Tensornet representation #167

MarshallYan · 2024-06-24T00:05:11Z

Description

Implement modelforge TensorNetRepresentation module, which matches the output of TensorEmbedding forward. In original implementation, TensorEmbedding is used by TensorNet class, where forward is called whenever updating TensorNet. The output of TensorEmbedding corresponds to "X" tensor in the TensorNet paper.

Todos

Set up TensorNet embedding module
Implement modelforge TensorNetRepresentation
Rename parameters from TensorNet with more intuitive names
Match output X tensors

Questions

static_shapes
What does the steps mean when static_shapes==True?

Status

Ready to go

…y, the array of atomic species was assumed to be unique; now we consider the order of the species array as well. Only entries with the same atomic species array that appear in order are considered to be the same molecule for purposes of grouping conformers. That is, if the same species array is encountered later in the array, it is treated as a new molecule. Molecule names are not just the string made from their species array, but also have a molecule number appended to them.

…ce 1 datasets.

…-curation

This revises how unique molecules are determined for ANI2x and updates dataset yaml files.

Merge macOS environments

MarshallYan · 2024-06-26T19:42:44Z

@wiederm
How edge_index, edge_vec, edge_weight are calculated are uncertain. The problem being:

edge_weight may not have units as distances, which causes confusion in the unit transformations in RSF.
The calculation is defined in torchmdnet.extensions.__init__, where torch.ops.torchmdnet_extensions.get_neighbor_pairs is called. I am unable to further trace back how this function is implemented.

MarshallYan · 2024-06-28T04:57:11Z

@wiederm
It seems to me that input wrappers (NNPInput and *NeuralNetworkData) are trying to generalize the information needed to form a model in separated layers. However, I can't find where box and batch in TensorNet should be put. Which data structure should be modified? Or how do we manage input that couldn't be generalized in designed ways?

Just as a reminder, edge_index, edge_weight, edge_vec = self.distance(pos, batch, box) is in the forward function of class TensorNet in torchmd-net.

MarshallYan · 2024-06-30T04:53:13Z

@wiederm
TensorNet uses pairlist with duplicated permute indices (0-1 and 1-0 are both included) and has a different order of these indices. But other than that, it seems that edge_weight==d_ij; edge_vec==r_ij. But is there an easier way to verify that? torch.allclose doesn't work directly since they are not the same in terms of dimensions and order.

… v.s. nanometer in TensorNet)

MarshallYan · 2024-07-02T21:52:06Z

@wiederm
I added a representation unit system to calculate the model with both angstrom and nanometer. Angstrom is used to compare the results with TensorNet, while I am also curious whether using nanometer inside makes a difference during training.

The only change I made that may affects 'main' branch is that 'CosineCutoff' now has a 'representation_unit' parameter that is set default with 'unit.nanometer'. (So I would assume that this won't even change anything outside tensornet.)

Does ANI use nanometer by their original implementation? If not, how did you manage to test ANI without using some inside unit transformation?

wiederm · 2024-07-03T11:56:44Z

It seems to me that input wrappers (NNPInput and *NeuralNetworkData) are trying to generalize the information needed to form a model in separated layers. However, I can't find where box and batch in TensorNet should be put. Which data structure should be modified? Or how do we manage input that couldn't be generalized in designed ways?

We discussed this offline: edge_weight is the pairwise distance, and edge_vec is the pairwise distance vector. Box vector and batch are passed to the input and don't need to be generated. The data structure is the NNPInput dataclass.

TensorNet uses pairlist with duplicated permute indices

So does modelforge! All pairwise interactions are listed, that means every atom $i$ encounters every atom $j$ withing.a neighborhood.

wiederm · 2024-07-03T12:03:18Z

ANI use nanometer by their original implementation

No, ANI uses angstrom. Testing is pretty simple, if you have a class that implements some transformation you can simply do:

input_data_in_angstrom = torch.abs(torch.randn(5,1)) * 5 # generate random data on interval [0,5]
reference_implementation = RefClass()
modelforge_implementation = MfClass()

# the rbf is defined on an interval [min_distance, max_distance]. 
# you can convert between rbfs in different length units by scaling the rbfs, 
# therefore you can convert from nm to A by multiplying with 10   

assert torch.allclose(modelforge_implementation(input_data_in_angstrom/10)*10, reference_implementation(input_data_in_angstrom))

MarshallYan · 2024-07-03T18:33:47Z

@wiederm
I may found a bug in TensorNet. They did apply cutoff function twice in their code, which is inconsistent with their paper. I currently sticked to they way they coded it, but we may need a further discussion on this issue.

