Summary:
This is causing type hint test errors on the latest numpy:
```
torch/testing/_internal/common_quantized.py:38: error: Module has no attribute "float"; maybe "float_", "cfloat", or "float64"? [attr-defined]
torch/testing/_internal/common_methods_invocations.py:758: error: Module has no attribute "bool"; maybe "bool_" or "bool8"? [attr-defined]
```
Runtime-wise, there's also a deprecation warning:
```
__main__:1: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
```
Fixes #{issue number}
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52103
Reviewed By: suo
Differential Revision: D26401210
Pulled By: albanD
fbshipit-source-id: a7cc12ca402c6645473c98cfc82caccf161160c9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49671
- Introduces the `torch.nn.quantizable` namespace
- Adds the `torch.nn.quantizable.LSTM` module
The point of the `quantizable` namespace is to segregate the purely quantized modules with the modules that could be quantized through a normal quantization flow, but are not using the quantized kernels explicitly.
That means the quantizable modules are functionally and numerically equivalent to the FP ones and can be used instead of the FP ones without any loss.
The main difference between the `torch.nn.LSTM` and the `torch.nn.quantizable.LSTM` is that the former one does not support observation for the linear layers, because all the computation is internal to the `aten` namespace.
The `torch.nn.quantizable.LSTM`, however, uses explicit linear layers that can be observed for further quantization.
Test Plan: Imported from OSS
Differential Revision: D25663870
Reviewed By: vkuzo
Pulled By: z-a-f
fbshipit-source-id: 70ff5463bd759b9a7922571a5712d3409dfdfa06
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/39677
Test Plan:
Moved a test class suite between files, wanted to have same functionality (simple code refactor) so tested to make sure the test output was the same before/after the refactor.
Image below shows the output of TestGraphModePostTrainingStatic before refactor
{F239676498}
This image shows the output of TestQuantizeScript (renamed version that is in test_quantize_script.py instead of test_quantize.py)
{F239676509}
Differential Revision: D21940638
Pulled By: edmundw314
fbshipit-source-id: 54160a5151aadf3a34bdac2bcaeb52904e6653ed
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39041
reduce_range option restricts the activation tensor to 7 bits instead of 8.
This is necessary to enable per channel quant for RNNs and LSTMs
Test Plan:
python test/test_quantization.py TestDynamicQuantizedLinear
Imported from OSS
Reviewed By: akinh
Differential Revision: D21769691
fbshipit-source-id: ef0e9873367f3c1b34091b0b3af788233ef60c6c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32479
Run dynamic quantization on mobile (similar to FBGEMM). Currently only implemented on linear operator
Test Plan:
python test/test_quantized.py TestDynamicQuantizedLinear.test_qlinear
Imported from OSS
Differential Revision: D19542980
fbshipit-source-id: c9f6e5e8ded4d62ae0f2ed99e478c8307dde22ed
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
