Related to the Reproducible Testing BE project. Goal is to print out the sample input that failed an OpInfo test.
Crazy idea: to avoid requiring widespread changes across tests that use OpInfo sample inputs, return a new special iterator type from `OpInfo.sample_inputs()`, etc. that tracks the most recent item seen. If a test fails later on, print out this info to identify the sample that failed the test.
This solves the problem that the test framework currently has no concept of which sample input is being operated on.
This PR contains the following changes:
* New `TrackedInputIter` that wraps a sample inputs func iterator and tracks the most recent input seen in a `TrackedInput` structure
* The information is stored in a dictionary on the test function itself, mapping `full test ID -> most recent TrackedInput`
* To determine the test function that is being run, we do some stack crawling hackery in `extract_test_fn_and_id()`
* Above applies only when one of the following is called: `OpInfo.sample_inputs()`, `OpInfo.error_inputs()`, `OpInfo.reference_inputs()`, and `OpInfo.conjugate_sample_inputs()`. This could easily be extended to `ModuleInfo`s and the sparse sample input funcs as well
Example output when a sample input causes a failure:
```
======================================================================
ERROR: test_foo_add_cpu_uint8 (__main__.TestFakeTensorCPU)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jbschlosser/branches/reproducible_testing/torch/testing/_internal/common_device_type.py", line 911, in test_wrapper
return test(*args, **kwargs)
File "/home/jbschlosser/branches/reproducible_testing/torch/testing/_internal/common_device_type.py", line 1097, in only_fn
return fn(slf, *args, **kwargs)
File "/home/jbschlosser/branches/reproducible_testing/test/test_ops.py", line 2211, in test_foo
self.fail('Example failure')
AssertionError: Example failure
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/jbschlosser/branches/reproducible_testing/torch/testing/_internal/common_utils.py", line 2436, in wrapper
method(*args, **kwargs)
File "/home/jbschlosser/branches/reproducible_testing/torch/testing/_internal/common_device_type.py", line 414, in instantiated_test
result = test(self, **param_kwargs)
File "/home/jbschlosser/branches/reproducible_testing/torch/testing/_internal/common_device_type.py", line 917, in test_wrapper
raise Exception(
Exception: Caused by sample input at index 2: SampleInput(input=Tensor[size=(5, 1), device="cpu", dtype=torch.uint8], args=TensorList[Tensor[size=(5,), device="cpu", dtype=torch.uint8]], kwargs={}, broadcasts_input=True, name='')
To execute this test, run the following from the base repo dir:
python test/test_ops.py -k test_foo_add_cpu_uint8
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
----------------------------------------------------------------------
```
This notably doesn't print the actual `SampleInput` values, as that's hard without fully reproducible random sample generation. I went down this path for a while and it seems infeasible without adding an untenable amount of overhead to set the random seed per SampleInput (see https://github.com/pytorch/pytorch/issues/86694#issuecomment-1614943708 for more details). For now, I am settling for at least spitting out the index and some metadata of the `SampleInput`, as it seems better than nothing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99444
Approved by: https://github.com/janeyx99
#113340 was reverted initially due to a bad default parametrization name. The test looked like
```python
@common_utils.parametrize(
"type_fn",
[
type,
lambda obj: obj.__class__,
],
)
def test_access_class_method_from_user_class(self, type_fn):
```
This is a valid parametrization, but results in these default test names:
```bash
❯ pytest test/dynamo/test_export.py -k test_access_class_method_from_user_class --co -q
test/dynamo/test_export.py::ExportTests::test_access_class_method_from_user_class_type_fn_<class 'type'>
test/dynamo/test_export.py::ExportTests::test_access_class_method_from_user_class_type_fn_<function ExportTests_<lambda> at 0x7f3be5de0c10>
```
Ignoring the whitespace in the test names, which can lead to other issues down the line, the problem in #113340 was that the lambda parameter included a memory address. IIUC, internally, the tests are not collected and run in the same process. Meaning, the address of the lambda and in turn the test name is no longer valid on the runner. This is fixed earlier in the stack by giving the parametrization an explicit name with `subtest`, but this PR is about preventing issues in the default case.
`pytest` solves this by simply using the name of the parameter plus its index as id in the test name:
```python
import pytest
class Foo:
def __repr__(self):
return str(id(self))
@pytest.mark.parametrize(
"bar",
[
pytest.param(type),
pytest.param(lambda obj: obj.__class__),
pytest.param(Foo()),
],
)
def test_foo(bar):
pass
```
```
❯ pytest main.py --co -q
main.py::test_foo[type]
main.py::test_foo[<lambda>]
main.py::test_foo[bar2]
```
`pytest` has better defaults for `type` and `lambda` than we do, but is has a safe default for custom objects.
This PR aligns our default test name with `pytest`. Using the parametrization from above again, we now collect
```bash
❯ pytest test/dynamo/test_export.py -k test_access_class_method_from_user_class --co -q
test/dynamo/test_export.py::ExportTests::test_access_class_method_from_user_class_type_fn0
test/dynamo/test_export.py::ExportTests::test_access_class_method_from_user_class_type_fn1
```
which might not be as expressive at first glance, but at least prevents bugs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113856
Approved by: https://github.com/malfet, https://github.com/huydhn
ghstack dependencies: #113855
Summary:
If IS_FBCODE is False, then we print an OSS repro if a test fails. We do
set IS_FBCODE manually on most internal tests, but we don't do it for
all of them. This PR changes it so that the IS_FBCODE gets set to the
correct default value (and then tests are able to override them if
they'd like).
Test Plan:
- Tested locally
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112766
Approved by: https://github.com/williamwen42
Use conditional imports: when running under dynamo, import the original NumPy not torch._numpy. This is what we want to trace, not our implementation.
