| # Owner(s): ["oncall: mobile"] | |
| import unittest | |
| import torch | |
| import torch.nn as nn | |
| import torch.utils.bundled_inputs | |
| from torch.testing._internal.common_utils import TestCase, run_tests, skipIfNoXNNPACK | |
| from torch.testing._internal.jit_utils import get_forward, get_forward_graph | |
| from torch.utils.mobile_optimizer import (LintCode, | |
| generate_mobile_module_lints, | |
| optimize_for_mobile, | |
| MobileOptimizerType) | |
| from torch.nn import functional as F | |
| from torch.testing._internal.common_quantized import override_quantized_engine | |
| try: | |
| import torchvision | |
| HAS_TORCHVISION = True | |
| except ImportError: | |
| HAS_TORCHVISION = False | |
| FileCheck = torch._C.FileCheck | |
| class TestOptimizer(TestCase): | |
| @skipIfNoXNNPACK | |
| def test_optimize_for_mobile(self): | |
| batch_size = 2 | |
| input_channels_per_group = 6 | |
| height = 16 | |
| width = 16 | |
| output_channels_per_group = 6 | |
| groups = 4 | |
| kernel_h = kernel_w = 3 | |
| stride_h = stride_w = 1 | |
| pad_h = pad_w = 1 | |
| dilation = 1 | |
| input_channels = input_channels_per_group * groups | |
| output_channels = output_channels_per_group * groups | |
| kernels = (kernel_h, kernel_w) | |
| strides = (stride_h, stride_w) | |
| paddings = (pad_h, pad_w) | |
| dilations = (dilation, dilation) | |
| conv_weight_shape = (output_channels, input_channels_per_group, kernel_h, kernel_w) | |
| conv_bias_shape = (output_channels) | |
| input_data = torch.rand((batch_size, input_channels, height, width)) | |
| conv_weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w)) | |
| conv_bias = torch.rand((output_channels)) | |
| result = F.conv2d(input_data, conv_weight, conv_bias, strides, paddings, dilations, groups) | |
| weight_output_dim = 24 | |
| linear_input_shape = result.shape[1] | |
| linear_weight_shape = (weight_output_dim, linear_input_shape) | |
| class MyTestModule(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.conv_weight = torch.nn.Parameter(torch.rand(conv_weight_shape)) | |
| self.conv_bias = torch.nn.Parameter(torch.rand((conv_bias_shape))) | |
| self.linear_weight = torch.nn.Parameter(torch.rand(linear_weight_shape)) | |
| self.linear_bias = torch.nn.Parameter(torch.rand((weight_output_dim))) | |
| self.strides = strides | |
| self.paddings = paddings | |
| self.dilations = dilations | |
| self.groups = groups | |
| def forward(self, x): | |
| o = F.conv2d(x, self.conv_weight, self.conv_bias, | |
| self.strides, self.paddings, self.dilations, self.groups) | |
| o = F.relu(o) | |
| x = o.permute([0, 2, 3, 1]) | |
| o = F.linear(x, self.linear_weight, self.linear_bias) | |
| o = o + x | |
| return F.relu(o) | |
| @torch.jit.export | |
| def foo(self, x): | |
| o = F.conv2d(x, self.conv_weight, self.conv_bias, | |
| self.strides, self.paddings, self.dilations, self.groups) | |
| o = F.relu(o) | |
| x = o.permute([0, 2, 3, 1]) | |
| o = F.linear(x, self.linear_weight, self.linear_bias) | |
| o = o + x | |
| return F.relu(o) | |
| class BNTestModule(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.conv = torch.nn.Conv2d(1, 20, 5, 1) | |
| self.bn = torch.nn.BatchNorm2d(num_features=20) | |
| self.bn.eps = 0.0023 | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.bn(x) | |
| return x | |
