vampnet/modules/transformer.py

import math
import logging
from typing import Optional, Tuple, Union, List

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
import loralib as lora
import audiotools as at

from .activations import get_activation
from .layers import CodebookEmbedding
from .layers import FiLM
from .layers import SequentialWithFiLM
from .layers import WNConv1d
from ..util import scalar_to_batch_tensor, codebook_flatten, codebook_unflatten
from ..mask import _gamma

LORA_R = 8

# def log(t, eps=1e-20):
#     return torch.log(t + eps)


def gumbel_noise_like(t):
    noise = torch.zeros_like(t).uniform_(1e-20, 1)
    return -torch.log(-torch.log(noise))


def gumbel_sample(t, temperature=1.0, dim=-1):
    return ((t / max(temperature, 1e-10)) + gumbel_noise_like(t)).argmax(dim=dim)


class RMSNorm(nn.Module):
    def __init__(self, hidden_size: int, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.var_eps = eps

    def forward(self, x):
        """Returns root mean square normalized version of input `x`
        # T5 uses a layer_norm which only scales and doesn't shift, which is also known
        # as Root Mean Square Layer Normalization https://arxiv.org/abs/1910.07467
        # thus varience is calculated w/o mean and there is no bias
        Parameters
        ----------
        x : Tensor[B x T x D]
        Returns
        -------
        Tensor[B x T x D]
        """
        var = x.pow(2).mean(-1, keepdim=True)
        x = x * torch.rsqrt(var + self.var_eps)

        return self.weight * x


class FeedForward(nn.Module):
    def __init__(
        self, d_model: int = 512, dropout: float = 0.1, activation: str = "geglu"
    ):
        super().__init__()
        factor = 2 if activation == "geglu" else 1
        self.w_1 = lora.Linear(d_model, d_model * 4, bias=False, r=LORA_R)
        self.w_2 = lora.Linear(d_model * 4 // factor, d_model, bias=False, r=LORA_R)
        self.drop = nn.Dropout(dropout)
        self.act = get_activation(activation)()

    def forward(self, x):
        """Computes position-wise feed-forward layer
        Parameters
        ----------
        x : Tensor[B x T x D]
        Returns
        -------
        Tensor[B x T x D]
        """
        x = self.w_1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.w_2(x)
        return x


class MultiHeadRelativeAttention(nn.Module):
    def __init__(
        self,
        n_head: int = 8,
        d_model: int = 512,
        dropout: float = 0.1,
        bidirectional: bool = True,
        has_relative_attention_bias: bool = True,
        attention_num_buckets: int = 32,
        attention_max_distance: int = 128,
    ):
        super().__init__()
        d_head = d_model // n_head
        self.n_head = n_head
        self.d_head = d_head
        self.bidirectional = bidirectional
        self.has_relative_attention_bias = has_relative_attention_bias
        self.attention_num_buckets = attention_num_buckets
        self.attention_max_distance = attention_max_distance

        # Create linear query, key, value projections
        self.w_qs = lora.Linear(d_model, d_model, bias=False, r=LORA_R)
        self.w_ks = nn.Linear(d_model, d_model, bias=False)
        self.w_vs = lora.Linear(d_model, d_model, bias=False, r=LORA_R)

        # Create linear final output projection
        self.fc = lora.Linear(d_model, d_model, bias=False, r=LORA_R)

        # Dropout for attention output weights
        self.dropout = nn.Dropout(dropout)

        # Create relative positional embeddings (if turned on)
        if has_relative_attention_bias:
            self.relative_attention_bias = nn.Embedding(attention_num_buckets, n_head)

    def _relative_position_bucket(self, relative_position):
        """Converts unbounded relative position into bounded set of buckets
        with half "exact" buckets (1 position = 1 bucket) and half "log-spaced"
        buckets
        Parameters
        ----------
        relative_position : Tensor[T_q x T_kv]
            Relative positions between queries and key_value items
        Returns
        -------
        Tensor[T_q x T_kv]
            Input relative positions converted into buckets
        """
        relative_buckets = 0
        num_buckets = self.attention_num_buckets
        max_distance = self.attention_max_distance

        # Convert relative position for (-inf, inf) to [0, inf]
        # Negative relative positions correspond to past
        # Positive relative positions correspond to future
        if self.bidirectional:
            # use half buckets for each side (past / future)
            num_buckets //= 2

