From 6b1ca2756cde650874fb7e5bf3f1025c3a84a83e Mon Sep 17 00:00:00 2001 From: fluffy Date: Thu, 28 Sep 2023 22:40:21 +0200 Subject: [PATCH 01/14] Add FSQ implementation --- .../finite_scalar_quantization.py | 55 +++++++++++++++++++ 1 file changed, 55 insertions(+) create mode 100644 vector_quantize_pytorch/finite_scalar_quantization.py diff --git a/vector_quantize_pytorch/finite_scalar_quantization.py b/vector_quantize_pytorch/finite_scalar_quantization.py new file mode 100644 index 0000000..c72c8d7 --- /dev/null +++ b/vector_quantize_pytorch/finite_scalar_quantization.py @@ -0,0 +1,55 @@ +""" +Finite Scalar Quantization: VQ-VAE Made Simple - https://arxiv.org/abs/2309.15505 +Code adapted from Jax version in Appendix A.1 +""" + +import torch +import torch.nn as nn + + +def round_ste(z: torch.Tensor) -> torch.Tensor: + """Round with straight through gradients.""" + zhat = z.round() + return z + (zhat - z).detach() + + +class FSQ(nn.Module): + def __init__(self, levels: list[int]): + self._levels = torch.tensor(levels, dtype=torch.int32) + self._basis = torch.cumprod(torch.tensor([1] + levels[:-1]), dtype=torch.int32) + + codebook_size = self._levels.prod() + self.implicit_codebook = self.indices_to_codes(torch.arange(codebook_size)) + + def bound(self, z: torch.Tensor, eps: float = 1e-3) -> torch.Tensor: + """Bound `z`, an array of shape (..., d).""" + half_l = (self._levels - 1) * (1 - eps) / 2 + offset = torch.where(self._levels % 2 == 0, 0.0, 0.5) + shift = (offset / half_l).tan() + return (z + shift).tanh() * half_l - offset + + def quantize(self, z: torch.Tensor) -> torch.Tensor: + """Quanitzes z, returns quantized zhat, same shape as z.""" + quantized = round_ste(self.bound(z)) + half_width = self._levels // 2 # Renormalize to [-1, 1]. + return quantized / half_width + + def _scale_and_shift(self, zhat_normalized: torch.Tensor) -> torch.Tensor: + half_width = self._levels // 2 + return (zhat_normalized * half_width) + half_width + + def _scale_and_shift_inverse(self, zhat: torch.Tensor) -> torch.Tensor: + half_width = self._levels // 2 + return (zhat - half_width) / half_width + + def codes_to_indices(self, zhat: torch.Tensor) -> torch.Tensor: + """Converts a `code` to an index in the codebook.""" + assert zhat.shape[-1] == len(self._levels) + zhat = self._scale_and_shift(zhat) + return (zhat * self._basis).sum(dim=-1).to(torch.int32) + + def indices_to_codes(self, indices: torch.Tensor) -> torch.Tensor: + """Inverse of `codes_to_indices`.""" + indices = indices.unsqueeze(-1) + codes_non_centered = (indices // self._basis) % self._levels + return self._scale_and_shift_inverse(codes_non_centered) From d3734638af81506c7eb442c098e0287e778c2046 Mon Sep 17 00:00:00 2001 From: fluffy Date: Fri, 29 Sep 2023 00:03:12 +0200 Subject: [PATCH 02/14] Make it importable --- vector_quantize_pytorch/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/vector_quantize_pytorch/__init__.py b/vector_quantize_pytorch/__init__.py index 74e5ec6..becc5e0 100644 --- a/vector_quantize_pytorch/__init__.py +++ b/vector_quantize_pytorch/__init__.py @@ -1,3 +1,4 @@ from vector_quantize_pytorch.vector_quantize_pytorch import VectorQuantize from vector_quantize_pytorch.residual_vq import ResidualVQ, GroupedResidualVQ -from vector_quantize_pytorch.random_projection_quantizer import RandomProjectionQuantizer \ No newline at end of file +from vector_quantize_pytorch.random_projection_quantizer import RandomProjectionQuantizer +from vector_quantize_pytorch.finite_scalar_quantization import FSQ \ No newline at end of file From 79aea468c36adc16c1b09e91f20aef0c1bee7ebd Mon Sep 17 00:00:00 2001 From: fluffy Date: Fri, 29 Sep 2023 00:04:04 +0200 Subject: [PATCH 03/14] Register buffers and add forward method --- .../finite_scalar_quantization.