| # Owner(s): ["module: nn"] |
| |
| import unittest |
| import sys |
| import os |
| import subprocess |
| |
| import torch |
| |
| import torch.nn.utils.stateless as stateless |
| from torch.testing._internal.common_cuda import TEST_MULTIGPU |
| from torch.testing._internal.common_utils import run_tests, TestCase, parametrize, instantiate_parametrized_tests, \ |
| subtest |
| |
| |
| class MockModule(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.l1 = torch.nn.Linear(1, 1) |
| self.register_buffer('buffer', torch.ones(1)) |
| |
| def forward(self, x): |
| return self.l1(x) + self.buffer |
| |
| class MockTiedModule(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.l1 = torch.nn.Linear(1, 1) |
| self.tied_bias = self.l1.bias |
| self.register_buffer('buffer', torch.ones(1)) |
| self.register_buffer('tied_buffer', self.buffer) |
| |
| def forward(self, x): |
| return self.l1(x) + self.tied_bias + self.buffer + self.tied_buffer |
| |
| |
| class TestStatelessFunctionalAPI(TestCase): |
| def _run_call_with_mock_module(self, module, functional_call, device='cpu', prefix=''): |
| |
| x = torch.rand((1, 1)).to(device) |
| weight = torch.tensor([[1.0]], device=device) |
| bias = torch.tensor([0.0], device=device) |
| buffer = torch.tensor([0.0], device=device) |
| if prefix != '': |
| parameters = {f'{prefix}.l1.weight': weight, |
| f'{prefix}.l1.bias': bias, |
| f'{prefix}.buffer': buffer} |
| else: |
| parameters = {'l1.weight': weight, |
| 'l1.bias': bias, |
| 'buffer': buffer} |
| to_check = module |
| if prefix != '': |
| to_check = getattr(module, prefix) |
| prev_weight = to_check.l1.weight.clone() |
| prev_buffer = to_check.buffer.clone() |
| # the parameters represent an identity function contrary to the |
| # existing params in module. So here we expect the result to be the |
| # same as the input if the weight swapping went well. |
| res = functional_call(module, parameters, x) |
| self.assertEqual(x, res) |
| # check that the weight remain unmodified |
| cur_weight = to_check.l1.weight |
| cur_buffer = to_check.buffer |
| self.assertEqual(cur_weight, prev_weight) |
| self.assertEqual(cur_buffer, prev_buffer) |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_functional_call(self, functional_call): |
| module = MockModule() |
| self._run_call_with_mock_module(module, functional_call) |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_functional_call_with_jit(self, functional_call): |
| module = MockModule() |
| jit_module = torch.jit.script(module) |
| with self.assertRaisesRegex( |
| RuntimeError, |
| r'used with Jitted modules' |
| ): |
| self._run_call_with_mock_module(jit_module, functional_call) |
| x = torch.rand((1, 1)) |
| traced_module = torch.jit.trace(module, x) |
| with self.assertRaisesRegex( |
| RuntimeError, |
| r'used with Jitted modules' |
| ): |
| self._run_call_with_mock_module(traced_module, functional_call) |
| |
| @unittest.skipIf(not TEST_MULTIGPU, 'multi-GPU not supported') |
| @unittest.skip("This doesn't work right now") |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_functional_call_with_data_parallel(self, functional_call): |
| module = MockModule() |
| module.cuda() |
| dp_module = torch.nn.DataParallel(module, [0, 1]) |
| self._run_call_with_mock_module(dp_module, functional_call, device='cuda', prefix='module') |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_functional_call_with_gradient(self, functional_call): |
| module = MockModule() |
| x = torch.rand((1, 1)) |
| weight = torch.tensor([[1.0]], requires_grad=True) |
| bias = torch.tensor([0.0], requires_grad=True) |
| buffer = torch.tensor([0.0]) |
| parameters = {'l1.weight': weight, |
| 'l1.bias': bias, |
| 'buffer': buffer} |
| res = functional_call(module, parameters, x) |
| # Check that a backward step calculates the gradient of the supplied parameters |
