The nn.LSTM is quantized through the custom module mechanism, which uses the nn.quantizable.LSTM for both observed and quantized paths. This is potentially a source of confusion. This creates a `quantized.LSTM` class, which completely takes the quantized path. Note that after this, the old usage will throw an error.
New way of using it:
```
>>> custom_module_config = {
... 'float_to_observed_custom_module_class': {
... nn.LSTM: nn.quantizable.LSTM,
... },
... 'observed_to_quantized_custom_module_class': {
... nn.quantizable.LSTM: nn.quantized.LSTM,
... }
... }
>>> tq.prepare(model, prepare_custom_module_class=custom_module_config)
>>> tq.convert(model, convert_custom_module_class=custom_module_config)
```
Differential Revision: [D33451338](https://our.internmc.facebook.com/intern/diff/D33451338/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D33451338/)!
[ghstack-poisoned]
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71453
As title
Test Plan: unit test
Reviewed By: frank-wei
Differential Revision: D33646384
fbshipit-source-id: d86326c93e4d6bd59c9152592721f0e6ecf7f6fb
(cherry picked from commit d886380ede)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68945
This PR enables the Python conversion functions for `Storage` (specifically `UntypedStorage`) and also cleans up some remnants of the deprecated typed storages from `DynamicTypes.cpp`.
ghstack-source-id: 147245110
Test Plan: Run the existing unit and integration tests.
Reviewed By: albanD
Differential Revision: D32676505
fbshipit-source-id: 3a3f6db4fb0da5c78dd406c96ab70bdc37015521
(cherry picked from commit d6427b94cf)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70476
1) Support a single dimension for inputs
2) Test several error cases
Partially addresses https://github.com/pytorch/pytorch/issues/65638
ghstack-source-id: 146307607
Test Plan: waitforbuildbot
Reviewed By: fduwjj
Differential Revision: D33344357
fbshipit-source-id: 4de7a7177452951dbcce76f27441703447609e6f
(cherry picked from commit 96dfded569)
Summary:
Following subfolders of the project were identified as one that can be
merged on github first and then asynchronously merged into Meta
codebase:
## ONNX exporter
PRs that include only files under `torch/onnx`, `torch/csrc/jit/passes/onnx` and `test/onnx` and are reviewed by garymm
## CUDA fusers
PRs that include only files under `torch/csrc/jit/codegen/fuser/cuda`, `torch/csrc/jit/codegen/cuda` or `benchmarks/cpp/nvfuser` and reviewed by csarofeen or ngimel
## OSS CI
PR that include only files under `.circleci`, `.github` and `.jenkins` and reviewed either by seemethere or myself
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71514
Reviewed By: bigfootjon
Differential Revision: D33673050
Pulled By: malfet
fbshipit-source-id: 21b909d49cb73ff79879b3ea0568e53ef65aa08c
(cherry picked from commit 520226c1bf)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71140
Structured kernels need to use the borrowing variants of the build APIs to TensorIterator. (I am working on a debug check for this, but it is currently too strict, and relaxing it does not catch these bugs.)
ghstack-source-id: 147191022
Test Plan: CI
Reviewed By: bdhirsh
Differential Revision: D33520003
fbshipit-source-id: 3b0ff9036acdb78ae6fc7489ed0ed487d5ff080f
(cherry picked from commit 80ef4e14e3)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71423
Replacing this math with a load seems to improve perf.
ghstack-source-id: 147171800
Test Plan: ptvsc2_predictor_bench runs on model from mikeiovine courtesy of mikeiovine
Reviewed By: mikeiovine, xiaomengy
Differential Revision: D33552176
fbshipit-source-id: f21a4cd66c13b9fcb7bcf48f356bdc85e94c4216
(cherry picked from commit 0354fcb988)
Summary:
From https://github.com/pytorch/pytorch/issues/67626: RRefProxy (rref.rpc_async, rref.rpc_sync, rref.remote) currently uses a blocking RPC call to the owner
This is done by chaining async calls. In the sync case we wait on the
resulting Future.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70206
Test Plan:
I ran rpc_tests using tensorpipe_rpc_agent_test_fixture.py and had to
adjust test_rref_proxy_timeout to the new behavior.
