Summary:
The premise of this approach is that a small subset of neural networks are well represented by a data flow graph. The README contains more information.
The name is subject to change, but I thought it was a cute reference to fire.
suo let me know if you'd prefer this in a different spot. Since it lowers a JIT'd module directly I assumed the JIT folder would be appropriate. There is no exposed Python interface yet (but is mocked up in `test_accelerant.py`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42753
Reviewed By: zou3519
Differential Revision: D23043771
Pulled By: bwasti
fbshipit-source-id: 5353731e3aae31c08b5b49820815da98113eb551
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42740
Adds a pass to hoist conv packed params to root module.
The benefit is that if there is nothing else in the conv module,
subsequent passes will delete it, which will reduce module size.
For context, freezing does not handle this because conv packed
params is a custom object.
Test Plan:
```
PYTORCH_JIT_LOG_LEVEL=">hoist_conv_packed_params.cpp" python test/test_mobile_optimizer.py TestOptimizer.test_hoist_conv_packed_params
```
Imported from OSS
Reviewed By: kimishpatel
Differential Revision: D23005961
fbshipit-source-id: 31ab1f5c42a627cb74629566483cdc91f3770a94
Summary:
[5/N] Implement Enum JIT support
Implement Enum class iteration
Add aten.ne for EnumType
Supported:
Enum-typed function arguments
using Enum type and comparing them
Support getting name/value attrs of enums
Using Enum value as constant
Support Enum-typed return values
Support iterating through Enum class (enum value list)
TODO:
Support serialization and deserialization
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42661
Reviewed By: SplitInfinity
Differential Revision: D22977364
Pulled By: gmagogsfm
fbshipit-source-id: 1a0216f91d296119e34cc292791f9aef1095b5a8
Summary:
in `_jit_pass_onnx`, symbolic functions are called for each node for conversion. However, there are nodes that cannot be converted without additional context. For example, the number of outputs from split (and whether it is static or dynamic) is unknown until the point where it is unpacked by listUnpack node. This pass does a preprocess, and prepares the nodes such that enough context can be received by the symbolic function.
* After preprocessing, `_jit_pass_onnx` should have enough context to produce valid ONNX nodes, instead of half baked nodes that replies on fixes from later postpasses.
* `_jit_pass_onnx_peephole` should be a pass that does ONNX specific optimizations instead of ONNX specific fixes.
* Producing more valid ONNX nodes in `_jit_pass_onnx` enables better utilization of the ONNX shape inference https://github.com/pytorch/pytorch/issues/40628.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41832
Reviewed By: ZolotukhinM
Differential Revision: D22968334
Pulled By: bzinodev
fbshipit-source-id: 8226f03c5b29968e8197d242ca8e620c6e1d42a5
Summary:
* move both under new file `fixup_onnx_controlflow`
* move the fixup to where the ONNX loop/if node is created, as oppose to running the fixup as postpass. This will help with enable onnx shape inference later.
* move `fuseSequenceSplitConcat` to `Peephole`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40943
Reviewed By: mrshenli
Differential Revision: D22709999
Pulled By: bzinodev
fbshipit-source-id: 51d316991d25dc4bb4047a6bb46ad1e2401d3d2d
Summary:
Raise and assert used to have a hard-coded error message "Exception". User provided error message was ignored. This PR adds support to represent user's error message in TorchScript.
This breaks backward compatibility because now we actually need to script the user's error message, which can potentially contain unscriptable expressions. Such programs can break when scripting, but saved models can still continue to work.
Increased an op count in test_mobile_optimizer.py because now we need aten::format to form the actual exception message.
This is built upon an WIP PR: https://github.com/pytorch/pytorch/pull/34112 by driazati
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41907
Reviewed By: ngimel
Differential Revision: D22778301
Pulled By: gmagogsfm
fbshipit-source-id: 2b94f0db4ae9fe70c4cd03f4048e519ea96323ad
Summary:
[3/N] Implement Enum JIT support
* Add enum value as constant support
* Add sugared value for EnumClass
Supported:
Enum-typed function arguments
using Enum type and comparing them
Support getting name/value attrs of enums
Using Enum value as constant
TODO:
Add PyThon sugared value for Enum
Support Enum-typed return values
Support serialization and deserialization
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42085
Reviewed By: eellison
Differential Revision: D22758042
Pulled By: gmagogsfm
fbshipit-source-id: 5c6e571686c0b60d7fbad59503f5f94b3b3cd125
Summary:
Currently constant pooling runs before const propagation, which can create more constants that need pooling. This can get in the way of serialization/deserialization stability because each time user serializes and deserializes a module, runCleanUpPasses is called upon it. Doing so multiple times would lead to different saved module.