With this, the test suite passes with and without `PYTORCH_TEST_WITH_DYNAMO=1` (modulo a couple of test modules which are not meant to be compiled, e.g. `test_nep50_examples`). There are two new decorators, `x{fail,pass}ifTorchDynamo`, the `xpass` in most cases indicates a graph break and a fallback to eager for things we do not implement.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110401
Approved by: https://github.com/lezcano
To reduce the amount of logs
* for successes, only print the part that says what tests ran and don't print the rest. Zip the log into an artifact. The line listing al the test names is really long, but if you view source of the raw logs, it will not wrap so it will only be one line. The log classifier can also be configured to ignored this line. Gets rid of lines like `test_ops.py::TestCommonCPU::test_multiple_devices_round_cpu_int64 SKIPPED [0.0010s] (Only runs on cuda) [ 9%]`
* for failures/reruns, print logs. Do not zip.
Also
* change log artifact name
Examples of various logs:
a074db0f7f failures
1b439e24c4 failures
possibly controversial haha
should i include an option for always printing?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110033
Approved by: https://github.com/huydhn
Enables two ruff rules derived from pylint:
* PLR1722 replaces any exit() calls with sys.exit(). exit() is only designed to be used in repl contexts as may not always be imported by default. This always use the version in the sys module which is better
* PLW3301 replaces nested min / max calls with simplified versions (ie. `min(a, min(b, c))` => `min(a, b. c)`). The new version is more idiomatic and more efficient.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109461
Approved by: https://github.com/ezyang
Fixes#68972
Relands #107246
To avoid causing Meta-internal CI failures, this PR avoids always asserting that the default dtype is float in the `TestCase.setUp/tearDown` methods. Instead, the assert is only done if `TestCase._default_dtype_check_enabled == True`. `_default_dtype_check_enabled` is set to True in the `if __name__ == "__main__":` blocks of all the relevant test files that have required changes for this issue
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108088
Approved by: https://github.com/ezyang
Summary:
[experimental] use EXCEPT_FOR env to suppress CPU tests from GPU RE -- alternative implementation to D48997976 using preexisting PYTORCH_TESTING_DEVICE_EXCEPT_FOR facility and building remaining logic (for assert-positive listers like test_transformers) on top of that.
Goal: save ~100 GPU (10% of capacity), enables us to fund more aggressive PyPer unit testing on GPU RE
Test Plan: sandcastle, github
Differential Revision: D48998582
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108672
Approved by: https://github.com/bertmaher
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
Sometimes test suite names include file/module names since they were imported from another file (ex _nvfuser.test_dynamo.TestNvFuserDynamo etc). This can sometimes make the autogenerated named by disable bot and the disable test button on hud incorrect which is annoying to track down, which leads to issues that are open but don't actually do anything, so my solution is to make the check between the issue name + the test more flexible. Instead of checking the entire test suite name, we chop off the file/module names and only look for the last part (ex TestNvFuserDynamo) and check if those are equal.
Also bundle both the check against the names in the slow test json and disable test issue names into one function for no reason other than less code.
Looked through logs to see what tests are skipped with this vs the old one and it looked the same.
Diff looks like a big change but its mostly a change in the indentation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104002
Approved by: https://github.com/ZainRizvi, https://github.com/huydhn
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
test_gradient_extreme_cases_* takes ~5 minutes on the inductor sm86 shard and possibly even longer on the inductor workflow since it's timing out right now although I'm not sure what the difference between the two is, and sometimes auto slow test detection isn't catching it
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107189
Approved by: https://github.com/ZainRizvi
Currently there are FFT operators which raise `UnsupportedOperatorException`
because their meta implementations sometimes give incorrect strides. This works
around the problem for static shapes by falling back to eager. Though we still
don't support calls with dynamic shapes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106319
Approved by: https://github.com/ezyang
Previously, you would get an error like
```
Dynamo input and output is a strict subset of traced input/output
```
now you get
```
Cannot export model which references tensors that are neither
buffers/parameters/constants nor are direct inputs. For each tensor, if you'd
like this tensor to be an explicit input, add it as a dummy argument
to the top-level model definition you are exporting; if you would
like its value to be embedded as an exported constant, wrap its access
in a function marked with @assume_constant_result.
G['bulbous_bouffant'], accessed at:
File "test_export.py", line N, in f
return bulbous_bouffant + y
```
This doesn't handle outputs, I'm going to hit that next.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106403
Approved by: https://github.com/tugsbayasgalan
Summary:
- PyTorch testing chokes sometimes when it sees an exception where the first
argument is not a string. fake_tensor.UnsupportedOperatorException's first
arg is an OpOverload. This PR fixes PyTorch testing to not choke. I'm not
really sure how to reproduce this in OSS.
- It turns out that if an operator does not have a meta kernel, the FakeTensor
rule is really slow (30ms in OSS in debug mode, 3s on some internal config).
The thing that is slow (aside from the previous diff) is waiting for the Dispatcher to
report NotImplemented and then attempting to catch that. I'm not really sure
why this is slow but it's easy to workaround so I added a workaround.
Test Plan: - existing tests
Differential Revision: D47917554
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106311
Approved by: https://github.com/eellison
In certain cases we capture ErrorMeta in a list. The ErrorMeta objects hold
tracebacks which contain a frame with a local variable that refers to that list.
This change mutates the list on exit from the frame so that it doesn't refer
to the ErrorMeta objects, breaking the cycle.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106328
Approved by: https://github.com/huydhn
setting TORCH_LINALG_PREFER_CUSOLVER=1
This will allow users to prefer cusolver as linear algebra backend in their container use case. The switch is not enabled by default so it won't change any existing default behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106226
Approved by: https://github.com/lezcano
