torchani 0.1

examples/training-benchmark.py

+import torch
+import torchani
+import torchani.data
+import tqdm
+import math
+import timeit
+import configs
+import functools
+configs.benchmark = True
+from common import *
+
+ds = torchani.data.load_dataset(configs.data_path)
+sampler = torchani.data.BatchSampler(ds, 256, 4)
+dataloader = torch.utils.data.DataLoader(
+    ds, batch_sampler=sampler, collate_fn=torchani.data.collate, num_workers=20)
+model = get_or_create_model('/tmp/model.pt')
+optimizer = torch.optim.Adam(model.parameters(), amsgrad=True)
+
+
+def benchmark(timer, index):
+    def wrapper(fun):
+        @functools.wraps(fun)
+        def wrapped(*args, **kwargs):
+            start = timeit.default_timer()
+            ret = fun(*args, **kwargs)
+            end = timeit.default_timer()
+            timer[index] += end - start
+            return ret
+        return wrapped
+    return wrapper
+
+
+timer = {'backward': 0}
+
+
+@benchmark(timer, 'backward')
+def optimize_step(a):
+    mse = a.avg()
+    optimizer.zero_grad()
+    mse.backward()
+    optimizer.step()
+
+
+start = timeit.default_timer()
+for batch in tqdm.tqdm(dataloader, total=len(sampler)):
+    a = Averager()
+    for molecule_id in batch:
+        _species = ds.species[molecule_id]
+        coordinates, energies = batch[molecule_id]
+        coordinates = coordinates.to(aev_computer.device)
+        energies = energies.to(aev_computer.device)
+        a.add(*evaluate(model, coordinates, energies, _species))
+    optimize_step(a)
+
+elapsed = round(timeit.default_timer() - start, 2)
+print('Radial terms:', aev_computer.timers['radial terms'])
+print('Angular terms:', aev_computer.timers['angular terms'])
+print('Terms and indices:', aev_computer.timers['terms and indices'])
+print('Combinations:', aev_computer.timers['combinations'])
+print('Mask R:', aev_computer.timers['mask_r'])
+print('Mask A:', aev_computer.timers['mask_a'])
+print('Assemble:', aev_computer.timers['assemble'])
+print('Total AEV:', aev_computer.timers['total'])
+print('NN:', model.timers['nn'])
+print('Total Forward:', model.timers['forward'])
+print('Total Backward:', timer['backward'])
+print('Epoch time:', elapsed)

index.md

+.. torchani documentation master file, created by
+   sphinx-quickstart on Tue May  1 00:00:05 2018.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to torchani's documentation!
+====================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+.. automodule:: torchani
+   :members:
+   :special-members: __init__, __call__
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`

setup.py

+from setuptools import setup
+from sphinx.setup_command import BuildDoc
+cmdclass = {'build_sphinx': BuildDoc}
+
+setup(name='torchani',
+      version='0.1',
+      description='ANI based on pytorch',
+      url='https://github.com/zasdfgbnm/torchani',
+      author='Xiang Gao',
+      author_email='qasdfgtyuiop@ufl.edu',
+      license='MIT',
+      packages=['torchani'],
+      include_package_data=True,
+      install_requires=[
+          'torch',
+          'lark-parser',
+          'h5py',
+      ],
+      test_suite='nose.collector',
+      tests_require=['nose', 'cntk'],
+      cmdclass=cmdclass,
+      )

torchani/__init__.py

+import pkg_resources
+import torch
+
+buildin_const_file = pkg_resources.resource_filename(
+    __name__, 'resources/ani-1x_dft_x8ens/rHCNO-5.2R_16-3.5A_a4-8.params')
+
+buildin_sae_file = pkg_resources.resource_filename(
+    __name__, 'resources/ani-1x_dft_x8ens/sae_linfit.dat')
+
+buildin_network_dir = pkg_resources.resource_filename(
+    __name__, 'resources/ani-1x_dft_x8ens/train0/networks/')
+
+buildin_model_prefix = pkg_resources.resource_filename(
+    __name__, 'resources/ani-1x_dft_x8ens/train')
+
+buildin_dataset_dir = pkg_resources.resource_filename(
+    __name__, 'resources/')
+
+default_dtype = torch.float32
+default_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+from .energyshifter import EnergyShifter
+from .nn import ModelOnAEV, PerSpeciesFromNeuroChem
+from .aev import SortedAEV
+import logging
+
+__all__ = ['SortedAEV', 'EnergyShifter', 'ModelOnAEV', 'PerSpeciesFromNeuroChem', 'data',
+           'buildin_const_file', 'buildin_sae_file', 'buildin_network_dir', 'buildin_dataset_dir',
+           'default_dtype', 'default_device']
+
+try:
+    from .neurochem_aev import NeuroChemAEV
+    __all__.append('NeuroChemAEV')
+except ImportError:
+    logging.log(logging.WARNING,
+                'Unable to import NeuroChemAEV, please check your pyNeuroChem installation.')

