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import os
from typing import List, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from e3nn import o3
from e3nn.nn import BatchNorm
from e3nn.o3 import Linear, TensorProduct
from torch_scatter import scatter, scatter_mean
from .layers import FCBlock
def get_irrep_seq(ns, nv, use_second_order_repr, reduce_pseudoscalars):
if use_second_order_repr:
irrep_seq = [
f"{ns}x0e",
f"{ns}x0e + {nv}x1o + {nv}x2e",
f"{ns}x0e + {nv}x1o + {nv}x2e + {nv}x1e + {nv}x2o",
f"{ns}x0e + {nv}x1o + {nv}x2e + {nv}x1e + {nv}x2o + {nv if reduce_pseudoscalars else ns}x0o",
]
else:
irrep_seq = [
f"{ns}x0e",
f"{ns}x0e + {nv}x1o",
f"{ns}x0e + {nv}x1o + {nv}x1e",
f"{ns}x0e + {nv}x1o + {nv}x1e + {nv if reduce_pseudoscalars else ns}x0o",
]
return irrep_seq
def irrep_to_size(irrep):
irreps = irrep.split(" + ")
size = 0
for ir in irreps:
m, (l, p) = ir.split("x")
size += int(m) * (2 * int(l) + 1)
return size
class FasterTensorProduct(torch.nn.Module):
# Implemented by Bowen Jing
def __init__(self, in_irreps, sh_irreps, out_irreps, **kwargs):
super().__init__()
assert (
o3.Irreps(sh_irreps) == o3.Irreps("1x0e+1x1o")
), "sh_irreps don't look like 1st order spherical harmonics"
self.in_irreps = o3.Irreps(in_irreps)
self.out_irreps = o3.Irreps(out_irreps)
in_muls = {"0e": 0, "1o": 0, "1e": 0, "0o": 0}
out_muls = {"0e": 0, "1o": 0, "1e": 0, "0o": 0}
for (m, ir) in self.in_irreps:
in_muls[str(ir)] = m
for (m, ir) in self.out_irreps:
out_muls[str(ir)] = m
self.weight_shapes = {
"0e": (in_muls["0e"] + in_muls["1o"], out_muls["0e"]),
"1o": (in_muls["0e"] + in_muls["1o"] + in_muls["1e"], out_muls["1o"]),
"1e": (in_muls["1o"] + in_muls["1e"] + in_muls["0o"], out_muls["1e"]),
"0o": (in_muls["1e"] + in_muls["0o"], out_muls["0o"]),
}
self.weight_numel = sum(a * b for (a, b) in self.weight_shapes.values())
def forward(self, in_, sh, weight):
in_dict, out_dict = {}, {"0e": [], "1o": [], "1e": [], "0o": []}
for (m, ir), sl in zip(self.in_irreps, self.in_irreps.slices()):
in_dict[str(ir)] = in_[..., sl]
if ir[0] == 1:
in_dict[str(ir)] = in_dict[str(ir)].reshape(
list(in_dict[str(ir)].shape)[:-1] + [-1, 3]
)
sh_0e, sh_1o = sh[..., 0], sh[..., 1:]
if "0e" in in_dict:
out_dict["0e"].append(in_dict["0e"] * sh_0e.unsqueeze(-1))
out_dict["1o"].append(in_dict["0e"].unsqueeze(-1) * sh_1o.unsqueeze(-2))
if "1o" in in_dict:
out_dict["0e"].append((in_dict["1o"] * sh_1o.unsqueeze(-2)).sum(-1) / np.sqrt(3))
out_dict["1o"].append(in_dict["1o"] * sh_0e.unsqueeze(-1).unsqueeze(-1))
out_dict["1e"].append(
torch.linalg.cross(in_dict["1o"], sh_1o.unsqueeze(-2), dim=-1) / np.sqrt(2)
)
if "1e" in in_dict:
out_dict["1o"].append(
torch.linalg.cross(in_dict["1e"], sh_1o.unsqueeze(-2), dim=-1) / np.sqrt(2)
