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Embedding output #73

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422f904
(probably incorrectly) returning embeddings
iamaray Mar 5, 2024
df26175
Outputting embeddings I think
iamaray Mar 8, 2024
178d572
added todo comment
iamaray Mar 8, 2024
fc7d92d
readout operation applied to atomic embeddings
iamaray Mar 11, 2024
37666a9
made an oopsy; fixed it
iamaray Mar 29, 2024
4bf1dff
Merge branch 'main' into embedding-output
iamaray Mar 29, 2024
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144 changes: 82 additions & 62 deletions matdeeplearn/models/torchmd_et.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,9 +16,9 @@
from matdeeplearn.models.torchmd_output_modules import Scalar, EquivariantScalar
from matdeeplearn.common.registry import registry
from matdeeplearn.preprocessor.helpers import node_rep_one_hot
@registry.register_model("torchmd_et")


@registry.register_model("torchmd_et")
class TorchMD_ET(BaseModel):
r"""The TorchMD equivariant Transformer architecture.

Expand Down Expand Up @@ -60,7 +60,7 @@ class TorchMD_ET(BaseModel):

def __init__(
self,
node_dim,
node_dim,
edge_dim,
output_dim,
hidden_channels=128,
Expand Down Expand Up @@ -110,7 +110,8 @@ def __init__(
self.distance_influence = distance_influence
self.max_z = max_z
self.pool = pool
assert pool_order in ['early', 'late'], f"{pool_order} is currently not supported"
assert pool_order in [
'early', 'late'], f"{pool_order} is currently not supported"
self.pool_order = pool_order
self.output_dim = output_dim
cutoff_lower = 0
Expand Down Expand Up @@ -159,10 +160,13 @@ def __init__(
self.post_lin_list = nn.ModuleList()
for i in range(self.num_post_layers):
if i == 0:
self.post_lin_list.append(nn.Linear(hidden_channels, post_hidden_channels))
self.post_lin_list.append(
nn.Linear(hidden_channels, post_hidden_channels))
else:
self.post_lin_list.append(nn.Linear(post_hidden_channels, post_hidden_channels))
self.post_lin_list.append(nn.Linear(post_hidden_channels, self.output_dim))
self.post_lin_list.append(
nn.Linear(post_hidden_channels, post_hidden_channels))
self.post_lin_list.append(
nn.Linear(post_hidden_channels, self.output_dim))

self.reset_parameters()

Expand All @@ -174,82 +178,93 @@ def reset_parameters(self):
for attn in self.attention_layers:
attn.reset_parameters()
self.out_norm.reset_parameters()

@conditional_grad(torch.enable_grad())
def _forward(self, data):

x = self.embedding(data.z)

#edge_index, edge_weight, edge_vec = self.distance(data.pos, data.batch)
#assert (
# edge_index, edge_weight, edge_vec = self.distance(data.pos, data.batch)
# assert (
# edge_vec is not None
#), "Distance module did not return directional information"
# ), "Distance module did not return directional information"
if self.otf_edge_index == True:
#data.edge_index, edge_weight, data.edge_vec, cell_offsets, offset_distance, neighbors = self.generate_graph(data, self.cutoff_radius, self.n_neighbors)
data.edge_index, data.edge_weight, data.edge_vec, _, _, _ = self.generate_graph(data, self.cutoff_radius, self.n_neighbors)
data.edge_attr = self.distance_expansion(data.edge_weight)

#mask = data.edge_index[0] != data.edge_index[1]
#data.edge_vec[mask] = data.edge_vec[mask] / torch.norm(data.edge_vec[mask], dim=1).unsqueeze(1)
data.edge_vec = data.edge_vec / torch.norm(data.edge_vec, dim=1).unsqueeze(1)

# data.edge_index, edge_weight, data.edge_vec, cell_offsets, offset_distance, neighbors = self.generate_graph(data, self.cutoff_radius, self.n_neighbors)
data.edge_index, data.edge_weight, data.edge_vec, _, _, _ = self.generate_graph(
data, self.cutoff_radius, self.n_neighbors)
data.edge_attr = self.distance_expansion(data.edge_weight)

# mask = data.edge_index[0] != data.edge_index[1]
# data.edge_vec[mask] = data.edge_vec[mask] / torch.norm(data.edge_vec[mask], dim=1).unsqueeze(1)
data.edge_vec = data.edge_vec / \
torch.norm(data.edge_vec, dim=1).unsqueeze(1)

if self.otf_node_attr == True:
data.x = node_rep_one_hot(data.z).float()
data.x = node_rep_one_hot(data.z).float()

if self.neighbor_embedding is not None:
x = self.neighbor_embedding(data.z, x, data.edge_index, data.edge_weight, data.edge_attr)
x = self.neighbor_embedding(
data.z, x, data.edge_index, data.edge_weight, data.edge_attr)

vec = torch.zeros(x.size(0), 3, x.size(1), device=x.device)

for attn in self.attention_layers:
dx, dvec = attn(x, vec, data.edge_index, data.edge_weight, data.edge_attr, data.edge_vec)
dx, dvec = attn(x, vec, data.edge_index,
data.edge_weight, data.edge_attr, data.edge_vec)
x = x + dx
vec = vec + dvec
# just output the embeddings => stop before the prediction layer
x = self.out_norm(x)

if self.prediction_level == "graph":
if self.pool_order == 'early':
x = getattr(torch_geometric.nn, self.pool)(x, data.batch)
for i in range(0, len(self.post_lin_list) - 1):
x = self.post_lin_list[i](x)
x = getattr(F, self.activation)(x)
x = self.post_lin_list[-1](x)
if self.pool_order == 'late':
x = getattr(torch_geometric.nn, self.pool)(x, data.batch)
#x = self.pool.pre_reduce(x, vec, data.z, data.pos, data.batch)
#x = self.pool.reduce(x, data.batch)
elif self.prediction_level == "node":
for i in range(0, len(self.post_lin_list) - 1):
x = self.post_lin_list[i](x)
x = getattr(F, self.activation)(x)
x = self.post_lin_list[-1](x)


