Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54809
I'm going to post on dev-discuss soon with a more thorough explanation of the design and advantages of this shape analysis, so I'm leaving out that for now.
There is still a ton left to do, I'm posting this initial version so we can get something on master multiple can work on. List of many remaining steps to do:
- [ ] Add symbolic shapes support
- [ ] Bind shape functions for operators in C++
- [ ] Make classes of operators share the same shape function (e.g. pointwise, broadcast two inputs)
- [ ] Refactor APIs
- [ ] Only iteratively optimize shape function while a change has been made
- [ ] Expand coverage of coverage to common ops
- [ ] Add shape analysis pass on Graph that handles Ifs and Loops
- [ ] Allow concurrent reads to the operator map
- [ ] Successive applications of same inputs to same shape function (e.g. series of pointwise ops)
For this review, I am mostly looking for comments related to the implementation of symolic_shape_analysis.cpp, with the caveats listed above. I am not really looking for comments related to api/registration/graph level analysis as those are all planned to be changed. I am fine landing this as is or waiting until necessary components of the TODOs above are finished.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D27750998
Pulled By: eellison
fbshipit-source-id: 4338b99e8651df076291c6b781c0e36a1bcbec03
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54915
TorchScript and torch.package have different mangling schemes. To avoid
them interfering with each other, we should undo the torch.package
mangling before processing anything with TorchScript (since TS
independently makes sure that no names collide).
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Differential Revision: D27410472
Pulled By: suo
fbshipit-source-id: d1cc013c532d9abb7fb9615122bc465ded4785bb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52881
**This PR adds:**
1. logic to parse complex constants (complex literals of the form `bj`)
2. logic to parse complex lists
3. support for complex constructors: `complex(tensor/int/float/bool, tensor/int/float/bool)`
4. Limited operator support
- `add`, `sub`, `mul`, `torch.tensor`, `torch.as_tensor`
**Follow-up work:**
1. Add complex support for unary and other registered ops.
2. support complex constructor with string as input (this is supported in Python eager mode).
3. Test all emitXYZ for all XYZ in `ir_emitter.cpp` (currently only emitConst, emitValueToTensor are tested). e.g., test loops etc.
4. onnx doesn't support complex tensors, so we should error out with a clear and descriptive error message.
Test Plan: Imported from OSS
Reviewed By: bdhirsh
Differential Revision: D27245059
Pulled By: anjali411
fbshipit-source-id: af043b5159ae99a9cc8691b5a8401503fa8d6f05
Summary:
Previously, the graph might have been delete while Python still has iterators, leading to segfaults.
This does not fully work for iterators from Nodes and Blocks as they may be invalidated when the owning graph goes out of scope. I will look into these separately.
Fixes https://github.com/pytorch/pytorch/issues/50454
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51951
Reviewed By: mrshenli
Differential Revision: D26352629
Pulled By: SplitInfinity
fbshipit-source-id: 67299b6cbf1ac7ab77f8703a0ca8f1162e03fcd4
Summary:
Previously we might have gotten segfaults and all, now it raises an exception.
Thread safety hasn't been an objective.
I have a followup to expand the Python interface for the API.
Fixes https://github.com/pytorch/pytorch/issues/49969.
wanchaol
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50326
Reviewed By: pbelevich
Differential Revision: D26096234
Pulled By: gmagogsfm
fbshipit-source-id: 5425772002eb4deb3830ed51eaa3964f22505840
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50870
**Summary**
Module attributes whose types cannot be determined based on annotations
or inference based on their values at script time are added to the
concrete type of the corresponding module as "failed attributes". Any
attempt to access them in scripted code produces an error with a message
explaining that the attribute could not be contributed to a
corresponding attribute on the TorchScript module. However, this error
is not more specific than that.
This commit modifies `infer_type` in `_recursive.py` so that it returns
`c10::InferredType` instead, which allows more information about typing
failures to be communicated to the caller through the `reason()` method
on this class. This information is appended to the hint added to the
module concrete type for failed attributes.
**Testing**
This commit adds a unit test to `test_module_containers.py` that checks
that extra information is provided about the reason for the failure
when a module attribute consisting of a list of `torch.nn.Module` fails to convert.
