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from __future__ import annotations
from collections.abc import Callable
from typing import Any, cast
from torch import Tensor, nn
from torch.utils.checkpoint import checkpoint
from .vb_layers_attentionv2 import AttentionPairBias
from .vb_modules_utils import LinearNoBias, SwiGLU
class AdaLN(nn.Module):
"""Normalize activations and apply scale and shift from conditioning S."""
def __init__(self, dim: int, dim_single_cond: int) -> None:
super().__init__()
self.a_norm = nn.LayerNorm(dim, elementwise_affine=False, bias=False)
self.s_norm = nn.LayerNorm(dim_single_cond, bias=False)
self.s_scale = nn.Linear(dim_single_cond, dim)
self.s_bias = LinearNoBias(dim_single_cond, dim)
def forward(self, a: Tensor, s: Tensor) -> Tensor:
# a: (..., d); s: (..., d_c).
normalized = self.a_norm(a) # (..., d)
conditioning = self.s_norm(s) # (..., d_c)
# (..., d)
output = self.s_scale(conditioning).sigmoid() * normalized + self.s_bias(conditioning)
return cast(Tensor, output) # (..., d)
class ConditionedTransitionBlock(nn.Module):
"""Apply a conditioned gated transition without changing tensor shape."""
def __init__(
self,
dim_single: int,
dim_single_cond: int,
expansion_factor: float = 2,
) -> None:
super().__init__()
self.adaln = AdaLN(dim_single, dim_single_cond)
inner_dim = int(dim_single * expansion_factor)
self.swish_gate = nn.Sequential(
LinearNoBias(dim_single, inner_dim * 2),
SwiGLU(),
)
self.a_to_b = LinearNoBias(dim_single, inner_dim)
self.b_to_a = LinearNoBias(inner_dim, dim_single)
projection = nn.Linear(dim_single_cond, dim_single)
nn.init.zeros_(projection.weight) # (d, d_c)
nn.init.constant_(projection.bias, -2.0) # (d,)
self.output_projection = nn.Sequential(projection, nn.Sigmoid())
def forward(self, a: Tensor, s: Tensor) -> Tensor:
# a: (..., d); s: (..., d_c).
normalized = self.adaln(a, s) # (..., d)
hidden = self.swish_gate(normalized) * self.a_to_b(normalized) # (..., d_inner)
# (..., d)
return cast(Tensor, self.output_projection(s) * self.b_to_a(hidden))
class DiffusionTransformer(nn.Module):
"""Run conditioned transformer layers over token or atom states."""
def __init__(
self,
depth: int,
heads: int,
dim: int = 384,
dim_single_cond: int | None = None,
pair_bias_attn: bool = True,
activation_checkpointing: bool = False,
post_layer_norm: bool = False,
) -> None:
super().__init__()
conditioning_dim = dim if dim_single_cond is None else dim_single_cond
self.activation_checkpointing = activation_checkpointing
self.pair_bias_attn = pair_bias_attn
self.layers = nn.ModuleList(
[
DiffusionTransformerLayer(
heads,
dim,
conditioning_dim,
post_layer_norm,
)
for _ in range(depth)
]
)
def _split_pair_bias(self, bias: Tensor | None) -> Tensor | None:
# bias: (b_z, l_q, l_k, n_layer * h) or None.
if not self.pair_bias_attn:
return None
if bias is None:
raise ValueError("pair bias is required when pair_bias_attn=True")
batch_size, query_length, key_length, width = bias.shape # b_z, l_q, l_k, n_layer * h
depth = len(self.layers) # n_layer
if depth == 0 or width % depth:
raise ValueError("pair-bias width must be divisible by transformer depth")
# (b_z, l_q, l_k, n_layer, h)
return bias.view(batch_size, query_length, key_length, depth, width // depth)
def forward(
self,
a: Tensor,
s: Tensor,
bias: Tensor | None = None,
mask: Tensor | None = None,
to_keys: Callable[[Tensor], Tensor] | None = None,
multiplicity: int = 1,
) -> Tensor:
"""Transform A and preserve shape ``(b * m, l, d)``."""
# a: (b * m, l_q, d); s: (b * m, l_q, d_c).
# bias: (b, l_q, l_k, n_layer * h) or None.
# mask: (b * m, l_q) before an optional to_keys mapping; to_keys preserves d.
layer_biases = self._split_pair_bias(bias) # (b, l_q, l_k, n_layer, h) or None
output = a # (b * m, l_q, d)
for index, layer in enumerate(self.layers):
# (b, l_q, l_k, h) or None
layer_bias = None if layer_biases is None else layer_biases[..., index, :]
if self.activation_checkpointing:
output = checkpoint( # (b * m, l_q, d)
layer,
output,
s,
layer_bias,
mask,
to_keys,
multiplicity,
use_reentrant=False,
)
else:
output = layer( # (b * m, l_q, d)
output,
s,
layer_bias,
mask,
to_keys,
multiplicity,
)
return output # (b * m, l_q, d)
class DiffusionTransformerLayer(nn.Module):
"""One adaptive-normalization attention and transition block."""