I ran into test_tensorpipe_set_default_timeout failing due to the
timeout being too small. Doesn't look related to this change.
mrshenli
Fixes https://github.com/pytorch/pytorch/issues/67626
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Reviewed By: pritamdamania87
Differential Revision: D33243348
Pulled By: kumpera
fbshipit-source-id: e1e8c34bb3d170407c0a793e2e585357f905d3c6
(cherry picked from commit 1ad5a7ceea)
Summary:
The block and thread extent calculations in `cuda_codegen` should be using `int64_t` instead of `int`. The updated test, `test_dynamic_shapes`, fails without this change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71428
Reviewed By: samdow
Differential Revision: D33640374
Pulled By: navahgar
fbshipit-source-id: 64c340ad2a9a1fa1fe066cf1c5dfc3b546b7be6d
(cherry picked from commit 6ea546ce11)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71031
During the conversion stage, we might create some constants when size op is called and size is static. Raising error here causes problem for this case. Generally speaking it doesn't hurt to allow not const folding.
Test Plan:
Test with D33483843 on shufflenet.
Added unit tests.
Reviewed By: wushirong
Differential Revision: D33484183
fbshipit-source-id: 5b32c06297e56965befd7e83fe8ca273e3665cee
(cherry picked from commit e6b79bd3dd)
Summary:
This one, will react to `repo_dispatch` event sent by PyTorch Probot
when `pytorchbot merge this` command is issued
At the moment, workflow will only attempt to merge PRs which has not
been created from forked repo and that match rules defined in
`.github/merge_rules.json`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71488
Reviewed By: bigfootjon
Differential Revision: D33665142
Pulled By: malfet
fbshipit-source-id: e22daa1892523e62d7b7a941960636a6514cb7d7
(cherry picked from commit 92059bab07)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68953
This PR consolidates the almost identical lvalue and rvalue implementations of shallow_copy_and_detach into a single templated function.
ghstack-source-id: 147238376
Test Plan: Run existing unit tests.
Reviewed By: fduwjj
Differential Revision: D32679741
fbshipit-source-id: 89a870335d2e09ffd005c943733a787d20d352f9
(cherry picked from commit 750344c860)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70853
We support both configurations, so we should ensure they both work.
ghstack-source-id: 147170900
Test Plan: This is adding a test to CI.
Reviewed By: malfet
Differential Revision: D33304505
fbshipit-source-id: 7074b6b98d05f60801bb1d74bc9ac1458c768d28
(cherry picked from commit 8e4134b777)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70852
This is the first change that uses a common build file, build.bzl, to
hold most of the build logic.
ghstack-source-id: 147170895
Test Plan: Relying on internal and external CI.
Reviewed By: malfet
Differential Revision: D33299331
fbshipit-source-id: a66afffba6deec76b758dfb39bdf61d747b5bd99
(cherry picked from commit d9163c56f5)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70851
This is a step towards OSS/fbcode convergence since OSS uses this file
in both CMake and Bazel.
ghstack-source-id: 147170896
Test Plan: Relying on the extensive CI internal tests for this.
Reviewed By: malfet
Differential Revision: D33299102
fbshipit-source-id: c650dd4755f8d696d5fce81c583d5c73782e3990
(cherry picked from commit 741ca140c8)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71450
att
Test Plan: no test
Reviewed By: jfix71
Differential Revision: D33515471
fbshipit-source-id: ded40ca117f63c971d6c5ed4556932cc71c009ca
(cherry picked from commit a9f66d5921)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71457
Today DynamicType is hard to be created because we have separare APIs for different types. In this diff we introduce an easier API to create types like the following:
```
#include <ATen/core/type_factory.h>
auto type = dynT<ListType>(dynT<TensorType>()); // etc...
```
ghstack-source-id: 147211236
Test Plan: CI
Reviewed By: iseeyuan
Differential Revision: D33647746
fbshipit-source-id: c850cf31ae781244eac805906a2fc110ef065a70
(cherry picked from commit 8cfd51d75f)
Summary:
On a CPU-only build of pytorch `torch._C._jit_set_nvfuser_enabled(False)` would throw an error (even though it is a no-op operation), with this fix:
```
>>> torch._C._jit_set_nvfuser_enabled(False)
False
>>> torch._C._jit_set_nvfuser_enabled(True)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
RuntimeError: Running CUDA fuser is only supported on CUDA builds.
>>>
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71358
Reviewed By: eellison
Differential Revision: D33601135
Pulled By: jansel
fbshipit-source-id: c764df2fa197ce7b4f71e5df0a91cd988766e99c
(cherry picked from commit a801df9321)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71091
Fixes https://github.com/pytorch/pytorch/issues/65394
The masked sum on a full input tensor (of any layout) with an all-true mask is the same as the sum on the strided input tensor (after applying `to_dense` to sparse inputs).
