modeling_lfq_tokenizer.py

"""
Hugging Face compatible implementation of Open-MAGVIt2
Code reference: https://github.com/TencentARC/Open-MAGVIT2
"""


from math import log2, ceil
from collections import namedtuple

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, reduce, pack, unpack
from torch import einsum
from torch.nn import Module
from transformers import PreTrainedModel

from .configuration_lfq_tokenizer import LFQTokenizerConfig


def swish(x):
    # swish
    return x * torch.sigmoid(x)


class ResBlock(nn.Module):
    def __init__(self, 
                 in_filters,
                 out_filters,
                 use_conv_shortcut = False
                 ) -> None:
        super().__init__()

        self.in_filters = in_filters
        self.out_filters = out_filters
        self.use_conv_shortcut = use_conv_shortcut

        self.norm1 = nn.GroupNorm(32, in_filters, eps=1e-6)
        self.norm2 = nn.GroupNorm(32, out_filters, eps=1e-6)

        self.conv1 = nn.Conv2d(in_filters, out_filters, kernel_size=(3, 3), padding=1, bias=False)
        self.conv2 = nn.Conv2d(out_filters, out_filters, kernel_size=(3, 3), padding=1, bias=False)

        if in_filters != out_filters:
            if self.use_conv_shortcut:
                self.conv_shortcut = nn.Conv2d(in_filters, out_filters, kernel_size=(3, 3), padding=1, bias=False)
            else:
                self.nin_shortcut = nn.Conv2d(in_filters, out_filters, kernel_size=(1, 1), padding=0, bias=False)
    

    def forward(self, x, **kwargs):
        residual = x

        x = self.norm1(x)
        x = swish(x)
        x = self.conv1(x)
        x = self.norm2(x)
        x = swish(x)
        x = self.conv2(x)
        if self.in_filters != self.out_filters:
            if self.use_conv_shortcut:
                residual = self.conv_shortcut(residual)
            else:
                residual = self.nin_shortcut(residual)

        return x + residual

class Encoder(nn.Module):
    def __init__(self, *, ch, out_ch, in_channels, num_res_blocks, z_channels, ch_mult=(1, 2, 2, 4)):
        super().__init__()

        self.in_channels = in_channels
        self.z_channels = z_channels

        self.num_res_blocks = num_res_blocks
        self.num_blocks = len(ch_mult)
        
        self.conv_in = nn.Conv2d(in_channels,
                                 ch,
                                 kernel_size=(3, 3),
                                 padding=1,
                                 bias=False
        )

        ## construct the model
        self.down = nn.ModuleList()

        in_ch_mult = (1,)+tuple(ch_mult)
        for i_level in range(self.num_blocks):
            block = nn.ModuleList()
            block_in = ch*in_ch_mult[i_level] #[1, 1, 2, 2, 4]
            block_out = ch*ch_mult[i_level] #[1, 2, 2, 4]
            for _ in range(self.num_res_blocks):
                block.append(ResBlock(block_in, block_out))
                block_in = block_out
            
            down = nn.Module()
            down.block = block
            if i_level < self.num_blocks - 1:
                down.downsample = nn.Conv2d(block_out, block_out, kernel_size=(3, 3), stride=(2, 2), padding=1)

            self.down.append(down)
        
        ### mid
        self.mid_block = nn.ModuleList()
        for res_idx in range(self.num_res_blocks):
            self.mid_block.append(ResBlock(block_in, block_in))
        
        ### end
        self.norm_out = nn.GroupNorm(32, block_out, eps=1e-6)
        self.conv_out = nn.Conv2d(block_out, z_channels, kernel_size=(1, 1))
            
    def forward(self, x):

        ## down
        x = self.conv_in(x)
        for i_level in range(self.num_blocks):
            for i_block in range(self.num_res_blocks):
                x = self.down[i_level].block[i_block](x)
            
            if i_level <  self.num_blocks - 1:
                x = self.down[i_level].downsample(x)
        
