from __future__ import absolute_import, division, print_function, unicode_literals import unittest import torch import torch.nn as nn import torch.nn.quantized as nnq import torch.nn._intrinsic as nni import torch.nn._intrinsic.quantized as nniq import torch.nn._intrinsic.qat as nniqat from torch.quantization import \ QConfig_dynamic, default_weight_observer, \ quantize, prepare, convert, prepare_qat, quantize_qat, fuse_modules, \ quantize_dynamic, default_qconfig, default_qat_qconfig, \ default_dynamic_qconfig, Observer, QuantWrapper from common_utils import run_tests, tempfile from common_quantization import QuantizationTestCase, SingleLayerLinearModel, \ SkipQuantModel, QuantStubModel, \ ModelForFusion, ManualLinearQATModel, ManualConvLinearQATModel, \ ModForWrapping, \ test_only_eval_fn, test_only_train_fn, \ prepare_dynamic, convert_dynamic, SingleLayerLinearDynamicModel, \ TwoLayerLinearModel, NestedModel, ResNetBase from common_quantization import AnnotatedTwoLayerLinearModel, AnnotatedNestedModel, \ AnnotatedSubNestedModel, AnnotatedCustomConfigNestedModel from hypothesis import given from hypothesis import strategies as st @unittest.skipIf( not torch.fbgemm_is_cpu_supported(), " Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs" " with instruction set support avx2 or newer.", ) class PostTrainingQuantTest(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() model = prepare(model) # Check if observers and quant/dequant nodes are inserted self.checkNoPrepModules(model) self.checkHasPrepModules(model.fc1) self.checkObservers(model) test_only_eval_fn(model, self.calib_data) convert(model) def checkQuantized(model): self.checkNoPrepModules(model) self.checkHasPrepModules(model.fc1) self.checkWrappedQuantizedLinear(model.fc1) test_only_eval_fn(model, self.calib_data) self.checkScriptable(model, self.calib_data) checkQuantized(model) # test one line API model = quantize(SingleLayerLinearModel(), test_only_eval_fn, self.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 = AnnotatedTwoLayerLinearModel() model = prepare(model) self.checkNoPrepModules(model) self.checkObservers(model) self.checkNoPrepModules(model.fc1) self.checkHasPrepModules(model.fc2) test_only_eval_fn(model, self.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.checkWrappedQuantizedLinear(model.fc2) test_only_eval_fn(model, self.calib_data) self.checkScriptable(model, self.calib_data) checkQuantized(model) # test one line API model = quantize(AnnotatedTwoLayerLinearModel(), test_only_eval_fn, self.calib_data) 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 = AnnotatedNestedModel() 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) checkPrepModules(model, True) test_only_eval_fn(model, self.calib_data) convert(model) def checkQuantized(model): checkPrepModules(model) self.checkLinear(model.sub1.fc) self.checkWrappedQuantizedLinear(model.fc3) self.checkWrappedQuantizedLinear(model.sub2.fc1) self.checkLinear(model.sub2.fc2) test_only_eval_fn(model, self.calib_data) self.checkScriptable(model, self.calib_data) checkQuantized(model) # test one line API model = quantize(AnnotatedNestedModel(), test_only_eval_fn, self.calib_data) checkQuantized(model) def test_nested2(self): model = AnnotatedSubNestedModel() model = prepare(model) 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.checkHasPrepModules(model.sub2) self.checkNoPrepModules(model.sub2.module.fc1) self.checkNoPrepModules(model.sub2.module.fc2) self.checkHasPrepModules(model.fc3) checkPrepModules(model, True) test_only_eval_fn(model, self.