)
out_dict["1e"].append(in_dict["1e"] * sh_0e.unsqueeze(-1).unsqueeze(-1))
out_dict["0o"].append((in_dict["1e"] * sh_1o.unsqueeze(-2)).sum(-1) / np.sqrt(3))
if "0o" in in_dict:
out_dict["1e"].append(in_dict["0o"].unsqueeze(-1) * sh_1o.unsqueeze(-2))
out_dict["0o"].append(in_dict["0o"] * sh_0e.unsqueeze(-1))
weight_dict = {}
start = 0
for key in self.weight_shapes:
in_, out = self.weight_shapes[key]
weight_dict[key] = weight[..., start : start + in_ * out].reshape(
list(weight.shape)[:-1] + [in_, out]
) / np.sqrt(in_)
start += in_ * out
if out_dict["0e"]:
out_dict["0e"] = torch.cat(out_dict["0e"], dim=-1)
out_dict["0e"] = torch.matmul(
out_dict["0e"].unsqueeze(-2), weight_dict["0e"]
).squeeze(-2)
if out_dict["1o"]:
out_dict["1o"] = torch.cat(out_dict["1o"], dim=-2)
out_dict["1o"] = (
out_dict["1o"].unsqueeze(-2) * weight_dict["1o"].unsqueeze(-1)
).sum(-3)
out_dict["1o"] = out_dict["1o"].reshape(list(out_dict["1o"].shape)[:-2] + [-1])
if out_dict["1e"]:
out_dict["1e"] = torch.cat(out_dict["1e"], dim=-2)
out_dict["1e"] = (
out_dict["1e"].unsqueeze(-2) * weight_dict["1e"].unsqueeze(-1)
).sum(-3)
out_dict["1e"] = out_dict["1e"].reshape(list(out_dict["1e"].shape)[:-2] + [-1])
if out_dict["0o"]:
out_dict["0o"] = torch.cat(out_dict["0o"], dim=-1)
out_dict["0o"] = torch.matmul(
out_dict["0o"].unsqueeze(-2), weight_dict["0o"]
).squeeze(-2)
out = []
for _, ir in self.out_irreps:
out.append(out_dict[str(ir)])
return torch.cat(out, dim=-1)
def tp_scatter_simple(
tp,
fc_layer,
node_attr,
edge_index,
edge_attr,
edge_sh,
out_nodes=None,
reduce="mean",
edge_weight=1.0,
):
"""
Perform TensorProduct + scatter operation, aka graph convolution.
This function is only for edge_groups == 1. For multiple edge groups, and for larger graphs,
use tp_scatter_multigroup instead.
"""
assert isinstance(
edge_attr, torch.Tensor
), "This function is only for a single edge group, so edge_attr must be a tensor and not a list."
_device = node_attr.device
_dtype = node_attr.dtype
edge_src, edge_dst = edge_index
out_irreps = fc_layer(edge_attr).to(_device).to(_dtype)
out_irreps.mul_(edge_weight)
tp = tp(node_attr[edge_dst], edge_sh, out_irreps)
out_nodes = out_nodes or node_attr.shape[0]
out = scatter(tp, edge_src, dim=0, dim_size=out_nodes, reduce=reduce)
return out
def tp_scatter_multigroup(
tp: o3.TensorProduct,
fc_layer: Union[nn.Module, nn.ModuleList],
node_attr: torch.Tensor,
edge_index: torch.Tensor,
edge_attr_groups: List[torch.Tensor],
edge_sh: torch.Tensor,
out_nodes=None,
reduce="mean",
edge_weight=1.0,
):
"""
Perform TensorProduct + scatter operation, aka graph convolution.
To keep the peak memory usage reasonably low, this function does not concatenate the edge_attr_groups.
