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# Copyright 2021 AlQuraishi Laboratory
# 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
import torch
import torch.nn as nn
import torch.utils.checkpoint
from typing import Optional, List, Tuple
from dockformerpp.model.primitives import (
Linear,
LayerNorm,
Attention,
)
from dockformerpp.utils.tensor_utils import permute_final_dims
class SingleAttention(nn.Module):
def __init__(
self,
c_in,
c_hidden,
no_heads,
pair_bias=False,
c_z=None,
inf=1e9,
):
"""
Args:
c_in:
Input channel dimension
c_hidden:
Per-head hidden channel dimension
no_heads:
Number of attention heads
pair_bias:
Whether to use pair embedding bias
c_z:
Pair embedding channel dimension. Ignored unless pair_bias
is true
inf:
A large number to be used in computing the attention mask
"""
super(SingleAttention, self).__init__()
self.c_in = c_in
self.c_hidden = c_hidden
self.no_heads = no_heads
self.pair_bias = pair_bias
self.c_z = c_z
self.inf = inf
self.layer_norm_m = LayerNorm(self.c_in)
self.layer_norm_z = None
self.linear_z = None
if self.pair_bias:
self.layer_norm_z = LayerNorm(self.c_z)
self.linear_z = Linear(
self.c_z, self.no_heads, bias=False, init="normal"
)
self.mha = Attention(
self.c_in,
self.c_in,
self.c_in,
self.c_hidden,
self.no_heads,
)
def _prep_inputs(self,
m: torch.Tensor,
z: Optional[torch.Tensor],
mask: Optional[torch.Tensor],
inplace_safe: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
if mask is None:
# [*, N_res]
mask = m.new_ones(m.shape[:-1])
# [*, 1, 1, N_res]
mask_bias = (self.inf * (mask - 1))[..., :, None, None, :]
if (self.pair_bias and
z is not None and # For the
self.layer_norm_z is not None and # benefit of
self.linear_z is not None # TorchScript
):
chunks = []
for i in range(0, z.shape[-3], 256):
z_chunk = z[..., i: i + 256, :, :]
# [*, N_res, N_res, C_z]
z_chunk = self.layer_norm_z(z_chunk)
# [*, N_res, N_res, no_heads]
z_chunk = self.linear_z(z_chunk)
chunks.append(z_chunk)
z = torch.cat(chunks, dim=-3)
# [*, no_heads, N_res, N_res]
z = permute_final_dims(z, (2, 0, 1))
return m, mask_bias, z
def forward(self,
m: torch.Tensor,
z: Optional[torch.Tensor] = None,
mask: Optional[torch.Tensor] = None,
use_memory_efficient_kernel: bool = False,
use_lma: bool = False,
inplace_safe: bool = False,
) -> torch.Tensor:
"""
Args:
m:
[*, N_res, C_m] single embedding
z:
[*, N_res, N_res, C_z] pair embedding. Required only if pair_bias is True
mask:
[*, N_res] single mask
"""
m, mask_bias, z = self._prep_inputs(
m, z, mask, inplace_safe=inplace_safe
)
biases = [mask_bias]
if(z is not None):
biases.append(z)
m = self.layer_norm_m(m)
m = self.mha(
q_x=m,
kv_x=m,
biases=biases,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_lma=use_lma,
)
return m
class SingleRowAttentionWithPairBias(SingleAttention):
"""
Implements Algorithm 7.
"""
def __init__(self, c_m, c_z, c_hidden, no_heads, inf=1e9):
"""
Args:
c_m:
Input channel dimension
c_z:
Pair embedding channel dimension
c_hidden:
Per-head hidden channel dimension
no_heads:
Number of attention heads
inf:
Large number used to construct attention masks
"""
super(SingleRowAttentionWithPairBias, self).__init__(
c_m,
c_hidden,
no_heads,
pair_bias=True,
c_z=c_z,
inf=inf,
)
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