generated C++ through `g++ -o mobnet -std=c++14 -L./build/lib -ltorch_cpu -ltorch mobnet.cc`, and it succeeded.
Reviewed By: ZolotukhinM
Differential Revision:
D30149482
Pulled By: cheng-chang
fbshipit-source-id:
e77b189f0353e37cd309423a48a513e668d07675
Raghavan Raman [Thu, 26 Aug 2021 16:49:44 +0000 (09:49 -0700)]
[nnc] Sanitized the names of constants in the input graph. (#63990)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/63923
The input graph can contain constants whose names contain special characters. So, all names of constants in the input graph need to be sanitized.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63990
Reviewed By: ZolotukhinM
Differential Revision:
D30558432
Pulled By: navahgar
fbshipit-source-id:
de5b0c23d50ee8997f40f2c0fc605dda3719186f
Bert Maher [Thu, 26 Aug 2021 16:41:58 +0000 (09:41 -0700)]
[nnc] Fix dtype promotion involving scalars (#64002)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64002
Fixes https://github.com/pytorch/vision/issues/4315
Test Plan: Imported from OSS
Reviewed By: navahgar
Differential Revision:
D30566979
Pulled By: bertmaher
fbshipit-source-id:
eaa98b9534a926be7fcd337d46c5a0acb3243179
Jane Xu [Thu, 26 Aug 2021 16:27:47 +0000 (09:27 -0700)]
run_test.py: add option to run only core tests (#63976)
Summary:
This is in response to a feature request from some folks in the core team to have a local command that would only run relevant "core" tests. The idea is to have a local smoke test option for developers to run locally before making a PR in order to verify their changes did not break core functionality. These smoke tests are not targeted to be short but rather relevant.
This PR enables that by allowing developers to run `python test/run_test.py --core` or `python test/run_test.py -core` in order to run the CORE_TEST_LIST, which is currently test_nn.py, test_torch.py, and test_ops.py.
I am not the best person to judge what should be considered "core", so please comment which tests should be included and/or excluded from the CORE_TEST_LIST!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63976
Test Plan:
```
(pytorch) janeyx@janeyx-mbp test % python run_test.py --core -v
Selected tests: test_nn, test_ops, test_torch
Running test_nn ... [2021-08-25 14:48:28.865078]
Executing ['/Users/janeyx/miniconda3/envs/pytorch/bin/python', 'test_nn.py', '-v'] ... [2021-08-25 14:48:28.865123]
test_to (__main__.PackedSequenceTest) ... ok
test_to_memory_format (__main__.PackedSequenceTest) ... ok
```
Reviewed By: walterddr
Differential Revision:
D30575560
Pulled By: janeyx99
fbshipit-source-id:
3f151982c1e315e50e60cb0d818adaea34556a04
Don Jang [Thu, 26 Aug 2021 15:08:53 +0000 (08:08 -0700)]
[Static Runtime] Disable out variant of aten::clone (#63980)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63980
The out variant implementation of `aten::clone` causes a crash, which needs further investigation. This change disables it until the problem gets fixed.
Note that `inline_cvr` doesn't use `aten::clone` as of now, so no perf implication: https://www.internalfb.com/phabricator/paste/view/P446858755?lines=121
Test Plan: N/A
Reviewed By: hlu1
Differential Revision:
D30544149
fbshipit-source-id:
facb334d67473f622b36862fbdb2633358556fdf
Rong Rong (AI Infra) [Thu, 26 Aug 2021 15:00:48 +0000 (08:00 -0700)]
[CI] move distributed test into its own CI job (#62896)
Summary:
Moving distributed to its own job.
- [x] ensure there should be a distributed test job for every default test job matrix (on GHA)
- [x] ensure that circleci jobs works for distributed as well
- [x] waiting for test distributed to have its own run_test.py launch options, see https://github.com/pytorch/pytorch/issues/63147
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62896
Reviewed By: seemethere
Differential Revision:
D30230856
Pulled By: walterddr
fbshipit-source-id:
0cad620f6cd9e56c727c105458d76539a5ae976f
albanD [Thu, 26 Aug 2021 14:48:20 +0000 (07:48 -0700)]
remove special grad_mode tls handling (#63116)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63116
This PR removes the special flag to disable grad mode tracking on the ThreadLocalState and replaces it with an explicit setter that users can use.
