Applies the remaining flake8-comprehension fixes and checks. This changes replace all remaining unnecessary generator expressions with list/dict/set comprehensions which are more succinct, performant, and better supported by our torch.jit compiler. It also removes useless generators such as 'set(a for a in b)`, resolving it into just the set call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94676
Approved by: https://github.com/ezyang
Preferring dash over underscore in command-line options. Add `--command-arg-name` to the argument parser. The old arguments with underscores `--command_arg_name` are kept for backward compatibility.
Both dashes and underscores are used in the PyTorch codebase. Some argument parsers only have dashes or only have underscores in arguments. For example, the `torchrun` utility for distributed training only accepts underscore arguments (e.g., `--master_port`). The dashes are more common in other command-line tools. And it looks to be the default choice in the Python standard library:
`argparse.BooleanOptionalAction`: 4a9dff0e5a/Lib/argparse.py (L893-L895)
```python
class BooleanOptionalAction(Action):
def __init__(...):
if option_string.startswith('--'):
option_string = '--no-' + option_string[2:]
_option_strings.append(option_string)
```
It adds `--no-argname`, not `--no_argname`. Also typing `_` need to press the shift or the caps-lock key than `-`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94505
Approved by: https://github.com/ezyang, https://github.com/seemethere
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54571
Supports bfloat16 via a similar method to half: upconvert inputs to
fp32, do math, then downconvert outputs to bf16.
Resource strings are mostly derived from cuda-11 headers.
Fixes#53918, for the legacy fuser at least.
Test Plan: Imported from OSS
Reviewed By: ailzhang
Differential Revision: D27328987
Pulled By: bertmaher
fbshipit-source-id: 5c0eae44164623faa0c75cb818e8bf0211579fdc
Summary:
- The thresholds of some tests are bumped up. Depending on the random generator, sometimes these tests fail with things like 0.0059 is not smaller than 0.005. I ran `test_nn.py` and `test_torch.py` for 10+ times to check these are no longer flaky.
- Add `tf32_on_and_off` to new `matrix_exp` tests.
- Disable TF32 on test suites other than `test_nn.py` and `test_torch.py`
cc: ptrblck
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44240
Reviewed By: mruberry
Differential Revision: D23882498
Pulled By: ngimel
fbshipit-source-id: 44a9ec08802c93a2efaf4e01d7487222478b6df8
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:
fmax/fmin propagate the number if one argument is NaN, which doesn't match the eager mode behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43590
Reviewed By: mruberry
Differential Revision: D23338664
Pulled By: bertmaher
fbshipit-source-id: b0316a6f01fcf8946ba77621efa18f339379b2d0
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:
After an early return, we conditionalize all further execution. This means that currently the pattern of
`if return elif return elif return` generates better code than `if return if return if return`. It's obviously not good to have semantically equivalent code generate worse IR, so we should rewrite the graph to handle this case. This came up in https://github.com/pytorch/pytorch/pull/37171
```
torch.jit.script
def test_foo(x: bool, y: bool):
if x:
return 1
return 2
print(test_foo.code)
```
generates:
```
def test_foo(x: bool,
y: bool) -> int:
_0 = uninitialized(int)
if x:
_1, _2 = True, 1
else:
_1, _2 = False, _0
if _1:
_3 = _2
else:
_3 = 2
return _3
```
while
```
torch.jit.script
def test_foo(x: bool, y: bool):
if x:
return 1
else:
return 2
print(test_foo.code)
```
generates:
```
def test_foo(x: bool,
y: bool) -> int:
if x:
_0 = 1
else:
_0 = 2
return _0
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38282
Differential Revision: D21576733
Pulled By: eellison
fbshipit-source-id: 80cf1ad7fbda6d8d58557abbfb21c90eafae7488
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/34258
This PR allows both atol and rtol to be specified, uses defaults based on the prior analysis (spreadsheet attached to https://github.com/pytorch/pytorch/pull/32538), but retains the absolute tolerance behavior in cases where precision was previously specified explicitly.
Test Plan: Imported from OSS
Differential Revision: D21110255
Pulled By: nairbv
fbshipit-source-id: 57b3a004c7d5ac1be80ee765f03668b1b13f4a7e
Summary:
This test was failing because caching resulted into a function with multiple execution plans rather than multiple functions with a single execution plan each as a test writer intended.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35847
Differential Revision: D20839674
Pulled By: Krovatkin
fbshipit-source-id: 68f41610a823d94c1e744c85ac72652c741d73ae
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34980
We were passing sample inputs to `torch.jit.script` (as if it was
`torch.jit.trace`), but this parameter was treated as an optional
`optimize` parameter. That parameter is deprecated and that caused a
warning.
Differential Revision: D20520369
Test Plan: Imported from OSS
Pulled By: ZolotukhinM
fbshipit-source-id: 87b40a5e35bfc4a3d7a5d95494632bfe117e40b7
Summary:
With the profiling executor enabled the fuser won't be invoked until the second pass over a script function, so some of these tests weren't correctly comparing the fused output with the interpreter output. I've used the `checkScript` method where applicable, which seems to do the right thing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33944
Test Plan: Locally inject obvious errors into the fuser and verify that the updated tests fail when they're supposed to.
Differential Revision: D20162320
Pulled By: bertmaher
fbshipit-source-id: 4a2f3f2d2ff1d81f23db504dc8cd0d5417bdcc50
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
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31071
Previously the profiler would think Tensors would require grad, even
when the no_grad flag is enabled during execution. This makes the profiling
and guards respect the no_grad flag, which eliminates extra differentiable
graphs that appear in the backward graph (where no_grad is typically enabled).
Test Plan: Imported from OSS
Differential Revision: D18915468
Pulled By: zdevito
fbshipit-source-id: 1ae816a16ab78ae5352825cc6b4a68ed7681a089
Summary:
These unit tests pass after landing all the warp size awareness patches.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25963
Differential Revision: D17319124
Pulled By: bddppq
fbshipit-source-id: 22f5d5f1ca9c67e66a7ccf983b2d2f889a74e729
Summary:
As of ROCm 2.6, we support hiprtc - the HIP runtime compilation API. Enable the jit fusion feature depending on the existence of such an API. This entails
* new hipification rules for API_RTC
* add hiprtc APIs to the shim loader
* update cmake infrastructure to find the hiprtc library (it is part of the HIP package)
* enabling of unit tests in the jit_fuser test set
* special casing in resource strings for HIP - the typedefs CUDA requires would be redundant
* for now disable the occupancy calculation we do not support yet and hard-code
Thanks to t-vi for working with me on getting this integration done!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22872
Differential Revision: D17207425
Pulled By: bddppq
fbshipit-source-id: 93409f3051ad0ea06afacc2239fd6c402152debe
