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e0552b0 | 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 | from einops import rearrange
import torch
from torch import Tensor
from jaxtyping import Float, Int, Bool
from typing import Optional
from koja_diffuser.train.debug import Debug
from koja_diffuser.tokenizer.special import SpecialToken
class MMDLoss:
@staticmethod
def flatten_latent(z: Float[Tensor, "B L S"]) -> Float[Tensor, "B LS"]:
return rearrange(z, "b l s -> b (l s)")
@staticmethod
def pairwise_sq_dist(x: Tensor, y: Tensor) -> Tensor:
x_norm = (x**2).sum(dim=1, keepdim=True)
y_norm = (y**2).sum(dim=1, keepdim=True).transpose(0, 1)
dist = x_norm + y_norm - 2.0 * (x @ y.transpose(0, 1))
return dist.clamp_min(0.0)
@staticmethod
@torch.no_grad()
def estimate_bandwidth(
x: Float[Tensor, "B LS"], y: Float[Tensor, "B LS"], eps=1e-4
) -> Tensor:
z = torch.cat([x, y], dim=0)
d2 = MMDLoss.pairwise_sq_dist(z, z)
mask = d2 > 0
if mask.any():
sigma = d2[mask].median().sqrt() # median heuristic
return sigma.clamp_min(eps)
return z.new_tensor(1.0)
@staticmethod
def off_diagonal_mean(k: Tensor) -> Tensor:
n = k.size(0)
if n <= 1:
return k.mean()
mask = ~torch.eye(n, dtype=torch.bool, device=k.device)
return k[mask].mean()
@staticmethod
def mmd_rbf_loss(
z_fake: Float[Tensor, "B L S"], z_real: Float[Tensor, "B L S"]
) -> Tensor:
x = MMDLoss.flatten_latent(z_fake.float())
y = MMDLoss.flatten_latent(z_real.detach().float())
sigma = MMDLoss.estimate_bandwidth(x.detach(), y.detach())
sigmas = [sigma * 0.5, sigma, sigma * 2.0]
d_xx = MMDLoss.pairwise_sq_dist(x, x)
d_yy = MMDLoss.pairwise_sq_dist(y, y)
d_xy = MMDLoss.pairwise_sq_dist(x, y)
k_xx = torch.zeros_like(d_xx)
k_yy = torch.zeros_like(d_yy)
k_xy = torch.zeros_like(d_xy)
for s in sigmas:
denom = 2.0 * (s**2)
k_xx += torch.exp(-d_xx / denom)
k_yy += torch.exp(-d_yy / denom)
k_xy += torch.exp(-d_xy / denom)
k_xx /= len(sigmas)
k_yy /= len(sigmas)
k_xy /= len(sigmas)
return (
MMDLoss.off_diagonal_mean(k_xx)
+ MMDLoss.off_diagonal_mean(k_yy)
- 2.0 * k_xy.mean()
)
@staticmethod
def direct_domain_loss(
*,
z_ja_hat: Tensor,
z_ko_hat: Tensor,
z_ja: Tensor,
z_ko: Tensor,
d: Optional[Debug] = None,
):
loss_domain_ja = MMDLoss.mmd_rbf_loss(z_ja_hat, z_ja)
loss_domain_ko = MMDLoss.mmd_rbf_loss(z_ko_hat, z_ko)
total = loss_domain_ja + loss_domain_ko
if d is not None:
d.loss.domain_ja(loss_domain_ja)
d.loss.domain_ko(loss_domain_ko)
d.loss.domain_total(total)
return total
def repeat_penalty_loss(
logits: Tensor,
*,
temperature=1.0,
exclude_token_ids: tuple[int, ...] = (SpecialToken.eos,),
) -> Tensor:
probs = (logits.float() / temperature).softmax(dim=-1)
if exclude_token_ids:
probs = probs.clone()
for token_id in exclude_token_ids:
probs[..., token_id] = 0.0
probs = probs / probs.sum(dim=-1, keepdim=True).clamp_min(1e-8)
p_prev = probs[:, :-1, :]
p_next = probs[:, 1:, :]
repeat_prob = (p_prev * p_next).sum(dim=-1)
return repeat_prob.mean()
class CenterOneSepLoss:
def __init__(self, logits: Float[Tensor, "B L S"], temperature=1.0):
self.logits = logits
self.probs = (self.logits.float() / temperature).softmax(dim=-1)
self.device = logits.device
def get_len(self) -> Int[Tensor, "B"]:
# EOS 제외 길이
pred_tokens = self.probs.argmax(dim=-1)
is_eos = pred_tokens == SpecialToken.eos
has_eos = is_eos.any(dim=-1)
eos_indices = is_eos.int().argmax(dim=-1)
seq_len = self.probs.size(1)
return torch.where(
has_eos, eos_indices, torch.tensor(seq_len, device=self.device)
)
def mirror_tensor(
self, probs_len: Int[Tensor, "B"]
) -> tuple[Float[Tensor, "B L"], Bool[Tensor, "B L"]]:
max_len = self.logits.size(1)
pos = rearrange(torch.arange(max_len, device=self.device), "l -> 1 l")
mirror = torch.minimum(pos, probs_len[:, None] - 1 - pos)
peak = (probs_len[:, None] - 1) // 2
denom = peak.clamp_min(1)
out = mirror.float() / denom.float()
mask = pos < probs_len[:, None]
out = out.masked_fill(~mask, 0.0)
return out, mask
def loss(self):
probs_len = self.get_len()
sep_probs = self.probs[..., SpecialToken.sep]
mirror_tensor, mask = self.mirror_tensor(probs_len)
pad_mass = sep_probs.masked_fill(~mask, 0.0)
center_loss = (pad_mass * (1.0 - mirror_tensor)).sum(dim=-1).mean()
# Count Loss
expected_pad_count = pad_mass.sum(dim=-1)
target_count = (probs_len > 0).float()
count_loss = ((expected_pad_count - target_count) ** 2).mean()
# Peak Loss
max_pad_prob = pad_mass.max(dim=-1).values
peak_loss = ((max_pad_prob - target_count) ** 2).mean()
return center_loss, count_loss, peak_loss
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