This is a lot of files changed! Don't panic! Here's how it works:
* Previously, we set `follow_imports = silent` for our mypy.ini configuration. Per https://mypy.readthedocs.io/en/stable/running_mypy.html#follow-imports, what this does is whenever we have an import to a module which is not listed as a file to be typechecked in mypy, we typecheck it as normal but suppress all errors that occurred in that file.
* When mypy is run inside lintrunner, the list of files is precisely the files covered by the glob in lintrunner.toml, but with files in excludes excluded.
* The top-level directive `# mypy: ignore-errors` instructs mypy to typecheck the file as normal, but ignore all errors.
* Therefore, it should be equivalent to set `follow_imports = normal`, if we put `# mypy: ignore-errors` on all files that were previously excluded from the file list.
* Having done this, we can remove the exclude list from .lintrunner.toml, since excluding a file from typechecking is baked into the files themselves.
* torch/_dynamo and torch/_inductor were previously in the exclude list, because they were covered by MYPYINDUCTOR. It is not OK to mark these as `# mypy: ignore-errors` as this will impede typechecking on the alternate configuration. So they are temporarily being checked twice, but I am suppressing the errors in these files as the configurations are not quite the same. I plan to unify the configurations so this is only a temporary state.
* There were some straggler type errors after these changes somehow, so I fixed them as needed. There weren't that many.
In the future, to start type checking a file, just remove the ignore-errors directive from the top of the file.
The codemod was done with this script authored by GPT-4:
```
import glob
exclude_patterns = [
...
]
for pattern in exclude_patterns:
for filepath in glob.glob(pattern, recursive=True):
if filepath.endswith('.py'):
with open(filepath, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write('# mypy: ignore-errors\n\n' + content)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118414
Approved by: https://github.com/thiagocrepaldi, https://github.com/albanD
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73875
Previously we had a few settings:
- getExecutor - which toggled between Profiling Executor and Legacy
- getGraphOptimize - if true, overrides PE/Legacy to run with simple executor (no optimizations)
and then...
- getProfilingMode - which would set PE to 0 specializtions.
The last mode is redundant with getGraphOptimize, we should just remove it and use getGraphOptimize in these cases. It would lead to potentially invalid combinations of logic - what does mean if getProfilingMode is true but getExecutor is set to false ? This would lead to a bug in specialize_autograd_zero in this case, see: https://github.com/pytorch/pytorch/blob/master/torch%2Fcsrc%2Fjit%2Fpasses%2Fspecialize_autogradzero.cpp#L93.
The tests here are failing but get fixed with the PR above it, so i'll squash for landing.
Test Plan: Imported from OSS
Reviewed By: cpuhrsch
Differential Revision: D34938130
Pulled By: eellison
fbshipit-source-id: 1a9c0ae7f6d1cfddc2ed3499a5af611053ae5e1b
(cherry picked from commit cf69ce3d155ba7d334022c42fb2cee54bb088c23)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70465
These tests check to ensure that
(a) the result after nnc fusion (of a single op) is the same as the
unfused op
(b) for certain ops where fusion is expected to occur, ensure that
fusion does actually occur
Test Plan: Imported from OSS
Reviewed By: wenleix
Differential Revision: D33595240
Pulled By: davidberard98
fbshipit-source-id: e2e17a921bc30c313e92e8e5bbc6c1b5fcd14bc1
(cherry picked from commit b1ba221acc)
Summary:
Syncing nvfuser code base from devel branch, Listing a few of our development since last sync:
- Extends support to normalization and reduction kernels.
- Multiple kernel launch for single `CudaFusionGroup`. Hierarchical caching system has been updated to cache graph segmentation.
- profile_ivalue is enabled to convert dynamic scalar into compile time constants, which are required by the codegen. (e.g. reduction axes).
To keep this PR simple and relatively review-free. We stripped most external changes and submitted them as separate PRs, so this gigantic PR is easier to handle.
internal updates are files located in:
1. updates in nvfuser codegen `torch/csrc/jit/coddgen/cuda`
2. added nvfuser specific benchmarks `benchmarks/cpp/nvfuser`
3. nvfuser jit cpp tests `test/cpp/jit/test_gpu.cpp` `test/cpp/jit/test_gpu_shift.cpp` `test/cpp/jit/test_gpu_validator.h`
updates affecting integration:
1. profile_ivalue enabled for nvfuser. related changes are in `torch/csrc/jit/runtime/*`,
2. exposed a few more symbols `aten/src/ATen/core/*` used by codegen
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63745
Reviewed By: saketh-are
Differential Revision: D30752939
Pulled By: malfet
fbshipit-source-id: ce122e80f01bcd3865f5bd3c4dfde660665fd84c
Summary:
normalizing `__is__` to `eq`, and `__isnot__` to `ne` in the case of bools.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57862
Test Plan:
```
python test/test_jit.py TestPeephole
```
11 Tests, 1 skipped, no failures
Fixes https://github.com/pytorch/pytorch/issues/57387
Reviewed By: eellison
Differential Revision: D28335646
Pulled By: Gamrix
fbshipit-source-id: c9f885044b32897ba35483091bcf7037759b7517
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/54640
If we are running constant propagation on a graph that doesn't have any operators with constant inputs and any mutable inputs/outputs, we do not need to initialize an alias db. This is going to be used to speed up symbolic shape analysis.