        ## mid 
        for res in range(self.num_res_blocks):
            x = self.mid_block[res](x)
        

        x = self.norm_out(x)
        x = swish(x)
        x = self.conv_out(x)

        return x


class Decoder(nn.Module):
    def __init__(self, *, ch, out_ch, in_channels, num_res_blocks, z_channels, ch_mult=(1, 2, 2, 4)) -> None:
        super().__init__()

        self.ch = ch
        self.num_blocks = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.in_channels = in_channels

        block_in = ch*ch_mult[self.num_blocks-1]

        self.conv_in = nn.Conv2d(
            z_channels, block_in, kernel_size=(3, 3), padding=1, bias=True
        )

        self.mid_block = nn.ModuleList()
        for res_idx in range(self.num_res_blocks):
            self.mid_block.append(ResBlock(block_in, block_in))
        
        self.up = nn.ModuleList()

        for i_level in reversed(range(self.num_blocks)):
            block = nn.ModuleList()
            block_out = ch*ch_mult[i_level]
            for i_block in range(self.num_res_blocks):
                block.append(ResBlock(block_in, block_out))
                block_in = block_out
            
            up = nn.Module()
            up.block = block
            if i_level > 0:
                up.upsample = Upsampler(block_in)
            self.up.insert(0, up)
        
        self.norm_out = nn.GroupNorm(32, block_in, eps=1e-6)

        self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=(3, 3), padding=1)
    
    def forward(self, z):

        z = self.conv_in(z)

        ## mid
        for res in range(self.num_res_blocks):
            z = self.mid_block[res](z)
        
        ## upsample
        for i_level in reversed(range(self.num_blocks)):
            for i_block in range(self.num_res_blocks):
                z = self.up[i_level].block[i_block](z)
            
            if i_level > 0:
                z = self.up[i_level].upsample(z)
        
        z = self.norm_out(z)
        z = swish(z)
        z = self.conv_out(z)

        return z


def depth_to_space(x: torch.Tensor, block_size: int) -> torch.Tensor:
    """ Depth-to-Space DCR mode (depth-column-row) core implementation.

        Args:
            x (torch.Tensor): input tensor. The channels-first (*CHW) layout is supported.
            block_size (int): block side size
    """
    # check inputs
    if x.dim() < 3:
        raise ValueError(
            f"Expecting a channels-first (*CHW) tensor of at least 3 dimensions"
        )
    c, h, w = x.shape[-3:]

    s = block_size**2
    if c % s != 0:
        raise ValueError(
            f"Expecting a channels-first (*CHW) tensor with C divisible by {s}, but got C={c} channels"
        )

    outer_dims = x.shape[:-3]

    # splitting two additional dimensions from the channel dimension
    x = x.view(-1, block_size, block_size, c // s, h, w)

    # putting the two new dimensions along H and W
    x = x.permute(0, 3, 4, 1, 5, 2)

    # merging the two new dimensions with H and W
    x = x.contiguous().view(*outer_dims, c // s, h * block_size,
                            w * block_size)

    return x

class Upsampler(nn.Module):
    def __init__(
        self,
        dim,
        dim_out = None
    ):
        super().__init__()
        dim_out = dim * 4
        self.conv1 = nn.Conv2d(dim, dim_out, (3, 3), padding=1)
        self.depth2space = depth_to_space

    def forward(self, x):
        """
        input_image: [B C H W]
        """
        out = self.conv1(x)
        out = self.depth2space(out, block_size=2)
        return out


class AdaptiveGroupNorm(nn.Module):
    def __init__(self, z_channel, in_filters, num_groups=32, eps=1e-6):
        super().__init__()
        self.gn = nn.GroupNorm(num_groups=32, num_channels=in_filters, eps=eps, affine=False)
        # self.lin = nn.Linear(z_channels, in_filters * 2)
        self.gamma = nn.Linear(z_channel, in_filters)
        self.beta = nn.Linear(z_channel, in_filters)
        self.eps = eps
    
    def forward(self, x, quantizer):
        B, C, _, _ = x.shape
        # quantizer = F.adaptive_avg_pool2d(quantizer, (1, 1))
        ### calcuate var for scale
        scale = rearrange(quantizer, "b c h w -> b c (h w)")
        scale = scale.var(dim=-1) + self.eps #not unbias
        scale = scale.sqrt()
        scale = self.gamma(scale).view(B, C, 1, 1)