| data_shape = (batch_size, input_channels, height, width) | |
| input_data = torch.normal(1, 20, size=data_shape) | |
| scripted_model = torch.jit.script(MyTestModule()) | |
| scripted_model.eval() | |
| initial_result = scripted_model(input_data) | |
| initial_foo_result = scripted_model.foo(input_data) | |
| optimized_scripted_model = optimize_for_mobile(scripted_model, preserved_methods=['foo']) | |
| optimized_result = optimized_scripted_model(input_data) | |
| optimized_foo_result = optimized_scripted_model.foo(input_data) | |
| FileCheck().check_not("Tensor = aten::conv2d") \ | |
| .check_not("Tensor = prim::CallFunction") \ | |
| .check_not("prepacked::conv2d_clamp_prepack") \ | |
| .check_count("prepacked::conv2d_clamp_run", 1, exactly=True) \ | |
| .check_not("prepacked::linear_clamp_prepack") \ | |
| .check_count("prepacked::linear_clamp_run", 1, exactly=True) \ | |
| .check_not("aten::add(") \ | |
| .check_not("aten::relu(") \ | |
| .check_count("aten::_add_relu(", 1, exactly=True) \ | |
| .run(optimized_scripted_model.graph) | |
| torch.testing.assert_close(initial_result, optimized_result, rtol=1e-2, atol=1e-3) | |
| FileCheck().check_not("Tensor = aten::conv2d") \ | |
| .check_not("Tensor = prim::CallFunction") \ | |
| .check_not("prepacked::conv2d_clamp_prepack") \ | |
| .check_count("prepacked::conv2d_clamp_run", 1, exactly=True) \ | |
| .check_not("prepacked::linear_clamp_prepack") \ | |
| .check_count("prepacked::linear_clamp_run", 1, exactly=True) \ | |
| .check_not("aten::add(") \ | |
| .check_not("aten::relu(") \ | |
| .check_count("aten::_add_relu(", 1, exactly=True) \ | |
| .run(optimized_scripted_model.foo.graph) | |
| torch.testing.assert_close(initial_foo_result, optimized_foo_result, rtol=1e-2, atol=1e-3) | |
| optimization_blocklist_no_prepack = {MobileOptimizerType.INSERT_FOLD_PREPACK_OPS} | |
| optimized_scripted_model_no_prepack = optimize_for_mobile(scripted_model, optimization_blocklist_no_prepack) | |
| optimized_result_no_prepack = optimized_scripted_model_no_prepack(input_data) | |
| FileCheck().check_count("Tensor = aten::conv2d", 1, exactly=True) \ | |
| .check_not("prepacked::linear_clamp_run") \ | |
| .check_not("prepacked::conv2d_clamp_run") \ | |
| .run(optimized_scripted_model_no_prepack.graph) | |
| torch.testing.assert_close(initial_result, optimized_result_no_prepack, rtol=1e-2, atol=1e-3) | |
| bn_test_module = BNTestModule() | |
| bn_scripted_module = torch.jit.script(bn_test_module) | |
| bn_scripted_module.eval() | |
| self.assertEqual(len(torch.jit.export_opnames(bn_scripted_module)), 11) | |
| FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 2, exactly=True) \ | |
| .run(str(get_forward(bn_scripted_module._c).graph)) | |
| optimization_blocklist_no_prepack = {MobileOptimizerType.INSERT_FOLD_PREPACK_OPS} | |
| bn_fold_scripted_module = optimize_for_mobile(bn_scripted_module, optimization_blocklist_no_prepack) | |
| self.assertEqual(len(torch.jit.export_opnames(bn_fold_scripted_module)), 1) | |
| bn_input = torch.rand(1, 1, 6, 6) | |
| torch.testing.assert_close(bn_scripted_module(bn_input), bn_fold_scripted_module(bn_input), rtol=1e-2, atol=1e-3) | |
| optimization_blocklist_no_fold_bn = {MobileOptimizerType.CONV_BN_FUSION} | |
| no_bn_fold_scripted_module = optimize_for_mobile(bn_scripted_module, optimization_blocklist_no_fold_bn) | |
