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from math import sqrt, prod
import torch
from e3nn.o3._irreps import Irreps
from e3nn.util.jit import compile_mode
from onescience.datapipes.materials.nequip import AtomicDataDict
from ._graph_mixin import GraphModuleMixin
from .nonlinearities import ShiftedSoftplus
from typing import Optional, Final, Dict
_NONLINEARITY_MAP: Final[Dict[str, torch.nn.Module]] = {
# NOTE: we include str options for `None` so that the parser always works
None: torch.nn.Identity,
"None": torch.nn.Identity,
"null": torch.nn.Identity,
"silu": torch.nn.SiLU,
"mish": torch.nn.Mish,
"gelu": torch.nn.GELU,
"ssp": ShiftedSoftplus,
"tanh": torch.nn.Tanh,
# not 0 -> 0
"sigmoid": torch.nn.Sigmoid,
"softplus": torch.nn.Softplus,
}
@compile_mode("script")
class ScalarMLP(GraphModuleMixin, torch.nn.Module):
"""Apply an MLP to some scalar field."""
field: str
out_field: str
def __init__(
self,
output_dim: int,
hidden_layers_depth: int = 0,
hidden_layers_width: Optional[int] = None,
nonlinearity: Optional[str] = "silu",
bias: bool = False,
forward_weight_init: bool = True,
init_mode: str = "uniform",
parametrization: Optional[str] = None,
field: str = AtomicDataDict.NODE_FEATURES_KEY,
out_field: Optional[str] = None,
irreps_in=None,
):
super().__init__()
self.field = field
self.out_field = out_field if out_field is not None else field
self._init_irreps(
irreps_in=irreps_in,
required_irreps_in=[self.field],
)
assert len(self.irreps_in[self.field]) == 1
assert self.irreps_in[self.field][0].ir == (0, 1) # scalars
self.mlp_module = ScalarMLPFunction(
input_dim=self.irreps_in[self.field][0].mul,
output_dim=output_dim,
hidden_layers_depth=hidden_layers_depth,
hidden_layers_width=hidden_layers_width,
nonlinearity=nonlinearity,
bias=bias,
forward_weight_init=forward_weight_init,
init_mode=init_mode,
parametrization=parametrization,
)
self.irreps_out[self.out_field] = Irreps([(self.mlp_module.dims[-1], (0, 1))])
def forward(self, data: AtomicDataDict.Type) -> AtomicDataDict.Type:
data[self.out_field] = self.mlp_module(data[self.field])
return data
@compile_mode("script")
class ScalarMLPFunction(torch.nn.Module):
"""Module implementing an MLP according to provided options.
``input_dim`` and ``output_dim`` are mandatory arguments.
If only ``input_dim`` and ``output_dim`` are specified, this module defaults to a linear layer (corresponding to the default of ``hidden_layers_depth=0``).
If ``hidden_layers_depth!=0``, ``hidden_layers_width`` must be configured (an error will be raised if the default of ``hidden_layers_width=None`` is used).
Args:
nonlinearity (str): ``silu`` (default), ``mish``, ``gelu``, ``ssp``, ``tanh``, ``None``, ``null``, or ``"None"``
bias (bool): whether a bias is included (default ``False``)
forward_weight_init (bool): whether to initialize weights to preserve forward activation variance (default ``True``) or initialize weights to preserve backward gradient variance
"""
num_layers: int
bias: bool
is_nonlinear: bool
def __init__(
self,
input_dim: int,
output_dim: int,
hidden_layers_depth: int = 0,
hidden_layers_width: Optional[int] = None,
nonlinearity: Optional[str] = "silu",
bias: bool = False,
forward_weight_init: bool = True,
init_mode: str = "uniform",
parametrization: Optional[str] = None,
):
super().__init__()
self.bias = bias
# === process MLP dims ===
if hidden_layers_depth != 0:
assert hidden_layers_depth > 0 and hidden_layers_width > 0
hidden_layers_dims = hidden_layers_depth * [hidden_layers_width]
self.dims = [input_dim] + hidden_layers_dims + [output_dim]
self.num_layers = len(self.dims) - 1
assert self.num_layers >= 1
# NOTE: `input_dim` and `output_dim` are always mandatory, which default to at least a linear
# a one-layer MLP is a linear layer
# === handle nonlinearity ===
# TODO: maybe adapt gain to be nonlinearity dependent
if nonlinearity not in _NONLINEARITY_MAP:
available_options = list(_NONLINEARITY_MAP.keys())
raise ValueError(
f"Unknown nonlinearity '{nonlinearity}'. Available options: {available_options}"
)
nonlinearity_module = _NONLINEARITY_MAP[nonlinearity]
self.is_nonlinear = False # updated below in loop
# === build the MLP + weight init ===
mlp = torch.nn.Sequential()
for layer, (h_in, h_out) in enumerate(zip(self.dims, self.dims[1:])):
# === weight initialization ===
# normalize to preserve variance of forward activations or backward derivatives
# we use "relu" gain (sqrt(2)) as a stand-in for the smooth nonlinearities we use, and only apply them if there is a nonlinearity
# for forward (backward) norm, we don't include the gain for the first (last) layer
# see https://pytorch.org/docs/stable/nn.init.html#torch.nn.init.kaiming_uniform_
if forward_weight_init:
norm_dim = h_in
gain = 1.0 if nonlinearity is None or (layer == 0) else sqrt(2)
else:
norm_dim = h_out
gain = (
1.0
if nonlinearity is None or (layer == self.num_layers - 1)
else sqrt(2)
)
# === instantiate `Linear` ===
linear_layer = ScalarLinearLayer(
in_features=h_in,
out_features=h_out,
alpha=gain / sqrt(norm_dim),
bias=bias,
init_mode=init_mode,
)
# apply parametrization if specified
if parametrization == "spectral_norm":
torch.nn.utils.parametrizations.spectral_norm(
linear_layer, "weight", dim=1
)
elif parametrization == "weight_norm":
torch.nn.utils.parametrizations.weight_norm(
linear_layer, "weight", dim=1
)
elif parametrization == "orthogonal":
torch.nn.utils.parametrizations.orthogonal(linear_layer, "weight")
elif parametrization not in [None, "None", "null"]:
raise ValueError(
f"Unknown parametrization '{parametrization}'. "
"Available options: None, 'weight_norm', 'orthogonal', 'spectral_norm'"
)
mlp.append(linear_layer)
del gain, norm_dim
# === add nonlinearity (if any) except for last layer ===
if (layer != self.num_layers - 1) and (nonlinearity is not None):
# only update `self.is_nonlinear` when a nonlinearity is applied
mlp.append(nonlinearity_module())
self.is_nonlinear = True
# use `multidot` based implementation for deep linear net (no nonlinearity, no bias, more than one layer)
# otherwise use the `mlp` built in init
if (not self.is_nonlinear) and (not self.bias) and (self.num_layers > 1):
self.mlp = DeepLinearMLP(mlp)
del mlp
else:
self.mlp = mlp
def forward(self, x):
return self.mlp(x)
class DeepLinearMLP(torch.nn.Module):
def __init__(self, mlp) -> None:
super().__init__()
self.weights = torch.nn.ParameterList()
alphas = []
for this_idx, mlp_idx in enumerate(range(len(mlp))):
new_weight = torch.clone(mlp[mlp_idx].weight)
self.weights.append(new_weight)
del new_weight
alphas.append(mlp[mlp_idx].alpha)
alpha = prod(alphas)
# the constant has to be a buffer for constant-folding to happen with `torch.compile(...dynamic=True)`
# `persistent=False` for backwards compatibility of checkpoint files
# (and technically preserves the old behavior when using a float in that it's also not persistent)
# `alpha` is already a torch.Tensor here
self.register_buffer("alpha", alpha, persistent=False)
del alphas
def forward(self, input: torch.Tensor) -> torch.Tensor:
weight = torch.mul(
torch.linalg.multi_dot([weight for weight in self.weights]), self.alpha
)
return torch.mm(input, weight)
class ScalarLinearLayer(torch.nn.Module):
"""Module implementing a linear layer with a scaling factor `alpha` applied to the weights."""
in_features: int
out_features: int
def __init__(
self,
in_features: int,
out_features: int,
alpha: float = 1.0,
bias: bool = False,
init_mode: str = "uniform",
) -> None:
super().__init__()
self.in_features = in_features
self.out_features = out_features
# the constant has to be a buffer for constant-folding to happen with `torch.compile(...dynamic=True)`
# `persistent=False` for backwards compatibility of checkpoint files
# (and technically preserves the old behavior when using a float in that it's also not persistent)
self.register_buffer("alpha", torch.tensor(alpha), persistent=False)
self.weight = torch.nn.Parameter(torch.empty((in_features, out_features)))
# initialize weights based on init_mode
if init_mode == "uniform":
# initialize weights to uniform distribution with mean 0 variance 1
torch.nn.init.uniform_(self.weight, -sqrt(3), sqrt(3))
elif init_mode == "normal":
# initialize weights to normal distribution with mean 0 std 1
torch.nn.init.normal_(self.weight, mean=0.0, std=1.0)
else:
raise ValueError(
f"Unknown init_mode: {init_mode}. Must be 'uniform' or 'normal'."
)
# initialize bias (if any) to zeros
if bias:
self.bias = torch.nn.Parameter(torch.zeros(out_features))
else:
self.register_parameter("bias", None)
def forward(self, input: torch.Tensor) -> torch.Tensor:
# compute scaled weights separately to be constant folded
weight = self.weight * self.alpha
if self.bias is None:
return torch.mm(input, weight)
else:
return torch.addmm(self.bias, input, weight)
def extra_repr(self) -> str:
return f"in_features={self.in_features}, out_features={self.out_features}, bias={self.bias is not None}, alpha={self.alpha:.6f}"
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