        ### calculate mean for bias
        bias = rearrange(quantizer, "b c h w -> b c (h w)")
        bias = bias.mean(dim=-1)
        bias = self.beta(bias).view(B, C, 1, 1)
       
        x = self.gn(x)
        x = scale * x + bias

        return x


# constants

LossBreakdown = namedtuple('LossBreakdown', ['per_sample_entropy', 'codebook_entropy', 'commitment', 'avg_probs'])

# helper functions

def exists(v):
    return v is not None

def default(*args):
    for arg in args:
        if exists(arg):
            return arg() if callable(arg) else arg
    return None

def pack_one(t, pattern):
    return pack([t], pattern)

def unpack_one(t, ps, pattern):
    return unpack(t, ps, pattern)[0]

# entropy

def entropy(prob):
    return (-prob * torch.log(prob + 1e-5)).sum(dim=-1)

# class

def mult_along_first_dims(x, y):
    """
    returns x * y elementwise along the leading dimensions of y
    """
    ndim_to_expand = x.ndim - y.ndim
    for _ in range(ndim_to_expand):
        y = y.unsqueeze(-1)
    return x * y

def masked_mean(x, m):
    """
    takes the mean of the elements of x that are not masked
    the mean is taken along the shared leading dims of m
    equivalent to: x[m].mean(tuple(range(m.ndim)))

    The benefit of using masked_mean rather than using
    tensor indexing is that masked_mean is much faster
    for torch-compile on batches.

    The drawback is larger floating point errors
    """
    x = mult_along_first_dims(x, m)
    x = x / m.sum()
    return x.sum(tuple(range(m.ndim)))

def entropy_loss(
    logits,
    mask=None,
    temperature=0.01,
    sample_minimization_weight=1.0,
    batch_maximization_weight=1.0,
    eps=1e-5,
):
    """
    Entropy loss of unnormalized logits

    logits: Affinities are over the last dimension

    https://github.com/google-research/magvit/blob/05e8cfd6559c47955793d70602d62a2f9b0bdef5/videogvt/train_lib/losses.py#L279
    LANGUAGE MODEL BEATS DIFFUSION — TOKENIZER IS KEY TO VISUAL GENERATION (2024)
    """
    probs = F.softmax(logits / temperature, -1)
    log_probs = F.log_softmax(logits / temperature + eps, -1)

    if mask is not None:
        avg_probs = masked_mean(probs, mask)
    else:
        avg_probs = reduce(probs, "... D -> D", "mean")

    avg_entropy = -torch.sum(avg_probs * torch.log(avg_probs + eps))

    sample_entropy = -torch.sum(probs * log_probs, -1)
    if mask is not None:
        sample_entropy = masked_mean(sample_entropy, mask).mean()
    else:
        sample_entropy = torch.mean(sample_entropy)

    loss = (sample_minimization_weight * sample_entropy) - (
        batch_maximization_weight * avg_entropy
    )

    return sample_entropy, avg_entropy, loss


class LFQ(Module):
    def __init__(
        self,
        *,
        dim = None,
        codebook_size = None,
        num_codebooks = 1,
        sample_minimization_weight=1.0,
        batch_maximization_weight=1.0,
        token_factorization = False,
    ):
        super().__init__()

        # some assert validations

        assert exists(dim) or exists(codebook_size), 'either dim or codebook_size must be specified for LFQ'
        assert not exists(codebook_size) or log2(codebook_size).is_integer(), f'your codebook size must be a power of 2 for lookup free quantization (suggested {2 ** ceil(log2(codebook_size))})'

        self.codebook_size = default(codebook_size, lambda: 2 ** dim)
        self.codebook_dim = int(log2(codebook_size))

        codebook_dims = self.codebook_dim * num_codebooks
        dim = default(dim, codebook_dims)

        has_projections = dim != codebook_dims
        self.has_projections = has_projections

        self.dim = dim
        self.codebook_dim = self.codebook_dim
        self.num_codebooks = num_codebooks
        
