By just calling `std_mps` and `mean` in sequence
Move `var_mean` decomp to `ReduceOps.mm`, as it should be faster to skip dispatching to a Python, which one can validate by running the following script:
```python
from timeit import default_timer
import torch
from torch.utils.benchmark import Measurement, Timer
def bench_var_mean(
m, n, k,
dtype = torch.float32,
device:str = "cpu",
) -> Measurement:
setup = f"""
x = torch.rand({m}, {n}, {k}, dtype={dtype}, device="{device}")
"""
t = Timer(
stmt="torch.var_mean(x, dim=1)", setup=setup, language="python", timer=default_timer
)
return t.blocked_autorange()
for x in [100, 1000]:
rc = bench_var_mean(1000, x, 100, device="mps")
print(f"{x:5} : {rc.mean*1e6:.2f} usec")
```
which before the change reports 681 and 1268 usec and after 668 and 684 (which probably means that GPU is not saturated, but overhead from switching between native and interpretable runtimes are shorter.
Fixes https://github.com/pytorch/pytorch/issues/119663
TODOs:
- Refactor the codebase and implement proper composite function (that must be faster)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119777
Approved by: https://github.com/albanD
Prerequisite for adding more complex type support and FFT operation
Check using `conjugateWithTensor:name:` selector defined as follows
```objc
/// Returns the complex conjugate of the input tensor elements.
///
/// - Parameters:
/// - tensor: The input tensor.
/// - name: An optional string which serves as an identifier for the operation..
/// - Returns: A valid `MPSGraphTensor` object containing the elementwise result of the applied operation.
-(MPSGraphTensor *) conjugateWithTensor:(MPSGraphTensor *) tensor
name:(NSString * _Nullable) name
MPS_AVAILABLE_STARTING(macos(14.0), ios(17.0), tvos(17.0))
MPS_SWIFT_NAME( conjugate(tensor:name:) );
```
- Rename `isOnMacOS13orNewer(unsigned minor)` hook to `isOnMacOSorNewer(major, minor)`
- Replace `torch._C.__mps_is_on_macos_13_or_newer` with `torch._C._mps_is_on_macos_or_newer`
- Add `torch.backends.mps.is_macos_or_newer` public API
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115512
Approved by: https://github.com/albanD
Fixes#112633
Fixed errors relating to pydocstyle in the following files. The remaining errors are not covered in this issue. `torch/utils/dlpack.py` was not modified as the errors are relating to the function signature in the first line in the docstring which must be maintained as is for proper Sphinx interpretation.
```python
def from_dlpack(ext_tensor: Any) -> 'torch.Tensor':
"""from_dlpack(ext_tensor) -> Tensor
.....
"""
```
pydocstyle torch/utils/_contextlib.py --count
before: 4
after: 0
pydocstyle torch/backends/mps/__init__.py --count
before: 8
after: 1
**remaining errors**
```
torch/backends/mps/__init__.py:1 at module level:
D104: Missing docstring in public package
```
pydocstyle torch/backends/xeon/run_cpu.py --count
before: 13
after: 1
**remaining errors**
```
torch/backends/xeon/run_cpu.py:864 in public function `main`:
D103: Missing docstring in public function
```
pydocstyle torch/backends/cpu/__init__.py --count
before: 2
after: 1
**remaining errors**
```
torch/backends/cpu/__init__.py:1 at module level:
D104: Missing docstring in public package
```
pydocstyle torch/utils/cpp_backtrace.py --count
before: 4
after: 1
**remaining errors**
```
torch/utils/cpp_backtrace.py:1 at module level:
D100: Missing docstring in public module
```
pydocstyle torch/utils/bundled_inputs.py --count
before: 8
after: 1
**remaining errors**
```
torch/utils/bundled_inputs.py:1 at module level:
D100: Missing docstring in public module
```
pydocstyle torch/utils/file_baton.py --count
before: 8
after: 1
**remaining errors**
```
torch/utils/file_baton.py:1 at module level:
D100: Missing docstring in public module
```
pydocstyle torch/utils/mobile_optimizer.py --count
before: 6
after: 1
**remaining errors**
```
torch/utils/mobile_optimizer.py:8 in public class `LintCode`:
D101: Missing docstring in public class
```
pydocstyle torch/backends/opt_einsum/__init__.py --count
before: 7
after: 5
**remaining errors**
```