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/vector_quantize_pytorch/finite_scalar_quantization.py b/vector_quantize_pytorch/finite_scalar_quantization.py index c72c8d7..825082d 100644 --- a/vector_quantize_pytorch/finite_scalar_quantization.py +++ b/vector_quantize_pytorch/finite_scalar_quantization.py @@ -15,11 +15,21 @@ def round_ste(z: torch.Tensor) -> torch.Tensor: class FSQ(nn.Module): def __init__(self, levels: list[int]): - self._levels = torch.tensor(levels, dtype=torch.int32) - self._basis = torch.cumprod(torch.tensor([1] + levels[:-1]), dtype=torch.int32) + super().__init__() + _levels = torch.tensor(levels, dtype=torch.int32) + self.register_buffer("_levels", _levels) + + _basis = torch.cumprod(torch.tensor([1] + levels[:-1]), dim=0, dtype=torch.int32) + self.register_buffer("_basis", _basis) codebook_size = self._levels.prod() - self.implicit_codebook = self.indices_to_codes(torch.arange(codebook_size)) + implicit_codebook = self.indices_to_codes(torch.arange(codebook_size)) + self.register_buffer("implicit_codebook", implicit_codebook) + + def forward(self, z: torch.Tensor) -> torch.Tensor: + zhat = self.quantize(z) + indices = self.codes_to_indices(zhat) + return self.implicit_codebook[indices], indices def bound(self, z: torch.Tensor, eps: float = 1e-3) -> torch.Tensor: """Bound `z`, an array of shape (..., d).""" From 01d9a1f1a835bf30e0c6d8c6d028ba37ecfd6a3c Mon Sep 17 00:00:00 2001 From: fluffy Date: Fri, 29 Sep 2023 00:04:26 +0200 Subject: [PATCH 04/14] Add FSQ Autoencoder example --- examples/autoencoder_fsq.py | 96 +++++++++++++++++++++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 examples/autoencoder_fsq.py diff --git a/examples/autoencoder_fsq.py b/examples/autoencoder_fsq.py new file mode 100644 index 0000000..aa0ce3b --- /dev/null +++ b/examples/autoencoder_fsq.py @@ -0,0 +1,96 @@ +# FashionMnist VQ experiment with various settings, using FSQ. +# From https://github.com/minyoungg/vqtorch/blob/main/examples/autoencoder.py + +from tqdm.auto import trange + +import math +import torch +import torch.nn as nn +from torchvision import datasets, transforms +from torch.utils.data import DataLoader + +from vector_quantize_pytorch import FSQ + + +lr = 3e-4 +train_iter = 1000 +levels = [8, 6, 5] # target size 2^8, actual size 240 +num_codes = math.prod(levels) +seed = 1234 +device = "cuda" if torch.cuda.is_available() else "cpu" + + +class SimpleFSQAutoEncoder(nn.Module): + def __init__(self, levels: list[int]): + super().