mirror of https://github.com/hpcaitech/ColossalAI
130 lines
3.7 KiB
Python
130 lines
3.7 KiB
Python
# 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 .primitives import Linear
|
|
from .tensor_utils import chunk_layer
|
|
|
|
|
|
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)
|
|
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
|
|
) -> 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]
|
|
m = self.layer_norm(m)
|
|
|
|
# [*, N_seq, N_res, C]
|
|
mask = mask.unsqueeze(-1)
|
|
a = self.linear_1(m) * mask
|
|
b = self.linear_2(m) * mask
|
|
|
|
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)
|
|
|
|
# [*, N_res, N_res, C_z]
|
|
outer = outer / (self.eps + norm)
|
|
|
|
return outer
|