This PR moves constant pooling after const propagation, which may slow down const propagation a little bit, but would otherwise side-step aforementioned problem.
test_constant_insertion in test_jit.py is also updated because after fixing the pass ordering, the number of constants is no longer a constant and it is extremely difficult to get the exact number with the current convoluted test structure. So for now, I changed the test to check only that CSE doesn't change number of "prim::constant" rather than comparing against a known number. Also left a TODO to improve this test.
ConstantPropagation pass is replaced by ConstantPropagationImmutableTypes because the latter is used in runCleanUpPasses. If not replaced, the former would create new CSE opportunities by folding more constants. This voids the purpose of the test case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41891
Reviewed By: colesbury
Differential Revision: D22701540
Pulled By: gmagogsfm
fbshipit-source-id: 8e60dbdcc54a93dac111d81b8d88fb39387224f5
Summary:
Add pass that fuses Conv and Batchnormalization nodes into one node Conv.
This pass is only applied in inference mode (training is None or TrainingMode.Eval).
Since this pass needs access to param_dict it is written outside peephole file where these kind of passes (fusing multiple nodes into one) is usually placed.
This PR also adds wrapper skipIfNoEmbed to skip debug_embed_params test:
Pass that fuses Conv and Batchnorm changes the params of resnet model and parameters of onnx and pytorch model won't match. Since parameters are not matching, debug_embed_params test for test_resnet will fail and that is expected, therefore debug_embed_params test for test_resnet should be skipped.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40547
Reviewed By: gchanan
Differential Revision: D22631687
Pulled By: bzinodev
fbshipit-source-id: fe45812400398a32541e797f727fd8697eb6d8c0
Summary:
* Add EnumType and AnyEnumType as first-class jit type
* Add Enum-typed IValue
* Enhanced aten::eq to support Enum
Supported:
Enum-typed function targuments
using Enum type and comparing them
TODO:
Add PyThon sugared value for Enum
Support getting name/value attrs of enums
Support Enum-typed return values
Support enum values of different types in same Enum class
Support serialization and deserialization
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41390
Reviewed By: eellison
Differential Revision: D22524388
Pulled By: gmagogsfm
fbshipit-source-id: 1627154a64e752d8457cd53270f3d14aea4b1150
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41414
This diff exports replaceAllUsesAfterNodeWith to PythonAPI.
Test Plan: Tested locally. Please let me know if there is a set of unit tests to be passed outside of the default ones triggered by Sandcastle.
Reviewed By: soumith
Differential Revision: D22523211
fbshipit-source-id: 3f075bafa6208ada462abc57d495c15179a6e53d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41146
**Summary**
This commit adds support for using `Modules` that have been lowered as
submodules in `ScriptModules`.
**Test Plan**
This commit adds execution and save/load tests to test_backends.py for
backend-lowered submodules.
**Fixes**
This commit fixes#40069.
Test Plan: Imported from OSS
Reviewed By: ailzhang
Differential Revision: D22459543
Pulled By: SplitInfinity
fbshipit-source-id: 02e0c0ccdce26c671ade30a34aca3e99bcdc5ba7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40252
As title says.
Test Plan:
python test/test_mobile_optimizer.py
Imported from OSS
Differential Revision: D22126825
fbshipit-source-id: a1880587ba8db9dee0fa450bc463734e4a8693d9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39343
Building on top of previous PR that adds fused add_relu op, this PR adds
a JIT pass to transform input graph to find all fusable instancs of add
+ relu and fuses them.
Test Plan:
python test/test_jit.py TestJit.test_add_relu_fusion
Imported from OSS
Differential Revision: D21822396
fbshipit-source-id: 12c7e8db54c6d70a2402b32cc06c7e305ffbb1be
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40718
Currently only constant except tensor must be inlined during serialization.
Tensor are stored in the contant table. This patch generalizes this capability
to any IValue. This is particularly useful for non ASCII string literal that
cannot be inlined.
Test Plan: Imported from OSS
Differential Revision: D22298169
Pulled By: bzinodev
fbshipit-source-id: 88cc59af9cc45e426ca8002175593b9e431f4bac
Summary:
This should be in its own file...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41137
Reviewed By: jamesr66a
Differential Revision: D22437922
Pulled By: eellison
fbshipit-source-id: 1b62dde1a4ebac673b5c60aea4f398f734d62501
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40902
See the bottom of this stack for context.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D22360210
Pulled By: suo
fbshipit-source-id: 4275127173a36982ce9ad357aa344435b98e1faf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40841
**Summary**
This commit adds support for using `Modules` that have been lowered as
submodules in `ScriptModules`.
**Test Plan**
This commit adds execution and save/load tests to test_backends.py for
backend-lowered submodules.
**Fixes**
This commit fixes#40069.
