modeling_vqvae.py

"""
MIT License

Copyright (c) 2021 Wilson Yan

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


This file is copied from https://github.com/wilson1yan/VideoGPT/blob/master/videogpt/vqvae.py
We adapted it to Hugging Face AutoModel for easier model loading.
"""


import os
import math
import numpy as np

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist

from .attention import MultiHeadAttention
from ._utils import shift_dim
from transformers import PreTrainedModel
from .configuration_vqvae import VQVAEConfig


class VQVAE(PreTrainedModel):
    config_class = VQVAEConfig

    def __init__(self, config):
        super().__init__(config)
        self.embedding_dim = config.embedding_dim
        self.n_codes = config.n_codes

        self.encoder = Encoder(config.n_hiddens, config.n_res_layers, config.downsample)
        self.decoder = Decoder(config.n_hiddens, config.n_res_layers, config.downsample)

        self.pre_vq_conv = SamePadConv3d(config.n_hiddens, config.embedding_dim, 1)
        self.post_vq_conv = SamePadConv3d(config.embedding_dim, config.n_hiddens, 1)

        self.codebook = Codebook(config.n_codes, config.embedding_dim)

    @property
    def latent_shape(self):
        input_shape = (self.args.sequence_length, self.args.resolution,
                       self.args.resolution)
        return tuple([s // d for s, d in zip(input_shape,
                                             self.args.downsample)])

    def encode(self, x, include_embeddings=False):
        h = self.pre_vq_conv(self.encoder(x))
        vq_output = self.codebook(h)
        if include_embeddings:
            return vq_output['encodings'], vq_output['embeddings']
        else:
            return vq_output['encodings']

    def decode(self, encodings):
        h = F.embedding(encodings, self.codebook.embeddings)
        h = self.post_vq_conv(shift_dim(h, -1, 1))
        return self.decoder(h)

    def decode_from_embeddings(self, embeddings):
        # embeddings: [b, c, t, h, w]
        encodings = self.codebook.search_indices(embeddings)
        return self.decode(encodings)

    def forward(self, x):
        z = self.pre_vq_conv(self.encoder(x))
        vq_output = self.codebook(z)
        x_recon = self.decoder(self.post_vq_conv(vq_output['embeddings']))
        recon_loss = F.mse_loss(x_recon, x) / 0.06

        return recon_loss, x_recon, vq_output


class AxialBlock(nn.Module):
    def __init__(self, n_hiddens, n_head):
        super().__init__()
        kwargs = dict(shape=(0,) * 3, dim_q=n_hiddens,
                      dim_kv=n_hiddens, n_head=n_head,
                      n_layer=1, causal=False, attn_type='axial')
        self.attn_w = MultiHeadAttention(attn_kwargs=dict(axial_dim=-2),
                                         **kwargs)
        self.attn_h = MultiHeadAttention(attn_kwargs=dict(axial_dim=-3),
                                         **kwargs)
        self.attn_t = MultiHeadAttention(attn_kwargs=dict(axial_dim=-4),
                                         **kwargs)

    def forward(self, x):
        x = shift_dim(x, 1, -1)
        x = self.attn_w(x, x, x) + self.attn_h(x, x, x) + self.attn_t(x, x, x)
        x = shift_dim(x, -1, 1)
        return x


class AttentionResidualBlock(nn.Module):
    def __init__(self, n_hiddens):
        super().__init__()
        self.block = nn.Sequential(
            nn.BatchNorm3d(n_hiddens),
            nn.ReLU(),
            SamePadConv3d(n_hiddens, n_hiddens // 2, 3, bias=False),
            nn.BatchNorm3d(n_hiddens // 2),
            nn.ReLU(),
            SamePadConv3d(n_hiddens // 2, n_hiddens, 1, bias=False),
            nn.BatchNorm3d(n_hiddens),
            nn.ReLU(),
            AxialBlock(n_hiddens, 2)
        )

    def forward(self, x):
        return x + self.block(x)