        # for entropy loss
        self.sample_minimization_weight = sample_minimization_weight
        self.batch_maximization_weight = batch_maximization_weight

        # for no auxiliary loss, during inference
        self.token_factorization = token_factorization ## only utilized in second stage
        if not self.token_factorization: #for first stage model
            self.register_buffer('mask', 2 ** torch.arange(self.codebook_dim - 1, -1, -1), persistent=False)
        else:
            k = self.codebook_dim // 2
            self.register_buffer("mask", 2 ** torch.arange(k - 1, -1, -1), persistent=False)

        self.register_buffer('zero', torch.tensor(0.), persistent = False)

        # codes
        all_codes = torch.arange(codebook_size)
        bits = self.indices_to_bits(all_codes)
        codebook = bits * 2.0 - 1.0

        self.register_buffer('codebook', codebook, persistent = False)

    @property
    def dtype(self):
        return self.codebook.dtype
    
    def indices_to_bits(self, x):
        """
        x: long tensor of indices for constructing codebook, but actually not utilized in all the experiments.

        returns big endian bits
        """
        mask = 2 ** torch.arange(self.codebook_dim, device=x.device, dtype=torch.long)
        # x is now big endian bits, the last dimension being the bits
        x = (x.unsqueeze(-1) & mask) != 0
        return x

    def get_codebook_entry(self, x, bhwc):
        if self.token_factorization:
            k = self.codebook_dim // 2
            mask = 2 ** torch.arange(k - 1, -1, -1, device=x.device, dtype=torch.long)
        else:
            mask = 2 ** torch.arange(self.codebook_dim-1, -1, -1, device=x.device, dtype=torch.long)
        
        x = (x.unsqueeze(-1) & mask) != 0
        x = x * 2.0 - 1.0 #back to the float
        ## scale back to the desired shape
        b, h, w, c = bhwc
        x = rearrange(x, "b (h w) c -> b h w c", h=h, w=w, c=c)
        x = rearrange(x, "b h w c -> b c h w")
        return x

    def bits_to_indices(self, bits):
        """
        bits: bool tensor of big endian bits, where the last dimension is the bit dimension

        returns indices, which are long integers from 0 to self.codebook_size
        """
        assert bits.shape[-1] == self.codebook_dim
        indices = 2 ** torch.arange(
            0,
            self.codebook_dim,
            1,
            dtype=torch.long,
            device=bits.device,
        )
        return (bits * indices).sum(-1)
    
    def decode(self, x):
        """
        x: ... NH
            where NH is number of codebook heads
            A longtensor of codebook indices, containing values from
            0 to self.codebook_size
        """
        x = self.indices_to_bits(x)
        # to some sort of float
        x = x.to(self.dtype)
        # -1 or 1
        x = x * 2 - 1
        x = rearrange(x, "... NC Z-> ... (NC Z)")
        return x

    def forward(
        self,
        x,
        return_loss_breakdown = False,
        mask = None,
        return_loss = True,
    ):
        """
        einstein notation
        b - batch
        n - sequence (or flattened spatial dimensions)
        d - feature dimension, which is also log2(codebook size)
        c - number of codebook dim
        """


        x = rearrange(x, 'b d ... -> b ... d')
        x, ps = pack_one(x, 'b * d')
        # split out number of codebooks

        x = rearrange(x, 'b n (c d) -> b n c d', c = self.num_codebooks)


        codebook_value = torch.Tensor([1.0]).to(device=x.device, dtype=x.dtype)
        quantized = torch.where(x > 0, codebook_value, -codebook_value) # higher than 0 filled 

        # calculate indices
        if self.token_factorization:
            k = self.codebook_dim // 2
            indices_pre = reduce((quantized[..., :k] > 0).int() * self.mask.int(), "b n c d -> b n c", "sum")
            indices_post = reduce((quantized[..., k:] > 0).int() * self.mask.int(), "b n c d -> b n c", "sum")
            # indices_post = 2**k + indices_post #shifter to the 1024
        else:
            indices = reduce((quantized > 0).int() * self.mask.int(), 'b n c d -> b n c', 'sum')