| FileCheck().check_count("aten::batch_norm", 1, exactly=True) \ | |
| .run(str(get_forward_graph(no_bn_fold_scripted_module._c))) | |
| bn_input = torch.rand(1, 1, 6, 6) | |
| torch.testing.assert_close(bn_scripted_module(bn_input), no_bn_fold_scripted_module(bn_input), rtol=1e-2, atol=1e-3) | |
| class MyMobileOptimizedTagTest(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.linear_weight = torch.nn.Parameter(torch.rand(linear_weight_shape)) | |
| self.linear_bias = torch.nn.Parameter(torch.rand((weight_output_dim))) | |
| def forward(self, x): | |
| o = F.linear(x, self.linear_weight, self.linear_bias) | |
| return F.relu(o) | |
| mobile_optimized_tag_module = MyMobileOptimizedTagTest() | |
| m = torch.jit.script(mobile_optimized_tag_module) | |
| m.eval() | |
| opt_m = optimize_for_mobile(m) | |
| tag = getattr(opt_m, "mobile_optimized", None) | |
| self.assertTrue(tag) | |
| class MyPreserveMethodsTest(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.linear_weight = torch.nn.Parameter(torch.rand(linear_weight_shape)) | |
| self.linear_bias = torch.nn.Parameter(torch.rand((weight_output_dim))) | |
| def forward(self, x): | |
| o = F.linear(x, self.linear_weight, self.linear_bias) | |
| return F.relu(o) | |
| @torch.jit.export | |
| def preserveThis(self): | |
| pass | |
| preserve_method_module = MyPreserveMethodsTest() | |
| m = torch.jit.script(preserve_method_module) | |
| m.eval() | |
| opt_m = optimize_for_mobile(m) | |
| no_preserveThis = getattr(opt_m, "preserveThis", None) | |
| self.assertEqual(no_preserveThis, None) | |
| opt_m = optimize_for_mobile(m, preserved_methods=["preserveThis"]) | |
| preserveThis = getattr(opt_m, "preserveThis", None) | |
| self.assertNotEqual(preserveThis, None) | |
| class OptimizeNoForwardTest(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.l = nn.Linear(10, 100) | |
| self.l2 = nn.Linear(100, 1) | |
| self.d = nn.Dropout(p=0.2) | |
| @torch.jit.export | |
| def foo(self, x): | |
| x = self.d(F.relu(self.l(x))) | |
| x = self.l2(x) | |
| x = x + torch.ones(1, 100) | |
| return F.relu(x) | |
| input_data = torch.ones(1, 10) | |
| m = torch.jit.script(OptimizeNoForwardTest()) | |
| m.eval() | |
| initial_result = m.foo(input_data) | |
| optimized_scripted_model = optimize_for_mobile(m, preserved_methods=['foo']) | |
| optimized_result = optimized_scripted_model.foo(input_data) | |
| FileCheck().check_not("dropout.__") \ | |
| .check_count("aten::_add_relu(", 1, exactly=True) \ | |
| .run(optimized_scripted_model.foo.graph) | |
| torch.testing.assert_close(initial_result, optimized_result, rtol=1e-2, atol=1e-3) | |
| class BNTestNoForwardModule(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.conv = torch.nn.Conv2d(1, 20, 5, 1) | |
| self.bn = torch.nn.BatchNorm2d(num_features=20) | |
| self.bn.eps = 0.0023 | |
| @torch.jit.export | |
| def foo(self, x): | |
| x = self.conv(x) | |
| x = self.bn(x) | |
| return x | |
| bn_test_no_forward_module = BNTestNoForwardModule() | |
| bn_no_forward_scripted_module = torch.jit.script(bn_test_no_forward_module) | |
| bn_no_forward_scripted_module.eval() | |
| self.assertEqual(len(torch.jit.export_opnames(bn_no_forward_scripted_module)), 11) | |
| FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 2, exactly=True) \ | |
| .run(bn_no_forward_scripted_module.foo.graph) | |
