| from __future__ import absolute_import, division, print_function, unicode_literals |
| import torch |
| import torch.nn.quantized as nnq |
| from torch.quantization import default_eval_fn, QConfig, default_qconfig, \ |
| default_observer, quantize, prepare, convert |
| |
| from common_utils import run_tests |
| from common_quantization import QuantizationTestCase, SingleLayerLinearModel, \ |
| TwoLayerLinearModel, NestedModel, WrappedModel, ManualQuantModel |
| |
| |
| calib_data = [torch.rand(20, 5, dtype=torch.float) for _ in range(20)] |
| |
| class ModelQuantizeAPITest(QuantizationTestCase): |
| |
| def test_single_layer(self): |
| r"""Quantize SingleLayerLinearModel which has one Linear module, make sure it is swapped |
| to nnq.Linear which is the quantized version of the module |
| """ |
| model = SingleLayerLinearModel() |
| qconfig_dict = { |
| '': default_qconfig |
| } |
| model = prepare(model, qconfig_dict) |
| # Check if observers and quant/dequant nodes are inserted |
| self.checkNoPrepModules(model) |
| self.checkHasPrepModules(model.fc1) |
| self.checkObservers(model) |
| |
| default_eval_fn(model, calib_data) |
| convert(model) |
| |
| def checkQuantized(model): |
| self.checkNoPrepModules(model) |
| self.checkHasPrepModules(model.fc1) |
| self.checkQuantizedLinear(model.fc1) |
| default_eval_fn(model, calib_data) |
| |
| checkQuantized(model) |
| |
| # test one line API |
| model = quantize(SingleLayerLinearModel(), default_eval_fn, calib_data, qconfig_dict) |
| checkQuantized(model) |
| |
| def test_two_layers(self): |
| r"""TwoLayerLinearModel has two Linear modules but we only quantize the second one |
| `fc2`, and `fc1`is not quantized |
| """ |
| model = TwoLayerLinearModel() |
| qconfig_dict = { |
| 'fc2': default_qconfig |
| } |
| model = prepare(model, qconfig_dict) |
| |
| self.checkNoPrepModules(model) |
| self.checkObservers(model) |
| self.checkNoPrepModules(model.fc1) |
| self.checkHasPrepModules(model.fc2) |
| |
| default_eval_fn(model, calib_data) |
| convert(model) |
| |
| def checkQuantized(model): |
| self.checkNoPrepModules(model) |
| self.checkNoPrepModules(model.fc1) |
| self.checkHasPrepModules(model.fc2) |
| self.assertEqual(type(model.fc1), torch.nn.Linear) |
| self.checkQuantizedLinear(model.fc2) |
| default_eval_fn(model, calib_data) |
| |
| checkQuantized(model) |
| |
| # test one line API |
| model = quantize(TwoLayerLinearModel(), default_eval_fn, calib_data, qconfig_dict) |
| checkQuantized(model) |
| |
| def test_nested1(self): |
| r"""Test quantization for nested model, top level 'fc3' and |
| 'fc1' of submodule 'sub2', 'sub2.fc2' is not quantized |
| """ |
| model = NestedModel() |
| qconfig_dict = { |
| 'fc3': default_qconfig, |
| 'sub2.fc1': default_qconfig |
| } |
| |
| def checkPrepModules(model, before_calib=False): |
| if before_calib: |
| self.checkObservers(model) |
| self.checkNoPrepModules(model) |
| self.checkNoPrepModules(model.sub1) |
| self.checkNoPrepModules(model.sub1.fc) |
| self.checkNoPrepModules(model.sub1.relu) |
| self.checkNoPrepModules(model.sub2) |
| self.checkHasPrepModules(model.sub2.fc1) |
| self.checkNoPrepModules(model.sub2.fc2) |
| self.checkHasPrepModules(model.fc3) |
| |
| model = prepare(model, qconfig_dict) |
| checkPrepModules(model, True) |
| default_eval_fn(model, calib_data) |
| convert(model) |
| |
| def checkQuantized(model): |
| checkPrepModules(model) |
| self.checkLinear(model.sub1.fc) |
| self.checkQuantizedLinear(model.fc3) |
| self.checkQuantizedLinear(model.sub2.fc1) |
| self.checkLinear(model.sub2.fc2) |
| default_eval_fn(model, calib_data) |
| |
| checkQuantized(model) |
| |
| # test one line API |
| model = quantize(NestedModel(), default_eval_fn, calib_data, qconfig_dict) |
| checkQuantized(model) |
| |
| |
| def test_nested2(self): |
| r"""Another test case for quantized, we will quantize all submodules |
| of submodule sub2, this will include redundant quant/dequant, to |
| remove them we need to manually call QuantWrapper or insert |
| QuantStub/DeQuantStub, see `test_quant_dequant_wrapper` and |
| `test_manual` |
| """ |
| model = NestedModel() |
| qconfig_dict = { |
| 'fc3': default_qconfig, |