__init__() + self.layers = nn.ModuleList( + [ + nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.GELU(), + nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1), + nn.Conv2d(32, 32, kernel_size=6, stride=3, padding=0), + FSQ(levels), + nn.ConvTranspose2d(32, 32, kernel_size=6, stride=3, padding=0), + nn.Conv2d(32, 16, kernel_size=4, stride=1, padding=2), + nn.GELU(), + nn.Upsample(scale_factor=2, mode="nearest"), + nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=2), + ] + ) + return + + def forward(self, x): + for layer in self.layers: + if isinstance(layer, FSQ): + print(x.shape) + x_flat, indices = layer(x) + else: + x = layer(x) + + return x.clamp(-1, 1), indices + + +def train(model, train_loader, train_iterations=1000): + def iterate_dataset(data_loader): + data_iter = iter(data_loader) + while True: + try: + x, y = next(data_iter) + except StopIteration: + data_iter = iter(data_loader) + x, y = next(data_iter) + yield x.to(device), y.to(device) + + for _ in (pbar := trange(train_iterations)): + opt.zero_grad() + x, _ = next(iterate_dataset(train_loader)) + out, indices = model(x) + print("out:", out.shape) + rec_loss = (out - x).abs().mean() + rec_loss.backward() + + opt.step() + pbar.set_description( + f"rec loss: {rec_loss.item():.3f} | " + + f"active %: {indices.unique().numel() / num_codes * 100:.3f}" + ) + return + + +transform = transforms.Compose( + [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))] +) +train_dataset = DataLoader( + datasets.FashionMNIST( + root="~/data/fashion_mnist", train=True, download=True, transform=transform + ), + batch_size=256, + shuffle=True, +) + +print("baseline") +torch.random.manual_seed(seed) +model = SimpleFSQAutoEncoder(levels).to(device) +opt = torch.optim.AdamW(model.parameters(), lr=lr) +train(model, train_dataset, train_iterations=train_iter) From 1245c290b86083c953855fa740e018329b69b9f3 Mon Sep 17 00:00:00 2001 From: fluffy Date: Fri, 29 Sep 2023 00:06:38 +0200 Subject: [PATCH 05/14] Remove print --- examples/autoencoder_fsq.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/autoencoder_fsq.py b/examples/autoencoder_fsq.py index aa0ce3b..795f358 100644 --- a/examples/autoencoder_fsq.py +++ b/examples/autoencoder_fsq.py @@ -43,7 +43,6 @@ def __init__(self, levels: list[int]): def forward(self, x): for layer in self.layers: if isinstance(layer, FSQ): - print(x.shape) x_flat, indices = layer(x) else: x = layer(x) From e7549b29fb55dd94d8ea2dc184c3ca00020130bd Mon Sep 17 00:00:00 2001 From: fluffy Date: Fri, 29 Sep 2023 00:07:27 +0200 Subject: [PATCH 06/14] That one too... --- examples/autoencoder_fsq.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/autoencoder_fsq.py b/examples/autoencoder_fsq.py index 795f358..1ba188a 100644 --- a/examples/autoencoder_fsq.py +++ b/examples/autoencoder_fsq.py @@ -65,7 +65,6 @@ def iterate_dataset(data_loader): opt.zero_grad() x, _ = next(iterate_dataset(train_loader)) out, indices = model(x) - print("out:", out.shape) rec_loss = (out - x).abs().mean() rec_loss.backward() From a832fc9aa9d4997de1aa5f70f07c561a0f13c4e8 Mon Sep 17 00:00:00 2001 From: fluffy Date: Fri, 29 Sep 2023 00:38:31 +0200 Subject: [PATCH 07/14] Add offset to even instead of odd --- vector_quantize_pytorch/finite_scalar_quantization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vector_quantize_pytorch/finite_scalar_quantization.py b/vector_quantize_pytorch/finite_scalar_quantization.py index 825082d..356a38f 100644 --- a/vector_quantize_pytorch/finite_scalar_quantization.py +++ b/vector_quantize_pytorch/finite_scalar_quantization.py @@ -34,7 +34,7 @@ def forward(self, z: torch.Tensor) -> torch.Tensor: def bound(self, z: torch.Tensor, eps: float = 1e-3) -> torch.Tensor: """Bound `z`, an array of shape (..., d).""" half_l = (self._levels - 1) * (1 - eps) / 2 - offset = torch.where(self._levels % 2 == 0, 0.0, 0.5) + offset = torch.where(self._levels % 2 == 0, 0.5, 0.0) shift = (offset / half_l).tan() return (z + shift).tanh() * half_l - offset From c989d30b8f7a4d4158ca9c30f70003dbb1b1f5d1 Mon Sep 17 00:00:00 2001 From: fluffy Date: Fri, 29 Sep 2023 00:44:10 +0200 Subject: [PATCH 08/14] Return zhat in forward --- vector_quantize_pytorch/finite_scalar_quantization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vector_quantize_pytorch/finite_scalar_quantization.py b/vector_quantize_pytorch/finite_scalar_quantization.py index 356a38f..dd32b02 100644 --- a/vector_quantize_pytorch/finite_scalar_quantization.py +++ b/vector_quantize_pytorch/finite_scalar_quantization.py @@ -29,7 +29,7 @@ def __init__(self, levels: list[int]): def forward(self, z: torch.Tensor) -> torch.Tensor: zhat = self.quantize(z) indices = self.codes_to_indices(zhat) - return self.implicit_codebook[indices], indices + return zhat, indices def bound(self, z: torch.Tensor, eps: float = 1e-3) -> torch.Tensor: """Bound `z`, an array of shape (..., d).""" From d4032485e5f3358eb9a07865c633f6c579b11517 Mon Sep 17 00:00:00 2001 From: fluffy Date: Fri, 29 Sep 2023 01:27:09 +0200 Subject: [PATCH 09/14] Fix gradient flow and reduce param count on FSQ model --- examples/autoencoder.py | 2 +- examples/autoencoder_fsq.py | 10 +++++----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/autoencoder.py b/examples/autoencoder.py index 0be3f53..676814a 100644 --- a/examples/autoencoder.py +++ b/examples/autoencoder.py @@ -41,7 +41,7 @@ def __init__(self, **vq_kwargs): def forward(self, x): for layer in self.layers: if isinstance(layer, VectorQuantize): - x_flat, indices, commit_loss = layer(x) + x, indices, commit_loss = layer(x) else: x = layer(x) diff --git a/examples/autoencoder_fsq.py b/examples/autoencoder_fsq.py index 1ba188a..2f3e9fc 100644 --- a/examples/autoencoder_fsq.py +++ b/examples/autoencoder_fsq.py @@ -28,11 +28,11 @@ def __init__(self, levels: list[int]): nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1), nn.MaxPool2d(kernel_size=2, stride=2), nn.GELU(), - nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1), - nn.Conv2d(32, 32, kernel_size=6, stride=3, padding=0), + nn.Conv2d(16, 8, kernel_size=3, stride=1, padding=1), + nn.Conv2d(8, 8, kernel_size=6, stride=3, padding=0), FSQ(levels), - nn.ConvTranspose2d(32, 32, kernel_size=6, stride=3, padding=0), - nn.Conv2d(32, 16, kernel_size=4, stride=1, padding=2), + nn.ConvTranspose2d(8, 8, kernel_size=6, stride=3, padding=0), + nn.Conv2d(8, 16, kernel_size=4, stride=1, padding=2), nn.GELU(), nn.Upsample(scale_factor=2, mode="nearest"), nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=2), @@ -43,7 +43,7 @@ def __init__(self, levels: list[int]): def forward(self, x): for layer in self.layers: if isinstance(layer, FSQ): - x_flat, indices = layer(x) + x, indices = layer(x) else: x = layer(x) From 0e66f53ef30ffe72d871116ebe4bf4d24b75c7fe Mon Sep 17 00:00:00 2001 From: fluffy Date: Fri, 29 Sep 2023 17:32:14 +0200 Subject: [PATCH 10/14] Add dim and n_codes properties --- vector_quantize_pytorch/finite_scalar_quantization.