This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
New elements added to a tensor by `torch.Tensor.resize_` are set to NaN/MAX_INT when deterministic mode is turned on.
When `torch.Tensor.resize_` is called on a quantized tensor and deterministic mode is turned on, a nondeterministic error is raised.
Part of #82004
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104300
Approved by: https://github.com/albanD
Summary:
This diff is reverting D46920584
D46920584: Make `torch.empty*` deterministic by filling with NaN or max int value (#101849) by generatedunixname499836121 has been identified to be causing the following test or build failures:
Tests affected:
- [torchrec/distributed/composable/tests:test_fsdp - torchrec.distributed.composable.tests.test_fsdp.FullyShardTest: test_composable_checkpoint](https://www.internalfb.com/intern/test/281475062923125/)
Here's the Multisect link:
https://www.internalfb.com/multisect/2341386
Here are the tasks that are relevant to this breakage:
We're generating a revert to back out the changes in this diff, please note the backout may land if someone accepts it.
If you believe this diff has been generated in error you may Commandeer and Abandon it.
Test Plan: NA
Reviewed By: huydhn, osalpekar
Differential Revision: D46997394
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104302
Approved by: https://github.com/osalpekar
Fixes#92576 , checking the following as described in the documentation:
"source.shape[dim] == len(index) and source.shape[i] == self.shape[i] for i != dim"
Would be happy to iterate on this if there are any issues, and would be happy to implement the checking for the CUDA and MPS implementations of index_add_.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100321
Approved by: https://github.com/lezcano
This PR addresses #101690. This PR implement faster data elements swap in `_StorageBase` using C++ rather than using Python.
This PR helps such a situation that a large model saved on a little-endian machine will be loaded on a big-endian machine.
TODO:
- [x] Add test cases
- [x] Add performance comparison before and after the PR
- [ ] (Optional) Investigate further opportunities for performance improvements by [SIMDization](https://dev.to/wunk/fast-array-reversal-with-simd-j3p)
Fixes#101690
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101925
Approved by: https://github.com/mikaylagawarecki
# Motivate
Without this PR:
```python
>>>import torch
>>>torch.IntTensor.is_cuda
False
>>>torch.IntTensor.is_xpu
<attribute 'is_xpu' of 'torch._C._TensorBase' objects>
```
With this PR:
```python
>>>import torch
>>>torch.IntTensor.is_xpu
False
```
Align to CUDA, some customer code use is_xpu to check the backend. Without this PR, the check is always True which result in an unexpected behavior
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101072
Approved by: https://github.com/mikaylagawarecki
Description:
Context: In torchvision we ensure that functional ops are torchscriptable. Recently exposed `torch.backends.cpu.get_cpu_capability()` in https://github.com/pytorch/pytorch/pull/100164 is failing in torchvision CI
```
RuntimeError:
Python builtin <built-in function _get_cpu_capability> is currently not supported in Torchscript:
File "/usr/local/lib/python3.10/dist-packages/torch/backends/cpu/__init__.py", line 17
- "AVX512"
"""
return torch._C._get_cpu_capability()
~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
```
Ref: https://github.com/pytorch/vision/pull/7557
In this PR, `torch._C._get_cpu_capability()` is explicitly registered for JIT and tested.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100723
Approved by: https://github.com/albanD
Description:
- As suggested by Nikita, created `torch.backends.cpu` submodule and exposed `get_cpu_capability`.
- In torchvision Resize method we want to know current cpu capability in order to pick appropriate codepath depending on cpu capablities
Newly coded vectorized resize of uint8 images on AVX2 supported CPUs is now faster than older way (uint8->float->resize->uint8). However, on non-avx hardware (e.g. Mac M1) certain configs are slower using native uint8.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100164
Approved by: https://github.com/albanD, https://github.com/malfet
Fixes #ISSUE_NUMBER
Without affecting the existing cpu/cuda logic, a separate interface is provided for the custom backend and users can choose whether to use the interface function which provides 10 tensor types with custom backend variations.
Therefore, users can use torch.set_deafult_tensor_type to set the default device tensor type, or use torch.xxx.dtypetensor to create a tensor.For example，torch.set_deafult_tensor_type(torch.foo.DoubleTensor) or torch.foo.DoubleTensor([]).
@albanD , please review my changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99521
Approved by: https://github.com/albanD
Issue: #93684
# Problem
Reduce graph breaks when dynamo compiles python functions containing numpy functions and ndarray operations.
# Design (as I know it)
* Use torch_np.ndarray(a wrapper of tensor) to back a `VariableTracker`: `NumpyTensorVariable`.
* Translate all attributes and methods calls, on ndarray, to torch_np.ndarray equivalent.
This PR adds `NumpyTensorVariable` and supports:
1. tensor to ndarray, ndarray to tensor
2. numpy functions such as numpy.meshgrid()
3. ndarray attributes such as `itemsize`, `stride`
Next PR will handle returning `np.ndarray` and add support for ndarray methods
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95849
Approved by: https://github.com/ezyang
<!--
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### <samp>🤖 Generated by Copilot at a9fa438</samp>
Simplified a test function for `torch.masked_scatter` in `test/test_torch.py` by removing redundant and unnecessary code.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98015
Approved by: https://github.com/ezyang
__What?__
Per discussion at #94634, deprecate `masked_fill` with non-bool masks. Deprecation warnings were previously added by #22261, but not for Apple MPS. I can revert the MPS changes if deprecation warnings are wanted first tho. See also #96112.
Fixes#85063 and #89320.
__Further Development?__
- Fixed the mask dtype checking for the cuda dispatch for `masked_fill` in `aten/src/ATen/native/cuda/Indexing.cu`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96594
Approved by: https://github.com/malfet, https://github.com/ngimel