Test Plan: Imported from OSS
Reviewed By: nikithamalgifb
Differential Revision: D27340863
Pulled By: eellison
fbshipit-source-id: 087b2a33b42c58fa5dae405d652b056d0f1d72e7
Summary:
This is a second attempt to use graph executor to run forward on a gradient. This allows a secondary chance to profile intermediate tensor introduced by autodiff.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52136
Reviewed By: pbelevich
Differential Revision: D26693978
Pulled By: Krovatkin
fbshipit-source-id: 91dde8009a210950af8e5173668ada241e16dd52
Summary:
Fixes https://github.com/pytorch/pytorch/issues/52264
When CPU fusion is enabled without LLVM support in PyTorch, it causes huge slowdown (> 50x). This PR makes the LLVM backend the default backend for TE. Now, an error will be reported if CPU fusion is enabled without LLVM support, to avoid this performance regression.
This PR also updates the tests to not use LLVM, so that the old flow is continued. This is necessary because tests run in CI do not have LLVM.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52314
Reviewed By: ejguan
Differential Revision: D26491294
Pulled By: navahgar
fbshipit-source-id: 74561db1207da805d6d28039450db046ba2988fb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51648
The following code will throw during the call to `traced(5)`:
```python
class M(nn.Module):
def __init__(self):
super(M, self).__init__()
self.W = torch.nn.Parameter(torch.randn(5))
def forward(self, x):
return torch.dot(self.W, x)
traced = fx.symbolic_trace(M())
traced(5)
```
Traceback before:
```
Traceback (most recent call last):
File "test/tinytest.py", line 26, in <module>
traced(5)
File "/home/ansley/local/pytorch/torch/fx/graph_module.py", line 338, in wrapped_call
return self._cls_call(self, *args, **kwargs)
File "/home/ansley/local/pytorch/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "<eval_with_key_0>", line 4, in forward
TypeError: dot(): argument 'tensor' (position 2) must be Tensor, not int
```
Traceback after:
```
Traceback (most recent call last):
File "/home/ansley/local/pytorch/torch/fx/graph_module.py", line 338, in wrapped_call
return torch.nn.Module.__call__(self, *args, **kwargs)
File "/home/ansley/local/pytorch/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "<eval_with_key_1>", line 4, in forward
dot_1 = torch.dot(w, x); w = x = None
TypeError: dot(): argument 'tensor' (position 2) must be Tensor, not int
Call using an FX-traced Module, line 4 of the traced Module’s generated forward function:
w = self.W
dot_1 = torch.dot(w, x); w = x = None
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
relu_1 = dot_1.relu(); dot_1 = None
return relu_1
```
(Note that the same `TypeError` is thrown despite modifying the traceback.)
Test Plan: Imported from OSS
Reviewed By: jamesr66a
Differential Revision: D26424005
Pulled By: ansley
fbshipit-source-id: 368f46ba81fb3111bd09654825bb2ac5595207d1
Summary:
Previously `torch.jit.trace` relies on AutoGrad hooks to infer name of tensors in computation, including those of function/method arguments. This often doesn't work out because:
- These names often do not exist
- Tracer uses argument name of first tensor operation on each tensor as inferred argument names. These tensor operations have programmatically-generated names like `argument_1`
This PR extracts argument names directly from Python functions and pass them down to tracer, which then assigns them to correct graph inputs. This way, we always have the correct argument names captured in IR.
This is useful for both debugging and supporting using `InterfaceType` to represent traced modules.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51775
Reviewed By: izdeby
Differential Revision: D26273105
Pulled By: gmagogsfm
fbshipit-source-id: 934a385041137dc3731bb6fa8657b11532fed9e5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47695
The method_tests from common_methods_invoations.py are being migrated into a new OpInfo class-based testing framework. The work in this commit pulls out the functions embedded in the old method_tests logic and places them in a location that both the old method_tests and OpInfo tests can use
Specifically: created torch/testing/_internal/common_jit.py from functions and methods in torch/testing/_internal/jit_utils.py and test/test_jit.py. Also created new intermediate class JitCommonTestCase to house moved methods. Also slightly modified jit_metaprogramming_utils.py to work for OpInfo tests
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D25212437
Pulled By: Lilyjjo
fbshipit-source-id: 97bc52c95d776d567750e7478fac722da30f4985
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47374
A few small fixes needed to enable unary op cpu testing. If reviewers would prefer I split them up let me know.
