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)
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)
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_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 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)
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 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)
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_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_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_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)
def checkQuantized(model): self.checkNoPrepModules(model) self.checkHasPrepModules(model.fc1) self.checkQuantizedLinear(model.fc1) default_eval_fn(model, calib_data)
def checkQuantized(model): self.assertEqual(type(model.fc), nnq.Linear) default_eval_fn(model, calib_data)