module.py

import math

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor


class Transpose(nn.Identity):
    """(N, T, D) -> (N, D, T)"""

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return input.transpose(1, 2)


class AdaptiveLayerNorm(nn.Module):
    r"""Adaptive Layer Normalization"""

    def __init__(self, d_model, norm) -> None:
        super(AdaptiveLayerNorm, self).__init__()
        self.project_layer = nn.Linear(d_model, 2 * d_model)
        self.norm = norm
        self.d_model = d_model
        self.eps = self.norm.eps

    def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor:
        if isinstance(input, tuple):
            input, embedding = input
            weight, bias = torch.split(
                self.project_layer(embedding),
                split_size_or_sections=self.d_model,
                dim=-1,
            )
            return (weight * self.norm(input) + bias, embedding)

        weight, bias = torch.split(
            self.project_layer(embedding),
            split_size_or_sections=self.d_model,
            dim=-1,
        )
        return weight * self.norm(input) + bias


class TokenEmbedding(nn.Module):
    def __init__(
        self,
        dim_model: int,
        vocab_size: int,
        dropout: float = 0.0,
    ):
        super().__init__()

        self.vocab_size = vocab_size
        self.dim_model = dim_model

        self.dropout = torch.nn.Dropout(p=dropout)
        self.word_embeddings = nn.Embedding(self.vocab_size, self.dim_model)

    @property
    def weight(self) -> torch.Tensor:
        return self.word_embeddings.weight

    def embedding(self, index: int) -> torch.Tensor:
        return self.word_embeddings.weight[index : index + 1]

    def forward(self, x: torch.Tensor):
        X = self.word_embeddings(x)
        X = self.dropout(X)

        return X


class SinePositionalEmbedding(nn.Module):
    def __init__(
        self,
        dim_model: int,
        dropout: float = 0.0,
        scale: bool = False,
        alpha: bool = False,
    ):
        super().__init__()
        self.dim_model = dim_model
        self.x_scale = math.sqrt(dim_model) if scale else 1.0
        self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
        self.dropout = torch.nn.Dropout(p=dropout)

        self.reverse = False
        self.pe = None
        self.extend_pe(torch.tensor(0.0).expand(1, 4000))

    def extend_pe(self, x):
        """Reset the positional encodings."""
        if self.pe is not None:
            if self.pe.size(1) >= x.size(1):
                if self.pe.dtype != x.dtype or self.pe.device != x.device:
                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                return
        pe = torch.zeros(x.size(1), self.dim_model)
        if self.reverse:
            position = torch.arange(x.size(1) - 1, -1, -1.0, dtype=torch.float32).unsqueeze(1)
        else:
            position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
        div_term = torch.exp(
            torch.arange(0, self.dim_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.dim_model)
        )
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)
        self.pe = pe.to(device=x.device, dtype=x.dtype).detach()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        self.extend_pe(x)
        output = x.unsqueeze(-1) if x.ndim == 2 else x
        output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)]
        return self.dropout(output)


class PreNet(nn.Module):
    """PreNet for NAR model"""

    def __init__(self, nar_d_model=1024) -> None:
        super().__init__()
        # self.nar_text_prenet = nn.Sequential(
        #     Transpose(),
        #     nn.Conv1d(nar_d_model, nar_d_model, kernel_size=5, padding="same"),
        #     nn.BatchNorm1d(nar_d_model),
        #     nn.ReLU(),
        #     nn.Dropout(0.5),
        #     nn.Conv1d(nar_d_model, nar_d_model, kernel_size=5, padding="same"),
        #     nn.BatchNorm1d(nar_d_model),
        #     nn.ReLU(),
        #     nn.Dropout(0.5),
        #     nn.Conv1d(nar_d_model, nar_d_model, kernel_size=5, padding="same"),
        #     nn.BatchNorm1d(nar_d_model),
        #     nn.ReLU(),
        #     nn.Dropout(0.5),
        #     Transpose(),
        #     nn.Linear(nar_d_model, nar_d_model),
        # )
        self.nar_audio_prenet = nn.Sequential(
            nn.Linear(nar_d_model, 256),
            nn.ReLU(),
            nn.Dropout(0.25),
            nn.Linear(256, 256),
            nn.ReLU(),
            nn.Dropout(0.25),
            nn.Linear(256, nar_d_model),
        )

    def forward(self, input):
        return self.nar_audio_prenet(input)


# https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py
def top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1):
    """Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
    Args:
        logits: logits distribution shape (batch size, vocabulary size)
        if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
        if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
            Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
        Make sure we keep at least min_tokens_to_keep per batch example in the output
    From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
    """
    if top_k > 0:
        top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1))  # Safety check
        # Remove all tokens with a probability less than the last token of the top-k
        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
        logits[indices_to_remove] = filter_value

    if top_p < 1.0:
        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)

        # Remove tokens with cumulative probability above the threshold (token with 0 are kept)
        sorted_indices_to_remove = cumulative_probs > top_p
        if min_tokens_to_keep > 1:
            # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
            sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
        # Shift the indices to the right to keep also the first token above the threshold
        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
        sorted_indices_to_remove[..., 0] = 0

        # scatter sorted tensors to original indexing
        indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
        logits[indices_to_remove] = filter_value
    return logits


def top_k_sampling(logits, top_k=10, top_p=1.0, temperature=1.0):
    # temperature: (`optional`) float
    #     The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
    # top_k: (`optional`) int
    #     The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50.
    # top_p: (`optional`) float
    #     The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1.

    # Temperature (higher temperature => more likely to sample low probability tokens)
    if temperature != 1.0:
        logits = logits / temperature
    # Top-p/top-k filtering
    logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
    # Sample
    token = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
    return token