This allows to reduce complexity of ThreadLocalState.
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision:
D30388098
Pulled By: albanD
fbshipit-source-id:
85641b3d711179fb78ff6a41ed077548dc821a2f
Heitor Schueroff [Thu, 26 Aug 2021 14:17:24 +0000 (07:17 -0700)]
Added API tests to ReductionOpInfo and ported amax/amin/nansum tests (#62899)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/62899
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision:
D30408816
Pulled By: heitorschueroff
fbshipit-source-id:
6cb0aa7fa7edba93549ef873baa2fb8a003bd91d
Edward Yang [Thu, 26 Aug 2021 13:58:12 +0000 (06:58 -0700)]
Deify opmath_t into its own header, align with accscalar_t (#63986)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63986
Fixes #63985
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision:
D30555996
Pulled By: ezyang
fbshipit-source-id:
b6e4d56a5658ed028ffc105cc4b479faa6882b65
Heitor Schueroff [Thu, 26 Aug 2021 13:05:28 +0000 (06:05 -0700)]
[OpInfo] Added ReductionOpInfo subclass of OpInfo and ported sum test (#62737)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62737
ReductionOpInfo is a specialization of OpInfo for reduction operators. For now, it is designed to work with reductions that return a single tensor and that reduce all elements along one or more dimensions to a single value. In particular this excludes operators such as `max` and `min` that return multiple tensors and `quantile` that can return multiple values.
fixes https://github.com/pytorch/pytorch/issues/49746
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision:
D30406568
Pulled By: heitorschueroff
fbshipit-source-id:
218b1da1902f67bcf4c3681e2a0f0029a25d51f1
Luca Wehrstedt [Thu, 26 Aug 2021 12:43:05 +0000 (05:43 -0700)]
Update TensorPipe submodule
Summary: The bot failed to do it.
Test Plan:
D30542677
Reviewed By: beauby
Differential Revision:
D30573500
fbshipit-source-id:
50abd6fc415cead0a6b6d9290fa0e5f97d0e4989
Michael Dagitses [Thu, 26 Aug 2021 11:42:36 +0000 (04:42 -0700)]
use `const auto&` as type for grad alias (#63949)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63949
This is an extension of the discussion in
https://github.com/pytorch/pytorch/pull/63040#discussion_r687793027.
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision:
D30546789
Pulled By: dagitses
fbshipit-source-id:
3046aff4f129d5492d73dfb67717a824e16ffee8
Kefei Lu [Thu, 26 Aug 2021 07:51:53 +0000 (00:51 -0700)]
Add logging for _MinimizerBase
Summary: Add logging so we know which nodes are currently being visited
Test Plan: lint & SC tests
Reviewed By:
842974287
Differential Revision:
D30509865
fbshipit-source-id:
09e77e44c97c825242e0b24f90463b50f3ca19c6
Rohan Varma [Thu, 26 Aug 2021 06:48:58 +0000 (23:48 -0700)]
Fix issue re: DDP and create_graph=True (#63831)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63831
Closes https://github.com/pytorch/pytorch/issues/63812
`at::mul_out` is not supported when `grad` itself requires grad, which is useful for computing higher order derivatives.
In this case, fall back to a mul + copy instead of mul_out.
ghstack-source-id:
136614644
Test Plan: UT
Reviewed By: SciPioneer
Differential Revision:
D30505573
fbshipit-source-id:
83532b6207b3d80116fcc4dff0e5520d73b3454f
Marjan Fariborz [Thu, 26 Aug 2021 06:40:09 +0000 (23:40 -0700)]
Adding BFP16 quantization/dequantization support to OSS (#63059)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63059
Supporting BFP16 quantization method to OSS. Currently only support CPU
ghstack-source-id:
136639528
Test Plan: Imported from OSS
Reviewed By: wanchaol
Differential Revision:
D30194538
fbshipit-source-id:
ac248567ad8028457c2a91b77ef2ce81709fce53
Kiuk Chung [Thu, 26 Aug 2021 05:56:33 +0000 (22:56 -0700)]
(torch.distributed) Add torch.distributed.is_torchelastic_launched() util method + make init_method=tcp:// compatible with torchelastic (#63910)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63910
Addresses the current issue that `init_method=tcp://` is not compatible with `torch.distributed.run` and `torch.distributed.launch`. When running with a training script that initializes the process group with `init_method=tcp://localhost:$port` as such:
```
$ python -u -m torch.distributed.run --max_restarts 0 --nproc_per_node 1 --nnodes 1 --master_addr $(hostname) --master_port 6000 ~/tmp/test.py
```
An `Address in use` error is raised since the training script tries to create a TCPStore on port 6000, which is already taken since the elastic agent is already running a TCPStore on that port.