| res.backward() |
| self.assertIsNotNone(weight.grad) |
| self.assertIsNotNone(bias.grad) |
| self.assertIsNone(buffer.grad) |
| # Gradient was not calculated for the module stated and buffers |
| self.assertIsNone(module.l1.weight.grad) |
| self.assertIsNone(module.l1.bias.grad) |
| self.assertIsNone(module.buffer.grad) |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_functional_batch_norm(self, functional_call): |
| module = torch.nn.BatchNorm1d(10) |
| module.train() # Allow stats update |
| # lets replace the running_mean buffer and check if its correctly updated |
| x = torch.full((20, 10), 128.0) |
| rm = torch.zeros(10) |
| parameters = {'running_mean': rm} |
| prev_rm = module.running_mean.clone() |
| res = functional_call(module, parameters, x) |
| cur_rm = module.running_mean |
| self.assertEqual(cur_rm, prev_rm) |
| self.assertEqual(rm, torch.full((10,), 12.8)) |
| # Now run functional without reparametrization and check that the module has |
| # been updated |
| res = functional_call(module, {}, x) |
| self.assertEqual(module.running_mean, torch.full((10,), 12.8)) |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_circular_references(self, functional_call): |
| module = MockModule() |
| # Add a circular reference |
| module.l1.m = module |
| x = torch.rand((1, 1)) |
| weight = torch.tensor([[1.0]]) |
| bias = torch.tensor([0.0]) |
| buffer = torch.tensor([0.0]) |
| parameters = {'l1.m.l1.weight': weight, |
| 'l1.bias': bias, |
| 'l1.m.buffer': buffer} |
| prev_weight = module.l1.weight.clone() |
| prev_buffer = module.buffer.clone() |
| res = functional_call(module, parameters, x, tie_weights=False) |
| self.assertEqual(x, res) |
| # check that the weights remain unmodified and were correctly accesed |
| cur_weight = module.l1.weight |
| cur_buffer = module.buffer |
| self.assertEqual(cur_weight, prev_weight) |
| self.assertEqual(cur_buffer, prev_buffer) |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_reparametrized_module_change_parametrization_original(self, functional_call): |
| module = MockModule() |
| torch.nn.utils.parametrizations.spectral_norm(module.l1) |
| self.assertTrue('l1.parametrizations.weight.original' in dict(module.named_parameters())) |
| orig_sn_weight = module.l1.weight.clone() |
| x = torch.rand((1, 1)) |
| # We substitute the parameter inside the parametrization |
| # the parametrization itself is not overwritten so it will be applied with a different |
| # value for the original tensor |
| parameters = {'l1.parametrizations.weight.original': torch.nn.Parameter(torch.tensor([[1.0]])), |
| 'l1.bias': torch.tensor([0.0]), |
| 'buffer': torch.tensor([0.0])} |
| res = functional_call(module, parameters, x) |
| self.assertEqual(x, res) |
| # verify that the spectral normalization is still applied |
| self.assertTrue('l1.parametrizations.weight.original' in dict(module.named_parameters())) |
| self.assertEqual(orig_sn_weight, module.l1.weight) |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_reparamertize_module_fail_reset_to_original(self, functional_call): |
| module = MockModule() |
| torch.nn.utils.parametrizations.spectral_norm(module.l1) |
| self.assertTrue('l1.parametrizations.weight.original' in dict(module.named_parameters())) |
| orig_sn_weight = module.l1.weight.clone() |
| # We substitute the parameter inside the parametrization |
| # the parametrization itself is not overwritten so it will be applied with a different |
| # value for the original tensor |
| parameters = {'l1.parametrizations.weight.original': torch.nn.Parameter(torch.tensor([[1.0]])), |
| 'l1.bias': torch.tensor([0.0]), |
| 'buffer': torch.tensor([0.0])} |
| with self.assertRaisesRegex(RuntimeError, "shapes cannot be multiplied"): |
| x = torch.rand((4, 5)) # to work, it should be of size (1, 1) |
