Structure confidence heads

Linear-Stack pLDDT Head

MediumRaw PyTorchAbout 20 min2 public examples plus private tests

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Your task

Produce one unnormalized score per pLDDT local-confidence bin for each atom slot of each token. Higher logits favor their corresponding bins; this head returns those scores. Normalize the feature vector, apply two affine hidden layers without activation, then project to atom/bin logits.

Function signature

plddt_linear_stack_head(
    activations: dict,
    parameters: dict,
    masks: dict,
    norm_eps: float = 1e-05,
) -> torch.Tensor

Worked example

LayerNorm with norm_eps=3 gives [-0.5, 0.5] and [0.5, -0.5]. Both hidden weights and the output weight are identities; the second bias adds 0.25, giving [-0.25, 0.75] and [0.75, -0.25]. Negative entries stay negative: there is no ReLU in this head.

import torch

activations = {
    'single': torch.tensor([[[1.0, 3.0], [2.0, 0.0]]], dtype=torch.float64, requires_grad=True),
    'W1': torch.tensor([[1.0, 0.0], [0.0, 1.0]], dtype=torch.float64, requires_grad=True),
    'W2': torch.tensor([[1.0, 0.0], [0.0, 1.0]], dtype=torch.float64, requires_grad=True),
}
parameters = {
    'single_scale': torch.tensor([1.0, 1.0], dtype=torch.float64, requires_grad=True),
    'single_offset': torch.tensor([0.0, 0.0], dtype=torch.float64, requires_grad=True),
    'b1': torch.tensor([0.0, 0.0], dtype=torch.float64, requires_grad=True),
    'b2': torch.tensor([0.25, 0.25], dtype=torch.float64, requires_grad=True),
    'W_plddt': torch.tensor([[[1.0, 0.0]], [[0.0, 1.0]]], dtype=torch.float64, requires_grad=True),
    'b_plddt': torch.tensor([[0.0, 0.0]], dtype=torch.float64, requires_grad=True),
}
masks = {
    'token_mask': torch.tensor([[True, True]], dtype=torch.bool),
    'atom_mask': torch.tensor([[[True], [True]]], dtype=torch.bool),
}
norm_eps = 3.0

result = plddt_linear_stack_head(activations=activations, parameters=parameters, masks=masks, norm_eps=norm_eps)

Expected return

torch.tensor([[[[-0.25, 0.75]], [[0.75, -0.25]]]], dtype=torch.float64)

Parameters

Parameter	Shape and meaning
`activations`dict	Supplied intermediate tensors.
`parameters`dict	Supplied model parameters, keyed as shown below.
`masks`dict	Supplied masks. Each field below defines what True includes or permits.
`norm_eps`floatDefault `1e-05`	Positive value inside the square root of every configured LayerNorm variance.

Return

torch.Tensor · $[B, N, A, L]$

Return Predicted logits in atom-slot/bin order.

Required behavior

single is $[B,N,Cs]$ ; token_mask is $[B,N]$ , atom_mask is $[B,N,A]$ , and True means present. Sanitize invalid single-token payloads before the learned head.
Apply affine population LayerNorm to single, then two affine hidden projections with NO intervening activation. For normalized heads use eps=norm_eps (default 1e-5) inside the population-variance square root.
Project hidden features with $W_{plddt}:[H_2,A,L]$ and add $b_{plddt}:[A,L]$ . Return $[B,N,A,L]$ in bin-logit order, without softmax or pLDDT scaling.
Zero every output logit whose token is absent or atom slot is absent, including after adding biases. Supply all parameters; do not initialize new layers.

Why this matters

Different heads answer different questions: pLDDT describes local accuracy, a distogram describes distance, and PAE describes error after alignment in a particular residue’s frame. Their probabilities feed separate summaries; they are not interchangeable confidence scales.

Starter code

You write the body of this function in the BioTorch workspace. Your code runs in an isolated PyTorch sandbox; Run checks the public examples and Submit grades the full test suite, including gradients.

import torch


def plddt_linear_stack_head(
    activations: dict,
    parameters: dict,
    masks: dict,
    norm_eps: float = 1e-05,
) -> torch.Tensor:
    """Return a tensor with shape (B, N, A, L)."""
    # Implement the operation. Inputs and test setup are supplied by BioTorch.
    raise NotImplementedError("Implement plddt_linear_stack_head")

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Sources and further reading

A primitive exercise, not an end-to-end reproduction of the named models. No affiliation or endorsement is implied.