osudiffusion/positional_embedding.py

import math

import torch


def encode_single(d_model, value, max_period=10000.0):
    """
    :param d_model: dimension of the model
    :param value: the value to encode
    :param max_period: the maximum allowed value
    :return: length*d_model position matrix
    """
    if d_model % 2 != 0:
        raise ValueError(
            "Cannot use sin/cos positional encoding with "
            "odd dim (got dim={:d})".format(d_model),
        )
    pe = torch.zeros(d_model)
    div_term = torch.exp(
        torch.arange(0, d_model, 2, dtype=torch.float)
        * -(math.log(max_period) / d_model),
    )
    pe[0::2] = torch.sin(value * div_term)
    pe[1::2] = torch.cos(value * div_term)

    return pe


def timestep_embedding(t, dim, max_period=10000):
    """
    Create sinusoidal timestep embeddings.
    :param t: a 1-D Tensor of N indices, one per batch element.
                      These may be fractional.
    :param dim: the dimension of the output.
    :param max_period: controls the minimum frequency of the embeddings.
    :return: an (N, D) Tensor of positional embeddings.
    """
    # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
    half = dim // 2
    freqs = torch.exp(
        -math.log(max_period)
        * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device)
        / half,
    )
    args = t[:, None].float() * freqs[None]
    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
    if dim % 2:
        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
    return embedding


def offset_sequence_embedding(t, dim, max_period=10000):
    """
    Create sinusoidal timestep embeddings.
    :param t: an (N, T) Tensor of sequences of time offsets
    :param dim: the dimension of the output.
    :param max_period: controls the minimum frequency of the embeddings.
    :return: an (N, T, dim) Tensor of positional embeddings.
    """
    N, T = t.shape
    flattened = torch.flatten(t)
    embedding = timestep_embedding(flattened, dim, max_period)
    return torch.reshape(embedding, (N, T, dim))


def position_sequence_embedding(t, dim, max_period=10000):
    """
    Create sinusoidal timestep embeddings.
    :param t: an (N, T, D) Tensor of sequences of D dimensional positions.
    :param dim: the dimension of the output.
    :param max_period: controls the minimum frequency of the embeddings.
    :return: an (N, T, D * dim) Tensor of positional embeddings.
    """
    N, T, D = t.shape
    flattened = torch.flatten(t)
    embedding = timestep_embedding(flattened, dim, max_period)
    return torch.reshape(embedding, (N, T, D * dim))


def positionalencoding(d_model, values, max_period=10000.0):
    """
    :param d_model: dimension of the model
    :param values: the values to encode
    :param max_period: the maximum allowed value
    :return: length*d_model position matrix
    """
    if d_model % 2 != 0:
        raise ValueError(
            "Cannot use sin/cos positional encoding with "
            "odd dim (got dim={:d})".format(d_model),
        )
    pe = torch.zeros(len(values), d_model)
    position = values.unsqueeze(1)
    div_term = torch.exp(
        torch.arange(0, d_model, 2, dtype=torch.float)
        * -(math.log(max_period) / d_model),
    )
    pe[:, 0::2] = torch.sin(position * div_term)
    pe[:, 1::2] = torch.cos(position * div_term)

    return pe


def positionalencoding1d(d_model, length):
    """
    :param d_model: dimension of the model
    :param length: length of positions
    :return: length*d_model position matrix
    """
    if d_model % 2 != 0:
        raise ValueError(
            "Cannot use sin/cos positional encoding with "
            "odd dim (got dim={:d})".format(d_model),
        )
    pe = torch.zeros(2, d_model)
    position = torch.arange(-50, 50, 100).unsqueeze(1)
    div_term = torch.exp(
        torch.arange(0, d_model, 2, dtype=torch.float) * -(math.log(10000.0) / d_model),
    )
    pe[:, 0::2] = torch.sin(position.float() * div_term)
    pe[:, 1::2] = torch.cos(position.float() * div_term)

    return pe


def positionalencoding2d(d_model, height, width):
    """
    :param d_model: dimension of the model
    :param height: height of the positions
    :param width: width of the positions
    :return: d_model*height*width position matrix
    """
    if d_model % 4 != 0:
        raise ValueError(
            "Cannot use sin/cos positional encoding with "
            "odd dimension (got dim={:d})".format(d_model),
        )
    pe = torch.zeros(d_model, height, width)
    # Each dimension use half of d_model
    d_model = int(d_model / 2)
    div_term = torch.exp(torch.arange(0.0, d_model, 2) * -(math.log(10000.0) / d_model))
    pos_w = torch.arange(0.0, width).unsqueeze(1)
    pos_h = torch.arange(0.0, height).unsqueeze(1)
    pe[0:d_model:2, :, :] = (
        torch.sin(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1)
    )
    pe[1:d_model:2, :, :] = (
        torch.cos(pos_w * div_term).transpose(0, 1).unsqueeze(1).repeat(1, height, 1)
    )
    pe[d_model::2, :, :] = (
        torch.sin(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width)
    )
    pe[d_model + 1 :: 2, :, :] = (
        torch.cos(pos_h * div_term).transpose(0, 1).unsqueeze(2).repeat(1, 1, width)
    )

    return pe


if __name__ == "__main__":
    import matplotlib.pyplot as plt

    pe = positionalencoding(128, torch.tensor([-50, 50]))
    plt.imshow(pe)
    plt.show()