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.module.fc1) self.checkQuantizedLinear(model.sub2.module.fc2) self.checkWrappedQuantizedLinear(model.fc3) test_only_eval_fn(model, self.calib_data) self.checkScriptable(model, self.calib_data) checkQuantized(model) # test one line API model = quantize(AnnotatedSubNestedModel(), test_only_eval_fn, self.calib_data) checkQuantized(model) def test_nested3(self): r"""More complicated nested test case with child qconfig overrides parent qconfig """ model = AnnotatedCustomConfigNestedModel() model = prepare(model) 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) test_only_eval_fn(model, self.calib_data) convert(model) def checkQuantized(model): checkPrepModules(model) self.checkWrappedQuantizedLinear(model.sub2.fc1) self.checkWrappedQuantizedLinear(model.sub2.fc2) self.checkWrappedQuantizedLinear(model.fc3) test_only_eval_fn(model, self.calib_data) self.checkScriptable(model, self.calib_data) checkQuantized(model) # test one line API model = quantize(AnnotatedCustomConfigNestedModel(), test_only_eval_fn, self.calib_data) checkQuantized(model) def test_skip_quant(self): r"""The case when we want to skip quantizing some layers """ model = SkipQuantModel() prepare(model) self.checkObservers(model) test_only_eval_fn(model, self.calib_data) convert(model) def checkQuantized(model): self.checkLinear(model.fc) self.checkQuantDequant(model.sub) self.checkQuantizedLinear(model.sub.module.fc1) self.checkQuantizedLinear(model.sub.module.fc2) self.assertEqual(type(model.sub.module.relu), nnq.ReLU) self.checkScriptable(model, self.calib_data) checkQuantized(model) # test one line API model = quantize(SkipQuantModel(), test_only_eval_fn, self.calib_data) checkQuantized(model) def test_manual(self): r"""User inserts QuantStub and DeQuantStub in model code and call the quantization utility functions. """ model = QuantStubModel() # propagate the qconfig of parents to children, model is changed # inplace prepare(model) self.checkObservers(model) test_only_eval_fn(model, self.calib_data) convert(model) def checkQuantized(model): self.assertEqual(type(model.fc), nnq.Linear) test_only_eval_fn(model, self.calib_data) self.checkScriptable(model, self.calib_data) checkQuantized(model) # test one line API model = quantize(QuantStubModel(), test_only_eval_fn, self.calib_data) checkQuantized(model) def test_resnet_base(self): r"""Test quantization for bottleneck topology used in resnet/resnext and add coverage for conversion of average pool and float functional """ model = ResNetBase().float().eval() model = QuantWrapper(model) model.qconfig = default_qconfig fuse_list = [['module.conv1', 'module.bn1', 'module.relu1']] fuse_modules(model, fuse_list) prepare(model) self.checkObservers(model) test_only_eval_fn(model, self.img_data) convert(model) def checkQuantized(model): self.assertEqual(type(model.module.conv1), nn._intrinsic.quantized.ConvReLU2d) self.assertEqual(type(model.module.myop), nn.quantized.QFunctional) self.assertEqual(type(model.module.avgpool), nn.AdaptiveAvgPool2d) test_only_eval_fn(model, self.img_data) checkQuantized(model) @unittest.skipIf( not torch.fbgemm_is_cpu_supported(), " Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs" " with instruction set support avx2 or newer.", ) class PostTrainingDynamicQuantTest(QuantizationTestCase): def test_single_layer(self): r"""Dynamic Quantize SingleLayerLinearDynamicModel which has one Linear module, make sure it is swapped to nnqd.Linear which is the quantized version of the module """ model = SingleLayerLinearDynamicModel().eval() qconfig_dict = { '': default_dynamic_qconfig } model = prepare_dynamic(model, qconfig_dict) convert_dynamic(model) def checkQuantized(model): self.checkDynamicQuantizedLinear(model.fc1) checkQuantized(model) # test one line API model = quantize_dynamic(SingleLayerLinearDynamicModel().eval(), 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().eval() qconfig_dict = { 'fc2': default_dynamic_qconfig } model = prepare_dynamic(model, qconfig_dict) convert_dynamic(model) def checkQuantized(model): self.assertEqual(type(model.fc1), torch.nn.Linear) self.checkDynamicQuantizedLinear(model.fc2) checkQuantized(model) # test one line API model = quantize_dynamic(TwoLayerLinearModel().eval(), 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().eval() qconfig_dict = { 'fc3': default_dynamic_qconfig, 'sub2.fc1': default_dynamic_qconfig } model = prepare_dynamic(model, qconfig_dict) convert_dynamic(model) def checkQuantized(model): self.checkLinear(model.sub1.fc) self.checkDynamicQuantizedLinear(model.fc3) self.checkDynamicQuantizedLinear(model.sub2.fc1) self.checkLinear(model.sub2.fc2) checkQuantized(model) # test one line API model = quantize_dynamic(NestedModel().eval(), qconfig_dict) checkQuantized(model) def test_nested2(self): r"""Another test case for quantized, we will quantize all submodules of submodule sub2 """ model = NestedModel().eval() qconfig_dict = { 'fc3': default_dynamic_qconfig, 'sub2': default_dynamic_qconfig } model = prepare_dynamic(model, qconfig_dict) convert_dynamic(model) def checkQuantized(model): self.checkLinear(model.sub1.fc) self.assertEqual(type(model.sub1.relu), torch.nn.ReLU) self.checkDynamicQuantizedLinear(model.sub2.fc1) self.checkDynamicQuantizedLinear(model.sub2.fc2) self.checkDynamicQuantizedLinear(model.fc3) checkQuantized(model) # test one line API model = quantize_dynamic(NestedModel().eval(), qconfig_dict) checkQuantized(model) def test_nested3(self): r"""More complicated nested test case with child qconfig overrides parent qconfig """ model = NestedModel().eval() custum_options = { 'dtype': torch.quint8, 'qscheme': torch.per_tensor_affine } custom_dynamic_qconfig = QConfig_dynamic(weight=default_weight_observer()) qconfig_dynamic_dict = { 'fc3': default_dynamic_qconfig, 'sub2': default_dynamic_qconfig, 'sub2.fc1': custom_dynamic_qconfig } model = prepare_dynamic(model, qconfig_dynamic_dict) convert_dynamic(model) def checkQuantized(model): self.checkDynamicQuantizedLinear(model.sub2.fc1) self.checkDynamicQuantizedLinear(model.sub2.fc2) self.checkDynamicQuantizedLinear(model.fc3) checkQuantized(model) # test one line API model = quantize_dynamic(NestedModel().eval(), qconfig_dynamic_dict) checkQuantized(model) def test_type_match_rule(self): r"""Test quantization for nested model, top level 'fc3' and 'fc1' of submodule 'sub2', All 'torch.nn.Linear' modules are quantized """ model = NestedModel().eval() qconfig_dict = { 'fc3': None, 'sub2.fc1': None, torch.nn.Linear: default_dynamic_qconfig } model = prepare_dynamic(model, qconfig_dict) test_only_eval_fn(model, self.calib_data) convert_dynamic(model) def checkQuantized(model): self.checkDynamicQuantizedLinear(model.sub1.fc) self.checkLinear(model.fc3) self.checkLinear(model.sub2.fc1) self.checkDynamicQuantizedLinear(model.sub2.fc2) test_only_eval_fn(model, self.calib_data) checkQuantized(model) # test one line API model = quantize_dynamic(NestedModel().eval(), qconfig_dict) checkQuantized(model) @unittest.skipIf( not torch.fbgemm_is_cpu_supported(), " Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs" " with instruction set support avx2 or newer.", ) class QuantizationAwareTrainingTest(QuantizationTestCase): def