            # Shift the position positions by `num_buckets` to wrap around
            # negative positions
            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
            relative_position = torch.abs(relative_position)
        else:
            # If not bidirectional, ignore positive positions and wrap
            # negative positions to positive
            relative_position = -torch.min(
                relative_position, torch.zeros_like(relative_position)
            )

        # Allocate half of the buckets are for exact increments in positions
        max_exact = num_buckets // 2
        is_small = relative_position < max_exact

        # The other half of the buckets are for logarithmically bigger bins in
        # positions up to `max_distance`
        relative_postion_if_large = max_exact + (
            torch.log(relative_position.float() / max_exact)
            / math.log(max_distance / max_exact)
            * (num_buckets - max_exact)
        ).to(torch.long)

        # Clip the max relative position to `num_buckets - 1`
        relative_postion_if_large = torch.min(
            relative_postion_if_large,
            torch.full_like(relative_postion_if_large, num_buckets - 1),
        )

        # Choose relative buckets based on small or large positions
        relative_buckets += torch.where(
            is_small, relative_position, relative_postion_if_large
        )

        return relative_buckets

    def compute_bias(self, query_length, key_length):
        """Computes a position bias scalar for each index in query_length x key_length
        Parameters
        ----------
        query_length : int
        key_length : int
        Returns
        -------
        Tensor[heads x 1 x T_q x T_kv]
            Position bias to be applied on attention logits
        """

        query_position = torch.arange(query_length, dtype=torch.long)[:, None]
        key_position = torch.arange(key_length, dtype=torch.long)[None, :]
        relative_position = key_position - query_position

        # Convert relative position to buckets
        relative_position_bucket = self._relative_position_bucket(relative_position)
        relative_position_bucket = relative_position_bucket.to(
            self.relative_attention_bias.weight.device
        )

        # Index attention bias values
        values = self.relative_attention_bias(relative_position_bucket)
        values = rearrange(values, "q k h -> h 1 q k")

        return values

    def forward(self, q, k, v, mask=None, position_bias=None):
        """Computes attention over (keys, values) for every timestep in query
        Parameters
        ----------
        q : Tensor[B x T_q x d_model]
            Query vectors
        k : Tensor[B x T_kv x d_model]
            Key vectors to compute attention over
        v : Tensor[B x T_kv x d_model]
            Value vectors corresponding to the keys
        mask : Tensor[B x T_q x T_kv], optional
        position_bias: Tensor[head x 1 x T_q x T_kv]
        Returns
        -------
        Tensor[B x T_q x d_model]
            Outputs after attending (key, value) using queries
        """
        # Compute query, key, value projections
        q = rearrange(self.w_qs(q), "b l (head k) -> head b l k", head=self.n_head)
        k = rearrange(self.w_ks(k), "b t (head k) -> head b t k", head=self.n_head)
        v = rearrange(self.w_vs(v), "b t (head k) -> head b t k", head=self.n_head)

        # Compute attention matrix
        attn = torch.einsum("hblk,hbtk->hblt", [q, k]) / np.sqrt(q.shape[-1])

        # Add relative position bias to attention scores
        if position_bias is None:
            if self.has_relative_attention_bias:
                position_bias = self.compute_bias(q.size(-2), k.size(-2))
            else:
                position_bias = torch.zeros_like(attn)
        attn += position_bias

        # Apply mask to attention scores to prevent looking up invalid locations
        if mask is not None:
            attn = attn.masked_fill(mask[None] == 0, -1e9)

        # Normalize attention scores and add dropout
        attn = torch.softmax(attn, dim=3)
        attn = self.dropout(attn)

        # Compute attended outputs (product of attention matrix and values)
        output = torch.einsum("hblt,hbtv->hblv", [attn, v])
        output = rearrange(output, "head b l v -> b l (head v)")
        output = self.fc(output)

        return output, position_bias


class TransformerLayer(nn.Module):
    def __init__(
        self,
        d_model: int = 512,
        d_cond: int = 64,
        n_heads: int = 8,
        bidirectional: bool = True,
        is_decoder: bool = False,
        has_relative_attention_bias: bool = False,
        flash_attn: bool = False,
        dropout: float = 0.1,
    ):
        super().__init__()
        # Store args
        self.is_decoder = is_decoder