torchani/aev.py

+import torch
+import itertools
+import numpy
+from .aev_base import AEVComputer
+from . import buildin_const_file, default_dtype, default_device
+
+
+def _cutoff_cosine(distances, cutoff):
+    """Compute the elementwise cutoff cosine function
+
+    The cutoff cosine function is define in https://arxiv.org/pdf/1610.08935.pdf equation 2
+
+    Parameters
+    ----------
+    distances : torch.Tensor
+        The pytorch tensor that stores Rij values. This tensor can have any shape since the cutoff
+        cosine function is computed elementwise.
+    cutoff : float
+        The cutoff radius, i.e. the Rc in the equation. For any Rij > Rc, the function value is defined to be zero.
+
+    Returns
+    -------
+    torch.Tensor
+        The tensor of the same shape as `distances` that stores the computed function values.
+    """
+    return torch.where(distances <= cutoff, 0.5 * torch.cos(numpy.pi * distances / cutoff) + 0.5, torch.zeros_like(distances))
+
+
+class SortedAEV(AEVComputer):
+    """The AEV computer assuming input coordinates sorted by species
+
+    Attributes
+    ----------
+    timers : dict
+        Dictionary storing the the benchmark result. It has the following keys:
+            radial_subaev : time spent on computing radial subaev
+            angular_subaev : time spent on computing angular subaev
+            total : total time for computing everything.
+    """
+
+    def __init__(self, benchmark=False, device=default_device, dtype=default_dtype, const_file=buildin_const_file):
+        super(SortedAEV, self).__init__(benchmark, dtype, device, const_file)
+        if benchmark:
+            self.radial_subaev_terms = self._enable_benchmark(
+                self.radial_subaev_terms, 'radial terms')
+            self.angular_subaev_terms = self._enable_benchmark(
+                self.angular_subaev_terms, 'angular terms')
+            self.terms_and_indices = self._enable_benchmark(
+                self.terms_and_indices, 'terms and indices')
+            self.combinations = self._enable_benchmark(
+                self.combinations, 'combinations')
+            self.compute_mask_r = self._enable_benchmark(
+                self.compute_mask_r, 'mask_r')
+            self.compute_mask_a = self._enable_benchmark(
+                self.compute_mask_a, 'mask_a')
+            self.assemble = self._enable_benchmark(self.assemble, 'assemble')
+            self.forward = self._enable_benchmark(self.forward, 'total')
+
+    def species_to_tensor(self, species):
+        """Convert species list into a long tensor.
+
+        Parameters
+        ----------
+        species : list
+            List of string for the species of each atoms.
+
+        Returns
+        -------
+        torch.Tensor
+            Long tensor for the species, where a value k means the species is
+            the same as self.species[k].
+        """
+        indices = {self.species[i]: i for i in range(len(self.species))}
+        values = [indices[i] for i in species]
+        return torch.tensor(values, dtype=torch.long, device=self.device)
+
+    def radial_subaev_terms(self, distances):
+        """Compute the radial subAEV terms of the center atom given neighbors
+
+        The radial AEV is define in https://arxiv.org/pdf/1610.08935.pdf equation 3.
+        The sum computed by this method is over all given neighbors, so the caller
+        of this method need to select neighbors if the caller want a per species subAEV.
+
+        Parameters
+        ----------
+        distances : torch.Tensor
+            Pytorch tensor of shape (..., neighbors) storing the |Rij| length where i are the
+            center atoms, and j are their neighbors.
+
+        Returns
+        -------
+        torch.Tensor
+            A tensor of shape (..., neighbors, `radial_sublength`) storing the subAEVs.
+        """
+        distances = distances.unsqueeze(-1).unsqueeze(-1) #TODO: allow unsqueeze to insert multiple dimensions
+        fc = _cutoff_cosine(distances, self.Rcr)
+        # Note that in the equation in the paper there is no 0.25 coefficient, but in NeuroChem there is such a coefficient. We choose to be consistent with NeuroChem instead of the paper here.
+        ret = 0.25 * torch.exp(-self.EtaR * (distances - self.ShfR)**2) * fc
+        return ret.flatten(start_dim=-2)
+
+    def angular_subaev_terms(self, vectors1, vectors2):
+        """Compute the angular subAEV terms of the center atom given neighbor pairs.
+
+        The angular AEV is define in https://arxiv.org/pdf/1610.08935.pdf equation 4.
+        The sum computed by this method is over all given neighbor pairs, so the caller
+        of this method need to select neighbors if the caller want a per species subAEV.
+
+        Parameters
+        ----------
+        vectors1, vectors2: torch.Tensor
+            Tensor of shape (..., pairs, 3) storing the Rij vectors of pairs of neighbors.
+            The vectors1(..., j, :) and vectors2(..., j, :) are the Rij vectors of the
+            two atoms of pair j.
+
+        Returns
+        -------
+        torch.Tensor
+            Tensor of shape (..., pairs, `angular_sublength`) storing the subAEVs.
+        """
+        vectors1 = vectors1.unsqueeze(
+            -1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1) #TODO: allow unsqueeze to plug in multiple dims
+        vectors2 = vectors2.unsqueeze(
+            -1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1) #TODO: allow unsqueeze to plug in multiple dims
+        distances1 = vectors1.norm(2, dim=-5)
+        distances2 = vectors2.norm(2, dim=-5)
+
+        # 0.95 is multiplied to the cos values to prevent acos from returning NaN.
+        cos_angles = 0.95 * \
+            torch.nn.functional.cosine_similarity(
+                vectors1, vectors2, dim=-5)
+        angles = torch.acos(cos_angles)
+
+        fcj1 = _cutoff_cosine(distances1, self.Rca)
+        fcj2 = _cutoff_cosine(distances2, self.Rca)
+        factor1 = ((1 + torch.cos(angles - self.ShfZ)) / 2) ** self.Zeta
+        factor2 = torch.exp(-self.EtaA *
+                            ((distances1 + distances2) / 2 - self.ShfA) ** 2)
+        ret = 2 * factor1 * factor2 * fcj1 * fcj2
+        # ret now have shape (..., pairs, ?, ?, ?, ?) where ? depend on constants
+        # flat the last 4 dimensions to view the subAEV as one dimension vector
+        return ret.flatten(start_dim=-4)
+
+    def terms_and_indices(self, coordinates):
+        """Compute radial and angular subAEV terms, and original indices.
+
+        Terms will be sorted according to their distances to central atoms, and only
+        these within cutoff radius are valid. The returned indices contains what would
+        their original indices be if they were unsorted.
+
+        Parameters
+        ----------
+        coordinates : torch.Tensor
+            The tensor that specifies the xyz coordinates of atoms in the molecule.
+            The tensor must have shape (conformations, atoms, 3)
+
+        Returns
+        -------
+        (radial_terms, angular_terms, indices_r, indices_a)
+        radial_terms : torch.Tensor
+            Tensor of shape (conformations, atoms, neighbors, `radial_sublength`) for
+            the (unsummed) radial subAEV terms.
+        angular_terms : torch.Tensor
+            Tensor of shape (conformations, atoms, pairs, `angular_sublength`) for the
+            (unsummed) angular subAEV terms.
+        indices_r : torch.Tensor
+            Tensor of shape (conformations, atoms, neighbors). Let l = indices_r(i,j,k),
+            then this means that radial_terms(i,j,k,:) is in the subAEV term of conformation
+            i between atom j and atom l. 
+        indices_a : torch.Tensor
+            Same as indices_r, except that the cutoff radius is Rca instead of Rcr.
+        """
+
+        vec = coordinates.unsqueeze(2) - coordinates.unsqueeze(1)
+        """Shape (conformations, atoms, atoms, 3) storing Rij vectors"""
+
+        distances = vec.norm(2, -1)
+        """Shape (conformations, atoms, atoms) storing Rij distances"""
+
+        distances, indices = distances.sort(-1)
+
+        min_distances, _ = distances.flatten(end_dim=1).min(0)
+        inRcr = (min_distances <= self.Rcr).nonzero().flatten()[1:]  #TODO: can we use something like find_first?
+        inRca = (min_distances <= self.Rca).nonzero().flatten()[1:]
+
+        distances = distances.index_select(-1, inRcr)
+        indices_r = indices.index_select(-1, inRcr)
+        radial_terms = self.radial_subaev_terms(distances)
+
+        indices_a = indices.index_select(-1, inRca)
+        new_shape = list(indices_a.shape) + [3]
+        _indices_a = indices_a.unsqueeze(-1).expand(*new_shape)  #TODO: can we add something like expand_dim(dim=0, repeat=3)
+        vec = vec.gather(-2, _indices_a)  #TODO: can we make gather broadcast??
+        vec = self.combinations(vec, -2)  #TODO: can we move combinations to ATen?
+        angular_terms = self.angular_subaev_terms(
+            *vec) if vec is not None else None
+
+        return radial_terms, angular_terms, indices_r, indices_a
+
+    def combinations(self, tensor, dim=0):
+        n = tensor.shape[dim]
+        r = torch.arange(n).type(torch.long).to(tensor.device)
+        grid_x, grid_y = torch.meshgrid([r, r])
+        index1 = grid_y[torch.triu(torch.ones(n, n), diagonal=1) == 1]
+        index2 = grid_x[torch.triu(torch.ones(n, n), diagonal=1) == 1]
+        if torch.numel(index1) == 0:
+            # TODO: pytorch are unable to handle size 0 tensor well. Is this an expected behavior?
+            # See: https://github.com/pytorch/pytorch/issues/5014
+            return None
+        return tensor.index_select(dim, index1), tensor.index_select(dim, index2)
+
+    def compute_mask_r(self, species_r):
+        """Partition indices according to their species, radial part
+
+        Parameters
+        ----------
+        species_r : torch.Tensor
+            Tensor of shape (conformations, atoms, neighbors) storing species of
+            neighbors.
+
+        Returns
+        -------
+        torch.Tensor
+            Tensor of shape (conformations, atoms, neighbors, all species) storing
+            the mask for each species.
+        """
+        mask_r = (species_r.unsqueeze(-1) ==
+                  torch.arange(len(self.species), device=self.device))
+        return mask_r
+
+
+    def compute_mask_a(self, species_a, present_species):
+        """Partition indices according to their species, angular part
+
+        Parameters
+        ----------
+        species_a : torch.Tensor
+            Tensor of shape (conformations, atoms, neighbors) storing the species of
+            neighbors
+        present_species : torch.Tensor
+            Long tensor for the species, already uniqued.
+
+        Returns
+        -------
+        torch.Tensor
+            Tensor of shape (conformations, atoms, pairs, present species, present species)
+            storing the mask for each pair.
+        """
+        species_a = self.combinations(species_a, -1)
+        if species_a is not None:
+            # TODO: can we remove this if pytorch support 0 size tensors?
+            species_a1, species_a2 = species_a
+
+        if species_a is not None:
+            mask_a1 = (species_a1.unsqueeze(-1) ==
+                       present_species).unsqueeze(-1)
+            mask_a2 = (species_a2.unsqueeze(-1).unsqueeze(-1)
+                       == present_species)
+            mask = mask_a1 * mask_a2
+            mask_rev = mask.permute(0, 1, 2, 4, 3)
+            mask_a = (mask + mask_rev) > 0
+            return mask_a
+        else:
+            return None
+
+    def assemble(self, radial_terms, angular_terms, present_species, mask_r, mask_a):
+        """Assemble radial and angular AEV from computed terms according to the given partition information.
+
+        Parameters
+        ----------
+        radial_terms : torch.Tensor
+            Tensor of shape (conformations, atoms, neighbors, `radial_sublength`) for
+            the (unsummed) radial subAEV terms.
+        angular_terms : torch.Tensor
+            Tensor of shape (conformations, atoms, pairs, `angular_sublength`) for the
+            (unsummed) angular subAEV terms.
+        present_species : torch.Tensor
+            Long tensor for species of atoms present in the molecules.
+        mask_r : torch.Tensor
+            Tensor of shape (conformations, atoms, neighbors, present species) storing
+            the mask for each species.
+        mask_a : torch.Tensor
+            Tensor of shape (conformations, atoms, pairs, present species, present species)
+            storing the mask for each pair.
+
+        Returns
+        -------
+        (torch.Tensor, torch.Tensor)
+            Returns (radial AEV, angular AEV), both are pytorch tensor of `dtype`.
+            The radial AEV must be of shape (conformations, atoms, radial_length)
+            The angular AEV must be of shape (conformations, atoms, angular_length)
+        """
+        conformations = radial_terms.shape[0]
+        atoms = radial_terms.shape[1]
+
+        # assemble radial subaev
+        present_radial_aevs = (radial_terms.unsqueeze(-2)
+                               * mask_r.unsqueeze(-1).type(self.dtype)).sum(-3)
+        """Tensor of shape (conformations, atoms, present species, radial_length)"""
+        radial_aevs = present_radial_aevs.flatten(start_dim=2)
+
+        # assemble angular subaev
+        rev_indices = {present_species[i].item(): i  #TODO: can we use find_first?
+                       for i in range(len(present_species))}
+        """Tensor of shape (conformations, atoms, present species, present species, angular_length)"""
+        angular_aevs = []
+        zero_angular_subaev = torch.zeros(  #TODO: can we make stack and cat broadcast?
+            conformations, atoms, self.angular_sublength, dtype=self.dtype, device=self.device)  #TODO: can we make torch.zeros, torch.ones typeless and deviceless?
+        for s1, s2 in itertools.combinations_with_replacement(range(len(self.species)), 2):
+            # TODO: can we remove this if pytorch support 0 size tensors?
+            if s1 in rev_indices and s2 in rev_indices and mask_a is not None:
+                i1 = rev_indices[s1]
+                i2 = rev_indices[s2]
+                mask = mask_a[..., i1, i2].unsqueeze(-1).type(self.dtype)
+                subaev = (angular_terms * mask).sum(-2)
+            else:
+                subaev = zero_angular_subaev
+            angular_aevs.append(subaev)
+
+        return radial_aevs, torch.cat(angular_aevs, dim=2)
+
+    def forward(self, coordinates, species):
+        species = self.species_to_tensor(species)
+        present_species = species.unique(sorted=True)
+
+        radial_terms, angular_terms, indices_r, indices_a = self.terms_and_indices(
+            coordinates)
+
+        species_r = species[indices_r]
+        mask_r = self.compute_mask_r(species_r)
+        species_a = species[indices_a]
+        mask_a = self.compute_mask_a(species_a, present_species)
+
+        return self.assemble(radial_terms, angular_terms, present_species, mask_r, mask_a)
+
+    def export_radial_subaev_onnx(self, filename):
+        """Export the operation that compute radial subaev into onnx format
+
+        Parameters
+        ----------
+        filename : string
+            Name of the file to store exported networks.
+        """
+        class M(torch.nn.Module):
+            def __init__(self, outerself):
+                super(M, self).__init__()
+                self.outerself = outerself
+
+            def forward(self, center, neighbors):
+                return self.outerself.radial_subaev(center, neighbors)
+        dummy_center = torch.randn(1, 3)
+        dummy_neighbors = torch.randn(1, 5, 3)
+        torch.onnx.export(M(self), (dummy_center, dummy_neighbors), filename)