For details see: https://github.com/pytorch/pytorch/issues/63874.
This change does a couple of things:
1. Adds `is_torchelastic_launched()` check function that users can use in the training scripts to see whether the script is launched via torchelastic.
1. Update the `torch.distributed` docs page to include the new `is_torchelastic_launched()` function.
1. Makes `init_method=tcp://` torchelastic compatible by modifying `_tcp_rendezvous_handler` in `torch.distributed.rendezvous` (this is NOT the elastic rendezvous, it is the old rendezvous module which is slotted for deprecation in future releases) to check `is_torchelastic_launched()` AND `torchelastic_use_agent_store()` and if so, only create TCPStore clients (no daemons, not even for rank 0).
1. Adds a bunch of unittests to cover the different code paths
NOTE: the issue mentions that we should fail-fast with an assertion on `init_method!=env://` when `is_torchelastic_launched()` is `True`. There are three registered init_methods in pytorch: env://, tcp://, file://. Since this diff makes tcp:// compatible with torchelastic and I've validated that file is compatible with torchelastic. There is no need to add assertions. I did update the docs to point out that env:// is the RECOMMENDED init_method. We should probably deprecate the other init_methods in the future but this is out of scope for this issue.
Test Plan: Unittests.
Reviewed By: cbalioglu
Differential Revision:
D30529984
fbshipit-source-id:
267aea6d4dad73eb14a2680ac921f210ff547cc5
Joseph Spisak [Thu, 26 Aug 2021 05:49:22 +0000 (22:49 -0700)]
Update persons_of_interest.rst (#63907)
Summary:
Fixes #{issue number}
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63907
Reviewed By: jspisak
Differential Revision:
D30534972
Pulled By: dzhulgakov
fbshipit-source-id:
ba726fc53e292a362c387cc8b5f7776ca2a2544c
Philip Meier [Thu, 26 Aug 2021 05:04:44 +0000 (22:04 -0700)]
enable equal_nan for complex values in isclose (#63571)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/63571
Test Plan: Imported from OSS
Reviewed By: malfet, ngimel
Differential Revision:
D30560127
Pulled By: mruberry
fbshipit-source-id:
8958121ca24e7c139d869607903aebbe87bc0740
nikithamalgi [Thu, 26 Aug 2021 04:47:50 +0000 (21:47 -0700)]
Clean up related to type refinements (#62444)
Summary:
Creates a helper function to refine the types into a torchScript compatible format in the monkeytype config for profile directed typing
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62444
Reviewed By: malfet
Differential Revision:
D30548159
Pulled By: nikithamalgifb
fbshipit-source-id:
7c09ce5f5e043d069313b87112837d7e226ade1f
Zeina Migeed [Thu, 26 Aug 2021 03:42:14 +0000 (20:42 -0700)]
inference for algebraic expressions (#63822)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63822
Infer algebraic expressions and add it to our symbolic inferencer. Works for conv2D and can be extended to other operations.
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision:
D30518469
Pulled By: migeed-z
fbshipit-source-id:
b92dfa40b2d834a535177da42b851701b8f7178c
Zafar Takhirov [Thu, 26 Aug 2021 03:37:56 +0000 (20:37 -0700)]
[quant] Fixing the conversion of the quantizable RNN (#63879)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63879
Quantizable RNN had a bug, where the `from_observed` was an instance method, instead of a class method. This caused the `tq.convert` to fail. This fixes the issue by making the `from_observed` a classmethod.
The tests were passing before because the unittests were not using the custom module path, but a conventional `from_float`, which is also supported.