Test Plan: Imported from OSS
Reviewed By: ansley
Differential Revision: D24805248
Pulled By: eellison
fbshipit-source-id: c2cfe2e3319a633e64da3366e68f5bf21d390cb7
Summary:
Follow-up of https://github.com/pytorch/pytorch/issues/46461 with a similar goal
Makes them more readable and possibly faster. Care has to be taken because `map` applies the function immediately while `(x for x in xs)` is a generator expression which gets evaluated later. This is a benefit in some cases where it is not required to actually create the list of values in memory (e.g. when passing to `tuple` or `extend` or `join`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46462
Reviewed By: zou3519
Differential Revision: D24422343
Pulled By: ezyang
fbshipit-source-id: 252e33499c92ac0b15238f2df32681dbbda2b237
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45940
**Summary**
In `try_ann_to_type`, if an annotation has an attribute named
`__torch_script_class__`, it is assumed to be a TorchScript class that
has already been scripted. However, if it is a class that extends
another class, this code path causes a crash because it looks up the
JIT type for the class by name in the compilation unit. This JIT type
obviously cannot exist because inheritance is not supported.
This commit fixes this by looking up the qualified name of a class
in torch.jit._state._script_class in order to ascertain whether it has
already been scripted (instead of looking for a `__torch_script_class__`
attribute on the class object.
**Test Plan**
This commit adds a unit test consisting of the code sample from the
issue that reported this problem.
**Fixes**
This commit fixes#45860.
Test Plan: Imported from OSS
Reviewed By: anjali411
Differential Revision: D24310027
Pulled By: SplitInfinity
fbshipit-source-id: 9f8225f3316fd50738d98e3544bf5562b16425b6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45789
Making sure that more tests invoke a run with a Fusion Group.
Test Plan: Imported from OSS
Reviewed By: Krovatkin
Differential Revision: D24169535
Pulled By: eellison
fbshipit-source-id: 54d7af434772ba52144b12d15d32ae30460c0c3c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45788
We were only running the traced graph once, which would not yet have been fused at that point. We should run for num_profiled_runs + 1, and also assert that all nodes in the graph were fused.
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D24169537
Pulled By: eellison
fbshipit-source-id: 8499bb1a5bd9d2221b1f1c54d6352558cf07ba9a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43631
I added a new test for just profiler stuff - I don't think the test should go in test_jit.py. Maybe this should just go in test_tensorexpr_fuser, but I'm not really testing tensorexpr stuff either... LMK
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D23358810
Pulled By: eellison
fbshipit-source-id: 074238e1b60e4c4a919a052b7a5312b790ad5d82
Summary:
This PR:
- updates test_op_normalization.py, which verifies that aliases are correctly translated in the JIT
- adds torch.linalg.det as an alias for torch.det
- moves the torch.linalg.outer alias to torch.outer (to be consistent with NumPy)
The torch.linalg.outer alias was put the linalg namespace erroneously as a placeholder since it's a "linear algebra op" according to NumPy but is actually still in the main NumPy namespace.
The updates to test_op_normalization are necessary. Previously it was using method_tests to generate tests, and method_tests assumes test suites using it also use the device generic framework, which test_op_normalization did not. For example, some ops require decorators like `skipCPUIfNoLapack`, which only works in device generic test classes. Moving test_op_normalization to the device generic framework also lets these tests run on CPU and CUDA.
Continued reliance on method_tests() is excessive since the test suite is only interested in testing aliasing, and a simpler and more readable `AliasInfo` class is used for the required information. An example impedance mismatch between method_tests and the new tests, for example, was how to handle ops in namespaces like torch.linalg.det. In the future this information will likely be folded into a common 'OpInfo' registry in the test suite.
The actual tests performed are similar to what they were previously: a scripted and traced version of the op is run and the test verifies that both graphs do not contain the alias name and do contain the aliased name.
The guidance for adding an alias has been updated accordingly.
cc mattip
Note:
ngimel suggests:
- deprecating and then removing the `torch.ger` name
- reviewing the implementation of `torch.outer`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42802
Reviewed By: zou3519
Differential Revision: D23059883
Pulled By: mruberry
fbshipit-source-id: 11321c2a7fb283a6e7c0d8899849ad7476be42d1
Summary:
Per title. Also updates our guidance for adding aliases to clarify interned_string and method_test requirements. The alias is tested by extending test_clamp to also test clip.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42770
Reviewed By: ngimel
Differential Revision: D23020655
Pulled By: mruberry
fbshipit-source-id: f1d8e751de9ac5f21a4f95d241b193730f07b5dc