| 'sub2': default_qconfig |
| } |
| model = prepare(model, qconfig_dict) |
| |
| def checkPrepModules(model, before_calib=False): |
| if before_calib: |
| self.checkObservers(model) |
| self.checkNoPrepModules(model) |
| self.checkNoPrepModules(model.sub1) |
| self.checkNoPrepModules(model.sub1.fc) |
| self.checkNoPrepModules(model.sub1.relu) |
| self.checkNoPrepModules(model.sub2) |
| self.checkHasPrepModules(model.sub2.fc1) |
| self.checkHasPrepModules(model.sub2.fc2) |
| self.checkHasPrepModules(model.fc3) |
| |
| checkPrepModules(model, True) |
| |
| default_eval_fn(model, calib_data) |
| convert(model) |
| |
| def checkQuantized(model): |
| checkPrepModules(model) |
| self.checkLinear(model.sub1.fc) |
| self.assertEqual(type(model.sub1.relu), torch.nn.ReLU) |
| self.checkQuantizedLinear(model.sub2.fc1) |
| self.checkQuantizedLinear(model.sub2.fc2) |
| self.checkQuantizedLinear(model.fc3) |
| default_eval_fn(model, calib_data) |
| |
| checkQuantized(model) |
| |
| # test one line API |
| model = quantize(NestedModel(), default_eval_fn, calib_data, qconfig_dict) |
| checkQuantized(model) |
| |
| def test_nested3(self): |
| r"""More complicated nested test case with child qconfig overrides |
| parent qconfig |
| """ |
| model = NestedModel() |
| custum_options = { |
| 'dtype': torch.quint8, |
| 'qscheme': torch.per_tensor_affine |
| } |
| custom_qconfig = QConfig(weight=default_observer(), |
| activation=default_observer(**custum_options)) |
| qconfig_dict = { |
| 'fc3': default_qconfig, |
| 'sub2': default_qconfig, |
| 'sub2.fc1': custom_qconfig |
| } |
| model = prepare(model, qconfig_dict) |
| |
| def checkPrepModules(model, before_calib=False): |
| if before_calib: |
| self.checkObservers(model) |
| self.checkNoPrepModules(model) |
| self.checkNoPrepModules(model.sub1) |
| self.checkNoPrepModules(model.sub1.fc) |
| self.checkNoPrepModules(model.sub1.relu) |
| self.checkNoPrepModules(model.sub2) |
| self.checkHasPrepModules(model.sub2.fc1) |
| self.checkHasPrepModules(model.sub2.fc2) |
| self.checkHasPrepModules(model.fc3) |
| |
| checkPrepModules(model, True) |
| |
| default_eval_fn(model, calib_data) |
| convert(model) |
| |
| def checkQuantized(model): |
| checkPrepModules(model) |
| self.checkQuantizedLinear(model.sub2.fc1) |
| self.checkQuantizedLinear(model.sub2.fc2) |
| self.checkQuantizedLinear(model.fc3) |
| default_eval_fn(model, calib_data) |
| |
| checkQuantized(model) |
| |
| # test one line API |
| model = quantize(NestedModel(), default_eval_fn, calib_data, qconfig_dict) |
| checkQuantized(model) |
| |
| def test_quant_wrapper(self): |
| r"""User need to modify the original code with QuantWrapper, |
| and call the quantization utility functions. |
| """ |
| model = WrappedModel() |
| |
| # since we didn't provide qconfig_dict, the model is modified inplace |
| # but we can do `model = prepare(model)` as well |
| prepare(model) |
| self.checkObservers(model) |
| |
| default_eval_fn(model, calib_data) |
| convert(model) |
| |
| def checkQuantized(model): |
| self.checkLinear(model.fc) |
| self.checkQuantDequant(model.sub) |
| self.assertEqual(type(model.sub.module.fc1), nnq.Linear) |
| self.assertEqual(type(model.sub.module.fc2), nnq.Linear) |
| self.assertEqual(type(model.sub.module.relu), nnq.ReLU) |
| default_eval_fn(model, calib_data) |
| |
| checkQuantized(model) |
| |
| # test one line API |
| model = quantize(WrappedModel(), default_eval_fn, calib_data, {}) |
| checkQuantized(model) |
| |
| |
| def test_manual(self): |
| r"""User inserts QuantStub and DeQuantStub in model code |
| and call the quantization utility functions. |
| """ |
| model = ManualQuantModel() |
| # propagate the qconfig of parents to children, model is changed |
| # inplace |
| prepare(model) |
| self.checkObservers(model) |
| |
| default_eval_fn(model, calib_data) |
| convert(model) |
| |
| def checkQuantized(model): |
| self.assertEqual(type(model.fc), nnq.Linear) |
| default_eval_fn(model, calib_data) |
| |
| checkQuantized(model) |
| |
| # test one line API |
| model = quantize(ManualQuantModel(), default_eval_fn, calib_data) |
| checkQuantized(model) |
| |
| |
| if __name__ == '__main__': |
| run_tests() |