def __init__(
self,
heads: int,
dim: int = 384,
dim_single_cond: int | None = None,
post_layer_norm: bool = False,
) -> None:
super().__init__()
conditioning_dim = dim if dim_single_cond is None else dim_single_cond
self.adaln = AdaLN(dim, conditioning_dim)
self.pair_bias_attn = AttentionPairBias(
c_s=dim,
num_heads=heads,
compute_pair_bias=False,
)
self.output_projection_linear = nn.Linear(conditioning_dim, dim)
nn.init.zeros_(self.output_projection_linear.weight) # (d, d_c)
nn.init.constant_(self.output_projection_linear.bias, -2.0) # (d,)
self.output_projection = nn.Sequential(
self.output_projection_linear,
nn.Sigmoid(),
)
self.transition = ConditionedTransitionBlock(dim, conditioning_dim)
self.post_lnorm = nn.LayerNorm(dim) if post_layer_norm else nn.Identity()
def forward(
self,
a: Tensor,
s: Tensor,
bias: Tensor | None = None,
mask: Tensor | None = None,
to_keys: Callable[[Tensor], Tensor] | None = None,
multiplicity: int = 1,
) -> Tensor:
"""Update activation tensor A with conditioning S and pair bias."""
# a: (b * m, l_q, d); s: (b * m, l_q, d_c).
# bias: (b, l_q, l_k, h) or None.
# mask: (b * m, l_q) or None; to_keys maps l_q to l_k.
if bias is None or mask is None:
raise ValueError("diffusion attention requires pair bias and a key mask")
normalized = self.adaln(a, s) # (b * m, l_q, d)
key_states = normalized # (b * m, l_q, d)
key_mask = mask # (b * m, l_q)
if to_keys is not None:
key_states = to_keys(normalized) # (b * m, l_k, d)
key_mask = to_keys(mask.unsqueeze(-1)).squeeze(-1) # (b * m, l_k)
attended = self.pair_bias_attn( # (b * m, l_q, d)
s=normalized,
z=bias,
mask=key_mask,
multiplicity=multiplicity,
k_in=key_states,
)
output = a + self.output_projection(s) * attended # (b * m, l_q, d)
output = output + self.transition(output, s) # (b * m, l_q, d)
return cast(Tensor, self.post_lnorm(output)) # (b * m, l_q, d)
class AtomTransformer(nn.Module):
"""Apply the diffusion transformer over fixed atom-query windows."""
def __init__(
self,
attn_window_queries: int,
attn_window_keys: int,
**diffusion_transformer_kwargs: Any,
) -> None:
super().__init__()
self.attn_window_queries = attn_window_queries
self.attn_window_keys = attn_window_keys
self.diffusion_transformer = DiffusionTransformer(
**diffusion_transformer_kwargs,
)
def forward(
self,
q: Tensor,
c: Tensor,
bias: Tensor,
to_keys: Callable[[Tensor], Tensor],
mask: Tensor,
multiplicity: int = 1,
) -> Tensor:
"""Transform atom tensor Q with shape ``(b, n_atoms, d)``."""
query_window = self.attn_window_queries # w_q
key_window = self.attn_window_keys # w_k
batch_size, atom_count, width = q.shape # b * m, n_atom, d
window_count = atom_count // query_window # k
# q, c: (b * m, n_atom, d); bias: (b, k, w_q, w_k, n_layer * h).
# mask: (b * m, n_atom); k = n_atom // w_q.
# (b * m * k, w_q, d)
query_states = q.view(batch_size * window_count, query_window, -1)
# (b * m * k, w_q, d_c)
conditioning = c.view(batch_size * window_count, query_window, -1)
# (b * m * k, w_q)
query_mask = mask.view(batch_size * window_count, query_window)
# (b * m, k, w_q, w_k, n_layer * h)
pair_bias = bias.repeat_interleave(multiplicity, dim=0)
pair_bias = pair_bias.view( # (b * m * k, w_q, w_k, n_layer * h)
pair_bias.shape[0] * window_count,
query_window,
key_window,
-1,
)
def windowed_keys(states: Tensor) -> Tensor:
# states: (b * m * k, w_q, d_x).
# (b * m, n_atom, d_x)
merged = states.view(batch_size, window_count * query_window, -1)
# (b * m * k, w_k, d_x)
return to_keys(merged).view(batch_size * window_count, key_window, -1)
output = self.diffusion_transformer( # (b * m * k, w_q, d)
a=query_states,
s=conditioning,
bias=pair_bias,
mask=query_mask.float(),
multiplicity=1,
to_keys=windowed_keys,
)
# (b * m, n_atom, d)
return cast(Tensor, output.view(batch_size, window_count * query_window, width))
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