torch/backends/opt_einsum/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/opt_einsum/__init__.py:67 in public function `set_flags`:
D103: Missing docstring in public function
torch/backends/opt_einsum/__init__.py:77 in public function `flags`:
D103: Missing docstring in public function
torch/backends/opt_einsum/__init__.py:93 in public class `OptEinsumModule`:
D101: Missing docstring in public class
torch/backends/opt_einsum/__init__.py:94 in public method `__init__`:
D107: Missing docstring in __init__
```
pydocstyle torch/utils/_device.py --count
before: 9
after: 6
**remaining errors**
```
torch/utils/_device.py:58 in public class `DeviceContext`:
D101: Missing docstring in public class
torch/utils/_device.py:59 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/_device.py:62 in public method `__enter__`:
D105: Missing docstring in magic method
torch/utils/_device.py:68 in public method `__exit__`:
D105: Missing docstring in magic method
torch/utils/_device.py:73 in public method `__torch_function__`:
D105: Missing docstring in magic method
torch/utils/_device.py:80 in public function `device_decorator`:
D103: Missing docstring in public function
```
pydocstyle torch/utils/_freeze.py --count
before: 15
after: 7
**remaining errors**
```
torch/utils/_freeze.py:77 in public function `indent_msg`:
D103: Missing docstring in public function
torch/utils/_freeze.py:89 in public class `FrozenModule`:
D101: Missing docstring in public class
torch/utils/_freeze.py:100 in public class `Freezer`:
D101: Missing docstring in public class
torch/utils/_freeze.py:101 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/_freeze.py:106 in public method `msg`:
D102: Missing docstring in public method
torch/utils/_freeze.py:185 in public method `get_module_qualname`:
D102: Missing docstring in public method
torch/utils/_freeze.py:206 in public method `compile_string`:
D102: Missing docstring in public method
```
pydocstyle torch/utils/throughput_benchmark.py --count
before: 25
after: 8
**remaining errors**
```
torch/utils/throughput_benchmark.py:1 at module level:
D100: Missing docstring in public module
torch/utils/throughput_benchmark.py:27 in public class `ExecutionStats`:
D101: Missing docstring in public class
torch/utils/throughput_benchmark.py:28 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/throughput_benchmark.py:33 in public method `latency_avg_ms`:
D102: Missing docstring in public method
torch/utils/throughput_benchmark.py:37 in public method `num_iters`:
D102: Missing docstring in public method
torch/utils/throughput_benchmark.py:46 in public method `total_time_seconds`:
D102: Missing docstring in public method
torch/utils/throughput_benchmark.py:50 in public method `__str__`:
D105: Missing docstring in magic method
torch/utils/throughput_benchmark.py:94 in public method `__init__`:
D107: Missing docstring in __init__
```
pydocstyle torch/utils/hooks.py --count
before: 14
after: 11
**remaining errors**
```
torch/utils/hooks.py:1 at module level:
D100: Missing docstring in public module
torch/utils/hooks.py:23 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/hooks.py:34 in public method `remove`:
D102: Missing docstring in public method
torch/utils/hooks.py:44 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/utils/hooks.py:50 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/hooks.py:64 in public method `__enter__`:
D105: Missing docstring in magic method
torch/utils/hooks.py:67 in public method `__exit__`:
D105: Missing docstring in magic method
torch/utils/hooks.py:82 in public function `warn_if_has_hooks`:
D103: Missing docstring in public function
torch/utils/hooks.py:103 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/hooks.py:188 in public method `setup_input_hook`:
D102: Missing docstring in public method
torch/utils/hooks.py:197 in public method `setup_output_hook`:
D102: Missing docstring in public method
```
pydocstyle torch/utils/_traceback.py --count
before: 19
after: 14
**remaining errors**
```
torch/utils/_traceback.py:47 in public function `report_compile_source_on_error`:
D103: Missing docstring in public function
torch/utils/_traceback.py:160 in public class `CapturedTraceback`:
D101: Missing docstring in public class
torch/utils/_traceback.py:163 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/_traceback.py:167 in public method `cleanup`:
D102: Missing docstring in public method
torch/utils/_traceback.py:170 in public method `summary`:
D102: Missing docstring in public method
torch/utils/_traceback.py:182 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/utils/_traceback.py:190 in public method `extract`:
D205: 1 blank line required between summary line and description (found 0)
torch/utils/_traceback.py:190 in public method `extract`:
D400: First line should end with a period (not 't')
torch/utils/_traceback.py:213 in public method `format`:
D205: 1 blank line required between summary line and description (found 0)
torch/utils/_traceback.py:213 in public method `format`:
D400: First line should end with a period (not 'f')
torch/utils/_traceback.py:213 in public method `format`:
D401: First line should be in imperative mood (perhaps 'Format', not 'Formats')
torch/utils/_traceback.py:224 in public method `format_all`:
D200: One-line docstring should fit on one line with quotes (found 3)
torch/utils/_traceback.py:247 in private function `_extract_symbolized_tb`:
D205: 1 blank line required between summary line and description (found 0)
torch/utils/_traceback.py:247 in private function `_extract_symbolized_tb`:
D400: First line should end with a period (not 'f')
```
pydocstyle torch/utils/mkldnn.py --count
before: 28
after: 26
**remaining errors**
```
torch/utils/mkldnn.py:1 at module level:
D100: Missing docstring in public module
torch/utils/mkldnn.py:4 in public class `MkldnnLinear`:
D101: Missing docstring in public class
torch/utils/mkldnn.py:5 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/mkldnn.py:19 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:23 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:29 in public method `forward`:
D102: Missing docstring in public method
torch/utils/mkldnn.py:75 in public class `MkldnnConv1d`:
D101: Missing docstring in public class
torch/utils/mkldnn.py:76 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/mkldnn.py:82 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:88 in public class `MkldnnConv2d`:
D101: Missing docstring in public class
torch/utils/mkldnn.py:89 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/mkldnn.py:100 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:110 in public class `MkldnnConv3d`:
D101: Missing docstring in public class
torch/utils/mkldnn.py:111 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/mkldnn.py:122 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:133 in public class `MkldnnBatchNorm`:
D101: Missing docstring in public class
torch/utils/mkldnn.py:136 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/mkldnn.py:155 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:163 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:171 in public method `forward`:
D102: Missing docstring in public method
torch/utils/mkldnn.py:184 in public class `MkldnnPrelu`:
D101: Missing docstring in public class
torch/utils/mkldnn.py:185 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/mkldnn.py:190 in public method `__getstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:194 in public method `__setstate__`:
D105: Missing docstring in magic method
torch/utils/mkldnn.py:199 in public method `forward`:
D102: Missing docstring in public method
torch/utils/mkldnn.py:205 in public function `to_mkldnn`:
D103: Missing docstring in public function
```
pydocstyle torch/utils/weak.py --count
before: 32
after: 30
**remaining errors**
```
torch/utils/weak.py:1 at module level:
D100: Missing docstring in public module
torch/utils/weak.py:42 in public class `WeakIdRef`:
D101: Missing docstring in public class
torch/utils/weak.py:45 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/weak.py:54 in public method `__call__`:
D102: Missing docstring in public method
torch/utils/weak.py:61 in public method `__hash__`:
D105: Missing docstring in magic method
torch/utils/weak.py:64 in public method `__eq__`:
D105: Missing docstring in magic method