| bn_fold_no_forward_scripted_module = optimize_for_mobile(bn_no_forward_scripted_module, preserved_methods=['foo']) | |
| self.assertEqual(len(torch.jit.export_opnames(bn_fold_no_forward_scripted_module)), 1) | |
| bn_input = torch.rand(1, 1, 6, 6) | |
| torch.testing.assert_close( | |
| bn_no_forward_scripted_module.foo(bn_input), | |
| bn_fold_no_forward_scripted_module.foo(bn_input), | |
| rtol=1e-2, | |
| atol=1e-3) | |
| @skipIfNoXNNPACK | |
| def test_quantized_conv_no_asan_failures(self): | |
| # There were ASAN failures when fold_conv_bn was run on | |
| # already quantized conv modules. Verifying that this does | |
| # not happen again. | |
| if 'qnnpack' not in torch.backends.quantized.supported_engines: | |
| return | |
| class Child(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.conv2 = nn.Conv2d(1, 1, 1) | |
| def forward(self, x): | |
| x = self.conv2(x) | |
| return x | |
| class Parent(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.quant = torch.ao.quantization.QuantStub() | |
| self.conv1 = nn.Conv2d(1, 1, 1) | |
| self.child = Child() | |
| self.dequant = torch.ao.quantization.DeQuantStub() | |
| def forward(self, x): | |
| x = self.quant(x) | |
| x = self.conv1(x) | |
| x = self.child(x) | |
| x = self.dequant(x) | |
| return x | |
| with override_quantized_engine('qnnpack'): | |
| model = Parent() | |
| model.qconfig = torch.ao.quantization.get_default_qconfig('qnnpack') | |
| torch.ao.quantization.prepare(model, inplace=True) | |
| model(torch.randn(4, 1, 4, 4)) | |
| torch.ao.quantization.convert(model, inplace=True) | |
| model = torch.jit.script(model) | |
| # this line should not have ASAN failures | |
| model_optim = optimize_for_mobile(model) | |
| def test_generate_mobile_module_lints(self): | |
| class MyTestModule(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.fc = torch.nn.Linear(4, 4) | |
| self.dropout = torch.nn.Dropout(p=0.5) | |
| def forward(self, inputs): | |
| out = self.fc(inputs) | |
| out = self.dropout(out) | |
| return out | |
| class MyBNModule(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.bn = torch.nn.BatchNorm2d(4, affine=True) | |
| def forward(self, inputs): | |
| bn = self.bn(inputs) | |
| return bn | |
| class MyBundledInputModule(torch.nn.Module): | |
| def forward(self, inputs): | |
| return inputs | |
| def get_lint_count_by_type(lint_type, module_lint_List): | |
| return len([lint_dict for lint_dict in module_lint_List if lint_dict['name'] == lint_type.name]) | |
| test_module = torch.jit.script(MyTestModule()) | |
| test_module_lint_list = generate_mobile_module_lints(test_module) | |
| self.assertEqual(len(test_module_lint_list), 4) | |
| self.assertEqual(get_lint_count_by_type(LintCode.BUNDLED_INPUT, test_module_lint_list), 1) | |
| self.assertEqual(get_lint_count_by_type(LintCode.DROPOUT, test_module_lint_list), 1) | |
| self.assertEqual(get_lint_count_by_type(LintCode.REQUIRES_GRAD, test_module_lint_list), 2) | |
| bn_module = torch.jit.script(MyBNModule()) | |
| bn_module_lint_list = generate_mobile_module_lints(bn_module) | |
| self.assertEqual(len(bn_module_lint_list), 4) | |
| self.assertEqual(get_lint_count_by_type(LintCode.BUNDLED_INPUT, bn_module_lint_list), 1) | |
| self.assertEqual(get_lint_count_by_type(LintCode.BATCHNORM, bn_module_lint_list), 1) | |