| functional_call(module, parameters, x) # this call will fail because x is the wrong size |
| |
| # verify that the spectral normalization is still applied |
| self.assertTrue('l1.parametrizations.weight.original' in dict(module.named_parameters())) |
| self.assertEqual(orig_sn_weight, module.l1.weight) |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_tied_weights_warns(self, functional_call): |
| module = MockModule() |
| module.tied_bias = module.l1.bias |
| module.register_buffer("tied_buffer", module.buffer) |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_reparamertize_tie_weights(self, functional_call): |
| module = MockTiedModule() |
| weight = torch.tensor([[2.0]],) |
| bias = torch.tensor([5.0]) |
| buffer = torch.tensor([3.0]) |
| |
| parameters = {'l1.weight': weight, |
| 'l1.bias': bias, |
| 'buffer': buffer} |
| x = torch.randn(1, 1) |
| out = functional_call(module, parameters, x, tie_weights=True) |
| self.assertEqual(out, x * weight + bias + bias + buffer + buffer) |
| |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_reparamertize_tie_some_weights(self, functional_call): |
| module = MockTiedModule() |
| weight = torch.tensor([[2.0]],) |
| buffer = torch.tensor([3.0]) |
| |
| parameters = {'l1.weight': weight, |
| 'buffer': buffer} |
| x = torch.randn(1, 1) |
| out = stateless.functional_call(module, parameters, x, tie_weights=True) |
| self.assertEqual(out, x * 2. + module.l1.bias + module.tied_bias + buffer + buffer) |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless._functional_call, "stateless") |
| ]) |
| def test_tied_weights_errors(self, functional_call): |
| module = MockTiedModule() |
| weight = torch.tensor([[1.0]],) |
| bias = torch.tensor([0.0]) |
| buffer = torch.tensor([0.0]) |
| |
| parameters = {'l1.weight': weight, |
| 'l1.bias': bias, |
| 'buffer': buffer} |
| x = torch.randn(1, 1) |
| self.assertNotWarn(lambda: functional_call(module, parameters, x, tie_weights=True)) |
| |
| # if tied values are the same tensors, shouldn't warn |
| parameters['tied_bias'] = bias |
| parameters['tied_buffer'] = buffer |
| self.assertNotWarn(lambda: functional_call(module, parameters, x, tie_weights=True)) |
| del parameters['tied_bias'] |
| del parameters['tied_buffer'] |
| |
| with self.assertRaisesRegex(ValueError, "functional_call got values for both (l1.bias|tied_bias)"): |
| parameters['tied_bias'] = torch.tensor([5.0]) |
| functional_call(module, parameters, x, tie_weights=True) |
| del parameters['tied_bias'] |
| |
| with self.assertRaisesRegex(ValueError, "functional_call got values for both (buffer|tied_buffer)"): |
| parameters['tied_buffer'] = torch.tensor([5.0]) |
| functional_call(module, parameters, x, tie_weights=True) |
| |
| |
| def test_tied_weights_no_error_without_flag(self): |
| module = MockTiedModule() |
| weight = torch.tensor([[1.0]],) |
| bias = torch.tensor([0.0]) |
| buffer = torch.tensor([0.0]) |
| |
| parameters = {'l1.weight': weight, |
| 'l1.bias': bias, |
| 'buffer': buffer} |
| x = torch.randn(1, 1) |
| self.assertNotWarn(lambda: stateless._functional_call(module, parameters, x, tie_weights=False)) |
| parameters['tied_bias'] = torch.tensor([5.0]) |
| self.assertNotWarn(lambda: stateless._functional_call(module, parameters, x, tie_weights=False)) |
| del parameters['tied_bias'] |
| parameters['tied_buffer'] = torch.tensor([5.0]) |
| self.assertNotWarn(lambda: stateless._functional_call(module, parameters, x, tie_weights=False)) |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_setattr(self, functional_call): |
| class Foo(torch.nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.register_buffer('foo', torch.zeros(())) |
| |
| def forward(self, x): |
| self.foo = self.foo + 1 |
| return x + self.foo |
| |
| a = {'foo': torch.zeros(())} |
| mod = Foo() |
| functional_call(mod, a, torch.ones(())) |