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D26091472
Pulled By: SplitInfinity
fbshipit-source-id: fcad6588b937520f250587f3d9e005662eb9af0d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50228
`fastmod -m 'expect(<((at|c10)::)?\w+Type>\(\)\s*)->'
'expectRef${1}.'`
Presuming it builds, this is a safe change: the result of `expect()`
wasn't being saved anywhere, so we didn't need it, so we can take a
reference instead of a new `shared_ptr`.
ghstack-source-id: 119782961
Test Plan: CI
Reviewed By: SplitInfinity
Differential Revision: D25837374
fbshipit-source-id: 86757b70b1520e3dbaa141001e7976400cdd3b08
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50074
Adds Conv-BN fusion for models that have been frozen. I haven't explicitly tested perf yet but it should be equivalent to the results from Chillee's PR [here](https://github.com/pytorch/pytorch/pull/476570) and [here](https://github.com/pytorch/pytorch/pull/47657#issuecomment-725752765). Click on the PR for details but it's a good speed up.
In a later PR in the stack I plan on making this optimization on by default as part of `torch.jit.freeze`. I will also in a later PR add a peephole so that there is not conv->batchnorm2d doesn't generate a conditional checking # dims.
Zino was working on freezing and left the team, so not really sure who should be reviewing this, but I dont care too much so long as I get a review �
Test Plan: Imported from OSS
Reviewed By: tugsbayasgalan
Differential Revision: D25856261
Pulled By: eellison
fbshipit-source-id: da58c4ad97506a09a5c3a15e41aa92bdd7e9a197
Summary:
`isCompleteTensor()` only returns true when both scalar type and shape is present. All dimensions in the shape must be static. This high requirement is unnecessary for many use cases such as when only rank or scalar type needs to be known.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48162
Reviewed By: malfet
Differential Revision: D25340823
Pulled By: bzinodev
fbshipit-source-id: 1fef61f44918f4339dd6654fb725b18cd58d99cf
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/46779
Test Plan: Used it in some notebooks.
Reviewed By: suo
Differential Revision: D24574005
Pulled By: dreiss
fbshipit-source-id: 78ba7a2bdb859fef5633212b73c7a3eb2cfbc380
Summary:
The record_stream method was hard coded for CUDA device. Define the record_stream in the native_functions.yaml to enable the dynamic dispatch to different end device.
Fixes https://github.com/pytorch/pytorch/issues/36556
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44301
Reviewed By: glaringlee
Differential Revision: D23763954
Pulled By: ezyang
fbshipit-source-id: e6d24f5e7892b56101fa858a6cad2abc5cdc4293
Summary:
* Support propagating `dim_param` in ONNX by encoding as `ShapeSymbol` in `SymbolicShape` of outputs. If export is called with `dynamic_axes` provided, shape inference will start with these axes set as dynamic.
* Add new test file `test_pytorch_onnx_shape_inference.py`, reusing all test cases from `test_pytorch_onnx_onnxruntime.py`, but focus on validating shape for all nodes in graph. Currently this is not enabled in the CI, since there are still quite some existing issues and corner cases to fix. The test is default to run only at opset 12.
* Bug fixes, such as div, _len, and peephole.cpp passes for PackPadded, and LogSoftmaxCrossEntropy.
* This PR depends on existing PR such as 44332.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44920
Reviewed By: eellison
Differential Revision: D23958398
Pulled By: bzinodev
fbshipit-source-id: 00479d9bd19c867d526769a15ba97ec16d56e51d
Summary:
Export of embedding bag with dynamic list of offsets.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44693
Reviewed By: malfet
Differential Revision: D23831980
Pulled By: bzinodev
fbshipit-source-id: 3eaff1a0f20d1bcfb8039e518d78c491be381e1a
Summary:
Optimize export_onnx api to reduce string and model proto exchange in export.cpp
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44332
Reviewed By: bwasti, eellison
Differential Revision: D23880129
Pulled By: bzinodev
fbshipit-source-id: 1d216d8f710f356cbba2334fb21ea15a89dd16fa
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44162
This diff exports Node::isBefore/isAfter method to PythonAPI.
Test Plan: Tested locally. Please let me know if there is a set of unit tests to be passed.
Reviewed By: soumith
Differential Revision: D23514448
fbshipit-source-id: 7ef709b036370217ffebef52fd93fbd68c464e89
Summary:
[Re-review tips: nothing changed other than a type in python_ir.cpp to fix a windows build failure]
Adds code printing for enum type
Enhance enum type to include all contained enum names and values
Adds code parsing for enum type in deserialization
Enabled serialization/deserialization test in most TestCases. (With a few dangling issues to be addressed in later PRs to avoid this PR grows too large)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43460
Reviewed By: albanD
Differential Revision: D23284929
Pulled By: gmagogsfm
fbshipit-source-id: e3e81d6106f18b7337ac3ff5cd1eeaff854904f3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42963
* Adds code printing for enum type
* Enhance enum type to include all contained enum names and values
* Adds code parsing for enum type in deserialization
* Enabled serialization/deserialization test in most TestCases. (With a few dangling issues to be addressed in later PRs to avoid this PR grows too large)
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Differential Revision: D23223281
Pulled By: gmagogsfm
fbshipit-source-id: 716d1866b7770dfb7bd8515548cfe7dc4c4585f7
Summary:
The ONNX spec for the Squeeze operator:
> Remove single-dimensional entries from the shape of a tensor. Takes a parameter axes with a list of axes to squeeze. If axes is not provided, all the single dimensions will be removed from the shape. If an axis is selected with shape entry not equal to one, an error is raised.
