models/simplefold/torch/confidence_module.py

#
# For licensing see accompanying LICENSE file.
# Copyright (c) 2025 Apple Inc. Licensed under MIT License.
#

import torch
from torch import nn


def compute_aggregated_metric(logits, end=1.0):
    """Compute the metric from the logits.

    Parameters
    ----------
    logits : torch.Tensor
        The logits of the metric
    end : float
        Max value of the metric, by default 1.0

    Returns
    -------
    Tensor
        The metric value

    """
    num_bins = logits.shape[-1]
    bin_width = end / num_bins
    bounds = torch.arange(
        start=0.5 * bin_width, end=end, step=bin_width, device=logits.device
    )
    probs = nn.functional.softmax(logits, dim=-1)
    plddt = torch.sum(
        probs * bounds.view(*((1,) * len(probs.shape[:-1])), *bounds.shape),
        dim=-1,
    )
    return plddt


class ConfidenceModule(nn.Module):
    def __init__(
        self,
        hidden_size,
        transformer_blocks,
        num_plddt_bins=50,
    ):
        super().__init__()
        self.transformer_blocks = transformer_blocks
        self.to_plddt_logits = nn.Sequential(
            nn.Linear(hidden_size, hidden_size),
            nn.LayerNorm(hidden_size),
            nn.SiLU(),
            nn.Linear(hidden_size, num_plddt_bins),
        )

    def forward(
        self,
        latent,
        feats,
    ):
        token_pe_pos = torch.cat(
            [
                feats["residue_index"].unsqueeze(-1).float(),        # (B, M, 1)
                feats["entity_id"].unsqueeze(-1).float(),            # (B, M, 1)
                feats["asym_id"].unsqueeze(-1).float(),              # (B, M, 1)
                feats["sym_id"].unsqueeze(-1).float(),               # (B, M, 1)
            ],
            dim=-1,
        )  

        latent = self.transformer_blocks(
            latents=latent, 
            c=None,
            pos=token_pe_pos,
        )

        # Compute the pLDDT
        plddt_logits = self.to_plddt_logits(latent)

        # Compute the aggregated pLDDT
        plddt = compute_aggregated_metric(plddt_logits)

        return dict(
            plddt=plddt,
            plddt_logits=plddt_logits,
        )