LDLMIn construction

Contextual evidence from Latent decoder

The latent decoder owns one position table, the mask token’s vector copied from the token table of a GPT-2 loaded for the purpose and frozen because the feature model is configured frozen, an MLP for the queries, the blocks and a scale for the prepared queries.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Latent decoder

Every query is the mask vector: it passes the MLP and a scale, positions are added; the latents take the same position table; the blocks are applied; every position counts as masked.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Latent decoder

The latent decoder: 12 heads of 64 features, query and key normalization, softcap 50, 6 layers, MLP multiplier 4; the token decoder has 3 blocks.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Latent decoder

The feature model is GPT-2, frozen; the hidden state at index −3 is extracted; the encoder and the decoder are of the Perceiver kind; the file’s vocabulary size and mask token ID are defaults that the trainer overwrites with the tokenizer’s; there are as many latents as positions, of 768 features.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from LayerNorm

The denoiser’s and the token decoder’s LayerNorm computes in float32 and owns a gain and no bias.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from LayerNorm

PyTorch 2.4.1 functional LayerNorm defaults to epsilon 1e-5.

torch-layer-norm · pytorch/pytorch / torch/nn/functional.py
Pinned revision ee1b6804381c ↗ · lines 2561–2577

def layer_norm(
    input: Tensor,
    normalized_shape: List[int],
    weight: Optional[Tensor] = None,
    bias: Optional[Tensor] = None,
    eps: float = 1e-5,
) -> Tensor:
    r"""Apply Layer Normalization for last certain number of dimensions.

    See :class:`~torch.nn.LayerNorm` for details.
    """
    if has_torch_function_variadic(input, weight, bias):
        return handle_torch_function(
            layer_norm, (input, weight, bias), input, normalized_shape, weight=weight, bias=bias, eps=eps
        )
    return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)

Contextual evidence from Add attention update

The residual update is scale · dropout(x) added to the residual; modulation is x · (1 + scale) + shift.

The implementation, pinned at revision e18c42acdfb7, not yet public

Contextual evidence from Add attention update

A block normalizes, modulates by the time condition’s shift and scale, projects to queries, keys and values, rotates queries and keys, attends over all positions with softcap, and adds the projected output scaled by a gate; the MLP branch does the same with its own shift, scale and gate.

The implementation, pinned at revision e18c42acdfb7, not yet public