Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49964
`torch.nn.modules.linear._LinearWithBias` is only used in the transformers, and is completely identical to the `torch.nn.Linear`.
This PR creates a mapping so that this module would be treated the same as the Linear.
Test Plan:
```
python test/test_quantization.py TestDynamicQuantizedModule TestStaticQuantizedModule
```
Differential Revision: D25731589
Reviewed By: jerryzh168
Pulled By: z-a-f
fbshipit-source-id: 1b2697014e250e97d3010cdb542f9d130b71fbc3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48192
This is to allow producing a backend independent quantized module
since some backend don't have packed weight for linear
Test Plan:
test_quantized_module.py
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D25061645
fbshipit-source-id: a65535e53f35af4f2926af0ee330fdaae6dae996
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48038
nn.ReLU works for both float and quantized input, we don't want to define an nn.quantized.ReLU
that does the same thing as nn.ReLU, similarly for nn.quantized.functional.relu
this also removes the numerical inconsistency for models quantizes nn.ReLU independently in qat mode
Test Plan:
Imported from OSS
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D25000462
fbshipit-source-id: e3609a3ae4a3476a42f61276619033054194a0d2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47415
nn.ReLU works for both float and quantized input, we don't want to define an nn.quantized.ReLU
that does the same thing as nn.ReLU, similarly for nn.quantized.functional.relu
this also removes the numerical inconsistency for models quantizes nn.ReLU independently in qat mode
Test Plan: Imported from OSS
Reviewed By: z-a-f
Differential Revision: D24747035
fbshipit-source-id: b8fdf13e513a0d5f0c4c6c9835635bdf9fdc2769
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46003
sparse is confusing because itt is used in training for sparse gradients
Test Plan: Imported from OSS
Reviewed By: radkris-git, qizzzh
Differential Revision: D24178248
fbshipit-source-id: 0a2b595f3873d33b2ce25839b6eee31d2bfd3b0d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44217
Move the tests to static ones as well
Test Plan:
python test/test_quantization.py TestStaticQuantizedModule.test_embedding_bag_api
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D23547386
fbshipit-source-id: 41f81c31e1613098ecf6a7eff601c7dcd4b09c76
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44208
Add quantized module in static quantization namespace. Embedding
quantization requires only weights to be quantized so it is static.
Internally it calls the embedding_bag_byte op with the offsets set corresponding to the
indices.
Future PR will move EmbeddingBag quantization from dynamic to static as well.
Test Plan:
python test/test_quantization.py test_embedding_api
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23547384
fbshipit-source-id: eddc6fb144b4a771060e7bab5853656ccb4443f0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43088
Create quantized module that the user can use to perform embedding bag quantization
The module uses the EmbeddingPackedParams to store the weights which can be serialized /deserialized
using TorchBind custom classes (C++ get/setstate code)
Following PR will add support for `from_float` to convert from float to quantized module
Test Plan:
python test/test_quantization.py TestDynamicQuantizedModule.test_embedding_bag_api
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23167519
fbshipit-source-id: 029d7bb44debf78c4ef08bfebf267580ed94d033
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39203
Adds logic and test coverage for optional weights and biases for
the quantized normalization operators. This was broken before this
PR because the `TORCH_LIBRARY` registration had these as required parameters
- removed it, and cleaned up the callsites.
Note: consolidating the registrations in `native_functions.yaml` as opposed to `library.cpp`
after a discussion with ezyang .
Test Plan:
```
python test/test_quantization.py TestQuantizedOps.test_qlayer_norm
python test/test_quantization.py TestQuantizedOps.test_group_norm
python test/test_quantization.py TestQuantizedOps.test_instance_norm
python test/test_quantization.py TestStaticQuantizedModule.test_layer_norm
python test/test_quantization.py TestStaticQuantizedModule.test_group_norm
python test/test_quantization.py TestStaticQuantizedModule.test_instance_norm
python test/test_quantization.py TestQuantizeScriptPTSQOps.test_layer_norm
python test/test_quantization.py TestQuantizeScriptPTSQOps.test_group_norm
python test/test_quantization.py TestQuantizeScriptPTSQOps.test_instance_norm
```
Imported from OSS
Differential Revision: D21885259
fbshipit-source-id: 978c7b8bd6c11a03e9e5fdb68f154cb80cc43599
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40101
Create three tests for LSTMs:
1. test_qlstm: Test to check numerics of quantized LSTM operator.
2. test_lstm_api: To check the LSTM module and compare
it with the quantized LSTM op
3. test_quantized_rnn: Check the dynamic quantization workflow, scriptability and serialization of quantized
LSTM
ghstack-source-id: 105997268
(Note: this ignores all push blocking failures!)