class Codebook(nn.Module):
    def __init__(self, n_codes, embedding_dim):
        super().__init__()
        self.register_buffer('embeddings', torch.randn(n_codes, embedding_dim))
        self.register_buffer('N', torch.zeros(n_codes))
        self.register_buffer('z_avg', self.embeddings.data.clone())

        self.n_codes = n_codes
        self.embedding_dim = embedding_dim
        self._need_init = True

    def _tile(self, x):
        d, ew = x.shape
        if d < self.n_codes:
            n_repeats = (self.n_codes + d - 1) // d
            std = 0.01 / np.sqrt(ew)
            x = x.repeat(n_repeats, 1)
            x = x + torch.randn_like(x) * std
        return x

    def _init_embeddings(self, z):
        # z: [b, c, t, h, w]
        self._need_init = False
        flat_inputs = shift_dim(z, 1, -1).flatten(end_dim=-2)
        y = self._tile(flat_inputs)

        d = y.shape[0]
        _k_rand = y[torch.randperm(y.shape[0])][:self.n_codes]
        if dist.is_initialized():
            dist.broadcast(_k_rand, 0)
        self.embeddings.data.copy_(_k_rand)
        self.z_avg.data.copy_(_k_rand)
        self.N.data.copy_(torch.ones(self.n_codes))

    def search_indices(self, z):
        # z: [b, c, t, h, w]
        flat_inputs = shift_dim(z, 1, -1).flatten(end_dim=-2)
        distances = (flat_inputs ** 2).sum(dim=1, keepdim=True) \
                    - 2 * flat_inputs @ self.embeddings.t() \
                    + (self.embeddings.t() ** 2).sum(dim=0, keepdim=True)

        encoding_indices = torch.argmin(distances, dim=1)
        encoding_indices = encoding_indices.view(z.shape[0], *z.shape[2:])
        return encoding_indices


    def forward(self, z):
        # z: [b, c, t, h, w]
        if self._need_init and self.training:
            self._init_embeddings(z)
        flat_inputs = shift_dim(z, 1, -1).flatten(end_dim=-2)
        distances = (flat_inputs ** 2).sum(dim=1, keepdim=True) \
                    - 2 * flat_inputs @ self.embeddings.t() \
                    + (self.embeddings.t() ** 2).sum(dim=0, keepdim=True)

        encoding_indices = torch.argmin(distances, dim=1)
        encode_onehot = F.one_hot(encoding_indices, self.n_codes).type_as(flat_inputs)
        encoding_indices = encoding_indices.view(z.shape[0], *z.shape[2:])

        embeddings = F.embedding(encoding_indices, self.embeddings)
        embeddings = shift_dim(embeddings, -1, 1)

        commitment_loss = 0.25 * F.mse_loss(z, embeddings.detach())

        # EMA codebook update
        if self.training:
            n_total = encode_onehot.sum(dim=0)
            encode_sum = flat_inputs.t() @ encode_onehot
            if dist.is_initialized():
                dist.all_reduce(n_total)
                dist.all_reduce(encode_sum)

            self.N.data.mul_(0.99).add_(n_total, alpha=0.01)
            self.z_avg.data.mul_(0.99).add_(encode_sum.t(), alpha=0.01)

            n = self.N.sum()
            weights = (self.N + 1e-7) / (n + self.n_codes * 1e-7) * n
            encode_normalized = self.z_avg / weights.unsqueeze(1)
            self.embeddings.data.copy_(encode_normalized)

            y = self._tile(flat_inputs)
            _k_rand = y[torch.randperm(y.shape[0])][:self.n_codes]
            if dist.is_initialized():
                dist.broadcast(_k_rand, 0)

            usage = (self.N.view(self.n_codes, 1) >= 1).float()
            self.embeddings.data.mul_(usage).add_(_k_rand * (1 - usage))

        embeddings_st = (embeddings - z).detach() + z

        avg_probs = torch.mean(encode_onehot, dim=0)
        perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))