        # Create self-attention layer
        self.norm_1 = RMSNorm(d_model)
        self.film_1 = FiLM(d_cond, d_model)
        self.flash_attn = flash_attn

        if flash_attn:
            from flash_attn.flash_attention import FlashMHA
            self.self_attn = FlashMHA(
                embed_dim=d_model,
                num_heads=n_heads,
                attention_dropout=dropout,
                causal=False,
            )
        else:
            self.self_attn = MultiHeadRelativeAttention(
                n_heads, d_model, dropout, bidirectional, has_relative_attention_bias
            )

        # (Optional) Create cross-attention layer
        if is_decoder:
            self.norm_2 = RMSNorm(d_model)
            self.film_2 = FiLM(d_cond, d_model)
            self.cross_attn = MultiHeadRelativeAttention(
                n_heads,
                d_model,
                dropout,
                bidirectional=True,
                has_relative_attention_bias=False,
            )

        # Create last feed-forward layer
        self.norm_3 = RMSNorm(d_model)
        self.film_3 = FiLM(d_cond, d_model)
        self.feed_forward = FeedForward(d_model=d_model, dropout=dropout)

        # Create dropout
        self.dropout = nn.Dropout(dropout)

    def forward(
        self,
        x,
        x_mask,
        cond,
        src=None,
        src_mask=None,
        position_bias=None,
        encoder_decoder_position_bias=None,
    ):
        """Computes one transformer layer consisting of self attention, (op) cross attention
        and feedforward layer
        Parameters
        ----------
        x : Tensor[B x T_q x D]
        x_mask : Tensor[B x T_q]
        src : Tensor[B x T_kv x D], optional
        src_mask : Tensor[B x T_kv x D], optional
        position_bias : Tensor[heads x B x T_q x T_q], optional
            Relative position bias for self attention layer
        encoder_decoder_position_bias : Tensor[heads x B x T_q x T_kv], optional
            Relative position bias for cross attention layer
        Returns
        -------
        Tensor[B x T_q x D]
        """
        y = self.norm_1(x)
        y = self.film_1(y.permute(0, 2, 1), cond).permute(0, 2, 1)
        if self.flash_attn:
            with torch.autocast(y.device.type, dtype=torch.bfloat16):
                y = self.self_attn(y)[0]
        else:
            y, position_bias = self.self_attn(y, y, y, x_mask, position_bias)
        x = x + self.dropout(y)

        if self.is_decoder:
            y = self.norm_2(x)
            y = self.film_2(y.permute(0, 2, 1), cond).permute(0, 2, 1)
            y, encoder_decoder_position_bias = self.cross_attn(
                y, src, src, src_mask, encoder_decoder_position_bias
            )
            x = x + self.dropout(y)

        y = self.norm_3(x)
        y = self.film_3(
            y.permute(
                0,
                2,
                1,
            ),
            cond,
        ).permute(0, 2, 1)
        y = self.feed_forward(y)
        x = x + self.dropout(y)

        return x, position_bias, encoder_decoder_position_bias


class TransformerStack(nn.Module):
    def __init__(
        self,
        d_model: int = 512,
        d_cond: int = 64,
        n_heads: int = 8,
        n_layers: int = 8,
        last_layer: bool = True,
        bidirectional: bool = True,
        flash_attn: bool = False,
        is_decoder: bool = False,
        dropout: float = 0.1,
    ):
        super().__init__()
        # Store args
        self.bidirectional = bidirectional
        self.is_decoder = is_decoder

        # Create transformer layers
        # In T5, relative attention bias is shared by all layers in the stack
        self.layers = nn.ModuleList(
            [
                TransformerLayer(
                    d_model,
                    d_cond,
                    n_heads,
                    bidirectional,
                    is_decoder,
                    has_relative_attention_bias=True if (i == 0) else False,
                    flash_attn=flash_attn,
                    dropout=dropout,
                )
                for i in range(n_layers)
            ]
        )

        # Perform last normalization
        self.norm = RMSNorm(d_model) if last_layer else None

    def subsequent_mask(self, size):
        return torch.ones(1, size, size).tril().bool()

    def forward(self, x, x_mask, cond=None, src=None, src_mask=None,
                return_activations: bool = False
        ):
        """Computes a full transformer stack
        Parameters
        ----------
        x : Tensor[B x T_q x D]
        x_mask : Tensor[B x T_q]
        src : Tensor[B x T_kv x D], optional
        src_mask : Tensor[B x T_kv], optional
        Returns
        -------
        Tensor[B x T_q x D]
        """