In class 'ExpNormalSmearing':

def forward(self, dist):
    dist = dist.unsqueeze(-1)
    return self.cutoff_fn(dist) * torch.exp(
        -self.betas
        * (torch.exp(self.alpha * (-dist + self.cutoff_lower)) - self.means) ** 2
    )

The output of this forward function is called 'edge_attr' in 'TensorEmbedding'. This should be $e_k^{RBF}$ in their paper, without cutoff function applied.

However, in 'TensorEmbedding':

def _get_tensor_messages(
    self, Zij: Tensor, edge_weight: Tensor, edge_vec_norm: Tensor, edge_attr: Tensor
) -> Tuple[Tensor, Tensor, Tensor]:
    C = self.cutoff(edge_weight).reshape(-1, 1, 1, 1) * Zij
    eye = torch.eye(3, 3, device=edge_vec_norm.device, dtype=edge_vec_norm.dtype)[
        None, None, ...
    ]
    Iij = self.distance_proj1(edge_attr)[..., None, None] * C * eye
    Aij = (
        self.distance_proj2(edge_attr)[..., None, None]
        * C
        * vector_to_skewtensor(edge_vec_norm)[..., None, :, :]
    )
    Sij = (
        self.distance_proj3(edge_attr)[..., None, None]
        * C
        * vector_to_symtensor(edge_vec_norm)[..., None, :, :]
    )
    return Iij, Aij, Sij

Cutoff function C is timed again to I, A, and S.

MarshallYan · 2024-07-03T18:52:14Z

@wiederm
The other thing that bothers me is that mathematically, Iij shouldn't be initialized with identity matrix, rather, it should be $\frac{1}{3}\rVert v\lVert^2\cdot I$ based on my calculation.

@wiederm I may found a bug in TensorNet. They did apply cutoff function twice in their code, which is inconsistent with their paper. I currently sticked to they way they coded it, but we may need a further discussion on this issue.

In class 'ExpNormalSmearing':
def forward(self, dist):
    dist = dist.unsqueeze(-1)
    return self.cutoff_fn(dist) * torch.exp(
        -self.betas
        * (torch.exp(self.alpha * (-dist + self.cutoff_lower)) - self.means) ** 2
    )
The output of this forward function is called 'edge_attr' in 'TensorEmbedding'. This should be ekRBF in their paper, without cutoff function applied.

However, in 'TensorEmbedding':
def _get_tensor_messages(
    self, Zij: Tensor, edge_weight: Tensor, edge_vec_norm: Tensor, edge_attr: Tensor
) -> Tuple[Tensor, Tensor, Tensor]:
    C = self.cutoff(edge_weight).reshape(-1, 1, 1, 1) * Zij
    eye = torch.eye(3, 3, device=edge_vec_norm.device, dtype=edge_vec_norm.dtype)[
        None, None, ...
    ]
    Iij = self.distance_proj1(edge_attr)[..., None, None] * C * eye
    Aij = (
        self.distance_proj2(edge_attr)[..., None, None]
        * C
        * vector_to_skewtensor(edge_vec_norm)[..., None, :, :]
    )
    Sij = (
        self.distance_proj3(edge_attr)[..., None, None]
        * C
        * vector_to_symtensor(edge_vec_norm)[..., None, :, :]
    )
    return Iij, Aij, Sij
Cutoff function C is timed again to I, A, and S.

…rent output from nn.Linear)

MarshallYan · 2024-07-05T01:39:21Z

@wiederm
The pairwise indices used in torchmd-net is different from that in tensornet, resulting in different dimensions inputed into nn.Linear, causing different output.

In modelforge, if we have 3 atoms [0, 1, 2, 3], pair_indices is: [[0, 0, 0, 1, 1, 2], [1, 2, 3, 2, 3, 3]]; meanwhile, in torchmd-net, edge_index is: [[1, 2, 2, 3, 3, 3, 0, 0, 1, 0, 1, 2], [0, 0, 1, 0, 1, 2, 1, 2, 2, 3, 3, 3]]. It is not a big problem since I just need to attach the other way around to the end. However, the radial symmetry vector corresponding to the indices needs to be input into a nn.Linear layer, and in the previous example, the output contains 6 v.s. 12 atom pairs.

The real problem here is that in torchmd-net, since the first 6 pairs are the same as the last 6 pairs, I assume the output of that linear layer should also be the case (the first 6 tensors are the same as the last 6 tensors), while it's not the case. Thus in later summation, results from two ways are different.