Since masked sum uses `torch.sparse.sum` then, for the simplicity of masked reductions implementations, its reduction behavior ought to be defined by the behavior of the `torch.sum`. This PR implements the behavioral connection with respect to the directional summation of empty sparse tensors that correspond to all-zero strided tensors.
cc nikitaved pearu cpuhrsch
Test Plan: Imported from OSS
Reviewed By: davidberard98
Differential Revision: D33651750
Pulled By: cpuhrsch
fbshipit-source-id: 703891bff88c8da6270b4272f5d2da81688db67d
(cherry picked from commit 53f97e80f7)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69060
Saved variable hooks checkpointing was added in https://github.com/pytorch/pytorch/pull/69508, this PR adds some tests for DDP.
Specifically, we can support almost all DDP use cases with this new API, such as dynamic module with find_unused_parameters=True. One case remains to be supported, which is static_graph + non-reentrant based checkpointing. The underlying reason this does not work is https://github.com/pytorch/pytorch/issues/58111.
ghstack-source-id: 147219887
Test Plan: CI
Reviewed By: zhaojuanmao
Differential Revision: D32712126
fbshipit-source-id: ba5ae9ca77fd8929ee020c7dc97838bae9a1931b
(cherry picked from commit 9c7f93e217)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71462
Fixes
```
6 aienv/aienv_ig_reels_base:caffe2/modules/detectron/upsample_nearest_op.h:65:1: error: loop not vectorized: the optimizer was unable to perform the requested transformation; the transformation might be disabled or specified as part of an unsupported transformation ordering [-Werror,-Wpass-failed=transform-warning]
6 deep_entity_classification/si_dec_gnn:caffe2/modules/detectron/upsample_nearest_op.h:65:1: error: loop not vectorized: the optimizer was unable to perform the requested transformation; the transformation might be disabled or specified as part of an unsupported transformation ordering [-Werror,-Wpass-failed=transform-warning]
6 feed_recommendation_infra/multifeed_execution_graph_service_nosan:caffe2/modules/detectron/upsample_nearest_op.h:65:1: error: loop not vectorized: the optimizer was unable to perform the requested transformation; the transformation might be disabled or specified as part of an unsupported transformation ordering [-Werror,-Wpass-failed=transform-warning]
12 mobile_cv/mobile-vision_experimental:caffe2/modules/detectron/upsample_nearest_op.h:65:1: error: loop not vectorized: the optimizer was unable to perform the requested transformation; the transformation might be disabled or specified as part of an unsupported transformation ordering [-Werror,-Wpass-failed=transform-warning]
30 mobile_cv/mobile-vision_xraymobilev2_detection_caffe2:caffe2/modules/detectron/upsample_nearest_op.h:65:1: error: loop not vectorized: the optimizer was unable to perform the requested transformation; the transformation might be disabled or specified as part of an unsupported transformation ordering [-Werror,-Wpass-failed=transform-warning]
42 aienv/aienv:caffe2/modules/detectron/upsample_nearest_op.h:65:1: error: loop not vectorized: the optimizer was unable to perform the requested transformation; the transformation might be disabled or specified as part of an unsupported transformation ordering [-Werror,-Wpass-failed=transform-warning]
128 feed_recommendation_infra/multifeed_recagg_dev:caffe2/modules/detectron/upsample_nearest_op.h:65:1: error: loop not vectorized: the optimizer was unable to perform the requested transformation; the transformation might be disabled or specified as part of an unsupported transformation ordering [-Werror,-Wpass-failed=transform-warning]
136 fluent2/fblearner_flow_projects_fluent2_nosan:caffe2/modules/detectron/upsample_nearest_op.h:65:1: error: loop not vectorized: the optimizer was unable to perform the requested transformation; the transformation might be disabled or specified as part of an unsupported transformation ordering [-Werror,-Wpass-failed=transform-warning]
1338 f6/f6_nosan:caffe2/modules/detectron/upsample_nearest_op.h:65:1: error: loop not vectorized: the optimizer was unable to perform the requested transformation; the transformation might be disabled or specified as part of an unsupported transformation ordering [-Werror,-Wpass-failed=transform-warning]
```
Test Plan: Sandcastle
Reviewed By: luciang
Differential Revision: D33641869
fbshipit-source-id: 8424849cfac5cb0109272dec2086863067bbde66
(cherry picked from commit d18429905c)
Summary:
Reference https://github.com/pytorch/pytorch/issues/69991
Refactored such that only `out` variant copies the result into `out` otherwise we just return the result of the composite functions as is.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70894
Reviewed By: samdow
Differential Revision: D33641742
Pulled By: zou3519
fbshipit-source-id: 671be13b31a7fff3afc0b7976706a5ecfc51ccac
(cherry picked from commit e7d5ac9af3)
Summary:
The sccache compilation log is often misleading.