        # Convert `src_mask` to (B x T_q x T_kv) shape for cross attention masking
        if self.is_decoder:
            src_mask = x_mask.unsqueeze(-1) * src_mask.unsqueeze(-2)

        # Convert `x_mask` to (B x T_q x T_q) shape for self attention masking
        x_mask = x_mask.unsqueeze(-2)
        if not self.bidirectional:
            x_mask = x_mask * self.subsequent_mask(x.size(1)).to(x_mask.device)

        # Initialize position biases
        position_bias = None
        encoder_decoder_position_bias = None

        # Compute transformer layers
        if return_activations:
            activations = []
        for layer in self.layers:
            x, position_bias, encoder_decoder_position_bias = layer(
                x=x,
                x_mask=x_mask,
                cond=cond,
                src=src,
                src_mask=src_mask,
                position_bias=position_bias,
                encoder_decoder_position_bias=encoder_decoder_position_bias,
            )
            if return_activations:
                activations.append(x.detach())

    
        out = self.norm(x) if self.norm is not None else x
        if return_activations:
            return out, torch.stack(activations)
        else:
            return out


class VampNet(at.ml.BaseModel):
    def __init__(
        self,
        n_heads: int = 20,
        n_layers: int = 16,
        r_cond_dim: int = 0,
        n_codebooks: int = 9,
        n_conditioning_codebooks: int = 0,
        latent_dim: int = 8,
        embedding_dim: int = 1280,
        vocab_size: int = 1024,
        flash_attn: bool = True,
        noise_mode: str = "mask",
        dropout: float = 0.1
    ):
        super().__init__()
        assert r_cond_dim == 0, f"r_cond_dim must be 0 (not supported), but got {r_cond_dim}"
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.r_cond_dim = r_cond_dim
        self.n_codebooks = n_codebooks
        self.n_conditioning_codebooks = n_conditioning_codebooks
        self.embedding_dim = embedding_dim
        self.vocab_size = vocab_size
        self.latent_dim = latent_dim
        self.flash_attn = flash_attn
        self.noise_mode = noise_mode

        assert self.noise_mode == "mask", "deprecated"

        self.embedding = CodebookEmbedding(
            latent_dim=latent_dim,
            n_codebooks=n_codebooks,
            vocab_size=vocab_size,
            emb_dim=embedding_dim,
            special_tokens=["MASK"],
        )
        self.mask_token = self.embedding.special_idxs["MASK"]

        self.transformer = TransformerStack(
            d_model=embedding_dim,
            d_cond=r_cond_dim,
            n_heads=n_heads,
            n_layers=n_layers,
            last_layer=True,
            bidirectional=True,
            flash_attn=flash_attn,
            is_decoder=False,
            dropout=dropout,
        )

        # Add final conv layer
        self.n_predict_codebooks = n_codebooks - n_conditioning_codebooks
        self.classifier = SequentialWithFiLM(
            WNConv1d(
                embedding_dim,
                vocab_size * self.n_predict_codebooks,
                kernel_size=1,
                padding="same",
                # groups=self.n_predict_codebooks,
            ),
        )

    def forward(self, x, return_activations: bool = False):
        x = self.embedding(x)
        x_mask = torch.ones_like(x, dtype=torch.bool)[:, :1, :].squeeze(1)

        x = rearrange(x, "b d n -> b n d")
        out = self.transformer(x=x, x_mask=x_mask, return_activations=return_activations)
        if return_activations:
            out, activations = out

        out = rearrange(out, "b n d -> b d n")

        out = self.classifier(out, None) # no cond here!

        out = rearrange(out, "b (p c) t -> b p (t c)", c=self.n_predict_codebooks)

        if return_activations:
            return out, activations
        else:
            return out
    
    def r_embed(self, r, max_positions=10000):
        if self.r_cond_dim > 0:
            dtype = r.dtype

            r = _gamma(r) * max_positions
            half_dim = self.r_cond_dim // 2

            emb = math.log(max_positions) / (half_dim - 1)
            emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()

            emb = r[:, None] * emb[None, :]
            emb = torch.cat([emb.sin(), emb.cos()], dim=1)

            if self.r_cond_dim % 2 == 1:  # zero pad
                emb = nn.functional.pad(emb, (0, 1), mode="constant")

            return emb.to(dtype)
        else:
            return r
    
    @torch.no_grad()
    def decode(self, z, codec):
        """
        convert a sequence of latents to a signal. 
        """
        assert z.ndim == 3

