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# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
import math
from typing import Optional, List
import torch
import torch.nn as nn
from openfold.model.primitives import Linear, LayerNorm, Attention
from openfold.model.dropout import (
DropoutRowwise,
DropoutColumnwise,
)
from openfold.model.pair_transition import PairTransition
from openfold.model.triangular_attention import (
TriangleAttentionStartingNode,
TriangleAttentionEndingNode,
)
from openfold.model.triangular_multiplicative_update import (
TriangleMultiplicationOutgoing,
TriangleMultiplicationIncoming,
)
from openfold.utils.checkpointing import checkpoint_blocks
from openfold.utils.tensor_utils import (
chunk_layer,
permute_final_dims,
flatten_final_dims,
)
class TemplatePointwiseAttention(nn.Module):
"""
Implements Algorithm 17.
"""
def __init__(self, c_t, c_z, c_hidden, no_heads, inf, **kwargs):
"""
Args:
c_t:
Template embedding channel dimension
c_z:
Pair embedding channel dimension
c_hidden:
Hidden channel dimension
"""
super(TemplatePointwiseAttention, self).__init__()
self.c_t = c_t
self.c_z = c_z
self.c_hidden = c_hidden
self.no_heads = no_heads
self.inf = inf
self.mha = Attention(
self.c_z,
self.c_t,
self.c_t,
self.c_hidden,
self.no_heads,
gating=False,
)
def _chunk(self,
z: torch.Tensor,
t: torch.Tensor,
biases: List[torch.Tensor],
chunk_size: int,
) -> torch.Tensor:
mha_inputs = {
"q_x": z,
"kv_x": t,
"biases": biases,
}
return chunk_layer(
self.mha,
mha_inputs,
chunk_size=chunk_size,
no_batch_dims=len(z.shape[:-2]),
)
def forward(self,
t: torch.Tensor,
z: torch.Tensor,
template_mask: Optional[torch.Tensor] = None,
chunk_size: Optional[int] = None
) -> torch.Tensor:
"""
Args:
t:
[*, N_templ, N_res, N_res, C_t] template embedding
z:
[*, N_res, N_res, C_t] pair embedding
template_mask:
[*, N_templ] template mask
Returns:
[*, N_res, N_res, C_z] pair embedding update
"""
if template_mask is None:
template_mask = t.new_ones(t.shape[:-3])
bias = self.inf * (template_mask[..., None, None, None, None, :] - 1)
# [*, N_res, N_res, 1, C_z]
z = z.unsqueeze(-2)
# [*, N_res, N_res, N_temp, C_t]
t = permute_final_dims(t, (1, 2, 0, 3))
# [*, N_res, N_res, 1, C_z]
biases = [bias]
if chunk_size is not None:
z = self._chunk(z, t, biases, chunk_size)
else:
z = self.mha(q_x=z, kv_x=t, biases=biases)
# [*, N_res, N_res, C_z]
z = z.squeeze(-2)
return z
class TemplatePairStackBlock(nn.Module):
def __init__(
self,
c_t: int,
c_hidden_tri_att: int,
c_hidden_tri_mul: int,
no_heads: int,
pair_transition_n: int,
dropout_rate: float,
inf: float,
**kwargs,
):
super(TemplatePairStackBlock, self).__init__()
self.c_t = c_t
self.c_hidden_tri_att = c_hidden_tri_att
self.c_hidden_tri_mul = c_hidden_tri_mul
self.no_heads = no_heads
self.pair_transition_n = pair_transition_n
self.dropout_rate = dropout_rate
self.inf = inf
self.dropout_row = DropoutRowwise(self.dropout_rate)
self.dropout_col = DropoutColumnwise(self.dropout_rate)
self.tri_att_start = TriangleAttentionStartingNode(
self.c_t,
self.c_hidden_tri_att,
self.no_heads,
inf=inf,
)
self.tri_att_end = TriangleAttentionEndingNode(
self.c_t,
self.c_hidden_tri_att,
self.no_heads,
inf=inf,
)
self.tri_mul_out = TriangleMultiplicationOutgoing(
self.c_t,
self.c_hidden_tri_mul,
)
self.tri_mul_in = TriangleMultiplicationIncoming(
self.c_t,
self.c_hidden_tri_mul,
)
self.pair_transition = PairTransition(
self.c_t,
self.pair_transition_n,
)
def forward(self,
z: torch.Tensor,
mask: torch.Tensor,
chunk_size: Optional[int] = None,
_mask_trans: bool = True
):
single_templates = [
t.unsqueeze(-4) for t in torch.unbind(z, dim=-4)
]
single_templates_masks = [
m.unsqueeze(-3) for m in torch.unbind(mask, dim=-3)
]
for i in range(len(single_templates)):
single = single_templates[i]
single_mask = single_templates_masks[i]
single = single + self.dropout_row(
self.tri_att_start(
single,
chunk_size=chunk_size,
mask=single_mask
)
)
single = single + self.dropout_col(
self.tri_att_end(
single,
chunk_size=chunk_size,
mask=single_mask
)
)
single = single + self.dropout_row(
self.tri_mul_out(
single,
mask=single_mask
)
)
single = single + self.dropout_row(
self.tri_mul_in(
single,
mask=single_mask
)
)
single = single + self.pair_transition(
single,
mask=single_mask if _mask_trans else None,
chunk_size=chunk_size,
)
single_templates[i] = single
z = torch.cat(single_templates, dim=-4)
return z
class TemplatePairStack(nn.Module):
"""
Implements Algorithm 16.
"""
def __init__(
self,
c_t,
c_hidden_tri_att,
c_hidden_tri_mul,
no_blocks,
no_heads,
pair_transition_n,
dropout_rate,
blocks_per_ckpt,
inf=1e9,
**kwargs,
):
"""
Args:
c_t:
Template embedding channel dimension
c_hidden_tri_att:
Per-head hidden dimension for triangular attention
c_hidden_tri_att:
Hidden dimension for triangular multiplication
no_blocks:
Number of blocks in the stack
pair_transition_n:
Scale of pair transition (Alg. 15) hidden dimension
dropout_rate:
Dropout rate used throughout the stack
blocks_per_ckpt:
Number of blocks per activation checkpoint. None disables
activation checkpointing
"""
super(TemplatePairStack, self).__init__()
self.blocks_per_ckpt = blocks_per_ckpt
self.blocks = nn.ModuleList()
for _ in range(no_blocks):
block = TemplatePairStackBlock(
c_t=c_t,
c_hidden_tri_att=c_hidden_tri_att,
c_hidden_tri_mul=c_hidden_tri_mul,
no_heads=no_heads,
pair_transition_n=pair_transition_n,
dropout_rate=dropout_rate,
inf=inf,
)
self.blocks.append(block)
self.layer_norm = LayerNorm(c_t)
def forward(
self,
t: torch.tensor,
mask: torch.tensor,
chunk_size: int,
_mask_trans: bool = True,
):
"""
Args:
t:
[*, N_templ, N_res, N_res, C_t] template embedding
mask:
[*, N_templ, N_res, N_res] mask
Returns:
[*, N_templ, N_res, N_res, C_t] template embedding update
"""
if(mask.shape[-3] == 1):
expand_idx = list(mask.shape)
expand_idx[-3] = t.shape[-4]
mask = mask.expand(*expand_idx)
t, = checkpoint_blocks(
blocks=[
partial(
b,
mask=mask,
chunk_size=chunk_size,
_mask_trans=_mask_trans,
)
for b in self.blocks
],
args=(t,),
blocks_per_ckpt=self.blocks_per_ckpt if self.training else None,
)
t = self.layer_norm(t)
return t
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