        # entropy aux loss

        if self.training and return_loss:
            logits = 2 * einsum('... i d, j d -> ... i j', x, self.codebook)
            # the same as euclidean distance up to a constant
            per_sample_entropy, codebook_entropy, entropy_aux_loss = entropy_loss(
                logits = logits,
                sample_minimization_weight = self.sample_minimization_weight,
                batch_maximization_weight = self.batch_maximization_weight
            )

            avg_probs = self.zero
        else:
            ## calculate the codebook_entropy needed for one batch evaluation
            #------------------------------------------------------------------
            # logits = 2 * einsum('... i d, j d -> ... i j', x, self.codebook)
            # probs = F.softmax(logits / 0.01, -1)
            # avg_probs = reduce(probs, "b n c d -> b d", "mean")
            # avg_probs = torch.sum(avg_probs, 0) #batch dimension
            #-------------------------------------------------------------------
            # if not training, just return dummy 0
            per_sample_entropy = codebook_entropy = self.zero
            entropy_aux_loss = self.zero
            avg_probs = self.zero

        # commit loss

        if self.training:
            commit_loss = F.mse_loss(x, quantized.detach(), reduction = 'none')

            if exists(mask):
                commit_loss = commit_loss[mask]

            commit_loss = commit_loss.mean()
        else:
            commit_loss = self.zero


        # use straight-through gradients (optionally with custom activation fn) if training

        quantized = x + (quantized - x).detach() #transfer to quantized

        # merge back codebook dim

        quantized = rearrange(quantized, 'b n c d -> b n (c d)')

        # reconstitute image or video dimensions

        quantized = unpack_one(quantized, ps, 'b * d')
        quantized = rearrange(quantized, 'b ... d -> b d ...')

        
        if self.token_factorization:
            indices_pre = unpack_one(indices_pre, ps, "b * c")
            indices_post = unpack_one(indices_post, ps, "b * c")
            indices_pre = indices_pre.flatten()
            indices_post = indices_post.flatten()
            indices = (indices_pre, indices_post)
        else:
            indices = unpack_one(indices, ps, 'b * c')
            indices = indices.flatten()

        ret = (quantized, entropy_aux_loss, indices)

        if not return_loss_breakdown:
            return ret

        return ret, LossBreakdown(per_sample_entropy, codebook_entropy, commit_loss, avg_probs)


class LFQTokenizer(PreTrainedModel):
    config_class = LFQTokenizerConfig

    def __init__(self, config: LFQTokenizerConfig):
        super().__init__(config)

        self.encoder = Encoder(**config.encoder_decoder_config)
        self.decoder = Decoder(**config.encoder_decoder_config)
        self.quantize = LFQ(**config.quantizer_config)

    def encode(self, x):
        h = self.encoder(x)
        (quant, emb_loss, info), loss_breakdown = self.quantize(h, return_loss_breakdown=True)
        return quant, emb_loss, info, loss_breakdown

    def decode(self, quant):
        return self.decoder(quant)

    def forward(self, input):
        quant, diff, _, loss_breakdown = self.encode(input)
        dec = self.decoder(quant)
        return dec, diff, loss_breakdown

    def tokenize(self, input):
        _, _, tokens, _ = self.encode(input)
        return tokens

    def get_last_layer(self):
        return self.decoder.conv_out.weight

    def decode_tokens(self, tokens, shape: tuple):        
        if self.quantize.token_factorization:
            tokens_pre, tokens_post = tokens[0], tokens[1]
            quant_pre = self.quantize.get_codebook_entry(tokens_pre, shape)
            quant_post = self.quantize.get_codebook_entry(tokens_post, shape)
            quant = torch.concat([quant_pre, quant_post], dim=1)
            return self.decode(quant)
        else:
            if tokens.ndim == 1:
                batch_size = shape[0]
                tokens = tokens.view(batch_size, -1)
            quant = self.quantize.get_codebook_entry(tokens, shape)
            return self.decode(quant)