        # remove mask token
        z = z.masked_fill(z == self.mask_token, 0)
        signal = at.AudioSignal(
            codec.decode(
                codec.quantizer.from_latents(self.embedding.from_codes(z, codec))[0]
            )["audio"],
            codec.sample_rate,
        )

        # find where the mask token is and replace it with silence in the audio
        for tstep in range(z.shape[-1]):
            if torch.all(z[:, :, tstep] == self.mask_token):
                sample_idx_0 = tstep * codec.hop_length
                sample_idx_1 = sample_idx_0 + codec.hop_length
                signal.samples[:, :, sample_idx_0:sample_idx_1] = 0.0

        return signal

    @torch.inference_mode()
    def generate(
        self, 
        codec,
        time_steps: int = 300,
        _sampling_steps: int = 12,
        start_tokens: Optional[torch.Tensor] = None,
        temperature: float = 1.0,
        mask: Optional[torch.Tensor] = None,
        mask_temperature: float = 10.5,
        typical_filtering=True,
        typical_mass=0.15,
        typical_min_tokens=64,
        top_p=None,
        seed: int = None,
        sample_cutoff: float = 1.0,
        return_signal=True,
        debug=False,
        causal_weight: float = 0.0,
        cfg_guidance: float = None,
    ):
        if seed is not None:
            at.util.seed(seed)
        sampling_steps = _sampling_steps
        logging.debug(f"beginning generation with {sampling_steps} steps")

        ##################### 
        # resolve initial z #
        #####################
        z = start_tokens
        nb = z.shape[0]

        if z is None:
            z = torch.full((1, self.n_codebooks, time_steps), self.mask_token).to(
                self.device
            )



        #################
        # resolve mask #
        #################

        if mask is None:
            mask = torch.ones_like(z).to(self.device).int()
            mask[:, : self.n_conditioning_codebooks, :] = 0.0
        if mask.ndim == 2:
            mask = mask[:, None, :].repeat(1, z.shape[1], 1)
        # init_mask = mask.clone()
        


        ###########
        # set up #
        ##########
        # apply the mask to z
        z_masked = z.masked_fill(mask.bool(), self.mask_token)
        # logging.debug(f"z_masked: {z_masked}")

        # how many mask tokens to begin with?
        num_mask_tokens_at_start = (z_masked == self.mask_token).sum()

        # how many codebooks are we inferring vs conditioning on?
        n_infer_codebooks = self.n_codebooks - self.n_conditioning_codebooks

        if cfg_guidance is not None:
            # we need to repeat our tensors
            z_uncond = torch.full_like(z, self.mask_token)

            z_masked = torch.cat(
                (z_masked, z_uncond), dim=0
            )
            z = torch.cat(
                (z, z_uncond), dim=0
            )
            mask = torch.cat(
                (mask, torch.full_like(mask, 1)), dim=0
            )

        #################
        # begin sampling #
        #################
        from tqdm import tqdm
        for i in range(sampling_steps):

            # our current schedule step
            r = scalar_to_batch_tensor(
                (i + 1) / sampling_steps, 
                z.shape[0]
            ).to(z.device)

            # get latents
            latents = self.embedding.from_codes(z_masked, codec)


            # infer from latents
            # NOTE: this collapses the codebook dimension into the sequence dimension
            logits = self.forward(latents) # b, prob, seq

            if cfg_guidance is not None:
                logits_cond, logits_uncond = logits[:nb], logits[nb:]
                logits_cond = cfg_guidance * logits_cond + cfg_guidance * (1 - logits_uncond)

            logits = logits.permute(0, 2, 1)  # b, seq, prob
            b = logits.shape[0]

            sampled_z, selected_probs = sample_from_logits(
                logits, sample=(
                   (i / sampling_steps) <= sample_cutoff
                ), 
                temperature=temperature,
                typical_filtering=typical_filtering, typical_mass=typical_mass,
                typical_min_tokens=typical_min_tokens,
                top_k=None, top_p=top_p, return_probs=True,
            )