Test Plan:
`buck test mode/dev //caffe2/test:quantization -- test_custom_module_lstm`
```
buck test mode/dev //caffe2/test:quantization -- test_custom_module_lstm
Parsing buck files: finished in 0.5 sec
Downloaded 0/2 artifacts, 0.00 bytes, 100.0% cache miss (for updated rules)
Building: finished in 9.2 sec (100%) 12622/12622 jobs, 2/12622 updated
Total time: 9.7 sec
More details at https://www.internalfb.com/intern/buck/build/
0d87b987-649f-4d06-b0e2-97b5077
Tpx test run coordinator for Facebook. See https://fburl.com/tpx for details.
Running with tpx session id:
cb99305f-65c9-438b-a99f-
a0a2a3089778
Trace available for this run at /tmp/tpx-
20210824-115652.540356/trace.log
Started reporting to test run: https://www.internalfb.com/intern/testinfra/testrun/
5066549645030046
✓ ListingSuccess: caffe2/test:quantization - main (12.550)
✓ Pass: caffe2/test:quantization - test_custom_module_lstm (quantization.core.test_quantized_op.TestQuantizedOps) (174.867)
Summary
Pass: 1
ListingSuccess: 1
If you need help understanding your runs, please follow the wiki: https://fburl.com/posting_in_tpx_users
Finished test run: https://www.internalfb.com/intern/testinfra/testrun/
5066549645030046
```
Reviewed By: jerryzh168, mtl67
Differential Revision:
D30520473
fbshipit-source-id:
bc5d0b5bb079fd146e2614dd42526fc7d4d4f3c6
Zhengxu Chen [Thu, 26 Aug 2021 03:09:12 +0000 (20:09 -0700)]
Make frozen symbol name customizable in torch deploy. (#63817)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63817
ghstack-source-id:
136699671
Test Plan: eyes
Reviewed By: wconstab
Differential Revision:
D29571559
fbshipit-source-id:
8e3caa4932ef8d7c8559f264f0e9bb5474ad2237
Natalia Gimelshein [Thu, 26 Aug 2021 01:17:10 +0000 (18:17 -0700)]
Compute cuda reduction buffer size in elements (#63969)
Summary:
Resubmit of https://github.com/pytorch/pytorch/issues/63885
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63969
Reviewed By: mruberry
Differential Revision:
D30549423
Pulled By: ngimel
fbshipit-source-id:
b16d25030d44ced789c125a333d72b02a8f45067
Jerry Zhang [Thu, 26 Aug 2021 00:50:48 +0000 (17:50 -0700)]
Back out "Revert
D30384746: [fx2trt] Add a test for quantized resnet18" (#63973)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63973
Original commit changeset:
b93235323e22
Test Plan: buck run mode/opt -c python.package_style=inplace caffe2:fx2trt_quantized_resnet_test
Reviewed By:
842974287
Differential Revision:
D30546036
fbshipit-source-id:
2c8302456f072d04da00cf9ad97aa8304bc5e43e
Philip Meier [Wed, 25 Aug 2021 23:42:14 +0000 (16:42 -0700)]
replace `self.assertTrue(torch.allclose(..))` with `self.assertEqual(…)` (#63637)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/63565
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63637
Reviewed By: malfet
Differential Revision:
D30541266
Pulled By: mruberry
fbshipit-source-id:
ab461949782c6908a589ea098fcfcf5c3e081ee6
David Riazati [Wed, 25 Aug 2021 22:54:31 +0000 (15:54 -0700)]
Remove render_test_results job (#63877)
Summary:
This removes the `render_test_results` job we had before which had been causing some confusion among devs when it failed and isn't really necessary now that we can actually render test results on the PR HUD.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63877
Reviewed By: walterddr, janeyx99
Differential Revision:
D30546705
Pulled By: driazati
fbshipit-source-id:
55fdafdb6f80924d941ffc15ee10787cb54f34a1
John Clow [Wed, 25 Aug 2021 22:27:37 +0000 (15:27 -0700)]
[EASY] Update the clang-tidy error message (#63370)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63370
As shown by this CI run, the actual thing that is incorrect is the prompt.
https://github.com/pytorch/pytorch/actions/runs/
1137298261
The CI runs the below command instead of the original command.
The original command errors out when importing another file on line 1.
