models/quant.py

from typing import List, Optional, Sequence, Tuple, Union

import numpy as np
import torch
from torch import distributed as tdist, nn as nn
from torch.nn import functional as F

import dist

# this file only provides the VectorQuantizer2 used in VQVAE
__all__ = ['VectorQuantizer2', ]


class VectorQuantizer2(nn.Module):
    # VQGAN originally use beta=1.0, never tried 0.25; SD seems using 0.25
    def __init__(
            self, vocab_size, Cvae, using_znorm, beta: float = 0.25,
            default_qresi_counts=0, v_patch_nums=None, quant_resi=0.5, share_quant_resi=4,  # share_quant_resi: args.qsr
    ):
        super().__init__()
        self.vocab_size: int = vocab_size
        self.Cvae: int = Cvae
        self.using_znorm: bool = using_znorm
        self.v_patch_nums: Tuple[int] = v_patch_nums

        self.quant_resi_ratio = quant_resi
        if share_quant_resi == 0:  # non-shared: \phi_{1 to K} for K scales
            self.quant_resi = PhiNonShared(
                [(Phi(Cvae, quant_resi) if abs(quant_resi) > 1e-6 else nn.Identity()) for _ in
                 range(default_qresi_counts or len(self.v_patch_nums))])
        elif share_quant_resi == 1:  # fully shared: only a single \phi for K scales
            self.quant_resi = PhiShared(Phi(Cvae, quant_resi) if abs(quant_resi) > 1e-6 else nn.Identity())
        else:  # partially shared: \phi_{1 to share_quant_resi} for K scales
            self.quant_resi = PhiPartiallyShared(nn.ModuleList(
                [(Phi(Cvae, quant_resi) if abs(quant_resi) > 1e-6 else nn.Identity()) for _ in
                 range(share_quant_resi)]))

        self.register_buffer('ema_vocab_hit_SV', torch.full((len(self.v_patch_nums), self.vocab_size), fill_value=0.0))
        self.record_hit = 0

        self.beta: float = beta
        self.embedding = nn.Embedding(self.vocab_size, self.Cvae)

        # only used for progressive training of VAR (not supported yet, will be tested and supported in the future)
        self.prog_si = -1  # progressive training: not supported yet, prog_si always -1

    def eini(self, eini):
        if eini > 0:
            nn.init.trunc_normal_(self.embedding.weight.data, std=eini)
        elif eini < 0:
            self.embedding.weight.data.uniform_(-abs(eini) / self.vocab_size, abs(eini) / self.vocab_size)

    def extra_repr(self) -> str:
        return f'{self.v_patch_nums}, znorm={self.using_znorm}, beta={self.beta}  |  S={len(self.v_patch_nums)}, quant_resi={self.quant_resi_ratio}'

    # ===================== `forward` is only used in VAE training =====================
    def forward(self, f_BChw: torch.Tensor, ret_usages=False) -> Tuple[torch.Tensor, List[float], torch.Tensor]:
        dtype = f_BChw.dtype
        if dtype != torch.float32: f_BChw = f_BChw.float()
        B, C, H, W = f_BChw.shape
        f_no_grad = f_BChw.detach()

        f_rest = f_no_grad.clone()
        f_hat = torch.zeros_like(f_rest)