Rather, we sum the output of the tensor product for each edge group, and then divide by the number of edges
"""
assert isinstance(edge_attr_groups, list), "This function is only for a list of edge groups"
assert reduce in {"mean", "sum"}, "Only 'mean' and 'sum' are supported for reduce"
_device = node_attr.device
_dtype = node_attr.dtype
edge_src, edge_dst = edge_index
edge_attr_lengths = [_edge_attr.shape[0] for _edge_attr in edge_attr_groups]
total_rows = sum(edge_attr_lengths)
assert (
total_rows == edge_index.shape[1]
), "Sum of edge_attr_groups must be equal to edge_index.shape[1]"
num_edge_groups = len(edge_attr_groups)
edge_weight_is_indexable = hasattr(edge_weight, "__getitem__")
out_nodes = out_nodes or node_attr.shape[0]
total_output_dim = sum([x.dim for x in tp.irreps_out])
final_out = torch.zeros((out_nodes, total_output_dim), device=_device, dtype=_dtype)
div_factors = torch.zeros(out_nodes, device=_device, dtype=_dtype)
cur_start = 0
for ii in range(num_edge_groups):
cur_length = edge_attr_lengths[ii]
cur_end = cur_start + cur_length
cur_edge_range = slice(cur_start, cur_end)
cur_edge_src, cur_edge_dst = edge_src[cur_edge_range], edge_dst[cur_edge_range]
cur_fc = fc_layer[ii] if isinstance(fc_layer, nn.ModuleList) else fc_layer
cur_out_irreps = cur_fc(edge_attr_groups[ii])
if edge_weight_is_indexable:
cur_out_irreps.mul_(edge_weight[cur_edge_range])
else:
cur_out_irreps.mul_(edge_weight)
summand = tp(node_attr[cur_edge_dst, :], edge_sh[cur_edge_range, :], cur_out_irreps)
final_out += scatter(summand, cur_edge_src, dim=0, dim_size=out_nodes, reduce="sum")
div_factors += torch.bincount(cur_edge_src, minlength=out_nodes)
cur_start = cur_end
del cur_out_irreps, summand
if reduce == "mean":
div_factors = torch.clamp(div_factors, torch.finfo(_dtype).eps)
final_out = final_out / div_factors[:, None]
return final_out
class TensorProductConvLayer(torch.nn.Module):
def __init__(
self,
in_irreps,
sh_irreps,
out_irreps,
n_edge_features,
residual=True,
batch_norm=True,
dropout=0.0,
hidden_features=None,
faster=False,
edge_groups=1,
tp_weights_layers=2,
activation="relu",
depthwise=False,
):
super(TensorProductConvLayer, self).__init__()
self.in_irreps = in_irreps
self.out_irreps = out_irreps
self.sh_irreps = sh_irreps
self.residual = residual
self.edge_groups = edge_groups
self.out_size = irrep_to_size(out_irreps)
self.depthwise = depthwise
if hidden_features is None:
hidden_features = n_edge_features
if depthwise:
in_irreps = o3.Irreps(in_irreps)
sh_irreps = o3.Irreps(sh_irreps)
out_irreps = o3.Irreps(out_irreps)
irreps_mid = []
instructions = []
for i, (mul, ir_in) in enumerate(in_irreps):
for j, (_, ir_edge) in enumerate(sh_irreps):
for ir_out in ir_in * ir_edge:
if ir_out in out_irreps:
k = len(irreps_mid)
irreps_mid.append((mul, ir_out))
instructions.append((i, j, k, "uvu", True))
irreps_mid = o3.Irreps(irreps_mid)
irreps_mid, p, _ = irreps_mid.sort()
instructions = [
(i_in1, i_in2, p[i_out], mode, train)
for i_in1, i_in2, i_out, mode, train in instructions
]
self.tp = TensorProduct(
in_irreps,
sh_irreps,
irreps_mid,
instructions,
shared_weights=False,
internal_weights=False,
