Summary: When we migrate to torch.export, we won't put L['self'] as the prefix for all the fqn in nn_module_stack. This diff adds the branch to handle the new case.
Test Plan: buck test mode/opt caffe2/test/quantization:test_quantization -- -r set_module_name
Differential Revision: D55436617
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122819
Approved by: https://github.com/tugsbayasgalan
Summary:
There was a bug in the module name filter for modules that had an underscore
already in them, as it was replaced with a "dot" notation.
This is because it was thought that underscores always meant a module separator,
but this isn't the case for modules whose name contains an underscore.
Test Plan:
Added a unit test. Before this change, that test failed (due to applying the wrong
qscheme). Now it passes.
Differential Revision: D53502771
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119344
Approved by: https://github.com/jerryzh168
Summary:
FX graph mode quant workflow and also pt2e flow relies on the `is_dynamic` flag in observer/quantizationspec to
convert an observer to dynamic quantization patterns (choose_qparams -> q -> dq), this PR added is_dynamic flag
for all observers so that it's possible to convert these observers to the pattern.
However, this dynamic quantization pattern (choose_qparams -> q -> dq) is actually only valid for MovingAverageObserver(averaging_constant=1)
for the computation before convert and after convert to match in the context of QAT. So we'll have some sanity
checks in other observers to make sure the is_dynamic is False.
Test Plan:
python test/test_quantization.py TestXNNPACKQuantizer.test_qat_dynamic_linear
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D51124725](https://our.internmc.facebook.com/intern/diff/D51124725)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113288
Approved by: https://github.com/kimishpatel
Summary: att, after SharedQuantizationSpec bug fix we are doing some checks before hand, this can simplify the logic when we insert observers
Test Plan:
contbuild & OSS CI, see bf998a2c5d
Test plan from GitHub:
python test/test_quantization.py TestQuantizePT2E
CIs
Differential Revision: D50816224
Pulled By: jerryzh168
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112453
Approved by: https://github.com/andrewor14
Summary:
att, after SharedQuantizationSpec bug fix we are doing some checks before hand, this can simplify the logic when we insert observers
Test Plan:
python test/test_quantization.py TestQuantizePT2E
CIs
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111828
Approved by: https://github.com/kimishpatel
ghstack dependencies: #111827
Summary:
Also added annotation support for conv1d_relu and conv1d in XNNPACKQuantizer, the quantized results still
matches fx quant path (didn't quantize conv1d) so tests are not disabled
Test Plan: with-proxy buck2 run executorch/examples/quantization:example -- -m=w2l --verify
Differential Revision: D49479546
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109830
Approved by: https://github.com/kimishpatel
Summary:
Previously we run propagate_annotation by default in quantization flow to propagate annotations for ops like reshape, view etc.
Not all quantizers would need this so we moved this to xnnpack_quantizer_utils for now.
Next Step:
* make propagate_annotation function configurable with a custom list of ops
* remove unneeded ops in `_is_share_obs_or_fq_op`
Test Plan:
python test/test_quantization.py TestQuantizePT2E
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D48856985](https://our.internmc.facebook.com/intern/diff/D48856985)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108320
Approved by: https://github.com/kimishpatel
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:
This is to allow sharing these annotate functions by other quantizers so that writing a new quantizer is easier
note that these annotation functions will be maintained by XNNPACKQuantizer developers instead of AO team
Test Plan:
python test/test_quantization.py TestQuantizePT2E
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106642
Approved by: https://github.com/andrewor14
Summary:
Added support to allow users to set configurations based on module type in XNNPACKQuantizer, can also serve as an example
for implementing new quantizers
Test Plan:
python test/test_quantization.py TestQuantizePT2E.test_xnnpack_quantizer_set_module_type
Summary:
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106094
Approved by: https://github.com/andrewor14
ghstack dependencies: #106087
Summary:
Added support to allow users to set configurations based on module name in XNNPACKQuantizer, can also serve as an example
for implementing new quantizers
Test Plan:
python test/test_quantization.py TestQuantizePT2E.test_xnnpack_quantizer_set_module_name
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106087
Approved by: 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