        with torch.cuda.amp.autocast(enabled=False):
            mean_vq_loss: torch.Tensor = 0.0
            vocab_hit_V = torch.zeros(self.vocab_size, dtype=torch.float, device=f_BChw.device)
            SN = len(self.v_patch_nums)
            for si, pn in enumerate(self.v_patch_nums):  # from small to large
                # find the nearest embedding
                if self.using_znorm:
                    rest_NC = F.interpolate(f_rest, size=(pn, pn), mode='bilinear').permute(0, 2, 3, 1).reshape(-1,
                                                                                                                C) if (
                                si != SN - 1) else f_rest.permute(0, 2, 3, 1).reshape(-1, C)
                    rest_NC = F.normalize(rest_NC, dim=-1)
                    idx_N = torch.argmax(rest_NC @ F.normalize(self.embedding.weight.data.T, dim=0), dim=1)
                else:
                    rest_NC = F.interpolate(f_rest, size=(pn, pn), mode='bilinear').permute(0, 2, 3, 1).reshape(-1,
                                                                                                                C) if (
                                si != SN - 1) else f_rest.permute(0, 2, 3, 1).reshape(-1, C)
                    d_no_grad = torch.sum(rest_NC.square(), dim=1, keepdim=True) + torch.sum(
                        self.embedding.weight.data.square(), dim=1, keepdim=False)
                    d_no_grad.addmm_(rest_NC, self.embedding.weight.data.T, alpha=-2, beta=1)  # (B*h*w, vocab_size)
                    idx_N = torch.argmin(d_no_grad, dim=1)

                hit_V = idx_N.bincount(minlength=self.vocab_size).float()
                if self.training:
                    if dist.initialized(): handler = tdist.all_reduce(hit_V, async_op=True)

                # calc loss
                idx_Bhw = idx_N.view(B, pn, pn)
                h_BChw = F.interpolate(self.embedding(idx_Bhw).permute(0, 3, 1, 2), size=(H, W),
                                       mode='bilinear').contiguous() if (si != SN - 1) else self.embedding(
                    idx_Bhw).permute(0, 3, 1, 2).contiguous()
                h_BChw = self.quant_resi[si / (SN - 1)](h_BChw)
                f_hat = f_hat + h_BChw
                f_rest -= h_BChw

                if self.training and dist.initialized():
                    handler.wait()
                    if self.record_hit == 0:
                        self.ema_vocab_hit_SV[si].copy_(hit_V)
                    elif self.record_hit < 100:
                        self.ema_vocab_hit_SV[si].mul_(0.9).add_(hit_V.mul(0.1))
                    else:
                        self.ema_vocab_hit_SV[si].mul_(0.99).add_(hit_V.mul(0.01))
                    self.record_hit += 1
                vocab_hit_V.add_(hit_V)
                mean_vq_loss += F.mse_loss(f_hat.data, f_BChw).mul_(self.beta) + F.mse_loss(f_hat, f_no_grad)

            mean_vq_loss *= 1. / SN
            f_hat = (f_hat.data - f_no_grad).add_(f_BChw)

        margin = tdist.get_world_size() * (f_BChw.numel() / f_BChw.shape[1]) / self.vocab_size * 0.08
        # margin = pn*pn / 100
        if ret_usages:
            usages = [(self.ema_vocab_hit_SV[si] >= margin).float().mean().item() * 100 for si, pn in
                      enumerate(self.v_patch_nums)]
        else:
            usages = None
        return f_hat, usages, mean_vq_loss

    # ===================== `forward` is only used in VAE training =====================

    def embed_to_fhat(self, ms_h_BChw: List[torch.Tensor], all_to_max_scale=True, last_one=False) -> Union[
        List[torch.Tensor], torch.Tensor]:
        ls_f_hat_BChw = []
        B = ms_h_BChw[0].shape[0]
        H = W = self.v_patch_nums[-1]
        SN = len(self.v_patch_nums)
        if all_to_max_scale:
            f_hat = ms_h_BChw[0].new_zeros(B, self.Cvae, H, W, dtype=torch.float32)
            for si, pn in enumerate(self.v_patch_nums):  # from small to large
                h_BChw = ms_h_BChw[si]
                if si < len(self.v_patch_nums) - 1:
                    h_BChw = F.interpolate(h_BChw, size=(H, W), mode='bilinear')
                h_BChw = self.quant_resi[si / (SN - 1)](h_BChw)
                f_hat.add_(h_BChw)
                if last_one:
                    ls_f_hat_BChw = f_hat
                else:
                    ls_f_hat_BChw.append(f_hat.clone())
        else:
            # WARNING: this is not the case in VQ-VAE training or inference (we'll interpolate every token map to the max H W, like above)
            # WARNING: this should only be used for experimental purpose
            f_hat = ms_h_BChw[0].new_zeros(B, self.Cvae, self.v_patch_nums[0], self.v_patch_nums[0],
                                           dtype=torch.float32)
            for si, pn in enumerate(self.v_patch_nums):  # from small to large
                f_hat = F.interpolate(f_hat, size=(pn, pn), mode='bilinear')
                h_BChw = self.quant_resi[si / (SN - 1)](ms_h_BChw[si])
                f_hat.add_(h_BChw)
                if last_one:
                    ls_f_hat_BChw = f_hat
                else:
                    ls_f_hat_BChw.append(f_hat)