Trying to fix the code to work with the original command causes the CI to error out.
We should actually ask the user to run
`python3 -m tools.linter.install.clang_tidy`
Test Plan: Imported from OSS
Reviewed By: janeyx99, heitorschueroff
Differential Revision:
D30530216
Pulled By: Gamrix
fbshipit-source-id:
2a2b8d539dcc2839e4000c13e82c207fa89bfc9f
Peter Bell [Wed, 25 Aug 2021 22:05:14 +0000 (15:05 -0700)]
Shard python_torch_functions.cpp (#62187)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62187
This file can take 3 minutes on its own to compile, and after
python_functions.cpp is the second limiting factor for compile time of
`libtorch_python` on a 32-core threadripper. This splits it into 3 files that
take around 1 minute each to compile.
Test Plan: Imported from OSS
Reviewed By: H-Huang
Differential Revision:
D29962048
Pulled By: albanD
fbshipit-source-id:
99016d75912bff483fe21b130cef43a6882f8c0e
Jithun Nair [Wed, 25 Aug 2021 22:00:47 +0000 (15:00 -0700)]
Add note on ifdefing based on CUDA_VERSION for ROCm path (#62850)
Summary:
CUDA_VERSION and HIP_VERSION follow very unrelated versioning schemes, so it does not make sense to use CUDA_VERSION to determine the ROCm path. This note explicitly addresses it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62850
Reviewed By: mruberry
Differential Revision:
D30547562
Pulled By: malfet
fbshipit-source-id:
02990fa66a88466c2330ab85f446b25b78545150
John Clow [Wed, 25 Aug 2021 21:49:06 +0000 (14:49 -0700)]
Small fixes to the Contributing.txt (#63385)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63385
Correcting a mistake for the pytorch uninstall, and
adding an extra note for Darwin.
Test Plan: Imported from OSS
Reviewed By: janeyx99, heitorschueroff
Differential Revision:
D30530234
fbshipit-source-id:
e0f88a1725eeadabfb4b28c1da11e369ee878ab4
Rong Rong (AI Infra) [Wed, 25 Aug 2021 21:34:40 +0000 (14:34 -0700)]
Back out "Temporary fix for remote gpu execution issue" (#63983)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63983
Test for fixes in
D30545351. it should resolve the remote execution flag being populated incorrectly issue.
Test Plan: CI
Reviewed By: malfet, seemethere
Differential Revision:
D30549443
fbshipit-source-id:
b3895909f5cd654ba163b77950872b332fbad3fe
Priya Ramani [Wed, 25 Aug 2021 20:08:12 +0000 (13:08 -0700)]
Shape Propagation Pass: Fix AdaptiveAveragePooling2d (#63629)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/63629
Test Plan: Imported from OSS
Reviewed By: ZolotukhinM
Differential Revision:
D30461727
Pulled By: priyaramani
fbshipit-source-id:
3873d1d636f79185680b82de06174d8de288c941
driazati [Wed, 25 Aug 2021 19:58:24 +0000 (12:58 -0700)]
Move existing target determinator to tools (#63809)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63809
This moves out the modulefinder determinator to `tools/testing` since it is supposed to be CI-only. This also simplifies run_test.py a little bit.
Test Plan: Imported from OSS
Reviewed By: malfet, seemethere, janeyx99
Differential Revision:
D30497438
Pulled By: driazati
fbshipit-source-id:
1d203037af5af6a20c1e7812da935e7cbb5cd82f
Yi Wang [Wed, 25 Aug 2021 19:46:09 +0000 (12:46 -0700)]
Add a comment on the potential implicit type up-casting (#63905)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63905
as title
ghstack-source-id:
136590703
Test Plan: N/A
Reviewed By: mrshenli
Differential Revision:
D30527929
fbshipit-source-id:
69402bbfa87cfd8fc166ce313cde9736ee072589
mingfeima [Wed, 25 Aug 2021 18:53:52 +0000 (11:53 -0700)]
add BFloat16 support for bernoulli and Dropout on CPU (#56372)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/56372
Test Plan: Imported from OSS
Reviewed By: heitorschueroff
Differential Revision:
D28836792
Pulled By: VitalyFedyunin
fbshipit-source-id:
ede951d172a59276e11383fd767778ab959b5a6b
Howard Huang [Wed, 25 Aug 2021 18:53:24 +0000 (11:53 -0700)]
Update torch.distributed.run OMP_NUM_THREADS message to log.warning (#63953)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63953
Closes #61138
Test:
`python -m torch.distributed.run --nproc_per_node 2 test.py`
Still outputs message
`LOGLEVEL=ERROR python -m torch.distributed.run --nproc_per_node 2 test.py`
Does not output message anymore
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse agolynski SciPioneer H-Huang mrzzd cbalioglu gcramer23
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision:
D30542997
Pulled By: H-Huang
fbshipit-source-id:
e7da30dcda51516abf4e56f1f510132e44397027
zhouzhuojie [Wed, 25 Aug 2021 18:30:28 +0000 (11:30 -0700)]
Fix ciflow/all label generation (#63954)
Summary:
the `ciflow/all` is automatically added but need to be added before we call `gen_root_job_condition`.