One way that can definitely fix this problem is to convert the modelforge index system to the torchmd-net one inside representation module. But I think that may not be what we want in modelforge. We should discuss how we can solve the problem.

wiederm · 2024-07-05T16:15:03Z

Note the boolean set in the Pairlist:

modelforge/modelforge/potential/models.py

Line 56 in 5a312b0

def __init__(self, only_unique_pairs: bool = False):

If this is set to False (the default property) it will return the same number of pairs as the pairlist in tensornet. ANI is an outlier and requires this boolean to be set to True.

wiederm · 2024-07-05T16:18:55Z

I am not compley sure that I follow this:

The real problem here is that in torchmd-net, since the first 6 pairs are the same as the last 6 pairs, I assume the output of that linear layer should also be the case (the first 6 tensors are the same as the last 6 tensors), while it's not the case. Thus in later summation, results from two ways are different.

Can you provide a minimum example that examplifies your concern?

wiederm · 2024-07-05T16:19:58Z

If possible, it might be very useful to discuss this in a working test

…ensornet_representation # Conflicts: # modelforge/potential/tensornet.py # modelforge/tests/test_tensornet.py

MarshallYan · 2024-07-05T19:09:31Z

Note the boolean set in the Pairlist:

modelforge/modelforge/potential/models.py

Line 56 in 5a312b0

def __init__(self, only_unique_pairs: bool = False):

If this is set to False (the default property) it will return the same number of pairs as the pairlist in tensornet. ANI is an outlier and requires this boolean to be set to True.

Thanks! It has been fixed by now.

wiederm · 2024-07-08T11:27:41Z

modelforge/potential/tensornet.py

@@ -10,11 +14,23 @@
 from modelforge.potential.utils import TensorNetRadialSymmetryFunction
 from modelforge.potential.utils import NeuralNetworkData
 from modelforge.potential.utils import NNPInput
+from .models import PairListOutputs
+
+ATOMIC_NUMBER_TO_INDEX_MAP = {


I'd suggest importing this from potential/ani2x.py

wiederm · 2024-07-08T11:30:41Z

modelforge/potential/tensornet.py

+ self.rsf_projection_I = nn.Linear(
+ number_of_radial_basis_functions,
+ hidden_channels,
+ dtype=dtype,


you don't have to set dtype here. All parameters in sub-models are automatically registered as nn.parameters and device and type can be set for each of the parameter at runtime.

wiederm · 2024-07-08T11:32:53Z

modelforge/potential/tensornet.py

+ self.rsf_projection_A = nn.Linear(
+ number_of_radial_basis_functions,
+ hidden_channels,
+ dtype=dtype,
+ )
+ self.rsf_projection_S = nn.Linear(
+ number_of_radial_basis_functions,
+ hidden_channels,
+ dtype=dtype,
+ )
+ self.atomic_number_i_embedding_layer = nn.Embedding(
+ max_atomic_number,
+ hidden_channels,
+ dtype=dtype,
+ )
+ self.atomic_number_ij_embedding_layer = nn.Linear(
+ 2 * hidden_channels,
+ hidden_channels,
+ dtype=dtype,
+ )


it might make sense to use a ModuleDict here: https://pytorch.org/docs/stable/generated/torch.nn.ModuleDict.html

self.rsf_projections = ModuleDict( { A : n.Linear( number_of_radial_basis_functions, hidden_channels, dtype=dtype, ), B: ..., )

wiederm · 2024-07-08T11:37:35Z

modelforge/potential/tensornet.py

+ for _ in range(3):
+ self.linears_tensor.append(
+ nn.Linear(hidden_channels, hidden_channels, bias=False)
+ )


are you sure that for each lineare layer bias=False?

and no activation function is used?

I'd prefer not to use append operations for `ModuleList. It makes it difficult to identify all to components of the list at one glance. In that case I'd suggest to use something like:

self.linears_tensor = nn.ModuleList( [nn.Linear(hidden_channels, hidden_channels, bias=False) for _ in range(3)] )

maybe use [nn.Sequential] (https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html)?

and maybe the Dense implementation of modelforge?

wiederm · 2024-07-08T11:39:57Z

modelforge/potential/tensornet.py

+ self.linears_scalar.append(
+ nn.Linear(hidden_channels, 2 * hidden_channels, bias=True, dtype=dtype)
+ )
+ self.linears_scalar.append(
+ nn.Linear(2 * hidden_channels, 3 * hidden_channels, bias=True, dtype=dtype)
+ )
+ self.init_norm = nn.LayerNorm(hidden_channels, dtype=dtype)


You can write this as:

self.linear_layers_for_scalar_properties = nn.ModuleList( nn.Linear(hidden_channels, 2 * hidden_channels, bias=True), nn.Linear(2 * hidden_channels, 3 * hidden_channels, bias=True) )

which is slightly easier to read.

