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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import typing as T
from contextlib import ExitStack
from dataclasses import dataclass
import torch
import torch.nn as nn
from openfold.model.structure_module import StructureModule
from esm.esmfold.v1.tri_self_attn_block import TriangularSelfAttentionBlock
@dataclass
class StructureModuleConfig:
c_s: int = 384
c_z: int = 128
c_ipa: int = 16
c_resnet: int = 128
no_heads_ipa: int = 12
no_qk_points: int = 4
no_v_points: int = 8
dropout_rate: float = 0.1
no_blocks: int = 8
no_transition_layers: int = 1
no_resnet_blocks: int = 2
no_angles: int = 7
trans_scale_factor: int = 10
epsilon: float = 1e-8
inf: float = 1e5
@dataclass
class FoldingTrunkConfig:
_name: str = "FoldingTrunkConfig"
num_blocks: int = 48
sequence_state_dim: int = 1024
pairwise_state_dim: int = 128
sequence_head_width: int = 32
pairwise_head_width: int = 32
position_bins: int = 32
dropout: float = 0
layer_drop: float = 0
cpu_grad_checkpoint: bool = False
max_recycles: int = 4
chunk_size: T.Optional[int] = None
structure_module: StructureModuleConfig = StructureModuleConfig()
def get_axial_mask(mask):
"""
Helper to convert B x L mask of valid positions to axial mask used
in row column attentions.
Input:
mask: B x L tensor of booleans
Output:
mask: B x L x L tensor of booleans
"""
if mask is None:
return None
assert len(mask.shape) == 2
batch_dim, seq_dim = mask.shape
m = mask.unsqueeze(1).expand(batch_dim, seq_dim, seq_dim)
m = m.reshape(batch_dim * seq_dim, seq_dim)
return m
class RelativePosition(nn.Module):
def __init__(self, bins, pairwise_state_dim):
super().__init__()
self.bins = bins
# Note an additional offset is used so that the 0th position
# is reserved for masked pairs.
self.embedding = torch.nn.Embedding(2 * bins + 2, pairwise_state_dim)
def forward(self, residue_index, mask=None):
"""
Input:
residue_index: B x L tensor of indices (dytpe=torch.long)
mask: B x L tensor of booleans
Output:
pairwise_state: B x L x L x pairwise_state_dim tensor of embeddings
"""
assert residue_index.dtype == torch.long
if mask is not None:
assert residue_index.shape == mask.shape
diff = residue_index[:, None, :] - residue_index[:, :, None]
diff = diff.clamp(-self.bins, self.bins)
diff = diff + self.bins + 1 # Add 1 to adjust for padding index.
if mask is not None:
mask = mask[:, None, :] * mask[:, :, None]
diff[mask == False] = 0
output = self.embedding(diff)
return output
class FoldingTrunk(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.cfg = FoldingTrunkConfig(**kwargs)
assert self.cfg.max_recycles > 0
c_s = self.cfg.sequence_state_dim
c_z = self.cfg.pairwise_state_dim
assert c_s % self.cfg.sequence_head_width == 0
assert c_z % self.cfg.pairwise_head_width == 0
block = TriangularSelfAttentionBlock
self.pairwise_positional_embedding = RelativePosition(self.cfg.position_bins, c_z)
self.blocks = nn.ModuleList(
[
block(
sequence_state_dim=c_s,
pairwise_state_dim=c_z,
sequence_head_width=self.cfg.sequence_head_width,
pairwise_head_width=self.cfg.pairwise_head_width,
dropout=self.cfg.dropout,
)
for i in range(self.cfg.num_blocks)
]
)
self.recycle_bins = 15
self.recycle_s_norm = nn.LayerNorm(c_s)
self.recycle_z_norm = nn.LayerNorm(c_z)
self.recycle_disto = nn.Embedding(self.recycle_bins, c_z)
self.recycle_disto.weight[0].detach().zero_()
self.structure_module = StructureModule(**self.cfg.structure_module) # type: ignore
self.trunk2sm_s = nn.Linear(c_s, self.structure_module.c_s)
self.trunk2sm_z = nn.Linear(c_z, self.structure_module.c_z)
self.chunk_size = self.cfg.chunk_size
def set_chunk_size(self, chunk_size):
# This parameter means the axial attention will be computed
# in a chunked manner. This should make the memory used more or less O(L) instead of O(L^2).
# It's equivalent to running a for loop over chunks of the dimension we're iterative over,
# where the chunk_size is the size of the chunks, so 128 would mean to parse 128-lengthed chunks.
self.chunk_size = chunk_size
def forward(self, seq_feats, pair_feats, true_aa, residx, mask, no_recycles: T.Optional[int] = None):
"""
Inputs:
seq_feats: B x L x C tensor of sequence features
pair_feats: B x L x L x C tensor of pair features
residx: B x L long tensor giving the position in the sequence
mask: B x L boolean tensor indicating valid residues
Output:
predicted_structure: B x L x (num_atoms_per_residue * 3) tensor wrapped in a Coordinates object
"""
device = seq_feats.device
s_s_0 = seq_feats
s_z_0 = pair_feats
if no_recycles is None:
no_recycles = self.cfg.max_recycles
else:
assert no_recycles >= 0, "Number of recycles must not be negative."
no_recycles += 1 # First 'recycle' is just the standard forward pass through the model.
def trunk_iter(s, z, residx, mask):
z = z + self.pairwise_positional_embedding(residx, mask=mask)
for block in self.blocks:
s, z = block(s, z, mask=mask, residue_index=residx, chunk_size=self.chunk_size)
return s, z
s_s = s_s_0
s_z = s_z_0
recycle_s = torch.zeros_like(s_s)
recycle_z = torch.zeros_like(s_z)
recycle_bins = torch.zeros(*s_z.shape[:-1], device=device, dtype=torch.int64)
assert no_recycles > 0
for recycle_idx in range(no_recycles):
with ExitStack() if recycle_idx == no_recycles - 1 else torch.no_grad():
# === Recycling ===
recycle_s = self.recycle_s_norm(recycle_s.detach())
recycle_z = self.recycle_z_norm(recycle_z.detach())
recycle_z += self.recycle_disto(recycle_bins.detach())
s_s, s_z = trunk_iter(s_s_0 + recycle_s, s_z_0 + recycle_z, residx, mask)
# === Structure module ===
structure = self.structure_module(
{"single": self.trunk2sm_s(s_s), "pair": self.trunk2sm_z(s_z)},
true_aa,
mask.float(),
)
recycle_s = s_s
recycle_z = s_z
# Distogram needs the N, CA, C coordinates, and bin constants same as alphafold.
recycle_bins = FoldingTrunk.distogram(
structure["positions"][-1][:, :, :3],
3.375,
21.375,
self.recycle_bins,
)
assert isinstance(structure, dict) # type: ignore
structure["s_s"] = s_s
structure["s_z"] = s_z
return structure
@staticmethod
def distogram(coords, min_bin, max_bin, num_bins):
# Coords are [... L x 3 x 3], where it's [N, CA, C] x 3 coordinates.
boundaries = torch.linspace(
min_bin,
max_bin,
num_bins - 1,
device=coords.device,
)
boundaries = boundaries**2
N, CA, C = [x.squeeze(-2) for x in coords.chunk(3, dim=-2)]
# Infer CB coordinates.
b = CA - N
c = C - CA
a = b.cross(c, dim=-1)
CB = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + CA
dists = (CB[..., None, :, :] - CB[..., :, None, :]).pow(2).sum(dim=-1, keepdims=True)
bins = torch.sum(dists > boundaries, dim=-1) # [..., L, L]
return bins
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