- fix the order of adding `ciflow/all`
- refactor all the string into global constants
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63954
Reviewed By: malfet
Differential Revision:
D30545596
Pulled By: zhouzhuojie
fbshipit-source-id:
83ab668f0234488afb855a72e3ebd4503f7f1a78
driazati [Wed, 25 Aug 2021 18:19:49 +0000 (11:19 -0700)]
Reformat run_test.py (#63808)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63808
`black run_test.py`
Test Plan: Imported from OSS
Reviewed By: seemethere
Differential Revision:
D30497437
Pulled By: driazati
fbshipit-source-id:
41b29b73f41fa4bb15fce5eaa69f8efe614e02f7
Raghavan Raman [Wed, 25 Aug 2021 18:12:57 +0000 (11:12 -0700)]
[Static Runtime] Added caching for the NNC code generated for Logit. (#63840)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63840
Added NNC generated code for Logit to the cache.
```
Logit NNC Benchmark Time (ns)
w/o cache w/ cache
logit_nnc_sleef/64 543 536
logit_nnc_sleef/512 3517 3465
logit_nnc_sleef/8192 88483 85881
logit_nnc_sleef/32768 337016 323090
logit_nnc_fast/64 167 163
logit_nnc_fast/512 866 817
logit_nnc_fast/8192 13069 12801
logit_nnc_fast/32768 53429 52530
logit_nnc_vml/64 164 151
logit_nnc_vml/512 783 769
logit_nnc_vml/8192 11563 11674
logit_nnc_vml/32768 46720 46452
```
Test Plan: Unit tests and inline_cvr model.
Reviewed By: hlu1
Differential Revision:
D30405424
fbshipit-source-id:
938b1b74758e2612ae151bac890c5f8ebbc42d50
Raghavan Raman [Wed, 25 Aug 2021 18:12:57 +0000 (11:12 -0700)]
[Static Runtime] Added a variable for clamp in the NNC code for Logit. (#63839)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63839
Replaced the use of a constant for clamp in the NNC code for Logit
with a variable. This makes it easier to enable caching for Logit.
There is no performance difference with this change, as shown in the micro-benchmarks below.
```
Logit NNC Benchmark Time (ns)
const-clamp var-clamp
logit_nnc_sleef/64 550 543
logit_nnc_sleef/512 3514 3517
logit_nnc_sleef/8192 85537 82900
logit_nnc_sleef/32768 347635 337016
logit_nnc_fast/64 173 167
logit_nnc_fast/512 829 866
logit_nnc_fast/8192 13286 13069
logit_nnc_fast/32768 51116 53429
logit_nnc_vml/64 146 164
logit_nnc_vml/512 773 783
logit_nnc_vml/8192 11556 11563
logit_nnc_vml/32768 44815 46720
```
Test Plan: SR unit tests and the inline_cvr model.
Reviewed By: bertmaher
Differential Revision:
D30405466
fbshipit-source-id:
adb891fdae5746439931ce5f43165291fec08f52
Raghavan Raman [Wed, 25 Aug 2021 18:12:57 +0000 (11:12 -0700)]
[Static Runtime] Moved NNC operator definitions to separate files. (#63838)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63838
Refactored NNC operator definitions code into separate files.