wiederm · 2024-07-08T12:04:27Z

modelforge/potential/utils.py

+ GaussianRadialBasisFunction(),
+ representation_unit: unit.Quantity = unit.angstrom,
+ ):
+ self.representation_unit = representation_unit


I don't think that this is necessary. All internal units are in nanometer, but for certain operations it might be useful to convert to angstrom.

wiederm · 2024-07-08T12:05:06Z

modelforge/potential/utils.py

+ _max_distance = self.max_distance.to(self.representation_unit).m
+ _min_distance = self.min_distance.to(self.representation_unit).m


convert everything to nanometer

wiederm · 2024-07-08T12:11:19Z

modelforge/potential/utils.py

 ):
 # TensorNet evenly distribute centers in log space
 start_value = torch.exp(
 torch.scalar_tensor(
- -_max_distance_in_nanometer*10 + _min_distance_in_nanometer*10, # in angstrom
+ -_max_distance + _min_distance,  # in angstrom


You can write this exactly as outlined here:

modelforge/modelforge/potential/utils.py

Line 810 in 5b0e9bc

def calculate_radial_basis_centers(

wiederm · 2024-07-08T12:21:17Z

modelforge/potential/utils.py

@@ -822,31 +857,35 @@ def calculate_radial_basis_centers(
 return centers

 def calculate_radial_scale_factor(


at first glance this should also match the PhysNetRadialBasisFunction. But please double check that claim!

wiederm · 2024-07-08T12:24:20Z

modelforge/potential/utils.py

+ _max_distance = self.max_distance.to(self.representation_unit).m
+ _min_distance = self.min_distance.to(self.representation_unit).m
+ alpha = 5.0 / (_max_distance - _min_distance)
+ d_ij = torch.exp(alpha * (-d_ij + _min_distance))


d_ij should always be the pairwise distance. If transformations are performed that are specific for the radial basis functions I think these should be moved inside the radial basis function implementation (and shouldn't overwrite d_ij).

ArnNag · 2024-07-20T21:36:35Z

@wiederm I may found a bug in TensorNet. They did apply cutoff function twice in their code, which is inconsistent with their paper. I currently sticked to they way they coded it, but we may need a further discussion on this issue.

In class 'ExpNormalSmearing':
def forward(self, dist):
    dist = dist.unsqueeze(-1)
    return self.cutoff_fn(dist) * torch.exp(
        -self.betas
        * (torch.exp(self.alpha * (-dist + self.cutoff_lower)) - self.means) ** 2
    )
The output of this forward function is called 'edge_attr' in 'TensorEmbedding'. This should be ekRBF in their paper, without cutoff function applied.

However, in 'TensorEmbedding':
def _get_tensor_messages(
    self, Zij: Tensor, edge_weight: Tensor, edge_vec_norm: Tensor, edge_attr: Tensor
) -> Tuple[Tensor, Tensor, Tensor]:
    C = self.cutoff(edge_weight).reshape(-1, 1, 1, 1) * Zij
    eye = torch.eye(3, 3, device=edge_vec_norm.device, dtype=edge_vec_norm.dtype)[
        None, None, ...
    ]
    Iij = self.distance_proj1(edge_attr)[..., None, None] * C * eye
    Aij = (
        self.distance_proj2(edge_attr)[..., None, None]
        * C
        * vector_to_skewtensor(edge_vec_norm)[..., None, :, :]
    )
    Sij = (
        self.distance_proj3(edge_attr)[..., None, None]
        * C
        * vector_to_symtensor(edge_vec_norm)[..., None, :, :]
    )
    return Iij, Aij, Sij
Cutoff function C is timed again to I, A, and S.

Was this resolved? It seems that they wanted to make sure that the outputs of intermediate linear layers also decay to 0 at the cutoff distance, even before they multiply by the cutoff-filtered distances.