test_manual(self): model = ManualLinearQATModel() model = prepare_qat(model) self.checkObservers(model) test_only_train_fn(model, self.train_data) convert(model) def checkQuantized(model): self.assertEqual(type(model.fc1), nnq.Linear) self.assertEqual(type(model.fc2), nnq.Linear) test_only_eval_fn(model, self.calib_data) self.checkScriptable(model, self.calib_data) checkQuantized(model) model = quantize_qat(ManualLinearQATModel(), test_only_train_fn, self.train_data) checkQuantized(model) def test_eval_only_fake_quant(self): r"""Using FakeQuant in evaluation only mode, this is useful for estimating accuracy loss when we quantize the network """ model = ManualLinearQATModel() model = prepare_qat(model) self.checkObservers(model) model.eval() test_only_eval_fn(model, self.calib_data) def test_conv_linear(self): model = ManualConvLinearQATModel() model = prepare_qat(model) self.checkObservers(model) test_only_train_fn(model, self.img_data) convert(model) def checkQuantized(model): self.assertEqual(type(model.conv), nnq.Conv2d) self.assertEqual(type(model.fc1), nnq.Linear) self.assertEqual(type(model.fc2), nnq.Linear) test_only_eval_fn(model, self.img_data) self.checkScriptable(model, self.img_data) checkQuantized(model) model = ManualConvLinearQATModel() model = quantize_qat(model, test_only_train_fn, self.img_data) checkQuantized(model) class ScriptabilityTest(QuantizationTestCase): def setUp(self): self.model_under_test = ModForWrapping(quantized=False) self.qmodel_under_test = ModForWrapping(quantized=True) self.qmodel_under_test = self.qmodel_under_test.from_float( self.model_under_test) self.x = torch.rand(10) self.qx = torch.quantize_linear(self.x.to(torch.float), scale=1.0, zero_point=0, dtype=torch.qint32) def test_scriptability_serialization(self): # test serialization of quantized functional modules with tempfile.TemporaryFile() as f: torch.save(self.qmodel_under_test, f) f.seek(0) loaded = torch.load(f) self.assertEqual(self.qmodel_under_test.myadd.zero_point, loaded.myadd.zero_point) state_dict = self.qmodel_under_test.state_dict() assert('myadd.zero_point' in state_dict.keys()) x = torch.rand(10, 1, dtype=torch.float) xq = torch.quantize_linear(x, 1.0, 0, torch.qint8) self.checkScriptable(self.qmodel_under_test, [(xq, xq)], check_save_load=True) self.checkScriptable(self.model_under_test, [(xq, xq)], check_save_load=True) @unittest.skipIf(not torch.fbgemm_is_cpu_supported(), 'Quantization requires FBGEMM. FBGEMM does not play' ' well with UBSAN at the moment, so we skip the test if' ' we are in a UBSAN environment.') class FusionTest(QuantizationTestCase): def test_fuse_module_train(self): model = ModelForFusion(default_qat_qconfig).train() fuse_modules(model, [['conv1', 'bn1', 'relu1'], ['sub1.conv', 'sub1.bn']]) self.assertEqual(type(model.conv1), nni.ConvBnReLU2d, "Fused Conv + BN + Relu first layer") self.assertEqual(type(model.bn1), torch.nn.Identity, "Fused Conv + BN + Relu (skipped BN)") self.assertEqual(type(model.relu1), torch.nn.Identity, "Fused Conv + BN + Relu (skipped Relu)") self.assertEqual(type(model.sub1.conv), nni.ConvBn2d, "Fused submodule Conv + BN") self.assertEqual(type(model.sub1.bn), torch.nn.Identity, "Fused submodule Conv + BN (skipped BN)") self.assertEqual(type(model.sub2.conv), torch.nn.Conv2d, "Non-fused submodule Conv") self.assertEqual(type(model.sub2.relu), torch.nn.ReLU, "Non-fused submodule ReLU") model = prepare_qat(model) self.checkObservers(model) def