        return ls_f_hat_BChw

    def f_to_idxBl_or_fhat(self, f_BChw: torch.Tensor, to_fhat: bool,
                           v_patch_nums: Optional[Sequence[Union[int, Tuple[int, int]]]] = None) -> List[
        Union[torch.Tensor, torch.LongTensor]]:  # z_BChw is the feature from inp_img_no_grad
        B, C, H, W = f_BChw.shape
        f_no_grad = f_BChw.detach()
        f_rest = f_no_grad.clone()
        f_hat = torch.zeros_like(f_rest)

        f_hat_or_idx_Bl: List[torch.Tensor] = []

        patch_hws = [(pn, pn) if isinstance(pn, int) else (pn[0], pn[1]) for pn in
                     (v_patch_nums or self.v_patch_nums)]  # from small to large
        assert patch_hws[-1][0] == H and patch_hws[-1][1] == W, f'{patch_hws[-1]=} != ({H=}, {W=})'

        SN = len(patch_hws)
        for si, (ph, pw) in enumerate(patch_hws):  # from small to large
            if 0 <= self.prog_si < si: break  # progressive training: not supported yet, prog_si always -1
            # find the nearest embedding
            z_NC = F.interpolate(f_rest, size=(ph, pw), mode='bilinear').permute(0, 2, 3, 1).reshape(-1, C) if (
                        si != SN - 1) else f_rest.permute(0, 2, 3, 1).reshape(-1, C)
            if self.using_znorm:
                z_NC = F.normalize(z_NC, dim=-1)
                idx_N = torch.argmax(z_NC @ F.normalize(self.embedding.weight.data.T, dim=0), dim=1)
            else:
                d_no_grad = torch.sum(z_NC.square(), dim=1, keepdim=True) + torch.sum(
                    self.embedding.weight.data.square(), dim=1, keepdim=False)
                d_no_grad.addmm_(z_NC, self.embedding.weight.data.T, alpha=-2, beta=1)  # (B*h*w, vocab_size)
                idx_N = torch.argmin(d_no_grad, dim=1)

            idx_Bhw = idx_N.view(B, ph, pw)
            h_BChw = F.interpolate(self.embedding(idx_Bhw).permute(0, 3, 1, 2), size=(H, W),
                                   mode='bilinear').contiguous() if (si != SN - 1) else self.embedding(idx_Bhw).permute(
                0, 3, 1, 2).contiguous()
            h_BChw = self.quant_resi[si / (SN - 1)](h_BChw)
            f_hat.add_(h_BChw)
            f_rest.sub_(h_BChw)
            f_hat_or_idx_Bl.append(f_hat.clone() if to_fhat else idx_N.reshape(B, ph * pw))

        return f_hat_or_idx_Bl

    # ===================== idxBl_to_var_input: only used in VAR training, for getting teacher-forcing input =====================
    def idxBl_to_var_input(self, gt_ms_idx_Bl: List[torch.Tensor]) -> torch.Tensor:
        next_scales = []
        B = gt_ms_idx_Bl[0].shape[0]
        C = self.Cvae
        H = W = self.v_patch_nums[-1]
        SN = len(self.v_patch_nums)

