Summary:
During convert step observers are first replaced by Q-DQ pair. In some
scenarios like following output DQ has a fan out.
---> OP2 -> Q -> DQ
/
OP -> Q -> DQ -
\
---> OP3 -> Q -> DQ
If either op OP2 or OP3 are configured to be quantized, then the input
is expected to quantized. In this case quantized equivalent of some
pattern, that quantizer asked to be quantized, should look like:
[DQ -> {pattern} -> Q]. However, in scenario like above where DQ node
is shared between multiple "quantized" patterns, boundary of "quantized"
pattern is not clear because DQ now belongs to multiple quantized
patterns.
This poses challenge for:
- Porting metadata: which "quantized" partition this DQ node belongs
- Quantized representation, equivalently, needs to identify
self-contained quantized pattern that is replaced by its equivalent pattern
that captures compute in the quantized precision.
Test Plan:
test_duplicate_dq_pass
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D48663147](https://our.internmc.facebook.com/intern/diff/D48663147)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107900
Approved by: https://github.com/jerryzh168, https://github.com/andrewor14, https://github.com/leslie-fang-intel
ghstack dependencies: #107105, #107106, #107899
**Summary**
Add linear and linear-unary post-op quantization recipe to x86 inductor quantizer. For PT2E with Inductor. With this, the quantization path will add `quant-dequant` pattern for linear and linear-unary post op.
**Test plan**
python test/test_quantization.py -k test_linear_with_quantizer_api
python test/test_quantization.py -k test_linear_unary_with_quantizer_api
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106781
Approved by: https://github.com/leslie-fang-intel, https://github.com/jgong5, https://github.com/jerryzh168
ghstack dependencies: #105818
Summary:
Currently in quantizer/quantize_pt2e we import things from specific quantizers (XNNPACKQuantizer, QuantizationConfig) etc.
this PR removes them so it's clearer that they are not part of the core quantization code base
This PR also removed get_supported_operators from main Quantizer since we haven't seen a clear need for this API
Test Plan:
CIs
Imported from OSS
Differential Revision: D48340367
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107259
Approved by: https://github.com/kimishpatel
**Summary**
Re-enable the test case of `test_conv2d_binary_with_quantizer_api` and `test_conv2d_binary_unary_with_quantizer_api` for X86InductorQuantizer. We disable these 2 testcases previously due to the time out issue in internal CI.
**Test Plan**
```
python -m pytest test_x86inductor_quantizer.py -k test_conv2d_binary_with_quantizer_api
python -m pytest test_x86inductor_quantizer.py -k test_conv2d_binary_unary_with_quantizer_api
```
Differential Revision: [D47745372](https://our.internmc.facebook.com/intern/diff/D47745372)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105638
Approved by: https://github.com/jerryzh168, https://github.com/andrewor14
Summary: moving quantizer to torch.ao.quantization to make it a public api, since pt2e is a folder for implementations
Test Plan:
CIs
sanity check: "buck test //executorch/backends/xnnpack/test:test_xnnpack_quantized_models -- test_resnet18"
Differential Revision: D47727838
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105885
Approved by: https://github.com/andrewor14
**Summary**
Reduce the test time of `test_conv2d_binary_with_quantizer_api` and `test_conv2d_binary_unary_with_quantizer_api`.
* For `test_conv2d_binary_with_quantizer_api`, reduce the number of test config from 12 to 2.
* For `test_conv2d_binary_unary_with_quantizer_api`, reduce the number of test config from 24 to 2.
**Test Plan**
```
python -m pytest test_x86inductor_quantizer.py -k test_conv2d_binary_with_quantizer_api
python -m pytest test_x86inductor_quantizer.py -k test_conv2d_binary_unary_with_quantizer_api
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104686
Approved by: https://github.com/jerryzh168