torch/utils/weak.py:84 in public class `WeakIdKeyDictionary`:
D101: Missing docstring in public class
torch/utils/weak.py:87 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/weak.py:131 in public method `__delitem__`:
D105: Missing docstring in magic method
torch/utils/weak.py:135 in public method `__getitem__`:
D105: Missing docstring in magic method
torch/utils/weak.py:138 in public method `__len__`:
D105: Missing docstring in magic method
torch/utils/weak.py:145 in public method `__repr__`:
D105: Missing docstring in magic method
torch/utils/weak.py:148 in public method `__setitem__`:
D105: Missing docstring in magic method
torch/utils/weak.py:151 in public method `copy`:
D102: Missing docstring in public method
torch/utils/weak.py:162 in public method `__deepcopy__`:
D105: Missing docstring in magic method
torch/utils/weak.py:172 in public method `get`:
D102: Missing docstring in public method
torch/utils/weak.py:175 in public method `__contains__`:
D105: Missing docstring in magic method
torch/utils/weak.py:182 in public method `items`:
D102: Missing docstring in public method
torch/utils/weak.py:189 in public method `keys`:
D102: Missing docstring in public method
torch/utils/weak.py:198 in public method `values`:
D102: Missing docstring in public method
torch/utils/weak.py:216 in public method `popitem`:
D102: Missing docstring in public method
torch/utils/weak.py:224 in public method `pop`:
D102: Missing docstring in public method
torch/utils/weak.py:228 in public method `setdefault`:
D102: Missing docstring in public method
torch/utils/weak.py:231 in public method `update`:
D102: Missing docstring in public method
torch/utils/weak.py:241 in public method `__ior__`:
D105: Missing docstring in magic method
torch/utils/weak.py:245 in public method `__or__`:
D105: Missing docstring in magic method
torch/utils/weak.py:252 in public method `__ror__`:
D105: Missing docstring in magic method
torch/utils/weak.py:262 in public method `__eq__`:
D105: Missing docstring in magic method
torch/utils/weak.py:276 in public method `__init__`:
D107: Missing docstring in __init__
torch/utils/weak.py:280 in public method `__call__`:
D102: Missing docstring in public method
```
@mikaylagawarecki @jbschlosser @svekars
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113311
Approved by: https://github.com/ezyang
Will be needed if one wants to make accurate XFAIL validation
I.e. `torch.backends.mps.is_macos13_or_newer()` will return True if PyTorch is running on MacOS 13.0 or newer, `torch.backends.mps.is_macos13_or_newer(1)` will return True if running on MacOS 13.1 or newer and `torch.backends.mps.is_macos13_or_newer(2)` will return True if running on MacOS 13.2 or newer
Do not use 13.3 check as `@available` does not really work for shared libraries
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95065
Approved by: https://github.com/albanD
- To check for Memory Leaks in `test_mps.py`, set the env-variable `PYTORCH_TEST_MPS_MEM_LEAK_CHECK=1` when running test_mps.py (used CUDA code as reference).
- Added support for the following new python interfaces in MPS module:
`torch.mps.[empty_cache(), set_per_process_memory_fraction(), current_allocated_memory(), driver_allocated_memory()]`
- Renamed `_is_mps_on_macos_13_or_newer()` to `_mps_is_on_macos_13_or_newer()`, and `_is_mps_available()` to `_mps_is_available()` to be consistent in naming with prefix `_mps`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94646
Approved by: https://github.com/malfet
Use Prims to implement group_norm, group_norm_backward and mean_var
Use `torch._ops.ops` instead of `torch.ops` in numerous subpackages in
order to be able to make them importable from `torch/backend/mps/__init__.py` as this alias is defined in
15af4b1cee/torch/__init__.py (L1095)
is executed last during init process.
Add `__all__` to `torch/backends/mps/__init__.py` as well as alias all imports as private
Add `TestNNMPS.test_group_norm_backward` that validates no NaNs are generated during the backward pass
Fixes https://github.com/pytorch/pytorch/issues/88331
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91190
Approved by: https://github.com/albanD