| self.assertEqual(mod.foo, torch.zeros(())) |
| self.assertEqual(a['foo'], torch.ones(())) |
| |
| @parametrize("functional_call", [ |
| subtest(torch.func.functional_call, "torch_func"), |
| subtest(stateless.functional_call, "stateless") |
| ]) |
| def test_functional_call_with_kwargs(self, functional_call): |
| class Foo(torch.nn.Module): |
| def __init__(self, x): |
| super().__init__() |
| self.x = x |
| |
| def forward(self, inp, *, other_inp): |
| return inp * self.x + other_inp |
| |
| a = {'x': torch.zeros(2, 3)} |
| mod = Foo(torch.randn(2, 3)) |
| inp, other_inp = torch.randn(2, 3), torch.randn(2, 3) |
| with self.assertRaisesRegex(TypeError, "missing 1 required keyword-only argument: 'other_inp'"): |
| functional_call(mod, a, inp) |
| res = functional_call(mod, a, inp, {'other_inp': other_inp}) |
| self.assertEqual(res, other_inp) |
| res_1 = functional_call(mod, a, (), {'inp': inp, 'other_inp': other_inp}) |
| self.assertEqual(res, res_1) |
| |
| |
| def test_functional_call_tuple_dicts(self): |
| mod = MockModule() |
| x = torch.rand((1, 1)) |
| parameters = {k: torch.ones_like(v) for k, v in mod.named_parameters()} |
| buffers = {k: torch.zeros_like(v) for k, v in mod.named_buffers()} |
| |
| # two dictionaries |
| res = torch.func.functional_call(mod, (parameters, buffers), x) |
| self.assertEqual(res, x + 1) |
| |
| # no dictionaries |
| res = torch.func.functional_call(mod, (), x) |
| self.assertEqual(res, mod(x)) |
| |
| # three dictonaries |
| a = ({'l1.weight': torch.ones(1, 1)}, {'l1.bias': torch.ones(1)}, {'buffer': torch.zeros(1)}) |
| res = torch.func.functional_call(mod, a, x) |
| self.assertEqual(res, x + 1) |
| |
| |
| def test_functional_call_multiple_dicts_error(self): |
| mod = MockModule() |
| x = torch.rand((1, 1)) |
| parameters = {'l1.weight': torch.zeros((1, 1)), 'l1.bias': torch.zeros((1, 1))} |
| repeated_parameters = {'l1.weight': torch.ones((1, 1))} |
| with self.assertRaisesRegex(ValueError, "l1.weight appeared in multiple dictionaries"): |
| torch.func.functional_call(mod, (parameters, repeated_parameters), x) |
| |
| |
| class TestStatelessDeprecation(TestCase): |
| def test_private_stateless_warns(self): |
| script = """ |
| import torch |
| import warnings |
| |
| with warnings.catch_warnings(record=True) as w: |
| from torch.nn.utils import _stateless |
| |
| exit(len(w)) |
| """ |
| try: |
| subprocess.check_output( |
| [sys.executable, '-W', 'all', '-c', script], |
| stderr=subprocess.STDOUT, |
| # On Windows, opening the subprocess with the default CWD makes `import torch` |
| # fail, so just set CWD to this script's directory |
| cwd=os.path.dirname(os.path.realpath(__file__)),) |
| except subprocess.CalledProcessError as e: |
| self.assertEqual(e.returncode, 1) |
| else: |
| self.assertTrue(False, "No warning was raised.") |
| |
| def test_stateless_functional_call_warns(self): |
| m = torch.nn.Linear(1, 1) |
| params = dict(m.named_parameters()) |
| x = torch.randn(3, 1) |
| with self.assertWarnsRegex(UserWarning, "Please use torch.func.functional_call"): |
| stateless.functional_call(m, params, x) |
| |
| class TestPythonOptimizeMode(TestCase): |
| def test_runs_with_optimize_flag(self): |
| script = """ |
| import torch |
| """ |
| try: |
| subprocess.check_output( |
| [sys.executable, '-OO', '-c', script], |
| stderr=subprocess.STDOUT, |
| # On Windows, opening the subprocess with the default CWD makes `import torch` |
| # fail, so just set CWD to this script's directory |
| cwd=os.path.dirname(os.path.realpath(__file__)),) |
| except subprocess.CalledProcessError as e: |
| self.assertFalse(e.returncode, "Import failed while running python in optimized mode") |
| |
| |
| instantiate_parametrized_tests( |
| TestStatelessFunctionalAPI, |
| ) |
| |
| if __name__ == '__main__': |
| run_tests() |