            # flatten z_masked and mask, so we can deal with the sampling logic
            # we'll unflatten them at the end of the loop for the next forward pass
            # remove conditioning codebooks, we'll add them back at the end
            z_masked = codebook_flatten(z_masked[:, self.n_conditioning_codebooks:, :])           

            mask = (z_masked == self.mask_token).int()
            
            # update the mask, remove conditioning codebooks from the mask
            # add z back into sampled z where the mask was false
            sampled_z = torch.where(
                mask.bool(), sampled_z, z_masked
            )

            # ignore any tokens that weren't masked
            selected_probs = torch.where(
                mask.bool(), selected_probs, torch.inf
            )

            # get the num tokens to mask, according to the schedule
            num_to_mask = torch.floor(_gamma(r) * num_mask_tokens_at_start).unsqueeze(1).long()
            logging.debug(f"num to mask: {num_to_mask}")

            if i != (sampling_steps - 1):
                num_to_mask = torch.maximum(
                    torch.tensor(1),
                    torch.minimum(
                        mask.sum(dim=-1, keepdim=True) - 1,
                        num_to_mask
                    )
                )


            # get our new mask
            mask = mask_by_random_topk(
                num_to_mask, selected_probs, mask_temperature * (1-r)
            )  

            # update the mask
            z_masked = torch.where(
                mask.bool(), self.mask_token, sampled_z
            )

            z_masked = codebook_unflatten(z_masked, n_infer_codebooks)
            mask = codebook_unflatten(mask, n_infer_codebooks)

            # add conditioning codebooks back to z_masked
            z_masked = torch.cat(
                (z[:, :self.n_conditioning_codebooks, :], z_masked), dim=1
            )

        # add conditioning codebooks back to sampled_z
        sampled_z = codebook_unflatten(sampled_z, n_infer_codebooks)
        sampled_z = torch.cat(
            (z[:, :self.n_conditioning_codebooks, :], sampled_z), dim=1
        )

        if cfg_guidance is not None:
            sampled_z = sampled_z[:nb]

        if return_signal:
            return self.decode(sampled_z, codec)
        else:
            return sampled_z




        
def sample_from_logits(
        logits, 
        sample: bool = True,
        temperature: float = 1.0,
        top_k: int = None,
        top_p: float = None,
        typical_filtering: bool = False,
        typical_mass: float = 0.2,
        typical_min_tokens: int = 1,
        return_probs: bool = False
    ):
    """Convenience function to sample from a categorial distribution with input as
    unnormalized logits.

    Parameters
    ----------
    logits : Tensor[..., vocab_size]
    config: SamplingConfig
        The set of hyperparameters to be used for sampling
        sample : bool, optional
            Whether to perform multinomial sampling, by default True
        temperature : float, optional
            Scaling parameter when multinomial samping, by default 1.0
        top_k : int, optional
            Restricts sampling to only `top_k` values acc. to probability,
            by default None
        top_p : float, optional
            Restricts sampling to only those values with cumulative
            probability = `top_p`, by default None

    Returns
    -------
    Tensor[...]
        Sampled tokens
    """
    shp = logits.shape[:-1]

    if typical_filtering:
        typical_filter(logits, 
                        typical_mass=typical_mass, 
                        typical_min_tokens=typical_min_tokens
        )

    # Apply top_k sampling
    if top_k is not None:
        v, _ = logits.topk(top_k)
        logits[logits < v[..., [-1]]] = -float("inf")

    # Apply top_p (nucleus) sampling
    if top_p is not None and top_p < 1.0:
        v, sorted_indices = logits.sort(descending=True)
        cumulative_probs = v.softmax(dim=-1).cumsum(dim=-1)

        sorted_indices_to_remove = cumulative_probs > top_p
        # Right shift indices_to_remove to keep 1st token over threshold
        sorted_indices_to_remove = F.pad(sorted_indices_to_remove, (1, 0), value=False)[
            ..., :-1
        ]

        # Compute indices_to_remove in unsorted array
        indices_to_remove = sorted_indices_to_remove.scatter(
            -1, sorted_indices, sorted_indices_to_remove
        )

        logits[indices_to_remove] = -float("inf")