torchani/aev_base.py

+import torch
+import torch.nn as nn
+from . import buildin_const_file, default_dtype, default_device
+from .benchmarked import BenchmarkedModule
+
+
+class AEVComputer(BenchmarkedModule):
+    __constants__ = ['Rcr', 'Rca', 'dtype', 'device', 'radial_sublength',
+        'radial_length', 'angular_sublength', 'angular_length', 'aev_length']
+
+    """Base class of various implementations of AEV computer
+
+    Attributes
+    ----------
+    benchmark : boolean
+        Whether to enable benchmark
+    dtype : torch.dtype
+        Data type of pytorch tensors for all the computations. This is also used
+        to specify whether to use CPU or GPU.
+    device : torch.Device
+        The device where tensors should be.
+    const_file : str
+        The name of the original file that stores constant.
+    Rcr, Rca : float
+        Cutoff radius
+    EtaR, ShfR, Zeta, ShfZ, EtaA, ShfA : torch.Tensor
+        Tensor storing constants.
+    radial_sublength : int
+        The length of radial subaev of a single species
+    radial_length : int
+        The length of full radial aev
+    angular_sublength : int
+        The length of angular subaev of a single species
+    angular_length : int
+        The length of full angular aev
+    aev_length : int
+        The length of full aev
+    """
+
+    def __init__(self, benchmark=False, dtype=default_dtype, device=default_device, const_file=buildin_const_file):
+        super(AEVComputer, self).__init__(benchmark)
+
+        self.dtype = dtype
+        self.const_file = const_file
+        self.device = device
+
+        # load constants from const file
+        with open(const_file) as f:
+            for i in f:
+                try:
+                    line = [x.strip() for x in i.split('=')]
+                    name = line[0]
+                    value = line[1]
+                    if name == 'Rcr' or name == 'Rca':
+                        setattr(self, name, float(value))
+                    elif name in ['EtaR', 'ShfR', 'Zeta', 'ShfZ', 'EtaA', 'ShfA']:
+                        value = [float(x.strip()) for x in value.replace(
+                            '[', '').replace(']', '').split(',')]
+                        value = torch.tensor(value, dtype=dtype, device=device)
+                        setattr(self, name, value)
+                    elif name == 'Atyp':
+                        value = [x.strip() for x in value.replace(
+                            '[', '').replace(']', '').split(',')]
+                        self.species = value
+                except:
+                    raise ValueError('unable to parse const file')
+
+        # Compute lengths
+        self.radial_sublength = self.EtaR.shape[0] * self.ShfR.shape[0]
+        self.radial_length = len(self.species) * self.radial_sublength
+        self.angular_sublength = self.EtaA.shape[0] * \
+            self.Zeta.shape[0] * self.ShfA.shape[0] * self.ShfZ.shape[0]
+        species = len(self.species)
+        self.angular_length = int(
+            (species * (species + 1)) / 2) * self.angular_sublength
+        self.aev_length = self.radial_length + self.angular_length
+
+        # convert constant tensors to a ready-to-broadcast shape
+        # shape convension (..., EtaR, ShfR)
+        self.EtaR = self.EtaR.view(-1, 1)
+        self.ShfR = self.ShfR.view(1, -1)
+        # shape convension (..., EtaA, Zeta, ShfA, ShfZ)
+        self.EtaA = self.EtaA.view(-1, 1, 1, 1)
+        self.Zeta = self.Zeta.view(1, -1, 1, 1)
+        self.ShfA = self.ShfA.view(1, 1, -1, 1)
+        self.ShfZ = self.ShfZ.view(1, 1, 1, -1)
+
+    def sort_by_species(self, data, species):
+        """Sort the data by its species according to the order in `self.species`
+
+        Parameters
+        ----------
+        data : torch.Tensor
+            Tensor of shape (conformations, atoms, ...) for data.
+        species : list
+            List storing species of each atom.
+
+        Returns
+        -------
+        (torch.Tensor, list)
+            Tuple of (sorted data, sorted species).
+        """
+        atoms = list(zip(species, torch.unbind(data, 1)))
+        atoms = sorted(atoms, key=lambda x: self.species.index(x[0]))
+        species = [s for s, _ in atoms]
+        data = torch.stack([c for _, c in atoms], dim=1)
+        return data, species
+
+    def forward(self, coordinates, species):
+        """Compute AEV from coordinates and species
+
+        Parameters
+        ----------
+        coordinates : torch.Tensor
+            The tensor that specifies the xyz coordinates of atoms in the molecule.
+            The tensor must have shape (conformations, atoms, 3)
+        species : torch.LongTensor
+            Long tensor for the species, where a value k means the species is
+            the same as self.species[k]
+
+        Returns
+        -------
+        (torch.Tensor, torch.Tensor)
+            Returns (radial AEV, angular AEV), both are pytorch tensor of `dtype`.
+            The radial AEV must be of shape (conformations, atoms, radial_length)
+            The angular AEV must be of shape (conformations, atoms, angular_length)
+        """
+        raise NotImplementedError('subclass must override this method')

torchani/benchmarked.py

+import torch
+import timeit
+import functools
+
+
+class BenchmarkedModule(torch.jit.ScriptModule):
+    """Module with member function benchmarking support.
+
+    The benchmarking is done by wrapping the original member function with
+    a wrapped function. The wrapped function will call the original function,
+    and accumulate its running time into `self.timers`. Different accumulators are
+    distinguished by different keys. All times should have unit seconds.
+
+    To enable benchmarking for member functions in a subclass, simply
+    call the `__init__` of this class with `benchmark=True`, and add the following
+    code to your subclass's `__init__`:
+    ```
+    if self.benchmark:
+        self._enable_benchmark(self.function_to_be_benchmarked, 'key1', 'key2')
+    ```
+
+    Example
+    -------
+    The following code implements a subclass for timing the running time of member function
+    `f` and `g` and the total of these two::
+    ```
+    class BenchmarkFG(BenchmarkedModule):
+        def __init__(self, benchmark=False)
+            super(BenchmarkFG, self).__init__(benchmark)
+            if benchmark:
+                self.f = self._enable_benchmark(self.f, 'function f', 'total')
+                self.g = self._enable_benchmark(self.g, 'function g', 'total')
+
+        def f(self):
+            print('in function f')
+
+        def g(self):
+            print('in function g')
+    ```
+
+    Attributes
+    ----------
+    benchmark : boolean
+        Whether benchmark is enabled
+    timers : dict
+        Dictionary storing the the benchmark result.
+    """
+
+    def _enable_benchmark(self, fun, *keys):
+        """Wrap a function to automatically benchmark it, and assign a key for it.
+
+        Parameters
+        ----------
+        keys
+            The keys in `self.timers` assigned. If multiple keys are specified, then
+            the time will be accumulated to all the keys.
+        func : function
+            The function to be benchmarked.
+
+        Returns
+        -------
+        function
+            Wrapped function that time the original function and update the corresponding
+            value in `self.timers` automatically.
+        """
+        for key in keys:
+            self.timers[key] = 0
+
+        @functools.wraps(fun)
+        def wrapped(*args, **kwargs):
+            start = timeit.default_timer()
+            ret = fun(*args, **kwargs)
+            end = timeit.default_timer()
+            for key in keys:
+                self.timers[key] += end - start
+            return ret
+        return wrapped
+
+    def reset_timers(self):
+        """Reset all timers. If benchmark is not enabled, a `ValueError` will be raised."""
+        if not self.benchmark:
+            raise ValueError('Can not reset timers, benchmark not enabled')
+        for i in self.timers:
+            self.timers[i] = 0
+
+    def __init__(self, benchmark=False):
+        super(BenchmarkedModule, self).__init__()
+        self.benchmark = benchmark
+        if benchmark:
+            self.timers = {}