We can move it to its own group so people don't see it right away
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71444
Reviewed By: atalman
Differential Revision: D33659650
Pulled By: janeyx99
fbshipit-source-id: f22fd21640a8747beeacce8857bbb8281efd76f4
(cherry picked from commit e25970abf9)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70266
Addresses some of the issues mentioned in
https://github.com/pytorch/pytorch/issues/65638. ShardedLinear implementation
only support 2D inputs.
On the other hand `nn.Linear` supports arbitrary dimensions for inputs and
outputs. As a result, in this PR I've added support to ensure that
ShardedLinear supports arbitrary input dims as well.
ghstack-source-id: 147206607
Test Plan: waitforbuildbot
Reviewed By: wanchaol
Differential Revision: D33267630
fbshipit-source-id: 0460994c3aa33348b80547d9274206ef90cb29b6
(cherry picked from commit 7c289e1dbf)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71461
After operator versioning work, the version in model file is used for operator versioning, while bytecode_version is used for bytecode versioning (for bytecode schema). They are two seperate things now and this comparison is not needed.
ghstack-source-id: 147209286
Test Plan: CI
Reviewed By: iseeyuan, tugsbayasgalan
Differential Revision: D33648592
fbshipit-source-id: beaa136a728f88435176a00c07b2d521210f107f
(cherry picked from commit e90e650e1a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70615
This adds `at::detail::empty_meta` and
`at::detail::empty_strided_meta` to complement the cpu API.
Test Plan: Imported from OSS
Reviewed By: samdow
Differential Revision: D33623678
Pulled By: ngimel
fbshipit-source-id: 59e003116361fb547ec2c633bbc15a7973e21d0e
(cherry picked from commit b4f5836fa1)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70614
This creates an `empty_strided_generic` function which, similar to
`empty_generic`, is a device-independent tensor constructor. This also
adds `at::detail::empty_strided_cpu` to complement
`at::detail::empty_cpu`.
Test Plan: Imported from OSS
Reviewed By: samdow
Differential Revision: D33623679
Pulled By: ngimel
fbshipit-source-id: 85994e88d664870bf425f398dfcdfc467885c694
(cherry picked from commit 2ff2a89df5)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71447
Changes the nightly build trigger to be based on pushes to the `nightly`
branch instead of being based on the tagged push. This aligns it with
our current CircleCI trigger and should make it so that it's easily
viewable using tools like https://hud.pytorch.org/ci/pytorch/pytorch/nightly
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D33647102
Pulled By: seemethere
fbshipit-source-id: c6757da35b7ec2d68bf36160dd7f3cb9ed040899
(cherry picked from commit 99b7b22650)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70613
This refactors `at::detail::empty_cpu` to use only `TensorBase` so you
can construct tensors without including `Tensor.h`. It also adds a
`TensorOptions` version to reduce friction in operators moving from
the `at::empty` API.
Test Plan: Imported from OSS
Reviewed By: samdow
Differential Revision: D33623682
Pulled By: ngimel
fbshipit-source-id: 7a7b08bc2ed06830a3d698197a0c8389a096dc1d
(cherry picked from commit 2e17ad0bbd)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71443
cogwheel test inline_cvr_infer_canary_pyper_model_publish is timing out.
The convert_fx call takes > 20 mins for local and local_ro sub modules, which used to take ~ 2 mins.
Test Plan:
Fblearn flow run
* the following cmd took 1113 seconds before the diff and 5002 seconds after.
flow-cli clone-locally 320014219 --run-as-secure-group pytorch_at_scale --operators pyper_model_publish_workflow.pyper_model_publish_workflow.process_torch_package_model_files.process_non_sparse_parameters[0]
Cogwheel test
* Cogwheel test with packages in B3588 (the last good run) took 4694.48s
* Cogwheel test with packages in B3590 (the first timeout) took 13975.83s
* Cogwheel test with the following packages took 4535.04s
* all packages in B3588 except the model publish
* the model publish built with D33469839 (043e84b3d2) reversed (created D33633570)
Reviewed By: albanD, jerryzh168
Differential Revision: D33633570
fbshipit-source-id: dc5e777c48a90c551641a3f79126461f6a60449e
(cherry picked from commit 03ab65023a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71417
I accidentally changed CPU_INSTANT_EVENT to CPU_OP, which broke TensorBoard.
Test Plan: Make memory profiling unit test check this case.
Reviewed By: aaronenyeshi
Differential Revision: D33637286
fbshipit-source-id: c95945f6b85cd4168820bd4d2a9203274a0a5bd6
(cherry picked from commit b1e258672a)