# if self.prediction_level == "graph":
# if self.pool_order == 'early':
# x = getattr(torch_geometric.nn, self.pool)(x, data.batch)
# for i in range(0, len(self.post_lin_list) - 1):
# x = self.post_lin_list[i](x)
# x = getattr(F, self.activation)(x)
# x = self.post_lin_list[-1](x)
# if self.pool_order == 'late':
# x = getattr(torch_geometric.nn, self.pool)(x, data.batch)
# # x = self.pool.pre_reduce(x, vec, data.z, data.pos, data.batch)
# # x = self.pool.reduce(x, data.batch)
# elif self.prediction_level == "node":
# for i in range(0, len(self.post_lin_list) - 1):
# x = self.post_lin_list[i](x)
# x = getattr(F, self.activation)(x)
# x = self.post_lin_list[-1](x)

# TODO: FIGURE OUT HOW TO ACCESS EMBEDDINGS; WE NEED THEM TO COMPUTE
# MOLECULAR FINGERPRINTS.

return x

def forward(self, data):

output = {}
out = self._forward(data)
output["output"] = out
output["output"] = out

if self.gradient == True and out.requires_grad == True:
volume = torch.einsum("zi,zi->z", data.cell[:, 0, :], torch.cross(data.cell[:, 1, :], data.cell[:, 2, :], dim=1)).unsqueeze(-1)
# this is skipped reached since we're not getting the prediction (I think?)
# even if it is reached, we're probably fine lol.
if self.gradient == True and out.requires_grad == True:
volume = torch.einsum("zi,zi->z", data.cell[:, 0, :], torch.cross(
data.cell[:, 1, :], data.cell[:, 2, :], dim=1)).unsqueeze(-1)
grad = torch.autograd.grad(
out,
[data.pos, data.displacement],
grad_outputs=torch.ones_like(out),
create_graph=self.training)
out,
[data.pos, data.displacement],
grad_outputs=torch.ones_like(out),
create_graph=self.training)
forces = -1 * grad[0]
stress = grad[1]
stress = stress / volume.view(-1, 1, 1)
stress = stress / volume.view(-1, 1, 1)

output["pos_grad"] = forces
output["cell_grad"] = stress
output["pos_grad"] = forces
output["cell_grad"] = stress
else:
output["pos_grad"] = None
output["cell_grad"] = None
return output
output["pos_grad"] = None
output["cell_grad"] = None

return output

def __repr__(self):
return (
Expand All @@ -267,6 +282,7 @@ def __repr__(self):
f"cutoff_lower={self.cutoff_lower}, "
f"self.cutoff_radius={self.self.cutoff_radius})"
)

@property
def target_attr(self):
return "y"
Expand All @@ -285,7 +301,8 @@ def __init__(
cutoff_upper,
aggregation,
):
super(EquivariantMultiHeadAttention, self).__init__(aggr=aggregation, node_dim=0)
super(EquivariantMultiHeadAttention, self).__init__(
aggr=aggregation, node_dim=0)
assert hidden_channels % num_heads == 0, (
f"The number of hidden channels ({hidden_channels}) "
f"must be evenly divisible by the number of "
Expand All @@ -307,7 +324,8 @@ def __init__(
self.v_proj = nn.Linear(hidden_channels, hidden_channels * 3)
self.o_proj = nn.Linear(hidden_channels, hidden_channels * 3)

self.vec_proj = nn.Linear(hidden_channels, hidden_channels * 3, bias=False)
self.vec_proj = nn.Linear(
hidden_channels, hidden_channels * 3, bias=False)

self.dk_proj = None
if distance_influence in ["keys", "both"]:
Expand Down Expand Up @@ -343,21 +361,23 @@ def forward(self, x, vec, edge_index, r_ij, f_ij, d_ij):
k = self.k_proj(x).reshape(-1, self.num_heads, self.head_dim)
v = self.v_proj(x).reshape(-1, self.num_heads, self.head_dim * 3)

vec1, vec2, vec3 = torch.split(self.vec_proj(vec), self.hidden_channels, dim=-1)
vec1, vec2, vec3 = torch.split(
self.vec_proj(vec), self.hidden_channels, dim=-1)
vec = vec.reshape(-1, 3, self.num_heads, self.head_dim)
vec_dot = (vec1 * vec2).sum(dim=1)

dk = (
self.act(self.dk_proj(f_ij)).reshape(-1, self.num_heads, self.head_dim)
self.act(self.dk_proj(f_ij)).reshape(-1,
self.num_heads, self.head_dim)
if self.dk_proj is not None
else None
)
dv = (
self.act(self.dv_proj(f_ij)).reshape(-1, self.num_heads, self.head_dim * 3)
self.act(self.dv_proj(f_ij)).reshape(-1,
self.num_heads, self.head_dim * 3)
if self.dv_proj is not None
else None
)


# propagate_type: (q: Tensor, k: Tensor, v: Tensor, vec: Tensor, dk: Tensor, dv: Tensor, r_ij: Tensor, d_ij: Tensor)
x, vec = self.propagate(
Expand Down
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