        f_hat = gt_ms_idx_Bl[0].new_zeros(B, C, H, W, dtype=torch.float32)
        pn_next: int = self.v_patch_nums[0]
        for si in range(SN - 1):
            if self.prog_si == 0 or (
                    0 <= self.prog_si - 1 < si): break  # progressive training: not supported yet, prog_si always -1
            h_BChw = F.interpolate(self.embedding(gt_ms_idx_Bl[si]).transpose_(1, 2).view(B, C, pn_next, pn_next),
                                   size=(H, W), mode='bilinear')
            f_hat.add_(self.quant_resi[si / (SN - 1)](h_BChw))
            pn_next = self.v_patch_nums[si + 1]
            next_scales.append(
                F.interpolate(f_hat, size=(pn_next, pn_next), mode='bilinear').view(B, C, -1).transpose(1, 2))
        return torch.cat(next_scales, dim=1) if len(next_scales) else None  # cat BlCs to BLC, this should be float32

    # ===================== get_next_autoregressive_input: only used in VAR inference, for getting next step's input =====================
    def get_next_autoregressive_input(self, si: int, SN: int, f_hat: torch.Tensor, h_BChw: torch.Tensor) -> Tuple[
        Optional[torch.Tensor], torch.Tensor]:  # only used in VAR inference
        HW = self.v_patch_nums[-1]
        if si != SN - 1:
            h = self.quant_resi[si / (SN - 1)](
                F.interpolate(h_BChw, size=(HW, HW), mode='bilinear'))  # conv after upsample
            f_hat.add_(h)
            return f_hat, F.interpolate(f_hat, size=(self.v_patch_nums[si + 1], self.v_patch_nums[si + 1]),
                                        mode='bilinear')
        else:
            h = self.quant_resi[si / (SN - 1)](h_BChw)
            f_hat.add_(h)
            return f_hat, f_hat


class Phi(nn.Conv2d):
    def __init__(self, embed_dim, quant_resi):
        ks = 3
        super().__init__(in_channels=embed_dim, out_channels=embed_dim, kernel_size=ks, stride=1, padding=ks // 2)
        self.resi_ratio = abs(quant_resi)

    def forward(self, h_BChw):
        return h_BChw.mul(1 - self.resi_ratio) + super().forward(h_BChw).mul_(self.resi_ratio)


class PhiShared(nn.Module):
    def __init__(self, qresi: Phi):
        super().__init__()
        self.qresi: Phi = qresi

    def __getitem__(self, _) -> Phi:
        return self.qresi


class PhiPartiallyShared(nn.Module):
    def __init__(self, qresi_ls: nn.ModuleList):
        super().__init__()
        self.qresi_ls = qresi_ls
        K = len(qresi_ls)
        self.ticks = np.linspace(1 / 3 / K, 1 - 1 / 3 / K, K) if K == 4 else np.linspace(1 / 2 / K, 1 - 1 / 2 / K, K)

    def __getitem__(self, at_from_0_to_1: float) -> Phi:
        return self.qresi_ls[np.argmin(np.abs(self.ticks - at_from_0_to_1)).item()]

    def extra_repr(self) -> str:
        return f'ticks={self.ticks}'


class PhiNonShared(nn.ModuleList):
    def __init__(self, qresi: List):
        super().__init__(qresi)
        # self.qresi = qresi
        K = len(qresi)
        self.ticks = np.linspace(1 / 3 / K, 1 - 1 / 3 / K, K) if K == 4 else np.linspace(1 / 2 / K, 1 - 1 / 2 / K, K)

    def __getitem__(self, at_from_0_to_1: float) -> Phi:
        return super().__getitem__(np.argmin(np.abs(self.ticks - at_from_0_to_1)).item())

    def extra_repr(self) -> str:
        return f'ticks={self.ticks}'