checkQAT(model): self.assertEqual(type(model.conv1), nniqat.ConvBnReLU2d) self.assertEqual(type(model.bn1), nn.Identity) self.assertEqual(type(model.relu1), nn.Identity) self.assertEqual(type(model.sub1.conv), nniqat.ConvBn2d) self.assertEqual(type(model.sub1.bn), nn.Identity) self.assertEqual(type(model.sub2.conv), nn.Conv2d) self.assertEqual(type(model.sub2.relu), nn.ReLU) checkQAT(model) test_only_train_fn(model, self.img_data) convert(model) def checkQuantized(model): self.assertEqual(type(model.conv1), nniq.ConvReLU2d) self.assertEqual(type(model.bn1), nn.Identity) self.assertEqual(type(model.relu1), nn.Identity) self.assertEqual(type(model.sub1.conv), nnq.Conv2d) self.assertEqual(type(model.sub1.bn), nn.Identity) self.assertEqual(type(model.sub2.conv), nn.Conv2d) self.assertEqual(type(model.sub2.relu), nn.ReLU) test_only_eval_fn(model, self.img_data) checkQuantized(model) model = ModelForFusion(default_qat_qconfig).train() fuse_modules(model, [['conv1', 'bn1', 'relu1'], ['sub1.conv', 'sub1.bn']]) model = quantize_qat(model, test_only_train_fn, self.img_data) checkQuantized(model) def test_fuse_module_eval(self): model = ModelForFusion(default_qconfig) model.eval() fuse_modules(model, [['conv1', 'bn1', 'relu1'] , ['sub1.conv', 'sub1.bn']]) self.assertEqual(type(model.conv1), nni.ConvReLU2d, "Fused Conv + BN + Relu first layer (BN is folded)") self.assertEqual(type(model.conv1[0]), nn.Conv2d, "Fused Conv + BN + Relu (Conv + folded BN only)") self.assertEqual(type(model.conv1[1]), nn.ReLU, "Fused Conv + BN + Relu second layer (Relu only)") self.assertEqual(type(model.bn1), nn.Identity, "Fused Conv + BN + Relu second layer (Skipped BN)") self.assertEqual(type(model.relu1), nn.Identity, "Fused Conv + BN + Relu second layer (Skipped Relu)") self.assertEqual(type(model.sub1.conv), nn.Conv2d, "Fused submodule Conv + folded BN") self.assertEqual(type(model.sub1.bn), nn.Identity, "Fused submodule (skipped BN)") self.assertEqual(type(model.sub2.conv), nn.Conv2d, "Non-fused submodule Conv") self.assertEqual(type(model.sub2.relu), torch.nn.ReLU, "Non-fused submodule ReLU") model = prepare(model) self.checkObservers(model) test_only_eval_fn(model, self.img_data) convert(model) def checkQuantized(model): self.assertEqual(type(model.conv1), nniq.ConvReLU2d) self.assertEqual(type(model.bn1), nn.Identity) self.assertEqual(type(model.relu1), nn.Identity) self.assertEqual(type(model.sub1.conv), nnq.Conv2d) self.assertEqual(type(model.sub1.bn), nn.Identity) self.assertEqual(type(model.sub2.conv), nn.Conv2d) self.assertEqual(type(model.sub2.relu), nn.ReLU) test_only_eval_fn(model, self.img_data) checkQuantized(model) model = ModelForFusion(default_qat_qconfig).eval() fuse_modules(model, [['conv1', 'bn1', 'relu1'], ['sub1.conv', 'sub1.bn']]) model = quantize(model, test_only_eval_fn, self.img_data) checkQuantized(model) class ObserverTest(QuantizationTestCase): @given(qdtype=st.sampled_from((torch.qint8, torch.quint8)), qscheme=st.sampled_from((torch.per_tensor_affine, torch.per_tensor_symmetric))) def test_observer(self, qdtype, qscheme): myobs = Observer(dtype=qdtype, qscheme=qscheme) x = torch.tensor([1.0, 2.0, 2.0, 3.0, 4.0, 5.0, 6.0]) y = torch.tensor([4.0, 5.0, 5.0, 6.0, 7.0, 8.0]) result = myobs(x) result = myobs(y) self.assertEqual(result, y) self.assertEqual(myobs.min_val, 1.0) self.assertEqual(myobs.max_val, 8.0) qparams = myobs.calculate_qparams() if qscheme == torch.per_tensor_symmetric: ref_scale = 0.062745 ref_zero_point = 0 if qdtype is torch.qint8 else 128 else: ref_scale = 0.0313725 ref_zero_point = -128 if qdtype is torch.qint8 else 0 self.assertEqual(qparams[1].item(), ref_zero_point) self.assertAlmostEqual(qparams[0].item(), ref_scale, delta=1e-5) if __name__ == '__main__': run_tests()