torchani/data.py

+from .pyanitools import anidataloader
+from os import listdir
+from os.path import join, isfile, isdir
+from torch import tensor, full_like, long
+from torch.utils.data import Dataset, Subset, TensorDataset, ConcatDataset
+from torch.utils.data.dataloader import default_collate
+from math import ceil
+from . import default_dtype
+from random import shuffle
+from itertools import chain, accumulate
+
+
+class ANIDataset(Dataset):
+    """Dataset with extra information for ANI applications
+
+    Attributes
+    ----------
+    dataset : Dataset
+        The dataset
+    sizes : sequence
+        Number of conformations for each molecule
+    cumulative_sizes : sequence
+        Cumulative sizes
+    """
+
+    def __init__(self, dataset, sizes, species):
+        super(ANIDataset, self).__init__()
+        self.dataset = dataset
+        self.sizes = sizes
+        self.cumulative_sizes = list(accumulate(sizes))
+        self.species = species
+
+    def __getitem__(self, idx):
+        return self.dataset[idx]
+
+    def __len__(self):
+        return len(self.dataset)
+
+
+def load_dataset(path, dtype=default_dtype):
+    """The returned dataset has cumulative_sizes and molecule_sizes"""
+    # get name of files storing data
+    files = []
+    if isdir(path):
+        for f in listdir(path):
+            f = join(path, f)
+            if isfile(f) and (f.endswith('.h5') or f.endswith('.hdf5')):
+                files.append(f)
+    elif isfile(path):
+        files = [path]
+    else:
+        raise ValueError('Bad path')
+
+    # read tensors from file and build a dataset
+    species = []
+    molecule_id = 0
+    datasets = []
+    for f in files:
+        for m in anidataloader(f):
+            coordinates = tensor(m['coordinates'], dtype=dtype)
+            energies = tensor(m['energies'], dtype=dtype)
+            _molecule_id = full_like(energies, molecule_id).type(long)
+            datasets.append(TensorDataset(_molecule_id, coordinates, energies))
+            species.append(m['species'])
+            molecule_id += 1
+    dataset = ConcatDataset(datasets)
+    sizes = [len(x) for x in dataset.datasets]
+    return ANIDataset(dataset, sizes, species)
+
+
+class BatchSampler(object):
+
+    def __init__(self, source, chunk_size, batch_chunks):
+        if not isinstance(source, ANIDataset):
+            raise ValueError("BatchSampler must take ANIDataset as input")
+        self.source = source
+        self.chunk_size = chunk_size
+        self.batch_chunks = batch_chunks
+
+    def _concated_index(self, molecule, conformation):
+        """
+        Get the index in the  dataset of the specified conformation
+        of the specified molecule.
+        """
+        src = self.source
+        cumulative_sizes = [0] + src.cumulative_sizes
+        return cumulative_sizes[molecule] + conformation
+
+    def __iter__(self):
+        molecules = len(self.source.sizes)
+        sizes = self.source.sizes
+        """Number of conformations of each molecule"""
+        unfinished = list(zip(range(molecules), [0] * molecules))
+        """List of pairs (molecule, progress) storing the current progress
+        of iterating each molecules."""
+
+        batch = []
+        batch_molecules = 0
+        """The number of molecules already in batch"""
+        while len(unfinished) > 0:
+            new_unfinished = []
+            for molecule, progress in unfinished:
+                size = sizes[molecule]
+                # the last incomplete chunk is not dropped
+                end = min(progress + self.chunk_size, size)
+                if end < size:
+                    new_unfinished.append((molecule, end))
+                batch += [self._concated_index(molecule, x)
+                          for x in range(progress, end)]
+                batch_molecules += 1
+                if batch_molecules >= self.batch_chunks:
+                    yield batch
+                    batch = []
+                    batch_molecules = 0
+            unfinished = new_unfinished
+
+        # the last incomplete batch is not dropped
+        if len(batch) > 0:
+            yield batch
+
+    def __len__(self):
+        sizes = self.source.sizes
+        chunks = [ceil(x/self.chunk_size) for x in sizes]
+        chunks = sum(chunks)
+        return ceil(chunks / self.batch_chunks)
+
+
+def collate(batch):
+    by_molecules = {}
+    for molecule_id, xyz, energy in batch:
+        molecule_id = molecule_id.item()
+        if molecule_id not in by_molecules:
+            by_molecules[molecule_id] = []
+        by_molecules[molecule_id].append((xyz, energy))
+    for i in by_molecules:
+        by_molecules[i] = default_collate(by_molecules[i])
+    return by_molecules
+
+
+def random_split(dataset, num_chunks, chunk_size):
+    """
+    Randomly split a dataset into non-overlapping new datasets of given lengths
+
+    The splitting is by chunk, which makes it possible for batching: The whole
+    dataset is first splitted into chunks of specified size, each chunk are different
+    conformation of the same isomer/molecule, then these chunks are randomly shuffled
+    and splitted accorting to the given `num_chunks`. After splitted, chunks belong to
+    the same molecule/isomer of the same subset will be merged to allow larger batch.
+
+    Parameters
+    ----------
+    dataset : Dataset:
+        Dataset to be split
+    num_chunks : sequence
+        Number of chuncks of splits to be produced
+    chunk_size : integer
+        Size of each chunk
+    """
+    chunks = list(BatchSampler(dataset, chunk_size, 1))
+    shuffle(chunks)
+    if sum(num_chunks) != len(chunks):
+        raise ValueError(
+            "Sum of input number of chunks does not equal the length of the total dataset!")
+    offset = 0
+    subsets = []
+    for i in num_chunks:
+        _chunks = chunks[offset:offset+i]
+        offset += i
+        # merge chunks by molecule
+        by_molecules = {}
+        for chunk in _chunks:
+            molecule_id = dataset[chunk[0]][0].item()
+            if molecule_id not in by_molecules:
+                by_molecules[molecule_id] = []
+            by_molecules[molecule_id] += chunk
+        _chunks = list(by_molecules.values())
+        shuffle(_chunks)
+        # construct subset
+        sizes = [len(j) for j in _chunks]
+        indices = list(chain.from_iterable(_chunks))
+        _dataset = Subset(dataset, indices)
+        _dataset = ANIDataset(_dataset, sizes, dataset.species)
+        subsets.append(_dataset)
+    return subsets

torchani/energyshifter.py

+from . import buildin_sae_file
+
+
+class EnergyShifter:
+    """Class that deal with self atomic energies.
+
+    Attributes
+    ----------
+    self_energies : dict
+        The dictionary that stores self energies of species.
+    """
+
+    def __init__(self, self_energy_file=buildin_sae_file):
+        # load self energies
+        self.self_energies = {}
+        with open(self_energy_file) as f:
+            for i in f:
+                try:
+                    line = [x.strip() for x in i.split('=')]
+                    name = line[0].split(',')[0].strip()
+                    value = float(line[1])
+                    self.self_energies[name] = value
+                except:
+                    pass  # ignore unrecognizable line
+
+    def subtract_sae(self, energies, species):
+        """Subtract self atomic energies from `energies`.
+
+        Parameters
+        ----------
+        energies : pytorch tensor of `dtype`
+            The tensor of any shape that stores the raw energies.
+        species : list of str
+            The list specifying the species of each atom. The length of the
+            list must be the same as the number of atoms.
+
+        Returns
+        -------
+        pytorch tensor of `dtype`
+            The tensor of the same shape as `energies` that stores the energies
+            with self atomic energies subtracted.
+        """
+        s = 0
+        for i in species:
+            s += self.self_energies[i]
+        return energies - s
+
+    def add_sae(self, energies, species):
+        """Add self atomic energies to `energies`
+
+        Parameters
+        ----------
+        energies : pytorch tensor of `dtype`
+            The tensor of any shape that stores the energies excluding self
+            atomic energies.
+        species : list of str
+            The list specifying the species of each atom. The length of the
+            list must be the same as the number of atoms.
+
+        Returns
+        -------
+        pytorch tensor of `dtype`
+            The tensor of the same shape as `energies` that stores the raw
+            energies, i.e. the energy including self atomic energies.
+        """
+        s = 0
+        for i in species:
+            s += self.self_energies[i]
+        return energies + s

