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
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42141
Update alias db in-place instead of having to construct alias db from scratch on each change, causing O(n^2) behavior.
Description from https://github.com/pytorch/pytorch/pull/37106 holds pretty well:
"""
Recomputing the aliasdb on every fusion iteration + in every subblock
is hugely expensive. Instead, update it in-place when doing fusion.
The graph fuser pass operates by pushing nodes into a fusion group. So
we start with
`x, y = f(a, b, c)`
and end with:
```
x_out, y_out = prim::fusionGroup(a, b, c)
x_in, y_in = f(a_in, b_in, c_in)
-> x_in, y_in
```
We destroy the x and y Value*s in the process. This operation is
easy to express as an update to the aliasDb--x_out just takes on all
the aliasing information x used to have. In particular, since we know
f and prim::fusionGroup are purely functional, we don't have to mess
with any write information.
"""
The one difficulty here is mapping x, y to x_out, y_out is not trivial in merging nodes into the autodiff subgraph node.
There are a few options:
- attempt to make all subgraph utils & ir cloning logic update a map
- mirror the subgraph utils implementation in create_autodiff_subgraph
- uniquely map x, y and x_in, y_in so you can back out the correspondence.
I went with the third option.
This shouldn't affect the results of the pass at all. LMK if you think there's anything else I should be doing to test, I was thinking about maybe exposing an option to run create autodiff subgraphs without the post processor and check that the alias db was correctly updated.
Test Plan: Imported from OSS
Reviewed By: SplitInfinity
Differential Revision: D22798377
Pulled By: eellison
fbshipit-source-id: 9a133bcaa3b051c0fb565afb23a3eed56dbe71f9
Summary:
Remove `skipIfRocm` from most jit tests and enable `RUN_CUDA_HALF` tests for ROCm.
These changes passed more than three rounds of CI testing against the ROCm CI.
CC ezyang xw285cornell sunway513
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40447
Differential Revision: D22190711
Pulled By: xw285cornell
fbshipit-source-id: bac44825a2675d247b3abe2ec2f80420a95348a3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40142
test_jit is becoming huge again, which makes editor hard to load and
write new tests, this split out the tracer related tests.
Test Plan: Imported from OSS
Reviewed By: ailzhang
Differential Revision: D22085035
Pulled By: wanchaol
fbshipit-source-id: 696bee84985ecfbfeac8e2ee5c27f1bdda8de394
Summary:
Enhance FileCheck util to check for highlighted source ranges. This is useful when writing tests regarding generated error messages that require source code highlighting.
Here is how the error looks like in different cases:
- In case of needed source code token not found at all in input string:
```
RuntimeError: Expected to find "invalid_token" but did not find it
Searched string:
... <--- HERE
def to_list_missing_type_annotation(x):
# type: (torch.Tensor) -> List[float]
From CHECK-SOURCE-HIGHLIGHTED: invalid_token
```
- In case of source code token not highlighted:
```
Traceback (most recent call last):
File "test_range.py", line 11, in <module>
FileCheck().check_source_highlighted("x.tolist()").run(s)
RuntimeError: Expected to find "~~~~~~~~~~" but did not find it
Searched string:
# type: (torch.Tensor) -> List[float]
li = x.tolist()
~~~~~~~~~ <--- HERE
~~~~~~~~~~~~~~~~~~~... <--- HERE
return li
```
It is a bit confusing since both input text (usually an error message) and generated error messages have their highlighted portions, but this is consistent of previous behavior. Another option is to generate plain error messages without additional range highlighting on input text.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39692
Test Plan:
Added unit test.
Closes https://github.com/pytorch/pytorch/issues/38698
Differential Revision: D22001765
Pulled By: gmagogsfm
fbshipit-source-id: 6681441eee5853ab061d198ccfe55ebffddca202
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38746
Factors out testing of op alias normalization so that there is a registry used for tests.
Test Plan: Imported from OSS
Differential Revision: D21673107
Pulled By: eellison
fbshipit-source-id: e06653cdf24f14a4253dd054e4d402d171d16a11
Summary:
The existing contextmanager only conditionally enabled_profiling_mode, which was counter intuitive. When we changed the default executor it broke internal benchmarking as a result.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37825
Differential Revision: D21404611
Pulled By: eellison
fbshipit-source-id: 306b3c333ef4eb44ab6a6e5ab4e0682e5ce312ce
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35615
Python 2 has reached end-of-life and is no longer supported by PyTorch.
Now we can clean up a lot of cruft that we put in place to support it.
These changes were all done manually, and I skipped anything that seemed
like it would take more than a few seconds, so I think it makes sense to
review it manually as well (though using side-by-side view and ignoring
whitespace change might be helpful).
Test Plan: CI
Differential Revision: D20842886
Pulled By: dreiss
fbshipit-source-id: 8cad4e87c45895e7ce3938a88e61157a79504aed
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33277
Currently we insert observer in the called graph, which is incorrect since graphs can be shared
and the decision of whether to insert observer or not might dependend on where the graph is called.
For example, for a call sequence `self.conv1(self.conv2(x))`, we can't inserting observer correctly
if `self.conv1` and `self.conv2` are sharing the same type in the current implementation, because we insert
observer in the graph of the forward method of Conv2d right now and this call sequence requires us to insert
only one observer for the output of self.conv1/input of self.conv2.
We'll need to insert observers for input/output values of the graph in call site instead.
Test Plan:
python test/test_jit.py
Imported from OSS
Differential Revision: D20208787
fbshipit-source-id: 739e1d877639c0d0ed24e573bbd36211defa6836
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33779
This should eliminate random warnings and print spew from test_jit.
It also fixes a bug where we weren't properly comparing captured outputs
(!)
Test Plan: Imported from OSS
Differential Revision: D20124224
Pulled By: suo
fbshipit-source-id: 9241d21fdf9470531b0437427b28e325cdf08d3a
Summary:
this adds enough infrastructure to run bailout checks in `checkScript`. I'll need to figure out the best way to enable it for nightly builds now.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32802
Differential Revision: D19974718
Pulled By: Krovatkin
fbshipit-source-id: 40485503f6d3ae14edcce98e1eec1f0559f3ad08
Summary:
The `not inline_everything` check was causing the jitter check to be skipped whenever we emitted a function. thanks SplitInfinity for pointing this out.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33468
Differential Revision: D19975934
Pulled By: eellison
fbshipit-source-id: 03faf8d2fd93f148100d8cf49cb67b8e15cf1f04
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30445
Create distributed and rpc directories under caffe/test for better management
of unit tests.
Differential Revision: D18702786
fbshipit-source-id: e9daeed0cfb846ef68806f6decfcb57c0e0e3606
