Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56830
Opt into formatting on GitHub and format everything. This is a trial run before turning on formatting for more and eventually all of the codebase.
Test Plan: CI
Reviewed By: zertosh
Differential Revision: D27979080
fbshipit-source-id: a80f0c48691c08ae8ca0af06377b87e6a2351151
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42612
Add a new Quantizer that supports an input zero point (bias) that can be float.
The quantization equation in this case is
Xq = (Xf - bias) * inv_scale, where bias is float zero_point value
We start with per-row implementation and can extend to per-tensor in the future, if necessary
Test Plan:
python test/test_quantization.py TestQuantizedTensor
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D22960142
fbshipit-source-id: ca9ab6c5b45115d3dcb1c4358897093594313706
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26339
Serializes per-channel tensor in both torch.serialization and jit. Since we didn't bind Quantizer properly yet, I chose to save a tuple representing quantizer settings. To avoid recursive tensor serialization calls, I'm using tuple instead of tensor to store scales and zero points.
driazati - please check the serialization logic. Is there a good test that compares that JIT serialization and python serialization are equivalent? (I haven't tested it yet)
Test Plan: Imported from OSS
Differential Revision: D17443222
Pulled By: dzhulgakov
fbshipit-source-id: a34758de1ffd2ec1cdc5355f5baf95284a4ccf4b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21852
To enable change of q_scale and q_zero_point in `copy_`
Differential Revision: D15793427
fbshipit-source-id: a7040b5b956d161fd6af6176287f4a4aa877c9be
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19381
Expose QScheme enum in frontend so that people can use it in
quantization configs in modules.
Differential Revision: D14922992
fbshipit-source-id: ab07b8a7ec42c1c1f5fe84a4a0c805adbcad408d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18960
empty_affine_quantized creates an empty affine quantized Tensor from scratch.
We might need this when we implement quantized operators.
Differential Revision: D14810261
fbshipit-source-id: f07d8bf89822d02a202ee81c78a17aa4b3e571cc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18230
Implementing minimum qtensor API to unblock other workstreams in quantization
Changes:
- Added Quantizer which represents different quantization schemes
- Added qint8 as a data type for QTensor
- Added a new ScalarType QInt8
- Added QTensorImpl for QTensor
- Added following user facing APIs
- quantize_linear(scale, zero_point)
- dequantize()
- q_scale()
- q_zero_point()
Reviewed By: dzhulgakov
Differential Revision: D14524641
fbshipit-source-id: c1c0ae0978fb500d47cdb23fb15b747773429e6c