torchani/neurochem_aev.py

+import torch
+import ase
+import pyNeuroChem
+import ase_interface
+import numpy
+from .aev_base import AEVComputer
+from . import buildin_const_file, buildin_sae_file, buildin_network_dir, default_dtype, default_device
+
+
+class NeuroChemAEV (AEVComputer):
+    """The AEV computer that dump out AEV from pyNeuroChem
+
+    Attributes
+    ----------
+    sae_file : str
+        The name of the original file that stores self atomic energies.
+    network_dir : str
+        The name ending with '/' of the directory that stores networks in NeuroChem's format.
+    nc : pyNeuroChem.molecule
+        The internal object of pyNeuroChem which can be used to dump out AEVs, energies, forces,
+        activations, etc.
+    """
+
+    def __init__(self, dtype=default_dtype, device=default_device, const_file=buildin_const_file, sae_file=buildin_sae_file, network_dir=buildin_network_dir):
+        super(NeuroChemAEV, self).__init__(False, dtype, device, const_file)
+        self.sae_file = sae_file
+        self.network_dir = network_dir
+        self.nc = pyNeuroChem.molecule(const_file, sae_file, network_dir, 0)
+
+    def _get_radial_part(self, fullaev):
+        """Get the radial part of AEV from the full AEV
+
+        Parameters
+        ----------
+        fullaev : pytorch tensor of `dtype`
+            The full AEV in shape (conformations, atoms, `radial_length()+angular_length()`).
+
+        Returns
+        -------
+        pytorch tensor of `dtype`
+            The radial AEV in shape(conformations, atoms, `radial_length()`)
+        """
+        radial_size = self.radial_length
+        return fullaev[:, :, :radial_size]
+
+    def _get_angular_part(self, fullaev):
+        """Get the angular part of AEV from the full AEV
+
+        Parameters
+        ----------
+        fullaev : pytorch tensor of `dtype`
+            The full AEV in shape (conformations, atoms, `radial_length()+angular_length()`).
+
+        Returns
+        -------
+        pytorch tensor of `dtype`
+            The radial AEV in shape (conformations, atoms, `angular_length()`)
+        """
+        radial_size = self.radial_length
+        return fullaev[:, :, radial_size:]
+
+    def _compute_neurochem_aevs_per_conformation(self, coordinates, species):
+        """Get the full AEV for a single conformation
+
+        Parameters
+        ----------
+        coordinates : pytorch tensor of `dtype`
+            The xyz coordinates in shape (atoms, 3).
+        species : list of str
+            The list specifying the species of each atom. The length of the
+            list must be the same as the number of atoms.
+
+        Returns
+        -------
+        pytorch tensor of `dtype`
+            The full AEV for all atoms in shape (atoms, `radial_length()+angular_length()`)
+        """
+        atoms = coordinates.shape[0]
+        mol = ase.Atoms(''.join(species), positions=coordinates)
+        mol.set_calculator(ase_interface.ANI(False))
+        mol.calc.setnc(self.nc)
+        _ = mol.get_potential_energy()
+        aevs = [self.nc.atomicenvironments(j) for j in range(atoms)]
+        aevs = numpy.stack(aevs)
+        return aevs
+
+    def __call__(self, coordinates, species):
+        conformations = coordinates.shape[0]
+        aevs = [self._compute_neurochem_aevs_per_conformation(
+            coordinates[i], species) for i in range(conformations)]
+        aevs = torch.from_numpy(numpy.stack(aevs)).type(
+            self.dtype).to(self.device)
+        return self._get_radial_part(aevs), self._get_angular_part(aevs)

