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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
from typing import Optional
import torch
import torch.nn as nn
from protenix.openfold_local.model.primitives import Linear
from protenix.openfold_local.utils.chunk_utils import chunk_layer
from protenix.openfold_local.utils.precision_utils import is_fp16_enabled
class OuterProductMean(nn.Module):
"""
Implements Algorithm 10.
"""
def __init__(self, c_m, c_z, c_hidden, eps=1e-3):
"""
Args:
c_m:
MSA embedding channel dimension
c_z:
Pair embedding channel dimension
c_hidden:
Hidden channel dimension
"""
super(OuterProductMean, self).__init__()
self.c_m = c_m
self.c_z = c_z
self.c_hidden = c_hidden
self.eps = eps
self.layer_norm = nn.LayerNorm(c_m)
self.linear_1 = Linear(c_m, c_hidden)
self.linear_2 = Linear(c_m, c_hidden)
self.linear_out = Linear(c_hidden**2, c_z, init="final")
def _opm(self, a, b):
# [*, N_res, N_res, C, C]
outer = torch.einsum("...bac,...dae->...bdce", a, b)
# [*, N_res, N_res, C * C]
outer = outer.reshape(outer.shape[:-2] + (-1,))
# [*, N_res, N_res, C_z]
outer = self.linear_out(outer)
return outer
@torch.jit.ignore
def _chunk(self, a: torch.Tensor, b: torch.Tensor, chunk_size: int) -> torch.Tensor:
# Since the "batch dim" in this case is not a true batch dimension
# (in that the shape of the output depends on it), we need to
# iterate over it ourselves
a_reshape = a.reshape((-1,) + a.shape[-3:])
b_reshape = b.reshape((-1,) + b.shape[-3:])
out = []
for a_prime, b_prime in zip(a_reshape, b_reshape):
outer = chunk_layer(
partial(self._opm, b=b_prime),
{"a": a_prime},
chunk_size=chunk_size,
no_batch_dims=1,
)
out.append(outer)
# For some cursed reason making this distinction saves memory
if len(out) == 1:
outer = out[0].unsqueeze(0)
else:
outer = torch.stack(out, dim=0)
outer = outer.reshape(a.shape[:-3] + outer.shape[1:])
return outer
def _forward(
self,
m: torch.Tensor,
mask: Optional[torch.Tensor] = None,
chunk_size: Optional[int] = None,
inplace_safe: bool = False,
) -> torch.Tensor:
"""
Args:
m:
[*, N_seq, N_res, C_m] MSA embedding
mask:
[*, N_seq, N_res] MSA mask
Returns:
[*, N_res, N_res, C_z] pair embedding update
"""
if mask is None:
mask = m.new_ones(m.shape[:-1])
# [*, N_seq, N_res, C_m]
ln = self.layer_norm(m)
# [*, N_seq, N_res, C]
mask = mask.unsqueeze(-1)
a = self.linear_1(ln)
a = a * mask
b = self.linear_2(ln)
b = b * mask
del ln
a = a.transpose(-2, -3)
b = b.transpose(-2, -3)
if chunk_size is not None:
outer = self._chunk(a, b, chunk_size)
else:
outer = self._opm(a, b)
# [*, N_res, N_res, 1]
norm = torch.einsum("...abc,...adc->...bdc", mask, mask)
norm = norm + self.eps
# [*, N_res, N_res, C_z]
if inplace_safe:
outer /= norm
else:
outer = outer / norm
return outer
def forward(
self,
m: torch.Tensor,
mask: Optional[torch.Tensor] = None,
chunk_size: Optional[int] = None,
inplace_safe: bool = False,
) -> torch.Tensor:
if is_fp16_enabled():
with torch.cuda.amp.autocast(enabled=False):
return self._forward(m.float(), mask, chunk_size, inplace_safe)
else:
return self._forward(m, mask, chunk_size, inplace_safe)
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