Currently, as explained in issue https://github.com/pytorch/pytorch/issues/36796, it is possible to export such a model to ONNX, and this results in an exception from ONNX runtime.
Fixes https://github.com/pytorch/pytorch/issues/36796.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38476
Reviewed By: hl475
Differential Revision: D22158024
Pulled By: houseroad
fbshipit-source-id: bed625f3c626eabcbfb2ea83ec2f992963defa19
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/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:
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 modifies type inference for `nn.Module` instance attributes
such that the type of a `NamedTuple` attribute is inferred correctly and
such that the field names of this `NamedTuple` instance can be used in
scripted methods. At present, the type of this attribute is inferred to be
`Tuple[T, U, ..., V]`, so the field must be referred to by index and
cannot be referred to by name.
**Test Plan**
This commit adds a unit test to test that a field of a `NamedTuple`
attribute can be referred to by name in a scripted method.
**Fixes**
This commit fixes https://github.com/pytorch/pytorch/issues/37668.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39116
Differential Revision: D22082806
Pulled By: SplitInfinity
fbshipit-source-id: 1227e243ab941376cd5e382fb093751e88dc8846
Summary:
Clearly expressing a type is inferred by PyTorch instead of explicitly annotated by user makes many error messages more user-friendly
Currently Type has two string conversion methods. str() for IR printing and python_str() for serialization and error message generation. If we want to include more information in type printing while maintaining serialization/deserialization correctness, we need to split python_str() into annotation_str() and repr_str().
annotation_str is solely responsible for serialization, it strictly matches format of python type annotation. repr_str() is responsible for generating a human-readable error message that includes information like "this type is inferred, not explicitly annotated"
Closes https://github.com/pytorch/pytorch/issues/39449
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39544
Differential Revision: D21978759
Pulled By: gmagogsfm
fbshipit-source-id: 733566f5a62e748b5ca4bb3c5943ebb6d5b664d0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36953
Add support for generic lists as a constant. generic dicts & tuples are already implemented. This is a pretty common pattern and cuts down on the number of non-tensor nodes executed in interpolate tests.
Test Plan: Imported from OSS
Differential Revision: D21160761
Pulled By: eellison
fbshipit-source-id: 1e6b7b25b7580f09067794772d44e615601c60c4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36727
Looks like this was renamed by accident in 0cbd7fa46f
Test Plan:
Unit test.
Lint.
Differential Revision: D21076697
Pulled By: dreiss
fbshipit-source-id: dbd18cb41c7b26479984a7a7b12ad41a4c5b7658
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35170
Looks like this was renamed by accident in 0cbd7fa46f
Test Plan:
Unit test.
Imported from OSS
Differential Revision: D20783298
fbshipit-source-id: 8fcc146284af022ec1afe8d651baf6721b190ad3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35115
This commit runs the newly added tools/clang_format.py on the JIT
codebase and includes all of the formatting changes thus produced.
Testing:
Ran the script, CI.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D20568523
Pulled By: SplitInfinity
fbshipit-source-id: e09bdb982ccf090eecfb7c7b461b8d0681eef82b
Summary:
Separating CUDA fuser from CPU fuser.
1. New node in IR - prim::CudaFusionGroup:
This enables the cuda fuser to co-exist along side the old fuser. Allows us
to incrementally build and expand cuda fuser.
2. copied FuseGraph optimization passes to CudaFuserGraph:
We will re-factor & reuse Chunk/Concat in the old fuser logic, which is
handled in the optimization pass at this moment. Unfortunately many code in
the pass is tightly binded with the legacy fuser, which makes code sharing
difficult.
The CudaFusionGraph will support only a subset of operations comparing to
legacy fuser (CUDA only). It is registered as a custom pass post fusion via
```torch._C._jit_register_cuda_fuser()```
To have it in effect, you should also turn off fusion on GPU via
```torch._C._jit_override_can_fuse_on_gpu(False)```
3. We don't have codegen in this PR yet (WIP). Currently we just fall back to
the old fuser.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33527
Differential Revision: D20171598
Pulled By: ZolotukhinM
fbshipit-source-id: 9a3c0f06f46da7eaa80ae7551c04869f5b03ef71