Made `TEWrapper` a class with a fixed set of methods and added separate definitions for them based on `TORCH_ENABLE_LLVM` to keep the same functionality as before.
Test Plan: Build and ran Static Runtime tests.
Reviewed By: hlu1
Differential Revision:
D30405467
fbshipit-source-id:
606ef852bb820d5e23a0f8af1bf5dc122e90bceb
Aayush Prakash [Wed, 25 Aug 2021 18:11:08 +0000 (11:11 -0700)]
[Reland] Replacing the p.data acccess in utils with tensor.set_ . Passes both test_post_localSGD_optimizer_pari and test_periodic_model_averager tests (#63895)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63895
When updating the model parameter, updating `parameter.data` is no longer recommended, because this `data` field will be deprecated in the future.
The replacement is `tensor.set_`.
ghstack-source-id:
136593433
Test Plan:
buck test mode/dev-nosan //caffe2/test/distributed:distributed_nccl_spawn -- test_periodic_model_averager
buck test mode/dev-nosan //caffe2/test/distributed:distributed_nccl_spawn -- test_post_localSGD_optimizer_parity
Reviewed By: SciPioneer
Differential Revision:
D30526178
fbshipit-source-id:
a1ac0ec3665d8623edd5bf94f01c1132daff5c00
albanD [Wed, 25 Aug 2021 18:07:24 +0000 (11:07 -0700)]
clean up engine.cpp thread state (#63115)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63115
This actually changes:
- callbacks now run with proper grad mode even in worker threads
- graphtask's Future callbacks now run with proper TLS when erroring
out from a worker thread
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision:
D30388100
Pulled By: albanD
fbshipit-source-id:
7ae9c461c2f0040548dd9e1e314f25e8da0c2e67
Shiyan Deng [Wed, 25 Aug 2021 17:22:17 +0000 (10:22 -0700)]
[fx2trt] Check input device in TRTModule (#63893)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63893
Add a check to ensure all the inputs are on cuda device.
Test Plan: CI
Reviewed By: kflu, houseroad
Differential Revision:
D30525265
fbshipit-source-id:
6e50b70fd535defc1f802d51e8bb991b2dd73741
riship [Wed, 25 Aug 2021 16:56:41 +0000 (09:56 -0700)]
bf16 Error message cleanup as well as addition of is_bf16_supported (#63798)
Summary:
ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63798
Reviewed By: heitorschueroff
Differential Revision:
D30526187
Pulled By: ngimel
fbshipit-source-id:
c484aec14638097c96c720095d3491249b6b2d14
Karen Zhou [Wed, 25 Aug 2021 16:55:02 +0000 (09:55 -0700)]
[pruner] add getter for pruned outputs in base pruner (#63520)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63520
Rather than having to call `module.parametrizations.weight[0].pruned_outputs` each time we need to access the set of pruned indices, we add a getter `get_module_pruned_outputs` which takes the module as an argument and returns the set.
This is used for testing.
ghstack-source-id:
136561130
Test Plan:
` buck test mode/dev-nosan //caffe2/test:ao -- TestBasePruner`
https://pxl.cl/1N4gK
Reviewed By: z-a-f
Differential Revision:
D30374558
fbshipit-source-id:
e38dfee0879cadde52b942e899a3d8d7151ee493
Karen Zhou [Wed, 25 Aug 2021 16:55:02 +0000 (09:55 -0700)]
[pruner] add support for pruning BatchNorm2d (#63519)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63519
If the pruner should be pruning biases along with weights, then if the model has BatchNorm2d following pruned Conv2d layers, then the corresponding channels of the BatchNorm must also be pruned.
Specifically, they need to zeroed out, rather than fully removed, since in eager mode, the dimensions between layers need to be preserved.
To do this, we add a pruning parametrization called `ZeroesParametrization` which zeroes out pruned channels, rather than removing them.