)
self.linear_2 = Linear(
irreps_in=irreps_mid.simplify(),
irreps_out=out_irreps,
internal_weights=True,
shared_weights=True,
)
else:
if faster:
print("Faster Tensor Product")
self.tp = FasterTensorProduct(in_irreps, sh_irreps, out_irreps)
else:
self.tp = o3.FullyConnectedTensorProduct(
in_irreps, sh_irreps, out_irreps, shared_weights=False
)
if edge_groups == 1:
self.fc = FCBlock(
n_edge_features,
hidden_features,
self.tp.weight_numel,
tp_weights_layers,
dropout,
activation,
)
else:
self.fc = [
FCBlock(
n_edge_features,
hidden_features,
self.tp.weight_numel,
tp_weights_layers,
dropout,
activation,
)
for _ in range(edge_groups)
]
self.fc = nn.ModuleList(self.fc)
self.batch_norm = BatchNorm(out_irreps) if batch_norm else None
def forward(
self,
node_attr,
edge_index,
edge_attr,
edge_sh,
out_nodes=None,
reduce="mean",
edge_weight=1.0,
):
if edge_index.shape[1] == 0 and node_attr.shape[0] == 0:
raise ValueError("No edges and no nodes")
_dtype = node_attr.dtype
if edge_index.shape[1] == 0:
out = torch.zeros((node_attr.shape[0], self.out_size), dtype=_dtype, device=node_attr.device)
else:
if self.edge_groups == 1:
out = tp_scatter_simple(
self.tp,
self.fc,
node_attr,
edge_index,
edge_attr,
edge_sh,
out_nodes,
reduce,
edge_weight,
)
else:
out = tp_scatter_multigroup(
self.tp,
self.fc,
node_attr,
edge_index,
edge_attr,
edge_sh,
out_nodes,
reduce,
edge_weight,
)
if self.depthwise:
out = self.linear_2(out)
if self.batch_norm:
out = self.batch_norm(out)
if self.residual:
padded = F.pad(node_attr, (0, out.shape[-1] - node_attr.shape[-1]))
out = out + padded
out = out.to(_dtype)
return out
class OldTensorProductConvLayer(torch.nn.Module):
def __init__(
self,
in_irreps,
sh_irreps,
out_irreps,
n_edge_features,
residual=True,
batch_norm=True,
dropout=0.0,
hidden_features=None,
):
super(OldTensorProductConvLayer, self).__init__()
self.in_irreps = in_irreps
self.out_irreps = out_irreps
self.sh_irreps = sh_irreps
self.residual = residual
if hidden_features is None:
hidden_features = n_edge_features
self.tp = tp = o3.FullyConnectedTensorProduct(
in_irreps, sh_irreps, out_irreps, shared_weights=False
)
self.fc = nn.Sequential(
nn.Linear(n_edge_features, hidden_features),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_features, tp.weight_numel),
)
self.batch_norm = BatchNorm(out_irreps) if batch_norm else None
def forward(
self, node_attr, edge_index, edge_attr, edge_sh, out_nodes=None, reduce="mean", edge_weight=1.0
):
edge_chunk_size = 100_000
num_edges = edge_attr.shape[0]
num_chunks = (
(num_edges // edge_chunk_size)
if num_edges % edge_chunk_size == 0
else (num_edges // edge_chunk_size) + 1
)
edge_ranges = np.array_split(np.arange(num_edges), num_chunks)
edge_attr_groups = [edge_attr[cur_range] for cur_range in edge_ranges]
out = tp_scatter_multigroup(
self.tp,
self.fc,
node_attr,
edge_index,
edge_attr_groups,
edge_sh,
out_nodes,
reduce,
edge_weight,
)
if self.residual:
padded = F.pad(node_attr, (0, out.shape[-1] - node_attr.shape[-1]))
out = out + padded
if self.batch_norm:
out = self.batch_norm(out)
out = out.to(node_attr.dtype)
return out
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