cosmos_predict1/tokenizer/networks/discrete_video.py

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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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"""The network definition for discrete video tokenizer with VQ, LFQ, FSQ or ResidualFSQ. """
from collections import OrderedDict, namedtuple

import torch
from loguru import logger as logging
from torch import nn

from cosmos_predict1.tokenizer.modules import Decoder3DType, DiscreteQuantizer, Encoder3DType
from cosmos_predict1.tokenizer.modules.layers3d import CausalConv3d
from cosmos_predict1.tokenizer.modules.quantizers import InvQuantizerJit

NetworkEval = namedtuple("NetworkEval", ["reconstructions", "quant_loss", "quant_info"])


class CausalDiscreteVideoTokenizer(nn.Module):
    def __init__(self, z_channels: int, z_factor: int, embedding_dim: int, **kwargs) -> None:
        super().__init__()
        self.name = kwargs.get("name", "CausalDiscreteVideoTokenizer")
        self.embedding_dim = embedding_dim

        encoder_name = kwargs.get("encoder", Encoder3DType.BASE.name)
        self.encoder = Encoder3DType[encoder_name].value(z_channels=z_factor * z_channels, **kwargs)

        decoder_name = kwargs.get("decoder", Decoder3DType.BASE.name)
        self.decoder = Decoder3DType[decoder_name].value(z_channels=z_channels, **kwargs)

        self.quant_conv = CausalConv3d(z_factor * z_channels, embedding_dim, kernel_size=1, padding=0)
        self.post_quant_conv = CausalConv3d(embedding_dim, z_channels, kernel_size=1, padding=0)

        quantizer_name = kwargs.get("quantizer", DiscreteQuantizer.RESFSQ.name)
        if quantizer_name == DiscreteQuantizer.VQ.name:
            assert "num_embeddings" in kwargs, f"`num_embeddings` must be provided for {quantizer_name}."
            kwargs.update(dict(embedding_dim=embedding_dim))
        elif quantizer_name == DiscreteQuantizer.LFQ.name:
            assert "codebook_size" in kwargs, f"`codebook_size` must be provided for {quantizer_name}."
            assert "codebook_dim" in kwargs, f"`codebook_dim` must be provided for {quantizer_name}."
        elif quantizer_name == DiscreteQuantizer.FSQ.name:
            assert "levels" in kwargs, f"`levels` must be provided for {quantizer_name}."
        elif quantizer_name == DiscreteQuantizer.RESFSQ.name:
            assert "levels" in kwargs, f"`levels` must be provided for {quantizer_name}."
            assert "num_quantizers" in kwargs, f"`num_quantizers` must be provided for {quantizer_name}."
        self.quantizer = DiscreteQuantizer[quantizer_name].value(**kwargs)
        logging.info(f"{self.name} based on {quantizer_name}-VAE, with {kwargs}.")

        num_parameters = sum(param.numel() for param in self.parameters())
        logging.info(f"model={self.name}, num_parameters={num_parameters:,}")
        logging.info(f"z_channels={z_channels}, embedding_dim={self.embedding_dim}.")

    def to(self, *args, **kwargs):
        setattr(self.quantizer, "dtype", kwargs.get("dtype", torch.bfloat16))
        return super(CausalDiscreteVideoTokenizer, self).to(*args, **kwargs)

    def encoder_jit(self):
        return nn.Sequential(
            OrderedDict(
                [
                    ("encoder", self.encoder),
                    ("quant_conv", self.quant_conv),
                    ("quantizer", self.quantizer),
                ]
            )
        )

    def decoder_jit(self):
        return nn.Sequential(
            OrderedDict(
                [
                    ("inv_quant", InvQuantizerJit(self.quantizer)),
                    ("post_quant_conv", self.post_quant_conv),
                    ("decoder", self.decoder),
                ]
            )
        )

    def last_decoder_layer(self):
        return self.decoder.conv_out

    def encode(self, x):
        h = self.encoder(x)
        h = self.quant_conv(h)
        return self.quantizer(h)

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

    def decode_code(self, code_b):
        quant_b = self.quantizer.indices_to_codes(code_b)
        quant_b = self.post_quant_conv(quant_b)
        return self.decoder(quant_b)

    def forward(self, input):
        quant_info, quant_codes, quant_loss = self.encode(input)
        reconstructions = self.decode(quant_codes)
        if self.training:
            return dict(
                reconstructions=reconstructions,
                quant_loss=quant_loss,
                quant_info=quant_info,
            )
        return NetworkEval(
            reconstructions=reconstructions,
            quant_loss=quant_loss,
            quant_info=quant_info,
        )