Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43902
Trace back from the weight node util we hit getattr, reconstruct the graph module with the traced nodes
and run the graph module to pack the weight. then replace the original chain of ops with the packed weight.
Test Plan:
Imported from OSS
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23432431
fbshipit-source-id: 657f21a8287494f7f87687a9d618ca46376d3aa3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43901
Add similar APIs like eager and graph mode on torchscript
- fuse_fx
- quantize_fx (for both post training static and qat)
- quantize_dynamic_fx (for post training dynamic)
- prepare_fx (for both post training static and qat)
- prepare_dynamic_fx (for post training dynamic)
- convert_fx (for all modes)
Test Plan:
Imported from OSS
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23432430
fbshipit-source-id: fc99eb75cbecd6ee7a3aa6c8ec71cd499ff7e3c1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43728
Trace back from the weight node util we hit getattr, reconstruct the graph module with the traced nodes
and run the graph module to pack the weight. then replace the original chain of ops with the packed weight.
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23385090
fbshipit-source-id: 11341f0af525a02ecec36f163a9cd35dee3744a1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43581
Add similar APIs like eager and graph mode on torchscript
- fuse_fx
- quantize_fx (for both post training static and qat)
- quantize_dynamic_fx (for post training dynamic)
- prepare_fx (for both post training static and qat)
- prepare_dynamic_fx (for post training dynamic)
- convert_fx (for all modes)
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23385091
fbshipit-source-id: b789e54e1a0f3af6b026fd568281984e253e0433
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43587
Add tests for graph mode quantization on torchvision and make sure it matches
current eager mode quantization
Test Plan:
Imported from OSS
Imported from OSS
Reviewed By: z-a-f
Differential Revision: D23331253
fbshipit-source-id: 0445a44145d99837a2c975684cd0a0b7d965c8f9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43526
Add tests for graph mode quantization on torchvision and make sure it matches
current eager mode quantization
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23306683
fbshipit-source-id: 30d27e225d4557bfc1d9aa462086e416aa9a9c0e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43445
changed the interface for checkGraphModule to make the arguments more explicit
as requested in https://github.com/pytorch/pytorch/pull/43437
Test Plan:
TestQuantizeFx
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23280586
fbshipit-source-id: 5b5859e326d149a5aacb1d15cbeee69667cc9109
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42178
This otherwise introduces unnecessary calls to contiguous in the rest of
the network, where certain ops want channels last format.
Test Plan:
Quantization tests.
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D22796479
fbshipit-source-id: f1ada1c2eeed84991b9b195120699b943ef6e421
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/43015
Currently activation_post_process are inserted by default in qat modules, which is not
friendly to automatic quantization tools, this PR removes them.
Test Plan:
Imported from OSS
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23105059
fbshipit-source-id: 3439ac39e718ffb0390468163bcbffd384802b57
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42343
Currently activation_post_process are inserted by default in qat modules, which is not
friendly to automatic quantization tools, this PR removes them.
Test Plan: Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D22856816
fbshipit-source-id: 988a43bce46a992b38fd0d469929f89e5b046131
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42576
Previously we have qconfig propagate list and we only attach qconfig for modules
in the list, this works when everything is quantized in the form of module.
but now we are expanding quantization for functional/torch ops, we'll need to attach qconfig
to all modules
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D22939453
fbshipit-source-id: 7d6a1f73ff9bfe461b3afc75aa266fcc8f7db517
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42222
This change adds the necessary passes to perform FP16 dynamic quantization.
We skip inserting observers for activations based on the dtype (torch.float16) and only insert the Fp16Observer for weights
Test Plan:
python test/test_quantization.py TestQuantizeJitOps
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D22849220
fbshipit-source-id: 2c53594ecd2485e9e3dd0b380eceaf7c5ab5fc50
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41930
As title
ghstack-source-id: 108517079
Test Plan: CI
Reviewed By: jerryzh168
Differential Revision: D22698386
fbshipit-source-id: 4f748c9bae4a0b615aa69c7cc8d8e451e5d26863
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40396
Removes activation and normalization modules from eager mode QAT.
These were incorrectly added, but we don't actually need them.
Test Plan:
```
python test/test_quantization.py TestQuantizationAwareTraining
```
Imported from OSS
Differential Revision: D22169768
fbshipit-source-id: b5bd753dafe92e90e226fb773eb18c6aae179703
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