torchani/nn.py

+from .aev_base import AEVComputer
+import torch
+import bz2
+import os
+import lark
+import struct
+import copy
+import math
+from . import buildin_network_dir, buildin_model_prefix
+from .benchmarked import BenchmarkedModule
+
+# For python 2 compatibility
+if not hasattr(math, 'inf'):
+    math.inf = float('inf')
+
+
+class PerSpeciesFromNeuroChem(torch.jit.ScriptModule):
+    """Subclass of `torch.nn.Module` for the per atom aev->y transformation, loaded from NeuroChem network dir.
+
+    Attributes
+    ----------
+    dtype : torch.dtype
+        Pytorch data type for tensors
+    device : torch.Device
+        The device where tensors should be.
+    layers : int
+        Number of layers.
+    output_length : int
+        The length of output vector
+    layerN : torch.nn.Linear
+        Linear model for each layer.
+    activation : function
+        Function for computing the activation for all layers but the last layer.
+    activation_index : int
+        The NeuroChem index for activation.
+    """
+
+    def __init__(self, dtype, device, filename):
+        """Initialize from NeuroChem network directory.
+
+        Parameters
+        ----------
+        dtype : torch.dtype
+            Pytorch data type for tensors
+        filename : string
+            The file name for the `.nnf` file that store network hyperparameters. The `.bparam` and `.wparam`
+            must be in the same directory
+        """
+        super(PerSpeciesFromNeuroChem, self).__init__()
+
+        self.dtype = dtype
+        self.device = device
+        networ_dir = os.path.dirname(filename)
+        with open(filename, 'rb') as f:
+            buffer = f.read()
+            buffer = self._decompress(buffer)
+            layer_setups = self._parse(buffer)
+            self._construct(layer_setups, networ_dir)
+
+    def _decompress(self, buffer):
+        """Decompress the `.nnf` file
+
+        Parameters
+        ----------
+        buffer : bytes
+            The buffer storing the whole compressed `.nnf` file content.
+
+        Returns
+        -------
+        string
+            The string storing the whole decompressed `.nnf` file content.
+        """
+        # decompress nnf file
+        while buffer[0] != b'='[0]:
+            buffer = buffer[1:]
+        buffer = buffer[2:]
+        return bz2.decompress(buffer)[:-1].decode('ascii').strip()
+
+    def _parse(self, nnf_file):
+        """Parse the `.nnf` file
+
+        Parameters
+        ----------
+        nnf_file : string
+            The string storing the while decompressed `.nnf` file content.
+
+        Returns
+        -------
+        list of dict
+            Parsed setups as list of dictionary storing the parsed `.nnf` file content.
+            Each dictionary in the list is the hyperparameters for a layer.
+        """
+        # parse input file
+        parser = lark.Lark(r'''
+        identifier : CNAME
+
+        inputsize : "inputsize" "=" INT ";"
+
+        assign : identifier "=" value ";"
+
+        layer : "layer" "[" assign * "]"
+
+        atom_net : "atom_net" WORD "$" layer * "$"
+
+        start: inputsize atom_net
+
+        value : INT
+              | FLOAT
+              | "FILE" ":" FILENAME "[" INT "]"
+
+        FILENAME : ("_"|"-"|"."|LETTER|DIGIT)+
+
+        %import common.SIGNED_NUMBER
+        %import common.LETTER
+        %import common.WORD
+        %import common.DIGIT
+        %import common.INT
+        %import common.FLOAT
+        %import common.CNAME
+        %import common.WS
+        %ignore WS
+        ''')
+        tree = parser.parse(nnf_file)
+
+        # execute parse tree
+        class TreeExec(lark.Transformer):
+
+            def identifier(self, v):
+                v = v[0].value
+                return v
+
+            def value(self, v):
+                if len(v) == 1:
+                    v = v[0]
+                    if v.type == 'FILENAME':
+                        v = v.value
+                    elif v.type == 'INT':
+                        v = int(v.value)
+                    elif v.type == 'FLOAT':
+                        v = float(v.value)
+                    else:
+                        raise ValueError('unexpected type')
+                elif len(v) == 2:
+                    v = self.value([v[0]]), self.value([v[1]])
+                else:
+                    raise ValueError('length of value can only be 1 or 2')
+                return v
+
+            def assign(self, v):
+                name = v[0]
+                value = v[1]
+                return name, value
+
+            def layer(self, v):
+                return dict(v)
+
+            def atom_net(self, v):
+                layers = v[1:]
+                return layers
+
+            def start(self, v):
+                return v[1]
+
+        layer_setups = TreeExec().transform(tree)
+        return layer_setups
+
+    def _construct(self, setups, dirname):
+        """Construct model from parsed setups
+
+        Parameters
+        ----------
+        setups : list of dict
+            Parsed setups as list of dictionary storing the parsed `.nnf` file content.
+            Each dictionary in the list is the hyperparameters for a layer.
+        dirname : string
+            The directory where network files are stored.
+        """
+
+        # Activation defined in:
+        # https://github.com/Jussmith01/NeuroChem/blob/master/src-atomicnnplib/cunetwork/cuannlayer_t.cu#L868
+        self.activation_index = None
+        self.activation = None
+        self.layers = len(setups)
+        for i in range(self.layers):
+            s = setups[i]
+            in_size = s['blocksize']
+            out_size = s['nodes']
+            activation = s['activation']
+            wfn, wsz = s['weights']
+            bfn, bsz = s['biases']
+            if i == self.layers-1:
+                if activation != 6:  # no activation
+                    raise ValueError('activation in the last layer must be 6')
+            else:
+                if self.activation_index is None:
+                    self.activation_index = activation
+                    if activation == 5:  # Gaussian
+                        self.activation = lambda x: torch.exp(-x*x)
+                    elif activation == 9:  # CELU
+                        alpha = 0.1
+                        self.activation = lambda x: torch.where(
+                            x > 0, x, alpha * (torch.exp(x/alpha)-1))
+                    else:
+                        raise NotImplementedError(
+                            'Unexpected activation {}'.format(activation))
+                elif self.activation_index != activation:
+                    raise NotImplementedError(
+                        'different activation on different layers are not supported')
+            linear = torch.nn.Linear(in_size, out_size).type(self.dtype)
+            name = 'layer{}'.format(i)
+            setattr(self, name, linear)
+            if in_size * out_size != wsz or out_size != bsz:
+                raise ValueError('bad parameter shape')
+            wfn = os.path.join(dirname, wfn)
+            bfn = os.path.join(dirname, bfn)
+            self.output_length = out_size
+            self._load_param_file(linear, in_size, out_size, wfn, bfn)
+
+    def _load_param_file(self, linear, in_size, out_size, wfn, bfn):
+        """Load `.wparam` and `.bparam` files"""
+        wsize = in_size * out_size
+        fw = open(wfn, 'rb')
+        w = struct.unpack('{}f'.format(wsize), fw.read())
+        w = torch.tensor(w, dtype=self.dtype, device=self.device).view(
+            out_size, in_size)
+        linear.weight = torch.nn.parameter.Parameter(w, requires_grad=True)
+        fw.close()
+        fb = open(bfn, 'rb')
+        b = struct.unpack('{}f'.format(out_size), fb.read())
+        b = torch.tensor(b, dtype=self.dtype,
+                         device=self.device).view(out_size)
+        linear.bias = torch.nn.parameter.Parameter(b, requires_grad=True)
+        fb.close()
+
+    def get_activations(self, aev, layer):
+        """Compute the activation of the specified layer.
+
+        Parameters
+        ----------
+        aev : torch.Tensor
+            The pytorch tensor of shape (conformations, aev_length) storing AEV as input to this model.
+        layer : int
+            The layer whose activation is desired. The index starts at zero, that is
+            `layer=0` means the `activation(layer0(aev))` instead of `aev`. If the given
+            layer is larger than the total number of layers, then the activation of the last
+            layer will be returned.
+
+        Returns
+        -------
+        torch.Tensor
+            The pytorch tensor of activations of specified layer.
+        """
+        y = aev
+        for j in range(self.layers-1):
+            linear = getattr(self, 'layer{}'.format(j))
+            y = linear(y)
+            y = self.activation(y)
+            if j == layer:
+                break
+        if layer >= self.layers-1:
+            linear = getattr(self, 'layer{}'.format(self.layers-1))
+            y = linear(y)
+        return y
+
+    def forward(self, aev):
+        """Compute output from aev
+
+        Parameters
+        ----------
+        aev : torch.Tensor
+            The pytorch tensor of shape (conformations, aev_length) storing AEV as input to this model.
+
+        Returns
+        -------
+        torch.Tensor
+            The pytorch tensor of shape (conformations, output_length) for output.
+        """
+        return self.get_activations(aev, math.inf)
+
+
+class ModelOnAEV(BenchmarkedModule):
+    """Subclass of `torch.nn.Module` for the [xyz]->[aev]->[per_atom_y]->y pipeline.
+
+    Attributes
+    ----------
+    aev_computer : AEVComputer
+        The AEV computer.
+    output_length : int
+        The length of output vector
+    derivative : boolean
+        Whether to support computing the derivative w.r.t coordinates, i.e. d(output)/dR
+    derivative_graph : boolean
+        Whether to generate a graph for the derivative. This would be required only if the
+        derivative is included as part of the loss function.
+    model_X : nn.Module
+        Model for species X. There should be one such attribute for each supported species.
+    reducer : function
+        Function of (input, dim)->output that reduce the input tensor along the given dimension
+        to get an output tensor. This function will be called with the per atom output tensor
+        with internal shape as input, and desired reduction dimension as dim, and should reduce
+        the input into the tensor containing desired output.
+    timers : dict
+        Dictionary storing the the benchmark result. It has the following keys:
+            aev : time spent on computing AEV.
+            nn : time spent on computing output from AEV.
+            derivative : time spend on computing derivative w.r.t. coordinates after the outputs
+                is given. This key is only available if derivative computation is turned on.
+            forward : total time for the forward pass
+    """
+
+    def __init__(self, aev_computer, derivative=False, derivative_graph=False, benchmark=False, **kwargs):
+        """Initialize object from manual setup or from NeuroChem network directory.
+
+        The caller must set either `from_nc` in order to load from NeuroChem network directory,
+        or set `per_species` and `reducer`.
+
+        Parameters
+        ----------
+        aev_computer : AEVComputer
+            The AEV computer.
+        derivative : boolean
+            Whether to support computing the derivative w.r.t coordinates, i.e. d(output)/dR
+        derivative_graph : boolean
+            Whether to generate a graph for the derivative. This would be required only if the
+            derivative is included as part of the loss function. This argument must be set to
+            False if `derivative` is set to False.
+        benchmark : boolean
+            Whether to enable benchmarking
+
+        Other Parameters
+        ----------------
+        from_nc : string
+            Path to the NeuroChem network directory. If this parameter is set, then `per_species` and
+            `reducer` should not be set. If set to `None`, then the network ship with torchani will be
+            used.
+        ensemble : int
+            Number of models in the model ensemble. If this is not set, then `from_nc` would refer to
+            the directory storing the model. If set to a number, then `from_nc` would refer to the prefix
+            of directories.
+        per_species : dict
+            Dictionary with supported species as keys and objects of `torch.nn.Model` as values, storing
+            the model for each supported species. These models will finally become `model_X` attributes.
+        reducer : function
+            The desired `reducer` attribute.
+
+        Raises
+        ------
+        ValueError
+            If `from_nc`, `per_species`, and `reducer` are not properly set.
+        """
+
+        super(ModelOnAEV, self).__init__(benchmark)
+        self.derivative = derivative
+        self.output_length = None
+        if not derivative and derivative_graph:
+            raise ValueError(
+                'ModelOnAEV: can not create graph for derivative if the computation of derivative is turned off')
+        self.derivative_graph = derivative_graph
+
+        if benchmark:
+            self.compute_aev = self._enable_benchmark(self.compute_aev, 'aev')
+            self.aev_to_output = self._enable_benchmark(
+                self.aev_to_output, 'nn')
+            if derivative:
+                self.compute_derivative = self._enable_benchmark(
+                    self.compute_derivative, 'derivative')
+            self.forward = self._enable_benchmark(self.forward, 'forward')
+
+        if not isinstance(aev_computer, AEVComputer):
+            raise TypeError(
+                "ModelOnAEV: aev_computer must be a subclass of AEVComputer")
+        self.aev_computer = aev_computer
+
+        if 'from_nc' in kwargs and 'per_species' not in kwargs and 'reducer' not in kwargs:
+            if 'ensemble' not in kwargs:
+                if kwargs['from_nc'] is None:
+                    kwargs['from_nc'] = buildin_network_dir
+                network_dirs = [kwargs['from_nc']]
+                self.suffixes = ['']
+            else:
+                if kwargs['from_nc'] is None:
+                    kwargs['from_nc'] = buildin_model_prefix
+                network_prefix = kwargs['from_nc']
+                network_dirs = []
+                self.suffixes = []
+                for i in range(kwargs['ensemble']):
+                    suffix = '{}'.format(i)
+                    network_dir = os.path.join(
+                        network_prefix+suffix, 'networks')
+                    network_dirs.append(network_dir)
+                    self.suffixes.append(suffix)
+
+            self.reducer = torch.sum
+            for network_dir, suffix in zip(network_dirs, self.suffixes):
+                for i in self.aev_computer.species:
+                    filename = os.path.join(
+                        network_dir, 'ANN-{}.nnf'.format(i))
+                    model_X = PerSpeciesFromNeuroChem(
+                        self.aev_computer.dtype, self.aev_computer.device, filename)
+                    if self.output_length is None:
+                        self.output_length = model_X.output_length
+                    elif self.output_length != model_X.output_length:
+                        raise ValueError(
+                            'output length of each atomic neural network must match')
+                    setattr(self, 'model_' + i + suffix, model_X)
+        elif 'from_nc' not in kwargs and 'per_species' in kwargs and 'reducer' in kwargs:
+            self.suffixes = ['']
+            per_species = kwargs['per_species']
+            for i in per_species:
+                model_X = per_species[i]
+                if not hasattr(model_X, 'output_length'):
+                    raise ValueError(
+                        'atomic neural network must explicitly specify output length')
+                elif self.output_length is None:
+                    self.output_length = model_X.output_length
+                elif self.output_length != model_X.output_length:
+                    raise ValueError(
+                        'output length of each atomic neural network must match')
+                setattr(self, 'model_' + i, model_X)
+            self.reducer = kwargs['reducer']
+        else:
+            raise ValueError(
+                'ModelOnAEV: bad arguments when initializing ModelOnAEV')
+
+        if derivative and self.output_length != 1:
+            raise ValueError(
+                'derivative can only be computed for output length 1')
+
+    def compute_aev(self, coordinates, species):
+        """Compute full AEV
+
+        Parameters
+        ----------
+        coordinates : torch.Tensor
+            The pytorch tensor of shape (conformations, atoms, 3) storing
+            the coordinates of all atoms of all conformations.
+        species : list of string
+            List of string storing the species for each atom.
+
+        Returns
+        -------
+        torch.Tensor
+            Pytorch tensor of shape (conformations, atoms, aev_length) storing
+            the computed AEVs.
+        """
+        radial_aev, angular_aev = self.aev_computer(coordinates, species)
+        fullaev = torch.cat([radial_aev, angular_aev], dim=2)
+        return fullaev
+
+    def aev_to_output(self, aev, species):
+        """Compute output from aev
+
+        Parameters
+        ----------
+        aev : torch.Tensor
+            Pytorch tensor of shape (conformations, atoms, aev_length) storing
+            the computed AEVs.
+        species : list of string
+            List of string storing the species for each atom.
+
+        Returns
+        -------
+        torch.Tensor
+            Pytorch tensor of shape (conformations, output_length) for the
+            output of each conformation.
+        """
+        conformations = aev.shape[0]
+        atoms = len(species)
+        rev_species = species[::-1]
+        species_dedup = sorted(
+            set(species), key=self.aev_computer.species.index)
+        per_species_outputs = []
+        for s in species_dedup:
+            begin = species.index(s)
+            end = atoms - rev_species.index(s)
+            y = aev[:, begin:end, :].contiguous(
+            ).view(-1, self.aev_computer.aev_length)
+
+            def apply_model(suffix):
+                model_X = getattr(self, 'model_' + s + suffix)
+                return model_X(y)
+            ys = [apply_model(suffix) for suffix in self.suffixes]
+            y = sum(ys) / len(ys)
+            y = y.view(conformations, -1, self.output_length)
+            per_species_outputs.append(y)
+
+        per_species_outputs = torch.cat(per_species_outputs, dim=1)
+        molecule_output = self.reducer(per_species_outputs, dim=1)
+        return molecule_output
+
+    def compute_derivative(self, output, coordinates):
+        """Compute the gradient d(output)/d(coordinates)"""
+        # Since different conformations are independent, computing
+        # the derivatives of all outputs w.r.t. its own coordinate is equivalent
+        # to compute the derivative of the sum of all outputs w.r.t. all coordinates.
+        return torch.autograd.grad(output.sum(), coordinates, create_graph=self.derivative_graph)[0]
+
+    def forward(self, coordinates, species):
+        """Feed forward
+
+        Parameters
+        ----------
+        coordinates : torch.Tensor
+            The pytorch tensor of shape (conformations, atoms, 3) storing
+            the coordinates of all atoms of all conformations.
+        species : list of string
+            List of string storing the species for each atom.
+
+        Returns
+        -------
+        torch.Tensor or (torch.Tensor, torch.Tensor)
+            If derivative is turned off, then this function will return a pytorch
+            tensor of shape (conformations, output_length) for the output of each
+            conformation.
+            If derivative is turned on, then this function will return a pair of
+            pytorch tensors where the first tensor is the output tensor as when the
+            derivative is off, and the second tensor is a tensor of shape
+            (conformation, atoms, 3) storing the d(output)/dR.
+        """
+        if not self.derivative:
+            coordinates = coordinates.detach()
+        else:
+            coordinates = torch.tensor(coordinates, requires_grad=True)
+        _coordinates, _species = self.aev_computer.sort_by_species(
+            coordinates, species)
+        aev = self.compute_aev(_coordinates, _species)
+        output = self.aev_to_output(aev, _species)
+        if not self.derivative:
+            return output
+        else:
+            derivative = self.compute_derivative(output, coordinates)
+            return output, derivative
+
+    def export_onnx(self, dirname):
+        """Export atomic networks into onnx format
+
+        Parameters
+        ----------
+        dirname : string
+            Name of the directory to store exported networks.
+        """
+
+        aev_length = self.aev_computer.aev_length
+        dummy_aev = torch.zeros(1, aev_length)
+        for s in self.aev_computer.species:
+            nn_onnx = os.path.join(dirname, '{}.proto'.format(s))
+            model_X = getattr(self, 'model_' + s)
+            torch.onnx.export(model_X, dummy_aev, nn_onnx)