| self.assertEqual(get_lint_count_by_type(LintCode.REQUIRES_GRAD, bn_module_lint_list), 2) | |
| bi_module = torch.jit.script(MyBundledInputModule()) | |
| torch.utils.bundled_inputs.augment_model_with_bundled_inputs( | |
| bi_module, [(torch.tensor([1]),)], []) | |
| bi_module_lint_list = generate_mobile_module_lints(bi_module) | |
| self.assertEqual(len(bi_module_lint_list), 0) | |
| @skipIfNoXNNPACK | |
| def test_preserve_bundled_inputs_methods(self): | |
| class MyBundledInputModule(torch.nn.Module): | |
| def forward(self, inputs): | |
| return inputs | |
| class MyIncompleteBundledInputModule(torch.nn.Module): | |
| def forward(self, inputs): | |
| return inputs | |
| @torch.jit.export | |
| def get_all_bundled_inputs(self): | |
| pass | |
| bi_module = torch.jit.script(MyBundledInputModule()) | |
| module_optim_bi_not_preserved = optimize_for_mobile(bi_module) | |
| # Expected to be False since no bundled inputs methods were added | |
| self.assertFalse( | |
| hasattr(module_optim_bi_not_preserved, 'get_all_bundled_inputs') or | |
| hasattr(module_optim_bi_not_preserved, 'get_num_bundled_inputs') | |
| ) | |
| # Add bundled inputs methods to the module | |
| torch.utils.bundled_inputs.augment_model_with_bundled_inputs( | |
| bi_module, [(torch.tensor([1]),)], []) | |
| # Now they should be preserved | |
| module_optim_bi_preserved = optimize_for_mobile(bi_module) | |
| # All of the bundled inputs methods were preserved | |
| self.assertTrue( | |
| hasattr(module_optim_bi_preserved, 'get_all_bundled_inputs') and | |
| hasattr(module_optim_bi_preserved, 'get_num_bundled_inputs') | |
| ) | |
| bundled_input = module_optim_bi_preserved.get_all_bundled_inputs()[0] | |
| module_optim_bi_preserved(*bundled_input) | |
| # If not all 3 bundled inputs methods are present in the module, | |
| # we will not try to preserve them unless specified by the user. | |
| incomplete_bi_module = torch.jit.script(MyIncompleteBundledInputModule()) | |
| incomplete_bi_module_optim = optimize_for_mobile(incomplete_bi_module) | |
| self.assertFalse(hasattr(incomplete_bi_module_optim, 'get_all_bundled_inputs')) | |
| # Specifically preserve get_all_bundled_inputs even if it's the only one | |
| # bundled inputs method available. | |
| incomplete_bi_module_optim = optimize_for_mobile(incomplete_bi_module, preserved_methods=['get_all_bundled_inputs']) | |
| self.assertTrue(hasattr(incomplete_bi_module_optim, 'get_all_bundled_inputs')) | |
| @skipIfNoXNNPACK | |
| def test_hoist_conv_packed_params(self): | |
| if 'qnnpack' not in torch.backends.quantized.supported_engines: | |
| return | |
| class Standalone(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.quant = torch.ao.quantization.QuantStub() | |
| self.conv1 = nn.Conv2d(1, 1, 1) | |
| self.conv2 = nn.Conv2d(1, 1, 1) | |
| self.relu = nn.ReLU() | |
| self.dequant = torch.ao.quantization.DeQuantStub() | |
| def forward(self, x): | |
| x = self.quant(x) | |
| x = self.conv1(x) | |
| x = self.conv2(x) | |
| x = self.relu(x) | |
| x = self.dequant(x) | |
| return x | |
| def fuse_model(self): | |
| torch.ao.quantization.fuse_modules(self, [['conv2', 'relu']], inplace=True) | |
| pass | |
| class Child(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.conv1 = nn.Conv2d(1, 1, 1) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| return x | |