The user must provide in the config, a tuple of the Conv2d and BatchNorm layers that go together. The `prepare` method will add the tuple to the `module_groups`; then it will add a PruningParametrization to the Conv2d layer, and a ZeroesParametrization to BatchNorm, and then set their pruned sets to be the same set. That way, during `step`, both masks are updated with the same pruned indices.
ghstack-source-id:
136562278
Test Plan:
`buck test mode/dev-nosan //caffe2/test:ao -- TestBasePruner`
https://pxl.cl/1N1P6
Reviewed By: z-a-f
Differential Revision:
D30349855
fbshipit-source-id:
3199d3688d5a70963f9b32d7a8fdac3962ae6a65
Peter Bell [Wed, 25 Aug 2021 16:35:26 +0000 (09:35 -0700)]
Minor OptionalTensorRef updates (#63611)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63611
A few minor updates to `OptionalTensorRef`:
1. use `Tensor`'s `unsafe_borrow_t` constructor which avoids an unnecesary `nullptr` check.
2. copy constructor cannot defer to the `const Tensor&` constructor because it checks the tensor is
defined, and so would fail for disengaged optionals.
3. use copy-swap idiom to avoid issues with self-assignment. `x = x` should be a no-op, but the old
version would clear `x`.
4. Add pointer-like access for consistency with `optional` and `MaybeOwned`
Test Plan: Imported from OSS
Reviewed By: bdhirsh
Differential Revision:
D30484704
Pulled By: ezyang
fbshipit-source-id:
738f4bd22359eaecd0a519a04e89a4b44d92da5b
Nikita Shulga [Wed, 25 Aug 2021 16:24:27 +0000 (09:24 -0700)]
Update CMake minimum version to 3.10 (#63660)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/63660
Test Plan: Imported from OSS
Reviewed By: janeyx99, mruberry
Differential Revision:
D30543878
fbshipit-source-id:
a7d938807653f39727f2cc7d7ca167200567b6a0
Rong Rong (AI Infra) [Wed, 25 Aug 2021 16:04:28 +0000 (09:04 -0700)]
Temporary fix for remote gpu execution issue (#63899)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63899
See: T99020845
Test Plan: sandcastle
Reviewed By: heitorschueroff
Differential Revision:
D30527384
fbshipit-source-id:
ce9933e5e181322c02d4ed17f3fdaabe4c5ba29e
Ansley Ussery [Wed, 25 Aug 2021 16:01:50 +0000 (09:01 -0700)]
Fix bug in `check_empty_containers` (#63492)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/63492
Test Plan: Imported from OSS
Reviewed By: bdhirsh
Differential Revision:
D30402749
Pulled By: ansley
fbshipit-source-id:
7de533355fe91ca4f45b2bafc3bfb205a028c1ed
Jane Xu [Wed, 25 Aug 2021 16:00:13 +0000 (09:00 -0700)]
Swap CUDA 11.1 and 11.3 in CI to make 11.1 periodic (#63900)
Summary:
Preparing for supporting 11.3 in the next release.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63900
Reviewed By: malfet
Differential Revision:
D30541437
Pulled By: janeyx99
fbshipit-source-id:
a7297da7f7818a4291b1c321d62d76fc2c0f1f90
zhouzhuojie [Wed, 25 Aug 2021 15:50:00 +0000 (08:50 -0700)]
[skip ci] Add generated comment to ruleset json (#63896)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/63896
Reviewed By: heitorschueroff
Differential Revision:
D30529820
Pulled By: zhouzhuojie
fbshipit-source-id:
7529803af23ea36a7bcb673cd399da80da8e3feb
Alban Desmaison [Wed, 25 Aug 2021 14:15:18 +0000 (07:15 -0700)]
Revert
D30526034: [pytorch][PR] compute reduction intermediate buffer size in elements
Test Plan: revert-hammer
Differential Revision:
D30526034 (https://github.com/pytorch/pytorch/commit/
e69a1398cbe534874060460faf36af21d24ce6e7)
Original commit changeset:
0aca7f887974
fbshipit-source-id:
a22472723818d6fe0c11a6e134080df1ac408038
Linbin Yu [Wed, 25 Aug 2021 07:42:03 +0000 (00:42 -0700)]
Revert
D30384746: [fx2trt] Add a test for quantized resnet18
Test Plan: revert-hammer
Differential Revision:
D30384746 (https://github.com/pytorch/pytorch/commit/
10dfa58eba055a1bbc1cc89df033cd2815cbb403)
Original commit changeset:
1a8638777116
fbshipit-source-id:
b93235323e229b391f5456f6e3543988062dd0d4