torchani/pyanitools.py

+# Written by Roman Zubatyuk and Justin S. Smith
+import h5py
+import numpy as np
+import platform
+import os
+
+
+PY_VERSION = int(platform.python_version().split('.')[0]) > 3
+
+
+class datapacker(object):
+    def __init__(self, store_file, mode='w-', complib='gzip', complevel=6):
+        """Wrapper to store arrays within HFD5 file
+        """
+        # opening file
+        self.store = h5py.File(store_file, mode=mode)
+        self.clib = complib
+        self.clev = complevel
+
+    def store_data(self, store_loc, **kwargs):
+        """Put arrays to store
+        """
+        # print(store_loc)
+        g = self.store.create_group(store_loc)
+        for k, v, in kwargs.items():
+            # print(type(v[0]))
+
+            # print(k)
+            if type(v) == list:
+                if len(v) != 0:
+                    if type(v[0]) is np.str_ or type(v[0]) is str:
+                        v = [a.encode('utf8') for a in v]
+
+            g.create_dataset(k, data=v, compression=self.clib,
+                             compression_opts=self.clev)
+
+    def cleanup(self):
+        """Wrapper to close HDF5 file
+        """
+        self.store.close()
+
+
+class anidataloader(object):
+
+    ''' Contructor '''
+
+    def __init__(self, store_file):
+        if not os.path.exists(store_file):
+            exit('Error: file not found - '+store_file)
+        self.store = h5py.File(store_file, 'r')
+
+    ''' Group recursive iterator (iterate through all groups in all branches and return datasets in dicts) '''
+
+    def h5py_dataset_iterator(self, g, prefix=''):
+        for key in g.keys():
+            item = g[key]
+            path = '{}/{}'.format(prefix, key)
+            keys = [i for i in item.keys()]
+            if isinstance(item[keys[0]], h5py.Dataset):  # test for dataset
+                data = {'path': path}
+                for k in keys:
+                    if not isinstance(item[k], h5py.Group):
+                        dataset = np.array(item[k].value)
+
+                        if type(dataset) is np.ndarray:
+                            if dataset.size != 0:
+                                if type(dataset[0]) is np.bytes_:
+                                    dataset = [a.decode('ascii')
+                                               for a in dataset]
+
+                        data.update({k: dataset})
+
+                yield data
+            else:  # test for group (go down)
+                yield from self.h5py_dataset_iterator(item, path)
+
+    ''' Default class iterator (iterate through all data) '''
+
+    def __iter__(self):
+        for data in self.h5py_dataset_iterator(self.store):
+            yield data
+
+    ''' Returns a list of all groups in the file '''
+
+    def get_group_list(self):
+        return [g for g in self.store.values()]
+
+    ''' Allows interation through the data in a given group '''
+
+    def iter_group(self, g):
+        for data in self.h5py_dataset_iterator(g):
+            yield data
+
+    ''' Returns the requested dataset '''
+
+    def get_data(self, path, prefix=''):
+        item = self.store[path]
+        path = '{}/{}'.format(prefix, path)
+        keys = [i for i in item.keys()]
+        data = {'path': path}
+        # print(path)
+        for k in keys:
+            if not isinstance(item[k], h5py.Group):
+                dataset = np.array(item[k].value)
+
+                if type(dataset) is np.ndarray:
+                    if dataset.size != 0:
+                        if type(dataset[0]) is np.bytes_:
+                            dataset = [a.decode('ascii') for a in dataset]
+
+                data.update({k: dataset})
+        return data
+
+    ''' Returns the number of groups '''
+
+    def group_size(self):
+        return len(self.get_group_list())
+
+    def size(self):
+        count = 0
+        for g in self.store.values():
+            count = count + len(g.items())
+        return count
+
+    ''' Close the HDF5 file '''
+
+    def cleanup(self):
+        self.store.close()

torchani/resources/ani-1x_dft_x8ens/rHCNO-5.2R_16-3.5A_a4-8.params

+TM = 1
+Rcr = 5.2000e+00
+Rca = 3.5000e+00
+EtaR = [1.6000000e+01]
+ShfR = [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]
+Zeta = [3.2000000e+01]
+ShfZ = [1.9634954e-01,5.8904862e-01,9.8174770e-01,1.3744468e+00,1.7671459e+00,2.1598449e+00,2.5525440e+00,2.9452431e+00]
+EtaA = [8.0000000e+00]
+ShfA = [9.0000000e-01,1.5500000e+00,2.2000000e+00,2.8500000e+00]
+Atyp = [H,C,N,O]

