triangular_attention.py

# 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 partialmethod
import math
import torch
import torch.nn as nn

from openfold.model.primitives import Linear, Attention
from openfold.utils.tensor_utils import (
    chunk_layer,
    permute_final_dims,
    flatten_final_dims,
)


class TriangleAttention(nn.Module):
    def __init__(
        self, c_in, c_hidden, no_heads, starting, inf=1e9
    ):
        """
        Args:
            c_in:
                Input channel dimension
            c_hidden:
                Overall hidden channel dimension (not per-head)
            no_heads:
                Number of attention heads
        """
        super(TriangleAttention, self).__init__()

        self.c_in = c_in
        self.c_hidden = c_hidden
        self.no_heads = no_heads
        self.starting = starting
        self.inf = inf

        self.layer_norm = nn.LayerNorm(self.c_in)

        self.linear = Linear(c_in, 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 forward(self, x, chunk_size, mask=None):
        """
        Args:
            x:
                [*, I, J, C_in] input tensor (e.g. the pair representation)
        Returns:
            [*, I, J, C_in] output tensor
        """
        if mask is None:
            # [*, I, J]
            mask = x.new_ones(
                x.shape[:-1],
            )

        # Shape annotations assume self.starting. Else, I and J are flipped
        if not self.starting:
            x = x.transpose(-2, -3)
            mask = mask.transpose(-1, -2)

        # [*, I, J, C_in]
        x = self.layer_norm(x)

        # [*, I, 1, 1, J]
        mask_bias = (self.inf * (mask - 1))[..., :, None, None, :]

        # [*, H, I, J]
        triangle_bias = permute_final_dims(self.linear(x), (2, 0, 1))

        # [*, 1, H, I, J]
        triangle_bias = triangle_bias.unsqueeze(-4)

        mha_inputs = {
            "q_x": x,
            "k_x": x,
            "v_x": x,
            "biases": [mask_bias, triangle_bias],
        }
        if chunk_size is not None:
            x = chunk_layer(
                self.mha,
                mha_inputs,
                chunk_size=chunk_size,
                no_batch_dims=len(x.shape[:-2]),
            )
        else:
            x = self.mha(**mha_inputs)

        if not self.starting:
            x = x.transpose(-2, -3)

        return x


class TriangleAttentionStartingNode(TriangleAttention):
    """
    Implements Algorithm 13.
    """

    __init__ = partialmethod(TriangleAttention.__init__, starting=True)


class TriangleAttentionEndingNode(TriangleAttention):
    """
    Implements Algorithm 14.
    """

    __init__ = partialmethod(TriangleAttention.__init__, starting=False)