wiederm · 2024-07-23T07:54:24Z

@wiederm I may found a bug in TensorNet. They did apply cutoff function twice in their code, which is inconsistent with their paper. I currently sticked to they way they coded it, but we may need a further discussion on this issue.
In class 'ExpNormalSmearing':
def forward(self, dist):
    dist = dist.unsqueeze(-1)
    return self.cutoff_fn(dist) * torch.exp(
        -self.betas
        * (torch.exp(self.alpha * (-dist + self.cutoff_lower)) - self.means) ** 2
    )
The output of this forward function is called 'edge_attr' in 'TensorEmbedding'. This should be ekRBF in their paper, without cutoff function applied.
However, in 'TensorEmbedding':
def _get_tensor_messages(
    self, Zij: Tensor, edge_weight: Tensor, edge_vec_norm: Tensor, edge_attr: Tensor
) -> Tuple[Tensor, Tensor, Tensor]:
    C = self.cutoff(edge_weight).reshape(-1, 1, 1, 1) * Zij
    eye = torch.eye(3, 3, device=edge_vec_norm.device, dtype=edge_vec_norm.dtype)[
        None, None, ...
    ]
    Iij = self.distance_proj1(edge_attr)[..., None, None] * C * eye
    Aij = (
        self.distance_proj2(edge_attr)[..., None, None]
        * C
        * vector_to_skewtensor(edge_vec_norm)[..., None, :, :]
    )
    Sij = (
        self.distance_proj3(edge_attr)[..., None, None]
        * C
        * vector_to_symtensor(edge_vec_norm)[..., None, :, :]
    )
    return Iij, Aij, Sij
Cutoff function C is timed again to I, A, and S.
Was this resolved? It seems that they wanted to make sure that the outputs of intermediate linear layers also decay to 0 at the cutoff distance, even before they multiply by the cutoff-filtered distances.

Yes, this has been resolved! We came to the same conclusion and left it in the code.

wiederm · 2024-07-23T08:50:24Z

Closing PR since it is superseeded by PR #181

chrisiacovella and others added 13 commits June 17, 2024 13:48

Updates to curation routines.

a8679e0

updated ani2x test

dafb4ed

tensornet original embedding setup

60da38f

Added curation script for model system.

c4bc58e

fixed curation test from refactoring name.

209ccfd

refactoring names for defaults to follow new naming convention of spi…

4e5825c

…ce 1 datasets.

adding in mac specific dev file to fix issues installing ray on mac.

b842428

removing relative path in dataset_downloads test

bb0e2aa

Adding in check to ensure an entry isn't empty.

b5066ee

Merge remote-tracking branch 'origin/updating-curation' into updating…

215b112

…-curation

Merge pull request #163 from chrisiacovella/updating-curation

9c28f09

This revises how unique molecules are determined for ANI2x and updates dataset yaml files.

tensornet embedding

9e1f0cb

MarshallYan requested a review from wiederm June 24, 2024 00:05

static_shapes=False

d729bc6

wiederm assigned MarshallYan Jun 24, 2024

MarshallYan added 4 commits June 24, 2024 11:55

radial scale factor dtype

2724975

representation init with TensorNet code

56da513

Merge pull request #168 from choderalab/main

54ea7e8

Merge macOS environments

fix typo

b1f7bb8

MarshallYan added 2 commits June 26, 2024 15:57

init representation instance

8c4cd31

input wrappers, NNPInput & TensorNetNeuralNetworkData

e4199d6

wiederm and others added 2 commits June 28, 2024 18:27

add an example test

59b60fb

check model input (OptimizedDistance v.s. InputPreparation)

bc596b8

MarshallYan added 2 commits June 30, 2024 16:40

test model input

016b1ea

openff-units requires pint<=0.23

7cd9859

MarshallYan mentioned this pull request Jun 30, 2024

Pytest error with test_env #175

Closed

MarshallYan added 3 commits July 1, 2024 09:12

use representation module from modelforge TensorNet class

f74f1ce

representation in torchmd-net language

28bf03e

add representation module units (to compare difference using angstrom…

6a850b2

… v.s. nanometer in TensorNet)

rename test names & name consistency for representation module

76e3d42

MarshallYan added 2 commits July 4, 2024 21:02

torchmd-net differ from modelforge in pairwise indices (causing diffe…

1eefcf3

…rent output from nn.Linear)

del test nonsense

1f8d7b7

MarshallYan added 2 commits July 5, 2024 15:06

only_unique_pairs=False

26c175d

Merge remote-tracking branch 'origin/tensornet_representation' into t…

1a46c8d

…ensornet_representation # Conflicts: # modelforge/potential/tensornet.py # modelforge/tests/test_tensornet.py

with representation_unit, no accuracy issue exists

cfe5f17

MarshallYan mentioned this pull request Jul 6, 2024

Test Failures in TensorNet Branches #179

Closed

wiederm requested changes Jul 9, 2024

View reviewed changes

MarshallYan mentioned this pull request Jul 9, 2024

New tensornet Branch from main #181

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wiederm closed this Jul 23, 2024

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		_max_distance = self.max_distance.to(self.representation_unit).m
		_min_distance = self.min_distance.to(self.representation_unit).m

		@@ -822,31 +857,35 @@ def calculate_radial_basis_centers(
		return centers

		def calculate_radial_scale_factor(

Tensornet representation #167

Tensornet representation #167

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