Test Plan: Imported from OSS
Differential Revision: D22418716
Pulled By: SplitInfinity
fbshipit-source-id: d2b2c6d5d2cf3042a620b3bde7d494f1abe28dc1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40939
Previously, when we would do shape analysis by running the op with representative inputs, we would always set the grad property to false. This led to a wrong static analysis when we would create differentiable subgraphs, and propagate shapes without also propagating requires_grad, and then uninline them.
Test Plan: Imported from OSS
Differential Revision: D22394676
Pulled By: eellison
fbshipit-source-id: 254e6e9f964b40d160befe0e125abe1b7aa2bd5e
Summary:
Define static script implementation of __len__ and __contains__ on any subclass derived from a type such as ModuleList, Sequential, or ModuleDict. Implement getitem for classes derived from ModuleDict.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40789
Reviewed By: eellison
Differential Revision: D22325159
Pulled By: wconstab
fbshipit-source-id: fc1562c29640fe800e13b5a1dd48e595c2c7239b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37034
c10 takes a Stack* in boxed functions while JIT took Stack&.
c10 doesn't return anything while JIT returns an int which is always zero.
This changes JIT to follow the c10 behavior.
ghstack-source-id: 106834069
Test Plan: unit tests
Differential Revision: D20567950
fbshipit-source-id: 1a7aea291023afc52ae706957e9a5ca576fbb53b
Summary:
By default freeze_module pass, invoked from optimize_for_mobile,
preserves only forward method. There is an option to specify a list of
methods that can be preserved during freeze_module. This PR exposes that
to optimize_for_module pass.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40629
Test Plan: python test/test_mobile_optimizer.py
Reviewed By: dreiss
Differential Revision: D22260972
Pulled By: kimishpatel
fbshipit-source-id: 452c653269da8bb865acfb58da2d28c23c66e326
Summary:
Partial support for slicing of Sequential containers.
- works around missing Sequential slice functionality
by converting to tuple
- only supports iteration of resulting tuple values,
not direct call() on the sliced sequential
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40445
Differential Revision: D22192469
Pulled By: wconstab
fbshipit-source-id: 61c85deda2d58f6e3bea2f1fa1d5d5dde568b9b5
Summary:
**Summary**
This commit adds an instance method `_reconstruct` that permits users
to reconstruct a `ScriptModule` from a given C++ `Module` instance.
**Testing**
This commit adds a unit test for `_reconstruct`.
**Fixes**
This pull request fixes https://github.com/pytorch/pytorch/issues/33912.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39979
Differential Revision: D22172323
Pulled By: SplitInfinity
fbshipit-source-id: 9aa6551c422a5a324b822a09cd8d7c660f99ca5c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40270
Original commit changeset: 1227e243ab94
D22082806 (1e03d603c6) broke the model generation of pyper models. We trace the namedtuple as input. To unblock the development of PyPer project, let's revert the diff first.
Sorry about the inconvenience, SplitInfinity
ghstack-source-id: 106217609
Test Plan: buck run dper3/dper3_models/experimental/pytorch/feed:feed_generation_script -- --model_files_dir=/tmp/
Reviewed By: alyssawangqq
Differential Revision: D22132960
fbshipit-source-id: ce9278c8462602a341e231ea890e46f74e743ddf
Summary:
**Summary**
This commit adds support for with statements to PyTorch JIT. Each
of the with items in a with statement is represented in the JIT IR
as a pair of `prim::Enter` and `prim::Exit` nodes that call the
`__enter__` and `__exit__` methods defined on the context manager objects
returned by the expressions in the with item.
**Testing**
This commit adds unit tests for with statements with named with items,
nameless with items, and with statements that encounter exceptions.
```
$ python test/test_jit.py TestWith.test_with_as
Fail to import hypothesis in common_utils, tests are not derandomized
.
----------------------------------------------------------------------
Ran 1 test in 0.430s
OK
```
```
$ python test/test_jit.py TestWith.test_with_no_as
Fail to import hypothesis in common_utils, tests are not derandomized
.
----------------------------------------------------------------------
Ran 1 test in 0.264s
OK
```
```
$ python test/test_jit.py TestWith.test_with_exceptions
Fail to import hypothesis in common_utils, tests are not derandomized
Couldn't download test skip set, leaving all tests enabled...
.
----------------------------------------------------------------------
Ran 1 test in 1.053s
OK
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34705
Differential Revision: D22095945
Pulled By: SplitInfinity
fbshipit-source-id: f661565a834786725259b8ea014b4d7532f9419d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37462
Instead of running all the optimization pass in optimizeForMobile method,
introducing a whitelist optimizer dictionary as second param in the method,
when it is not passed during calling, the method will run all the optimization
passes, otherwise the method will read the dict and only run the pass with
value of True.
ghstack-source-id: 106104503
Test Plan:
python test/test_mobile_optimizer.py
Imported from OSS
Differential Revision: D22096029
fbshipit-source-id: daa9370c0510930f4c032328b225df0bcf97880f
