import os import torch import torchani import unittest import pickle import copy from torchani.testing import TestCase, make_tensor path = os.path.dirname(os.path.realpath(__file__)) skipIfNoGPU = unittest.skipIf(not torch.cuda.is_available(), 'There is no device to run this test') skipIfNoMultiGPU = unittest.skipIf(not torch.cuda.device_count() >= 2, 'There is not enough GPU devices to run this test') skipIfNoCUAEV = unittest.skipIf(not torchani.aev.has_cuaev, "only valid when cuaev is installed") @skipIfNoCUAEV class TestCUAEVNoGPU(TestCase): def testSimple(self): def f(coordinates, species, Rcr: float, Rca: float, EtaR, ShfR, EtaA, Zeta, ShfA, ShfZ, num_species: int): cuaev_computer = torch.classes.cuaev.CuaevComputer(Rcr, Rca, EtaR.flatten(), ShfR.flatten(), EtaA.flatten(), Zeta.flatten(), ShfA.flatten(), ShfZ.flatten(), num_species) return torch.ops.cuaev.run(coordinates, species, cuaev_computer) s = torch.jit.script(f) self.assertIn("cuaev::run", str(s.graph)) def testAEVComputer(self): path = os.path.dirname(os.path.realpath(__file__)) const_file = os.path.join(path, '../torchani/resources/ani-1x_8x/rHCNO-5.2R_16-3.5A_a4-8.params') # noqa: E501 consts = torchani.neurochem.Constants(const_file) aev_computer = torchani.AEVComputer(**consts, use_cuda_extension=True) s = torch.jit.script(aev_computer) # Computation of AEV using cuaev when there is no atoms does not require CUDA, and can be run without GPU species = make_tensor((8, 0), device='cpu', dtype=torch.int64, low=-1, high=4) coordinates = make_tensor((8, 0, 3), device='cpu', dtype=torch.float32, low=-5, high=5) self.assertIn("cuaev::run", str(s.graph_for((species, coordinates)))) def testPickle(self): path = os.path.dirname(os.path.realpath(__file__)) const_file = os.path.join(path, '../torchani/resources/ani-1x_8x/rHCNO-5.2R_16-3.5A_a4-8.params') # noqa: E501 consts = torchani.neurochem.Constants(const_file) aev_computer = torchani.AEVComputer(**consts, use_cuda_extension=True) tmpfile = '/tmp/cuaev.pkl' with open(tmpfile, 'wb') as file: pickle.dump(aev_computer, file) with open(tmpfile, 'rb') as file: aev_computer = pickle.load(file) os.remove(tmpfile) @skipIfNoGPU @skipIfNoCUAEV class TestCUAEV(TestCase): def setUp(self, device='cuda:0'): self.tolerance = 5e-5 self.device = device Rcr = 5.2000e+00 Rca = 3.5000e+00 EtaR = torch.tensor([1.6000000e+01], device=self.device) ShfR = torch.tensor([9.0000000e-01, 1.1687500e+00, 1.4375000e+00, 1.7062500e+00, 1.9750000e+00, 2.2437500e+00, 2.5125000e+00, 2.7812500e+00, 3.0500000e+00, 3.3187500e+00, 3.5875000e+00, 3.8562500e+00, 4.1250000e+00, 4.3937500e+00, 4.6625000e+00, 4.9312500e+00], device=self.device) Zeta = torch.tensor([3.2000000e+01], device=self.device) ShfZ = torch.tensor([1.9634954e-01, 5.8904862e-01, 9.8174770e-01, 1.3744468e+00, 1.7671459e+00, 2.1598449e+00, 2.5525440e+00, 2.9452431e+00], device=self.device) EtaA = torch.tensor([8.0000000e+00], device=self.device) ShfA = torch.tensor([9.0000000e-01, 1.5500000e+00, 2.2000000e+00, 2.8500000e+00], device=self.device) num_species = 4 self.aev_computer = torchani.AEVComputer(Rcr, Rca, EtaR, ShfR, EtaA, Zeta, ShfA, ShfZ, num_species) self.cuaev_computer = torchani.AEVComputer(Rcr, Rca, EtaR, ShfR, EtaA, Zeta, ShfA, ShfZ, num_species, use_cuda_extension=True) self.nn = torch.nn.Sequential(torch.nn.Linear(384, 1, False)).to(self.device) self.radial_length = self.aev_computer.radial_length def _double_backward_1_test(self, species, coordinates): def double_backward(aev_computer, species, coordinates): torch.manual_seed(12345) self.nn.zero_grad() _, aev = aev_computer((species, coordinates)) E = self.nn(aev).sum() force = -torch.autograd.grad(E, coordinates, create_graph=True, retain_graph=True)[0] force_true = torch.randn_like(force) loss = torch.abs(force_true - force).sum(dim=(1, 2)).mean() loss.backward() param = next(self.nn.parameters()) param_grad = copy.deepcopy(param.grad) return aev, force, param_grad aev, force_ref, param_grad_ref = double_backward(self.aev_computer, species, coordinates) cu_aev, force_cuaev, param_grad = double_backward(self.cuaev_computer, species, coordinates) self.assertEqual(cu_aev, aev, f'cu_aev: {cu_aev}\n aev: {aev}') self.assertEqual(force_cuaev, force_ref, f'\nforce_cuaev: {force_cuaev}\n force_ref: {force_ref}') self.assertEqual(param_grad, param_grad_ref, f'\nparam_grad: {param_grad}\n param_grad_ref: {param_grad_ref}', atol=5e-5, rtol=5e-5) def _double_backward_2_test(self, species, coordinates): def double_backward(aev_computer, species, coordinates): """ # We want to get the gradient of `grad_aev`, which requires `grad_aev` to be a leaf node # due to `torch.autograd`'s limitation. So we split the coord->aev->energy graph into two separate # graphs: coord->aev and aev->energy, so that aev and grad_aev are now leaves. """ torch.manual_seed(12345) # graph1 input -> aev coordinates = coordinates.clone().detach().requires_grad_() _, aev = aev_computer((species, coordinates)) # graph2 aev -> E aev_ = aev.clone().detach().requires_grad_() E = self.nn(aev_).sum() # graph2 backward aev_grad = torch.autograd.grad(E, aev_, create_graph=True, retain_graph=True)[0] # graph1 backward aev_grad_ = aev_grad.clone().detach().requires_grad_() force = torch.autograd.grad(aev, coordinates, aev_grad_, create_graph=True, retain_graph=True)[0] # force loss backward force_true = torch.randn_like(force) loss = torch.abs(force_true - force).sum(dim=(1, 2)).mean() aev_grad_grad = torch.autograd.grad(loss, aev_grad_, create_graph=True, retain_graph=True)[0] return aev, force, aev_grad_grad aev, force_ref, aev_grad_grad = double_backward(self.aev_computer, species, coordinates) cu_aev, force_cuaev, cuaev_grad_grad = double_backward(self.cuaev_computer, species, coordinates) self.assertEqual(cu_aev, aev, f'cu_aev: {cu_aev}\n aev: {aev}', atol=5e-5, rtol=5e-5) self.assertEqual(force_cuaev, force_ref, f'\nforce_cuaev: {force_cuaev}\n force_ref: {force_ref}', atol=5e-5, rtol=5e-5) self.assertEqual(cuaev_grad_grad, aev_grad_grad, f'\ncuaev_grad_grad: {cuaev_grad_grad}\n aev_grad_grad: {aev_grad_grad}', atol=5e-5, rtol=5e-5) def testSimple(self): coordinates = torch.tensor([ [[0.03192167, 0.00638559, 0.01301679], [-0.83140486, 0.39370209, -0.26395324], [-0.66518241, -0.84461308, 0.20759389], [0.45554739, 0.54289633, 0.81170881], [0.66091919, -0.16799635, -0.91037834]], [[-4.1862600, 0.0575700, -0.0381200], [-3.1689400, 0.0523700, 0.0200000], [-4.4978600, 0.8211300, 0.5604100], [-4.4978700, -0.8000100, 0.4155600], [0.00000000, -0.00000000, -0.00000000]] ], device=self.device) species = torch.tensor([[1, 0, 0, 0, 0], [2, 0, 0, 0, -1]], device=self.device) _, aev = self.aev_computer((species, coordinates)) _, cu_aev = self.cuaev_computer((species, coordinates)) self.assertEqual(cu_aev, aev) @skipIfNoMultiGPU def testMultiGPU(self): self.setUp(device='cuda:1') self.testSimple() self.testSimpleBackward() self.testSimpleDoubleBackward_1() self.testSimpleDoubleBackward_2() self.setUp(device='cuda:0') def testPickleCorrectness(self): ref_aev_computer = self.cuaev_computer tmpfile = '/tmp/cuaev.pkl' with open(tmpfile, 'wb') as file: pickle.dump(ref_aev_computer, file) with open(tmpfile, 'rb') as file: test_aev_computer = pickle.load(file) os.remove(tmpfile) coordinates = torch.rand([2, 50, 3], device=self.device) * 5 species = torch.randint(-1, 3, (2, 50), device=self.device) _, ref_aev = ref_aev_computer((species, coordinates)) _, test_aev = test_aev_computer((species, coordinates)) self.assertEqual(ref_aev, test_aev) def testSimpleBackward(self): coordinates = torch.tensor([ [[0.03192167, 0.00638559, 0.01301679], [-0.83140486, 0.39370209, -0.26395324], [-0.66518241, -0.84461308, 0.20759389], [0.45554739, 0.54289633, 0.81170881], [0.66091919, -0.16799635, -0.91037834]], [[-4.1862600, 0.0575700, -0.0381200], [-3.1689400, 0.0523700, 0.0200000], [-4.4978600, 0.8211300, 0.5604100], [-4.4978700, -0.8000100, 0.4155600], [0.00000000, -0.00000000, -0.00000000]] ], requires_grad=True, device=self.device) species = torch.tensor([[1, 0, 0, 0, 0], [2, 0, 0, 0, -1]], device=self.device) _, aev = self.aev_computer((species, coordinates)) aev.backward(torch.ones_like(aev)) force_ref = coordinates.grad coordinates = coordinates.clone().detach() coordinates.requires_grad_() _, cu_aev = self.cuaev_computer((species, coordinates)) cu_aev.backward(torch.ones_like(cu_aev)) force_cuaev = coordinates.grad self.assertEqual(cu_aev, aev, f'cu_aev: {cu_aev}\n aev: {aev}') self.assertEqual(force_cuaev, force_ref, f'\nforce_cuaev: {force_cuaev}\n aev_grad: {force_ref}') def testSimpleDoubleBackward_1(self): """ Test Double Backward (Force training) by parameters' gradient """ coordinates = torch.tensor([ [[0.03192167, 0.00638559, 0.01301679], [-0.83140486, 0.39370209, -0.26395324], [-0.66518241, -0.84461308, 0.20759389], [0.45554739, 0.54289633, 0.81170881], [0.66091919, -0.16799635, -0.91037834]], [[-4.1862600, 0.0575700, -0.0381200], [-3.1689400, 0.0523700, 0.0200000], [-4.4978600, 0.8211300, 0.5604100], [-4.4978700, -0.8000100, 0.4155600], [0.00000000, -0.00000000, -0.00000000]] ], requires_grad=True, device=self.device) species = torch.tensor([[1, 0, 0, 0, 0], [2, 0, 0, 0, -1]], device=self.device) self._double_backward_1_test(species, coordinates) def testSimpleDoubleBackward_2(self): """ Test Double Backward (Force training) directly. Double backward: Forward: input is dE/dAEV, output is force Backward: input is dLoss/dForce, output is dLoss/(dE/dAEV) """ coordinates = torch.tensor([ [[0.03192167, 0.00638559, 0.01301679], [-0.83140486, 0.39370209, -0.26395324], [-0.66518241, -0.84461308, 0.20759389], [0.45554739, 0.54289633, 0.81170881], [0.66091919, -0.16799635, -0.91037834]], [[-4.1862600, 0.0575700, -0.0381200], [-3.1689400, 0.0523700, 0.0200000], [-4.4978600, 0.8211300, 0.5604100], [-4.4978700, -0.8000100, 0.4155600], [0.00000000, -0.00000000, -0.00000000]] ], requires_grad=True, device=self.device) species = torch.tensor([[1, 0, 0, 0, 0], [2, 0, 0, 0, -1]], device=self.device) self._double_backward_2_test(species, coordinates) def testTripeptideMD(self): for i in range(100): datafile = os.path.join(path, 'test_data/tripeptide-md/{}.dat'.format(i)) with open(datafile, 'rb') as f: coordinates, species, *_ = pickle.load(f) coordinates = torch.from_numpy(coordinates).float().unsqueeze(0).to(self.device) species = torch.from_numpy(species).unsqueeze(0).to(self.device) _, aev = self.aev_computer((species, coordinates)) _, cu_aev = self.cuaev_computer((species, coordinates)) self.assertEqual(cu_aev, aev) def testTripeptideMDBackward(self): for i in range(100): datafile = os.path.join(path, 'test_data/tripeptide-md/{}.dat'.format(i)) with open(datafile, 'rb') as f: coordinates, species, *_ = pickle.load(f) coordinates = torch.from_numpy(coordinates).float().unsqueeze(0).to(self.device).requires_grad_(True) species = torch.from_numpy(species).unsqueeze(0).to(self.device) _, aev = self.aev_computer((species, coordinates)) aev.backward(torch.ones_like(aev)) aev_grad = coordinates.grad coordinates = coordinates.clone().detach() coordinates.requires_grad_() _, cu_aev = self.cuaev_computer((species, coordinates)) cu_aev.backward(torch.ones_like(cu_aev)) cuaev_grad = coordinates.grad