| class Parent(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.quant = torch.ao.quantization.QuantStub() | |
| self.conv1 = nn.Conv2d(1, 1, 1) | |
| self.child = Child() | |
| # TODO: test nn.Sequential after #42039 is fixed | |
| self.dequant = torch.ao.quantization.DeQuantStub() | |
| def forward(self, x): | |
| x = self.quant(x) | |
| x = self.conv1(x) | |
| x = self.child(x) | |
| x = self.dequant(x) | |
| return x | |
| def fuse_model(self): | |
| pass | |
| with override_quantized_engine('qnnpack'): | |
| def _quant_script_and_optimize(model): | |
| model.qconfig = torch.ao.quantization.get_default_qconfig('qnnpack') | |
| model.fuse_model() | |
| torch.ao.quantization.prepare(model, inplace=True) | |
| model(torch.randn(4, 1, 4, 4)) | |
| torch.ao.quantization.convert(model, inplace=True) | |
| model = torch.jit.script(model) | |
| model_optim = optimize_for_mobile(model) | |
| return model, model_optim | |
| # basic case | |
| m, m_optim = _quant_script_and_optimize(Standalone()) | |
| FileCheck().check_not("Conv2d = prim::GetAttr[name=\"conv1\"]") \ | |
| .check_count("__torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::Constant", 2, exactly=True) \ | |
| .run(m_optim.graph) | |
| self.assertFalse(hasattr(m_optim, "conv1")) | |
| self.assertFalse(hasattr(m_optim, "conv2")) | |
| data = torch.randn(4, 1, 4, 4) | |
| m_res = m(data) | |
| m_optim_res = m_optim(data) | |
| torch.testing.assert_close(m_res, m_optim_res, rtol=1e-2, atol=1e-3) | |
| # generic case | |
| m, m_optim = _quant_script_and_optimize(Parent()) | |
| FileCheck().check_not("Conv2d = prim::GetAttr[name=\"conv1\"]") \ | |
| .check_count("__torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::Constant", 2, exactly=True) \ | |
| .run(m_optim.graph) | |
| self.assertFalse(hasattr(m_optim, "conv1")) | |
| self.assertFalse(hasattr(m_optim, "child")) | |
| data = torch.randn(4, 1, 4, 4) | |
| m_res = m(data) | |
| m_optim_res = m_optim(data) | |
| torch.testing.assert_close(m_res, m_optim_res, rtol=1e-2, atol=1e-3) | |
| @skipIfNoXNNPACK | |
| @unittest.skipUnless(HAS_TORCHVISION, "Needs torchvision") | |
| def test_mobilenet_optimize_for_mobile(self): | |
| m = torchvision.models.mobilenet_v3_small() | |
| m = torch.jit.script(m) | |
| m = optimize_for_mobile(m) | |
| # run forward 3 times until segfault, see https://github.com/pytorch/pytorch/issues/52463 | |
| x = torch.zeros(1, 3, 56, 56) | |
| self.assertEqual(m(x).numel(), 1000) | |
| self.assertEqual(m(x).numel(), 1000) | |
| self.assertEqual(m(x).numel(), 1000) | |
| def test_clone_module_with_class(self): | |
| class MyInnerTestModule(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.pqr = torch.Tensor([10., 20., 30.]) | |
| def forward(self, inputs): | |
| return inputs | |
| @torch.jit.export | |
| def dummy_method_not_cloned(self): | |
| return 20 | |
| class MyTestModule(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.abc = 23 | |
| self.pqr = torch.Tensor([1., 2., 3.]) | |
| self.inner = MyInnerTestModule() | |
| def forward(self, inputs): | |
| x = self.dummy_method_cloned() | |
| # The call to self.inner.dummy_method_not_cloned should not raise an error | |
| y = self.inner.dummy_method_not_cloned() | |
| # The call to self.inner.pqr should not raise an error | |
| z = self.inner.pqr | |