torchani/resources/ani-1x_dft_x8ens/sae_linfit.dat

+H,0=-0.600952980000
+C,1=-38.08316124000
+N,2=-54.70775770000
+O,3=-75.19446356000

torchani/resources/ani-1x_dft_x8ens/train0/cost.dat

+1 0.00215381 0.000651299 1.79769e+308 1.79769e+308 inf inf
+2 0.000343021 0.000438866 1.79769e+308 1.79769e+308 inf inf
+3 0.000195355 0.000251078 0.000438866 1.79769e+308 13.1458 inf
+4 0.000158185 0.000184984 0.000251078 1.79769e+308 9.94316 inf
+5 0.000107284 0.000153077 0.000184984 1.79769e+308 8.53469 inf
+6 0.00601522 0.00038403 0.000153077 1.79769e+308 7.76383 inf
+7 0.000266499 0.000241021 0.000153077 1.79769e+308 7.76383 inf
+8 0.000236914 0.000199804 0.000153077 1.79769e+308 7.76383 inf
+9 0.000165818 0.00016792 0.000153077 1.79769e+308 7.76383 inf
+10 0.000158702 0.000154047 0.000153077 1.79769e+308 7.76383 inf
+11 0.000106782 0.000128321 0.000153077 1.79769e+308 7.76383 inf
+12 8.44243e-05 0.000116677 0.000128321 1.79769e+308 7.10835 inf
+13 0.00135905 0.000253787 0.000116677 1.79769e+308 6.77819 inf
+14 0.000140052 0.000220903 0.000116677 1.79769e+308 6.77819 inf
+15 0.000116882 0.000140909 0.000116677 1.79769e+308 6.77819 inf
+16 8.96803e-05 0.00012854 0.000116677 1.79769e+308 6.77819 inf
+17 7.88749e-05 0.000114086 0.000116677 1.79769e+308 6.77819 inf
+18 6.7099e-05 9.12768e-05 0.000114086 1.79769e+308 6.70248 inf
+19 5.71614e-05 9.10224e-05 9.12768e-05 1.79769e+308 5.99516 inf
+20 0.00025057 0.000125654 9.10224e-05 1.79769e+308 5.9868 inf
+21 6.2611e-05 9.311e-05 9.10224e-05 1.79769e+308 5.9868 inf
+22 5.47838e-05 8.82776e-05 9.10224e-05 1.79769e+308 5.9868 inf
+23 4.87376e-05 0.00025737 8.82776e-05 1.79769e+308 5.89584 inf
+24 4.41331e-05 7.20951e-05 8.82776e-05 1.79769e+308 5.89584 inf
+25 0.000269606 0.00010794 7.20951e-05 1.79769e+308 5.32811 inf
+26 5.92139e-05 8.65903e-05 7.20951e-05 1.79769e+308 5.32811 inf
+27 5.42871e-05 8.44167e-05 7.20951e-05 1.79769e+308 5.32811 inf
+28 4.8119e-05 7.63317e-05 7.20951e-05 1.79769e+308 5.32811 inf
+29 4.45345e-05 7.27833e-05 7.20951e-05 1.79769e+308 5.32811 inf
+30 0.000114552 7.21742e-05 7.20951e-05 1.79769e+308 5.32811 inf
+31 4.21754e-05 7.47768e-05 7.20951e-05 1.79769e+308 5.32811 inf
+32 3.95117e-05 6.41902e-05 7.20951e-05 1.79769e+308 5.32811 inf
+33 3.79761e-05 6.07997e-05 6.41902e-05 1.79769e+308 5.02753 inf
+34 3.53293e-05 6.12195e-05 6.07997e-05 1.79769e+308 4.89295 inf
+35 0.000112355 7.22487e-05 6.07997e-05 1.79769e+308 4.89295 inf
+36 3.78325e-05 6.49368e-05 6.07997e-05 1.79769e+308 4.89295 inf
+37 3.61781e-05 6.24098e-05 6.07997e-05 1.79769e+308 4.89295 inf
+38 3.42333e-05 5.62535e-05 6.07997e-05 1.79769e+308 4.89295 inf
+39 3.11644e-05 7.35091e-05 5.62535e-05 1.79769e+308 4.70647 inf
+40 7.0266e-05 6.14018e-05 5.62535e-05 1.79769e+308 4.70647 inf
+41 2.97229e-05 5.11057e-05 5.62535e-05 1.79769e+308 4.70647 inf
+42 3.00984e-05 5.54161e-05 5.11057e-05 1.79769e+308 4.48595 inf
+43 2.85385e-05 4.73022e-05 5.11057e-05 1.79769e+308 4.48595 inf
+44 2.77921e-05 4.79044e-05 4.73022e-05 1.79769e+308 4.3158 inf
+45 8.48964e-05 5.26919e-05 4.73022e-05 1.79769e+308 4.3158 inf
+46 2.66808e-05 4.53825e-05 4.73022e-05 1.79769e+308 4.3158 inf
+47 2.64211e-05 4.44367e-05 4.53825e-05 1.79769e+308 4.22731 inf
+48 2.79506e-05 4.38053e-05 4.44367e-05 1.79769e+308 4.18303 inf
+49 2.56196e-05 9.71927e-05 4.38053e-05 1.79769e+308 4.15321 inf
+50 2.98491e-05 4.57566e-05 4.38053e-05 1.79769e+308 4.15321 inf
+51 2.44843e-05 4.64266e-05 4.38053e-05 1.79769e+308 4.15321 inf
+52 2.38946e-05 4.21199e-05 4.38053e-05 1.79769e+308 4.15321 inf
+53 0.000126411 5.21673e-05 4.21199e-05 1.79769e+308 4.07253 inf
+54 2.21531e-05 4.45517e-05 4.21199e-05 1.79769e+308 4.07253 inf
+55 2.34767e-05 4.16752e-05 4.21199e-05 1.79769e+308 4.07253 inf
+56 2.29703e-05 6.00544e-05 4.16752e-05 1.79769e+308 4.05097 inf
+57 2.33132e-05 4.17647e-05 4.16752e-05 1.79769e+308 4.05097 inf
+58 2.22999e-05 3.79163e-05 4.16752e-05 1.79769e+308 4.05097 inf
+59 4.93435e-05 4.21772e-05 3.79163e-05 1.79769e+308 3.86397 inf
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torchani/resources/ani-1x_dft_x8ens/train0/inputtrain.ipt

+!InputFile for Force Prediction Network
+sflparamsfile=rHCNO-5.2R_16-3.5A_a4-8.params
+ntwkStoreDir=networks/
+atomEnergyFile=sae_linfit.dat
+nmax=0! Maximum number of iterations (0 = inf)
+tolr=100! Tolerance - early stopping
+emult=0.5!Multiplier by eta after tol switch
+eta=0.001! Eta -- Learning rate
+tcrit=1.0E-5! Eta termination criterion
+tmax=0! Maximum time (0 = inf)
+tbtchsz=2560
+vbtchsz=2560
+gpuid=0
+ntwshr=0
+nkde=2
+energy=1
+force=0
+fmult=0.0
+pbc=0
+cmult =0.001
+runtype=ANNP_CREATE_HDNN_AND_TRAIN!Create and train a HDN network
+network_setup {
+  inputsize=384;
+  atom_net H $
+    layer [
+          nodes=160;
+          activation=9;
+          type=0;
+          l2norm=1;
+          l2valu=0.0001;
+    ]
+    layer [
+          nodes=128;
+          activation=9;
+          type=0;
+          l2norm=1;
+          l2valu=0.00001;
+    ]
+    layer [
+          nodes=96;
+          activation=9;
+          type=0;
+          l2norm=1;
+          l2valu=0.000001;
+    ]
+    layer [
+          nodes=1;
+          activation=6;
+          type=0;
+    ]
+    $
+  atom_net C $
+    layer [
+          nodes=144;
+          activation=9;
+          type=0;
+          l2norm=1;
+          l2valu=0.0001;
+    ]
+    layer [
+          nodes=112;
+          activation=9;
+          type=0;
+          l2norm=1;
+          l2valu=0.00001;
+    ]
+    layer [
+          nodes=96;
+          activation=9;
+          type=0;
+          l2norm=1;
+          l2valu=0.000001;
+    ]
+    layer [
+          nodes=1;
+          activation=6;
+          type=0;
+    ]
+    $
+  atom_net N $
+    layer [
+          nodes=128;
+          activation=9;
+          type=0;
+          l2norm=1;
+          l2valu=0.0001;
+    ]
+    layer [
+          nodes=112;
+          activation=9;
+          type=0;
+          l2norm=1;
+          l2valu=0.00001;
+    ]
+    layer [
+          nodes=96;
+          activation=9;
+          type=0;
+          l2norm=1;
+          l2valu=0.000001;
+    ]
+    layer [
+          nodes=1;
+          activation=6;
+          type=0;
+    ]
+    $
+  atom_net O $
+    layer [
+          nodes=128;
+          activation=9;
+          type=0;
+          l2norm=1;
+          l2valu=0.0001;
+    ]
+    layer [
+          nodes=112;
+          activation=9;
+          type=0;
+          l2norm=1;
+          l2valu=0.00001;
+    ]
+    layer [
+          nodes=96;
+          activation=9;
+          type=0;
+          l2norm=1;
+          l2valu=0.000001;
+    ]
+    layer [
+          nodes=1;
+          activation=6;
+          type=0;
+    ]
+    $
+
+}
+adptlrn=OFF ! Adaptive learning (OFF,RMSPROP)
+decrate=0.9 !Decay rate of RMSPROP
+moment=ADAM! Turn on momentum or nesterov momentum (OFF,CNSTTEMP,TMANNEAL,REGULAR,NESTEROV)
+mu=0.99 ! Mu factor for momentum
+