self.assertEqual(cu_aev, aev) self.assertEqual(cuaev_grad, aev_grad, atol=5e-5, rtol=5e-5) def testTripeptideMDDoubleBackward_2(self): for i in range(100): datafile = os.path.join(path, 'test_data/tripeptide-md/{}.dat'.format(i)) with open(datafile, 'rb') as f: coordinates, species, *_ = pickle.load(f) coordinates = torch.from_numpy(coordinates).float().unsqueeze(0).to(self.device).requires_grad_(True) species = torch.from_numpy(species).unsqueeze(0).to(self.device) self._double_backward_2_test(species, coordinates) def testNIST(self): datafile = os.path.join(path, 'test_data/NIST/all') with open(datafile, 'rb') as f: data = pickle.load(f) for coordinates, species, _, _, _, _ in data: coordinates = torch.from_numpy(coordinates).to(torch.float).to(self.device) species = torch.from_numpy(species).to(self.device) _, aev = self.aev_computer((species, coordinates)) _, cu_aev = self.cuaev_computer((species, coordinates)) self.assertEqual(cu_aev, aev) def testNISTBackward(self): datafile = os.path.join(path, 'test_data/NIST/all') with open(datafile, 'rb') as f: data = pickle.load(f) for coordinates, species, _, _, _, _ in data[:10]: coordinates = torch.from_numpy(coordinates).to(torch.float).to(self.device).requires_grad_(True) species = torch.from_numpy(species).to(self.device) _, aev = self.aev_computer((species, coordinates)) aev.backward(torch.ones_like(aev)) aev_grad = coordinates.grad coordinates = coordinates.clone().detach() coordinates.requires_grad_() _, cu_aev = self.cuaev_computer((species, coordinates)) cu_aev.backward(torch.ones_like(cu_aev)) cuaev_grad = coordinates.grad self.assertEqual(cu_aev, aev) self.assertEqual(cuaev_grad, aev_grad, atol=5e-5, rtol=5e-5) def testNISTDoubleBackward_2(self): datafile = os.path.join(path, 'test_data/NIST/all') with open(datafile, 'rb') as f: data = pickle.load(f) for coordinates, species, _, _, _, _ in data[:3]: coordinates = torch.from_numpy(coordinates).to(torch.float).to(self.device).requires_grad_(True) species = torch.from_numpy(species).to(self.device) self._double_backward_2_test(species, coordinates) def testVeryDenseMolecule(self): """ Test very dense molecule for aev correctness, especially for angular kernel when center atom pairs are more than 32. issue: https://github.com/aiqm/torchani/pull/555 """ for i in range(5): datafile = os.path.join(path, 'test_data/tripeptide-md/{}.dat'.format(i)) with open(datafile, 'rb') as f: coordinates, species, *_ = pickle.load(f) # change angstrom coordinates to 10 times smaller coordinates = 0.1 * torch.from_numpy(coordinates).float().unsqueeze(0).to(self.device) species = torch.from_numpy(species).unsqueeze(0).to(self.device) _, aev = self.aev_computer((species, coordinates)) _, cu_aev = self.cuaev_computer((species, coordinates)) self.assertEqual(cu_aev, aev, atol=5e-5, rtol=5e-5) def testVeryDenseMoleculeBackward(self): for i in range(5): datafile = os.path.join(path, 'test_data/tripeptide-md/{}.dat'.format(i)) with open(datafile, 'rb') as f: coordinates, species, *_ = pickle.load(f) # change angstrom coordinates to 10 times smaller coordinates = 0.1 * torch.from_numpy(coordinates).float().unsqueeze(0).to(self.device) coordinates.requires_grad_(True) species = torch.from_numpy(species).unsqueeze(0).to(self.device) _, aev = self.aev_computer((species, coordinates)) aev.backward(torch.ones_like(aev)) aev_grad = coordinates.grad coordinates = coordinates.clone().detach() coordinates.requires_grad_() _, cu_aev = self.cuaev_computer((species, coordinates)) cu_aev.backward(torch.ones_like(cu_aev)) cuaev_grad = coordinates.grad self.assertEqual(cu_aev, aev, atol=5e-5, rtol=5e-5) self.assertEqual(cuaev_grad, aev_grad, atol=5e-4, rtol=5e-4) if __name__ == '__main__': unittest.main()