Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48331
Enables mypy to not ignore type errors in FX quantization files. Fixes the easy
typing errors inline, and comments out the harder errors to be fixed at a later time.
After this PR, mypy runs without errors on `torch/quantization`.
Test Plan:
```
> mypy torch/quantization/
Success: no issues found in 25 source files
```
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D25133348
fbshipit-source-id: 0568ef9405b292b80b3857eae300450108843e80
Summary:
Adds a standalone script which can be used to test different BLAS libraries. Right now I've deliberately kept it limited (only a couple BLAS libs and only GEMM and GEMV). It's easy enough to expand later.
CC ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47058
Reviewed By: zhangguanheng66
Differential Revision: D25078946
Pulled By: robieta
fbshipit-source-id: b5f7f7ec289d59c16c5370b7a6636c10a496b3ac
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47531
This is part of a stack of PRs that fixes mypy typing errors in the torch.distributed.* directory.
Test Plan:
python test_type_hints.py -v TestTypeHints.test_run_mypy
Imported from OSS
Reviewed By: walterddr
Differential Revision: D24952499
fbshipit-source-id: b193171e28c2211a71d28a544fa44770bf938a1e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46780
This is in prototype status, but pretty functional. There are two major
parts.
- Model converter. This is a pure Python component that consumes a
model in TorchScript format, converts the operations into NNAPI
semantics, and serializes the model in a custom format. It then wraps
the result in a new TorchScript model that can invoke NNAPI under the
hood.
- Runtime. This is a TorchBind object that deserializes the model and
sends the result to NNAPI. This is fairly simple since the serialized
format is basically just a list of NNAPI calls to make, so most of the
code is spent on bounds checking.
A few notes on the design.
- Currently, all tensor sizes need to be fixed, and those fixed sizes
are burned directly into the serialized model. This will probably
need to change. NNAPI supports variable-sized tensors, but the
important hardware backends do not. However, we're seeing use cases
crop up where the input size is not known until around the time that
the model is loaded (for example, it might depend on the camera aspect
ratio). I think the proper fix here is to remove the code in the
converter that eagerly calculates the sizes of the intermediate
tensors and replace it with a code generator that will generate some
TorchScript code that will perform those calculations at model load
time. This way, we will be able to support models that have
variable-sized inputs while still only showing fixed-sized operands to
NNAPI.
- The important hardware backends want operands to be in NHWC order, but
PyTorch natively represents all tensors and NCHW. The strategy for
this is to keep NCHW during most of the conversion process, but track
and additional value per operand representing the "dimension order".
The dimension order gets propagated through convolutions and pointwise
ops. When we're ready to serialize the model, we reorder the
dimensions for "channels last" operands to NHWC.
Test Plan:
Some local testing with FB prod models. I'll need to add some examples
and automated tests.
Reviewed By: iseeyuan
Differential Revision: D24574040
Pulled By: dreiss
fbshipit-source-id: 6adc8571b234877ee3666ec0c0de24da35c38a1f
Summary:
Fix `torch._C._autocast_*_nesting` declarations in __init__.pyi
Fix iterable constructor logic: not every iterable can be constructed using `type(val)(val)` trick, for example it would not work for `val=range(10)` although `isinstance(val, Iterable)` is True
Change optional resolution logic to meet mypy expectations
Fixes https://github.com/pytorch/pytorch/issues/45436
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45480
Reviewed By: walterddr
Differential Revision: D23982822
Pulled By: malfet
fbshipit-source-id: 6418a28d04ece1b2427dcde4b71effb67856a872
Summary:
This is a sub-task for addressing: https://github.com/pytorch/pytorch/issues/42969. We re-enable type check for `autocast_test_lists `.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45107
Test Plan:
`python test/test_type_hints.py` passed:
```
(pytorch) bash-5.0$ with-proxy python test/test_type_hints.py
....
----------------------------------------------------------------------
Ran 4 tests in 103.871s
OK
```
Reviewed By: walterddr
Differential Revision: D23842884
Pulled By: Hangjun
fbshipit-source-id: a39f3810e3abebc6b4c1cb996b06312f6d42ffd6
Summary:
Fixes a subtask of https://github.com/pytorch/pytorch/issues/42969
Tested the following and no warnings were seen.
python test/test_type_hints.py
....
----------------------------------------------------------------------
Ran 4 tests in 180.759s
OK
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44971
Reviewed By: walterddr
Differential Revision: D23822274
Pulled By: visweshfb
fbshipit-source-id: e3485021e348ee0a8508a9d128f04bad721795ef
Summary:
Enabled type checking in common_distributed by using tensors of ints
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44821
Test Plan: Run python test/test_type_hints.py, errors are no longer ingnored by mypy.ini
Reviewed By: walterddr
Differential Revision: D23747466
Pulled By: alanadakotashine
fbshipit-source-id: 820fd502d7ff715728470fbef0be90ae7f128dd6
Summary:
To help with further typing, move dynamically added native contributions from `torch.autograd` to `torch._C._autograd`
Fix invalid error handling pattern in
89ac30afb8/torch/csrc/autograd/init.cpp (L13-L15)
`PyImport_ImportModule` already raises Python exception and nullptr should be returned to properly propagate the to Python runtime.
And all native methods/types in `torch/autograd/__init.py` after `torch._C._init_autograd()` has been called
Use f-strings instead of `.format` in test_type_hints.py
Fixes https://github.com/pytorch/pytorch/issues/44450
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44451
Reviewed By: ezyang
Differential Revision: D23618261
Pulled By: malfet
fbshipit-source-id: fa5f739d7cff8410641128b55b810318c5f636ae