| return (inputs, x, y, z) | |
| @torch.jit.export | |
| def dummy_method_not_cloned2(self): | |
| # The call to self.inner.dummy_method_not_cloned should not raise an error | |
| y = self.inner.dummy_method_not_cloned() | |
| # The call to self.inner.pqr should not raise an error | |
| z = self.inner.pqr | |
| return self.pqr, self.dummy_method_not_cloned(), y, z | |
| @torch.jit.export | |
| def dummy_method_not_cloned(self): | |
| return None | |
| @torch.jit.export | |
| def dummy_method_cloned(self): | |
| return None | |
| @torch.jit.export | |
| def dummy_method_ref_attr_pqr(self): | |
| return self.pqr, self.inner.pqr | |
| m = torch.jit.script(MyTestModule()) | |
| # Check that the methods exist on the original model. | |
| self.assertEqual(hasattr(m, "dummy_method_not_cloned"), True) | |
| self.assertEqual(hasattr(m, "dummy_method_cloned"), True) | |
| self.assertEqual(hasattr(m, "dummy_method_not_cloned2"), True) | |
| self.assertEqual(hasattr(m, "pqr"), True) | |
| # Case-1: Successfully clone, ignoring 2 methods, keeping all attributes. | |
| cloned = torch._C._hack_do_not_use_clone_module_with_class( | |
| m._c, | |
| ["dummy_method_not_cloned", "dummy_method_not_cloned2"], # ignored_methods | |
| [], # ignored_attributes | |
| ) | |
| # Check that the ignored methods don't exist on the cloned model. | |
| self.assertEqual(hasattr(cloned, "dummy_method_not_cloned"), False) | |
| self.assertEqual(hasattr(cloned, "dummy_method_cloned"), True) | |
| self.assertEqual(hasattr(cloned, "dummy_method_not_cloned2"), False) | |
| self.assertEqual(hasattr(cloned, "pqr"), True) | |
| # Check that the cloned class has a classname that starts with __torch__. | |
| self.assertTrue( | |
| cloned.qualified_name.startswith('__torch__.'), | |
| ("Expected the cloned module's name to start with the string " | |
| "'__torch__.', but got: {0}").format(cloned.qualified_name), | |
| ) | |
| # Case-2: Successfully clone the module, ignoring the attribute pqr, and the method that references it. | |
| cloned = torch._C._hack_do_not_use_clone_module_with_class( | |
| m._c, | |
| ["dummy_method_not_cloned", "dummy_method_not_cloned2", "dummy_method_ref_attr_pqr"], | |
| ["pqr"], | |
| ) | |
| # Check that the ignored methods don't exist on the cloned model. | |
| self.assertEqual(hasattr(cloned, "dummy_method_not_cloned"), False) | |
| self.assertEqual(hasattr(cloned, "dummy_method_cloned"), True) | |
| self.assertEqual(hasattr(cloned, "dummy_method_not_cloned2"), False) | |
| self.assertEqual(hasattr(cloned, "dummy_method_ref_attr_pqr"), False) | |
| self.assertEqual(hasattr(cloned, "pqr"), False) | |
| # Case-3: The statement below will throw since dummy_method_cloned2 is preserved, | |
| # and references dummy_method_not_cloned, which is not cloned. | |
| with self.assertRaises(RuntimeError): | |
| cloned = torch._C._hack_do_not_use_clone_module_with_class(m._c, ["dummy_method_not_cloned"], []) | |
| # Case-4: The statement below will throw since dummy_method_ref_attr_pqr | |
| # is preserved, and references "pqr", which is not cloned. | |
| with self.assertRaises(RuntimeError): | |
| cloned = torch._C._hack_do_not_use_clone_module_with_class( | |
| m._c, | |
| ["dummy_method_not_cloned", "dummy_method_not_cloned2"], | |
| ["pqr"], | |
| ) | |
| if __name__ == '__main__': | |
| run_tests() |