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Running on Zero
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9d7cf7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | from __future__ import annotations
from functools import wraps, partial
from contextlib import nullcontext
from typing import List, Tuple
import torch
import torch.nn as nn
from torch.nn import Module
from torch import Tensor, int32
from torch.amp import autocast
from einops import rearrange, pack, unpack
# helper functions
def exists(v):
return v is not None
def default(*args):
for arg in args:
if exists(arg):
return arg
return None
def maybe(fn):
@wraps(fn)
def inner(x, *args, **kwargs):
if not exists(x):
return x
return fn(x, *args, **kwargs)
return inner
def pack_one(t, pattern):
return pack([t], pattern)
def unpack_one(t, ps, pattern):
return unpack(t, ps, pattern)[0]
# tensor helpers
def round_ste(z: Tensor) -> Tensor:
"""Round with straight through gradients."""
zhat = z.round()
return z + (zhat - z).detach()
# main class
class FSQ(Module):
def __init__(
self,
levels: List[int],
dim: int | None = None,
num_codebooks = 1,
keep_num_codebooks_dim: bool | None = None,
scale: float | None = None,
allowed_dtypes: Tuple[torch.dtype, ...] = (torch.float32, torch.float64),
channel_first: bool = False,
projection_has_bias: bool = True,
return_indices = True,
force_quantization_f32 = True
):
super().__init__()
_levels = torch.tensor(levels, dtype=int32)
self.register_buffer("_levels", _levels, persistent = False)
_basis = torch.cumprod(torch.tensor([1] + levels[:-1]), dim=0, dtype=int32)
self.register_buffer("_basis", _basis, persistent = False)
self.scale = scale
codebook_dim = len(levels)
self.codebook_dim = codebook_dim
effective_codebook_dim = codebook_dim * num_codebooks
self.num_codebooks = num_codebooks
self.effective_codebook_dim = effective_codebook_dim
keep_num_codebooks_dim = default(keep_num_codebooks_dim, num_codebooks > 1)
assert not (num_codebooks > 1 and not keep_num_codebooks_dim)
self.keep_num_codebooks_dim = keep_num_codebooks_dim
self.dim = default(dim, len(_levels) * num_codebooks)
self.channel_first = channel_first
has_projections = self.dim != effective_codebook_dim
self.project_in = nn.Linear(self.dim, effective_codebook_dim, bias = projection_has_bias) if has_projections else nn.Identity()
self.project_out = nn.Linear(effective_codebook_dim, self.dim, bias = projection_has_bias) if has_projections else nn.Identity()
self.has_projections = has_projections
self.return_indices = return_indices
if return_indices:
self.codebook_size: int = self._levels.prod().item()
implicit_codebook = self._indices_to_codes(torch.arange(self.codebook_size))
self.register_buffer("implicit_codebook", implicit_codebook, persistent = False)
self.allowed_dtypes = allowed_dtypes
self.force_quantization_f32 = force_quantization_f32
def bound(self, z, eps: float = 1e-3):
""" Bound `z`, an array of shape (..., d). """
half_l = (self._levels - 1) * (1 + eps) / 2
offset = torch.where(self._levels % 2 == 0, 0.5, 0.0)
shift = (offset / half_l).atanh()
return (z + shift).tanh() * half_l - offset
def quantize(self, z):
""" Quantizes 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):
half_width = self._levels // 2
return (zhat_normalized * half_width) + half_width
def _scale_and_shift_inverse(self, zhat):
half_width = self._levels // 2
return (zhat - half_width) / half_width
def _indices_to_codes(self, indices):
level_indices = self.indices_to_level_indices(indices)
codes = self._scale_and_shift_inverse(level_indices)
return codes
def codes_to_indices(self, zhat):
""" Converts a `code` to an index in the codebook. """
assert zhat.shape[-1] == self.codebook_dim
zhat = self._scale_and_shift(zhat)
return (zhat * self._basis).sum(dim=-1).to(int32)
def indices_to_level_indices(self, indices):
""" Converts indices to indices at each level, perhaps needed for a transformer with factorized embeddings """
indices = rearrange(indices, '... -> ... 1')
codes_non_centered = (indices // self._basis) % self._levels
return codes_non_centered
def indices_to_codes(self, indices):
""" Inverse of `codes_to_indices`. """
assert exists(indices)
is_img_or_video = indices.ndim >= (3 + int(self.keep_num_codebooks_dim))
codes = self._indices_to_codes(indices)
if self.keep_num_codebooks_dim:
codes = rearrange(codes, '... c d -> ... (c d)')
codes = self.project_out(codes)
if is_img_or_video or self.channel_first:
codes = rearrange(codes, 'b ... d -> b d ...')
return codes
def dequantize(self, indices):
codes = self._indices_to_codes(indices)
out = self.project_out(codes)
return out
def forward(self, z):
"""
einstein notation
b - batch
n - sequence (or flattened spatial dimensions)
d - feature dimension
c - number of codebook dim
"""
assert z.shape[-1] == self.dim, f'expected dimension of {self.dim} but found dimension of {z.shape[-1]}'
z = self.project_in(z)
z = rearrange(z, 'b n (c d) -> b n c d', c = self.num_codebooks)
# whether to force quantization step to be full precision or not
force_f32 = self.force_quantization_f32
quantization_context = partial(autocast, 'cuda', enabled = False) if force_f32 else nullcontext
with quantization_context():
orig_dtype = z.dtype
if force_f32 and orig_dtype not in self.allowed_dtypes:
z = z.float()
codes = self.quantize(z)
# returning indices could be optional
indices = None
if self.return_indices:
indices = self.codes_to_indices(codes)
codes = rearrange(codes, 'b n c d -> b n (c d)')
codes = codes.type(orig_dtype)
# project out
out = self.project_out(codes)
if not self.keep_num_codebooks_dim and self.return_indices:
indices = maybe(rearrange)(indices, '... 1 -> ...')
# return quantized output and indices
return out, indices, None |