    # Perform multinomial sampling after normalizing logits
    probs = (
        F.softmax(logits / temperature, dim=-1)
        if temperature > 0
        else logits.softmax(dim=-1)
    )
    token = (
        probs.view(-1, probs.size(-1)).multinomial(1).squeeze(1).view(*shp)
        if sample
        else logits.argmax(-1)
    )

    if return_probs:
        token_probs = probs.take_along_dim(token.unsqueeze(-1), dim=-1).squeeze(-1)
        return token, token_probs
    else:
        return token
    


def mask_by_random_topk(
        num_to_mask: int, 
        probs: torch.Tensor, 
        temperature: float = 1.0, 
    ):
    """
    Args:
        num_to_mask (int): number of tokens to mask
        probs (torch.Tensor): probabilities for each sampled event, shape (batch, seq)
        temperature (float, optional): temperature. Defaults to 1.0.
    """
    logging.debug(f"masking by random topk")
    logging.debug(f"num to mask: {num_to_mask}")
    logging.debug(f"probs shape: {probs.shape}")
    logging.debug(f"temperature: {temperature}")
    logging.debug("")

    noise = gumbel_noise_like(probs)
    temperature = temperature.unsqueeze(-1)
    confidence = torch.log(probs) + temperature * noise
    logging.debug(f"confidence shape: {confidence.shape}")

    sorted_confidence, sorted_idx = confidence.sort(dim=-1)
    logging.debug(f"sorted confidence shape: {sorted_confidence.shape}")
    logging.debug(f"sorted idx shape: {sorted_idx.shape}")

    # get the cut off threshold, given the mask length
    cut_off = torch.take_along_dim(
        sorted_confidence, num_to_mask, axis=-1
    )
    logging.debug(f"cut off shape: {cut_off.shape}")

    # mask out the tokens
    mask = confidence < cut_off
    logging.debug(f"mask shape: {mask.shape}")

    return mask

def typical_filter(
        logits, 
        typical_mass: float = 0.95,
        typical_min_tokens: int = 1,):
    nb, nt, _ = logits.shape
    x_flat = rearrange(logits, "b t l -> (b t ) l")
    x_flat_norm = torch.nn.functional.log_softmax(x_flat, dim=-1)
    x_flat_norm_p = torch.exp(x_flat_norm)
    entropy = -(x_flat_norm * x_flat_norm_p).nansum(-1, keepdim=True)

    c_flat_shifted = torch.abs((-x_flat_norm) - entropy)
    c_flat_sorted, x_flat_indices = torch.sort(c_flat_shifted, descending=False)
    x_flat_cumsum = (
        x_flat.gather(-1, x_flat_indices).softmax(dim=-1).cumsum(dim=-1)
    )

    last_ind = (x_flat_cumsum < typical_mass).sum(dim=-1)
    sorted_indices_to_remove = c_flat_sorted > c_flat_sorted.gather(
        1, last_ind.view(-1, 1)
    )
    if typical_min_tokens > 1:
        sorted_indices_to_remove[..., :typical_min_tokens] = 0
    indices_to_remove = sorted_indices_to_remove.scatter(
        1, x_flat_indices, sorted_indices_to_remove
    )
    x_flat = x_flat.masked_fill(indices_to_remove, -float("Inf"))
    logits = rearrange(x_flat, "(b t) l -> b t l", t=nt)
    return logits


if __name__ == "__main__":
    # import argbind
    from .layers import num_params

    VampNet = argbind.bind(VampNet)

    @argbind.bind(without_prefix=True)
    def try_model(device: str = "cuda", batch_size: int = 2, seq_len_s: float = 10.0):
        seq_len = int(32000 / 512 * seq_len_s)

        model = VampNet().to(device)

        z = torch.randint(
            0, model.vocab_size, size=(batch_size, model.n_codebooks, seq_len)
        ).to(device)

        r = torch.zeros(batch_size).to(device)
        
        z_mask_latent = torch.rand(
            batch_size, model.latent_dim * model.n_codebooks, seq_len
        ).to(device)
        z_hat = model(z_mask_latent)

        pred = z_hat.argmax(dim=1)
        pred = model.embedding.unflatten(pred, n_codebooks=model.n_predict_codebooks)

        logging.debug(f"model has {num_params(model)/1e6:<.3f}M parameters")
        logging.debug(f"prediction has shape {pred.shape}")

    args = argbind.parse_args()
    with argbind.scope(args):
        try_model()