        return dict(embeddings=embeddings_st, encodings=encoding_indices,
                    commitment_loss=commitment_loss, perplexity=perplexity)

    def dictionary_lookup(self, encodings):
        embeddings = F.embedding(encodings, self.embeddings)
        return embeddings

class Encoder(nn.Module):
    def __init__(self, n_hiddens, n_res_layers, downsample):
        super().__init__()
        n_times_downsample = np.array([int(math.log2(d)) for d in downsample])
        self.convs = nn.ModuleList()
        max_ds = n_times_downsample.max()
        for i in range(max_ds):
            in_channels = 3 if i == 0 else n_hiddens
            stride = tuple([2 if d > 0 else 1 for d in n_times_downsample])
            conv = SamePadConv3d(in_channels, n_hiddens, 4, stride=stride)
            self.convs.append(conv)
            n_times_downsample -= 1
        self.conv_last = SamePadConv3d(in_channels, n_hiddens, kernel_size=3)

        self.res_stack = nn.Sequential(
            *[AttentionResidualBlock(n_hiddens)
              for _ in range(n_res_layers)],
            nn.BatchNorm3d(n_hiddens),
            nn.ReLU()
        )

    def forward(self, x):
        h = x
        for conv in self.convs:
            h = F.relu(conv(h))
        h = self.conv_last(h)
        h = self.res_stack(h)
        return h


class Decoder(nn.Module):
    def __init__(self, n_hiddens, n_res_layers, upsample):
        super().__init__()
        self.res_stack = nn.Sequential(
            *[AttentionResidualBlock(n_hiddens)
              for _ in range(n_res_layers)],
            nn.BatchNorm3d(n_hiddens),
            nn.ReLU()
        )

        n_times_upsample = np.array([int(math.log2(d)) for d in upsample])
        max_us = n_times_upsample.max()
        self.convts = nn.ModuleList()
        for i in range(max_us):
            out_channels = 3 if i == max_us - 1 else n_hiddens
            us = tuple([2 if d > 0 else 1 for d in n_times_upsample])
            convt = SamePadConvTranspose3d(n_hiddens, out_channels, 4,
                                           stride=us)
            self.convts.append(convt)
            n_times_upsample -= 1

    def forward(self, x):
        h = self.res_stack(x)
        for i, convt in enumerate(self.convts):
            h = convt(h)
            if i < len(self.convts) - 1:
                h = F.relu(h)
        return h


# Does not support dilation
class SamePadConv3d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, bias=True):
        super().__init__()
        if isinstance(kernel_size, int):
            kernel_size = (kernel_size,) * 3
        if isinstance(stride, int):
            stride = (stride,) * 3

        # assumes that the input shape is divisible by stride
        total_pad = tuple([k - s for k, s in zip(kernel_size, stride)])
        pad_input = []
        for p in total_pad[::-1]: # reverse since F.pad starts from last dim
            pad_input.append((p // 2 + p % 2, p // 2))
        pad_input = sum(pad_input, tuple())
        self.pad_input = pad_input

        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size,
                              stride=stride, padding=0, bias=bias)

    def forward(self, x):
        return self.conv(F.pad(x, self.pad_input))


class SamePadConvTranspose3d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, bias=True):
        super().__init__()
        if isinstance(kernel_size, int):
            kernel_size = (kernel_size,) * 3
        if isinstance(stride, int):
            stride = (stride,) * 3

        total_pad = tuple([k - s for k, s in zip(kernel_size, stride)])
        pad_input = []
        for p in total_pad[::-1]: # reverse since F.pad starts from last dim
            pad_input.append((p // 2 + p % 2, p // 2))
        pad_input = sum(pad_input, tuple())
        self.pad_input = pad_input

        self.convt = nn.ConvTranspose3d(in_channels, out_channels, kernel_size,
                                        stride=stride, bias=bias,
                                        padding=tuple([k - 1 for k in kernel_size]))

    def forward(self, x):
        return self.convt(F.pad(x, self.pad_input))