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/vector_quantize_pytorch/finite_scalar_quantization.py b/vector_quantize_pytorch/finite_scalar_quantization.py index dd32b02..e34493f 100644 --- a/vector_quantize_pytorch/finite_scalar_quantization.py +++ b/vector_quantize_pytorch/finite_scalar_quantization.py @@ -22,8 +22,9 @@ def __init__(self, levels: list[int]): _basis = torch.cumprod(torch.tensor([1] + levels[:-1]), dim=0, dtype=torch.int32) self.register_buffer("_basis", _basis) - codebook_size = self._levels.prod() - implicit_codebook = self.indices_to_codes(torch.arange(codebook_size)) + self.dim = len(levels) + self.n_codes = self._levels.prod().item() + implicit_codebook = self.indices_to_codes(torch.arange(self.n_codes)) self.register_buffer("implicit_codebook", implicit_codebook) def forward(self, z: torch.Tensor) -> torch.Tensor: @@ -54,7 +55,7 @@ def _scale_and_shift_inverse(self, zhat: torch.Tensor) -> torch.Tensor: def codes_to_indices(self, zhat: torch.Tensor) -> torch.Tensor: """Converts a `code` to an index in the codebook.""" - assert zhat.shape[-1] == len(self._levels) + assert zhat.shape[-1] == self.dim zhat = self._scale_and_shift(zhat) return (zhat * self._basis).sum(dim=-1).to(torch.int32) From 1f684762b4029802d78deca8e44267b14ab560d3 Mon Sep 17 00:00:00 2001 From: fluffy Date: Fri, 29 Sep 2023 17:32:33 +0200 Subject: [PATCH 11/14] Bump version to 1.8.0 --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index bb688a6..61e541f 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ setup( name = 'vector_quantize_pytorch', packages = find_packages(), - version = '1.7.1', + version = '1.8.0', license='MIT', description = 'Vector Quantization - Pytorch', long_description_content_type = 'text/markdown', From 64ce9e9342561fac69f2e35a5dc8fe2949f4de98 Mon Sep 17 00:00:00 2001 From: fluffy Date: Fri, 29 Sep 2023 18:40:07 +0200 Subject: [PATCH 12/14] Add FSQ entry to README --- README.md | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/README.md b/README.md index 9e9b9a1..7692f1a 100644 --- a/README.md +++ b/README.md @@ -251,6 +251,33 @@ indices = quantizer(x) # (1, 1024, 16) - (batch, seq, num_codebooks) This repository should also automatically synchronizing the codebooks in a multi-process setting. If somehow it isn't, please open an issue. You can override whether to synchronize codebooks or not by setting `sync_codebook = True | False` +### Finite Scalar Quantization + + + +TODO: Write description + +arxiv: https://arxiv.org/abs/2309.15505 + +```python +import torch +from vector_quantize_pytorch import FSQ + +levels = [8,5,5,5] # see 4.1 and A.4.1 in the paper +quantizer = FSQ(levels) + +x = torch.randn(1, 1024, quantizer.dim) +xhat, indices = quantizer(x) + +print(xhat.shape) # (1, 1024, 4) - (batch, seq, dim) +print(indices.shape) # (1, 1024) - (batch, seq) + +assert torch.all(xhat == quantizer.indices_to_codes(indices)) +assert torch.all(xhat == quantizer.implicit_codebook[indices]) +``` + + + ## Todo - [x] allow for multi-headed codebooks @@ -384,3 +411,14 @@ This repository should also automatically synchronizing the codebooks in a multi primaryClass = {cs.CV} } ``` + +```bibtex +@misc{mentzer2023finite, + title = {Finite Scalar Quantization: VQ-VAE Made Simple}, + author = {Fabian Mentzer and David Minnen and Eirikur Agustsson and Michael Tschannen}, + year = {2023}, + eprint = {2309.15505}, + archivePrefix = {arXiv}, + primaryClass = {cs.CV} +} +``` From bb9f7a92999b7e9bb49356eb13579562ae2beb1f Mon Sep 17 00:00:00 2001 From: fluffy Date: Fri, 29 Sep 2023 18:43:10 +0200 Subject: [PATCH 13/14] Add FSQ png --- fsq.png | Bin 0 -> 294924 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 fsq.png diff --git a/fsq.png b/fsq.png new file mode 100644 index 0000000000000000000000000000000000000000..0d30ad0102c905c3d5c5e214891b4188bf7376a3 GIT binary patch literal 294924 zcmeEu2Uk0z2 zQl(3c^iC*&(2{(ogy+8Z-uDl@-+1PTFgzsZ?6db;bIrB(Itje1rg&)o@%;z{;?SMj z@)`)lU#1AezQiMRz>%PcFAKl{UTZnIyVe#K2*mBchzQHO@AcRl@T*cYFK--`4Y(`! zR^Xg(_!0f{9qC_hOvl|-c=W1>Rxs$*Wra5iH0*{EJhlof2ZEo|vN^vHqHQd3WW9G& z&jEKVj>w5DqkQmevnM-E4m-7NI?$YY_2NNwlc%{xN!7s5&|@4d&3kd@LlPoumI=;# zaNVU$h|KSvCAhZDEC!~x);kkLMP*Mm`JX!@66YWEG%Km=jeKrWVGN_xSfX^l8UDG5 z(;b;cyU#1PRtjys8=Gq}@?qVNILW_t;xNUZ;)D;_%H=4|M*EG@6AWfDw3<{!{Qf3FYh(6T&|x}4m*8| zf^M@^FexARK>T(>oZ7DN7>PhQFVf!FYDcNt6=TD_5R!{am-c?3czlgGbcn&1cKQb0 zd0Oqnznq^jiSCK4V!Mi9|3l+-^T=<+#woh9v@}0xi%sdzBNR-zuQH_FxL}1)O=SH@ z7xzc1h&JoRz#m}_y0B+QRGvrO*gO1eAH(x+M~)@ky!htmm%}qRZXG$1G`!PlD?KIn`yQe3h zPn7=YOYf02hsz)LPgQL$83UIKP_N-!V%@kRb@0GQ)Y3`XBRn-X!kLWxUOO-`p5+LX z-TOo5%MpIX*|$1hPRz(Ho|e7Ow&`1wXmy@fiTy#~lUtjgHoj_g>1c_i?RTN=em1Kx zB=`EW{MRBK%VL z^>97MvLgShC@B@MbhrGCYs>eRul*I{_sK6nK1n`XKKHw7ptV3`+D(_lTX$De8@{4a znJ@b#D~)w*A4sqr`vP@e2=T{dT_6p*thZ zt~+w=UCTRZ9#7S$I-$ar{0&ip;nyz^Z*xWVMiL`<Hr=6Cbsk_p0xlR`{ebl2((! zBiF7Yrk1U;oHQD8RQ0DmzfP*k%)5>Q$!Fi!s+j7CdVb(};QqjJ;^2hj1kJ=g*2tg) zrFf;-MrQ5j0tVtr`6~H}+Fz2tOlC!<_av>~=ESz@gxy1a8BUSGR(CM``kmRHD%SGu z;j-?v47Q$VUcJs+m$Y+@S1Z_V1!uBiU3BNNu4LFHaeg++^j4(r5$g`@zLs&RXn%R5 zmv%?mmiFh8vw%G%KMr0j;*x7q6;a1(;v*_ze7=!=NHU~0I$pZJ=zzI8lqRUt_NigHolEa*Z@pyw+j_+i?&x1CgQ`zs3DI5`#V@f&7sRwir+#=D z(;fXfXXcUmQ?YITYyE?*Ua_0*tBh>PY%^>r7sxqX`5}dm3O*Xw>N6K7o3;2w1ZhWW z$3^^#vF5b#?$WpV{D}FXru}NOPWP3Xg7&9DpX&n0K8}~=T>8fRJAa_X=|Yh~QPcvH zC*QX0HVu8+iv+odT1`dP2CD`(#kcPRJ0GSrwOU!Crbw-yrS@?p^nPb9jxK)WIOB4z zc&kXKSjMUIDRYraY3-u$BK=@PPa0NLe_)12%8iR&D&8$eN>duWR^#UGq3tP6yh3PQ zTU`CL`G{b>?mlHZWiYz^TfI}H7Tb_8$~}r3M{=8T-#vcnU>D=yQMc>2ubjW~RgmMv zJog}nRAH;xP_@hZ)4Zqmojx+siRC))gy_g9o?hp9fo!r*@%J=ym5bZmL^DH~HLM>X=WADXo#(F-9aB>|F zwI6wuI*0Yon(L*jtSg06>hn+fyERVf zoI1}jWLI3ePT$3tjXXH7SDx~uZK~!~je!E4!h}L_-Oi{1@;F&{d8}jjYx$mY$>$W; 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description +| | VQ | FSQ | +|------------------|----|-----| +| Quantization | argmin_c \|\| z-c \|\| | round(f(z)) | +| Gradients | Straight Through Estimation (STE) | STE | +| Auxiliary Losses | Commitment, codebook, entropy loss, ... | N/A | +| Tricks | EMA on codebook, codebook splitting, projections, ...| N/A | +| Parameters | Codebook | N/A | + +[This](https://arxiv.org/abs/2309.15505) work out of Google Deepmind aims to vastly simplify the way vector quantization is done for generative modeling, removing the need for commitment losses, EMA updating of the codebook, as well as tackle the issues with codebook collapse or insufficient utilization. They simply round each scalar into discrete levels with straight through gradients; the codes become uniform points in a hypercube. -arxiv: https://arxiv.org/abs/2309.15505 ```python import torch