Test Plan:
buck test caffe2/test:quantization -- 'test_lstm_api \(quantization\.test_quantized_module\.TestDynamicQuantizedModule\)' --print-passing-details
buck test caffe2/test:quantization -- 'test_quantized_rnn \(quantization\.test_quantize\.TestPostTrainingDynamic\)'
buck test caffe2/test:quantization -- 'test_qlstm \(quantization\.test_quantized_op\.TestDynamicQuantizedRNNOp\)' --print-passing-details
Differential Revision: D22070826
fbshipit-source-id: 46c333e19b9eab8fa5cab6f132e89b80a635791a
Summary:
Create three tests for LSTMs:
1. test_qlstm: Test to check numerics of quantized LSTM operator.
2. test_lstm_api: To check the LSTM module and compare
it with the quantized LSTM op
3. test_quantized_rnn: Check the dynamic quantization workflow, scriptability and serialization of quantized
LSTM
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38851
ghstack-source-id: 105945574
(Note: this ignores all push blocking failures!)
Test Plan:
buck test caffe2/test:quantization -- 'test_lstm_api \(quantization\.test_quantized_module\.TestDynamicQuantizedModule\)' --print-passing-details
buck test caffe2/test:quantization -- 'test_quantized_rnn \(quantization\.test_quantize\.TestPostTrainingDynamic\)'
buck test caffe2/test:quantization -- 'test_qlstm \(quantization\.test_quantized_op\.TestDynamicQuantizedRNNOp\)' --print-passing-details
Differential Revision: D21628596
fbshipit-source-id: 4aeda899f2e5f14bfbe3d82096cb4ce89c725fa1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39090
Makes quantized GroupNorm work in eager mode post training static quant.
Test Plan:
```
python test/test_quantization.py TestPostTrainingStatic.test_normalization
python test/test_quantization.py TestStaticQuantizedModule.test_group_norm
```
Imported from OSS
Differential Revision: D21885262
fbshipit-source-id: 58b0ffb59c601fcb4c79f711c7c98a667ffc6170
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39125
switch to setting reduce_range to true for version > 3.
Models serialized with older state_dict will have version <=3 so will be run with reduce_range=false
Verified with backward compatibility tests (works with no changes to these tests)
Test Plan:
python test/test_quantization.py
Imported from OSS
Differential Revision: D21769689
fbshipit-source-id: 131f2ae736e31705222e82bdc77480f2f1826fe8
Summary:
This updates assertEqual and assertEqual-like functions to either require both or neither of atol and rtol be specified. This should improve clarity around handling precision in the test suite, and it allows us to remove the legacy positional atol argument from assertEqual. In addition, the "message" kwarg is replace with a kwarg-only "msg" argument whose name is consistent with unittest's assertEqual argument.
In the future we could make "msg" an optional third positional argument to be more consistent with unittest's assertEqual, but requiring it be specified should be clear, and we can easily update the signature to make "msg" an optional positional argument in the future, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38872
Differential Revision: D21740237
Pulled By: mruberry
fbshipit-source-id: acbc027aa1d7877a49664d94db9a5fff91a07042
Summary:
This updates assertEqual and assertEqual-like functions to either require both or neither of atol and rtol be specified. This should improve clarity around handling precision in the test suite, and it allows us to remove the legacy positional atol argument from assertEqual. In addition, the "message" kwarg is replace with a kwarg-only "msg" argument whose name is consistent with unittest's assertEqual argument.
In the future we could make "msg" an optional third positional argument to be more consistent with unittest's assertEqual, but requiring it be specified should be clear, and we can easily update the signature to make "msg" an optional positional argument in the future, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38872
Differential Revision: D21717199
Pulled By: mruberry
fbshipit-source-id: 9feb856f94eee911b44f6c7140a1d07c1b026d3a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37622
Enable channelwise quantized test on qlinear and qconv.
Dynmaic linear to follow.
Test Plan:
pytest test/quantization/test_quantized.py
pytest test/quantization/test_quantized_module.py
Imported from OSS
Differential Revision: D21339046
fbshipit-source-id: 32377680d3a6424ca1e98d3707b82839eeb349a7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38283
Adds support for the modules and tests
Test Plan:
python test/test_quantization.py TestStaticQuantizedModule.test_conv1d_api
Imported from OSS
Differential Revision: D21553665
fbshipit-source-id: 7ea28da024bdf59f87f300d616c266f2b41f0bcd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37964
I thought it was because of flakiness that we didn't pass the conv2d_relu test, but turns out to
be a typo in the implementation
Also re-enabled the `use_fused` option in `test_conv2d_api`
Test Plan:
.
Imported from OSS
Differential Revision: D21434776
fbshipit-source-id: 7c24c13cde0a96807e8dfbd1deabf47e8280fdb7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37366
- we can put both fake quant module and observer module tests in the test_workflow_module.py
- added test_quantized_functional.py
- moved tests in test_numerics.py to test_quantize.py and removed test_numerics.py
Test Plan:
python test/test_quantization.py
Imported from OSS
Differential Revision: D21282198
fbshipit-source-id: 60107cee7d1ed2cd14a45650e91ec28b8a262c52
