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from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Optional, Tuple, Union, List
import math
import numpy as np
import torch
import torch.nn.functional as F
from src.meissonic.scheduler import mask_by_random_topk
@dataclass
class SchedulerStepOutput:
new_latents: torch.Tensor
@dataclass
class SchedulerApproxGuidanceOutput:
new_latents: torch.Tensor
log_prob_proposal: torch.Tensor
log_prob_diffusion: torch.Tensor
class BaseScheduler(ABC):
@abstractmethod
def step(
self,
latents: torch.Tensor,
step: int,
logits: torch.Tensor,
) -> SchedulerStepOutput:
pass
@abstractmethod
def set_timesteps(self, num_inference_steps: int):
pass
@abstractmethod
def step_with_approx_guidance(
self,
latents: torch.Tensor,
step: int,
logits: torch.Tensor,
approx_guidance: torch.Tensor,
) -> SchedulerApproxGuidanceOutput:
pass
def sum_masked_logits(
logits: torch.Tensor,
preds: torch.Tensor,
mask: torch.Tensor
) -> torch.Tensor:
"""
Sum logits at `preds` indices, masked by `mask`, handling invalid `preds`.
Args:
logits: Tensor of shape (B, H, W, C) - logits over C classes.
preds: Tensor of shape (B, H, W) - predicted class indices.
mask: Tensor of shape (B, H, W) - binary mask to include positions.
Returns:
Tensor of shape (B,) - sum of selected logits per batch item.
"""
B, H, W, C = logits.shape
# Ensure preds are in valid index range [0, C-1]
valid = (preds >= 0) & (preds <= preds[mask].max())
# Replace invalid preds with a dummy index (0), which we will mask later
safe_preds = preds.masked_fill(~valid, 0)
# Gather logits at predicted indices
selected = torch.gather(logits, dim=3, index=safe_preds.unsqueeze(-1)).squeeze(-1)
# Zero out contributions from invalid preds and masked positions
selected = selected * valid * mask
# Sum over H, W dimension
return selected.sum(dim=(1, 2))
def log1mexp(x: torch.Tensor) -> torch.Tensor:
"""
Numerically stable computation of log(1 - exp(x)) for x < 0.
"""
return torch.where(
x > -1,
torch.log(-torch.expm1(x)),
torch.log1p(-torch.exp(x)),
)
class MeissonicScheduler(BaseScheduler):
def __init__(self,
mask_token_id: int,
masking_schedule: str = "cosine",
device: Union[str, torch.device] = 'cpu',
):
self.mask_token_id = mask_token_id
self.masking_schedule = masking_schedule
self.device = device
def set_timesteps(self, num_inference_steps: int, temperature: Union[int, Tuple[int, int], List[int]] = (2, 0)):
self.num_inference_steps = num_inference_steps
self.timesteps = torch.arange(num_inference_steps, device=self.device).flip(0)
if isinstance(temperature, (tuple, list)):
self.temperatures = torch.linspace(temperature[0], temperature[1], num_inference_steps, device=self.device)
else:
self.temperatures = torch.linspace(temperature, 0.01, num_inference_steps, device=self.device)
def step(
self,
latents: torch.Tensor,
step: int,
logits: torch.Tensor,
) -> SchedulerStepOutput:
batch_size, height, width, vocab_size = logits.shape
sample = latents.reshape(batch_size, height * width)
model_output = logits.reshape(batch_size, height * width, vocab_size)
unknown_map = sample == self.mask_token_id
probs = model_output.softmax(dim=-1)
device = probs.device
probs_ = probs
if probs_.device.type == "cpu" and probs_.dtype != torch.float32:
probs_ = probs_.float() # multinomial is not implemented for cpu half precision
probs_ = probs_.reshape(-1, probs.size(-1))
pred_original_sample = torch.multinomial(probs_, 1).to(device=device)
pred_original_sample = pred_original_sample[:, 0].view(*probs.shape[:-1])
pred_original_sample = torch.where(unknown_map, pred_original_sample, sample)
timestep = self.num_inference_steps - 1 - step
if timestep == 0:
prev_sample = pred_original_sample
else:
seq_len = sample.shape[1]
step_idx = (self.timesteps == timestep).nonzero()
ratio = (step_idx + 1) / len(self.timesteps)
if self.masking_schedule == "cosine":
mask_ratio = torch.cos(ratio * math.pi / 2)
elif self.masking_schedule == "linear":
mask_ratio = 1 - ratio
else:
raise ValueError(f"unknown masking schedule {self.masking_schedule}")
mask_len = (seq_len * mask_ratio).floor().long()
# do not mask more than amount previously masked
mask_len = torch.min(unknown_map.sum(dim=-1, keepdim=True) - 1, mask_len)
# mask at least one
mask_len = torch.max(torch.tensor([1], device=model_output.device), mask_len)
selected_probs = torch.gather(probs, -1, pred_original_sample[:, :, None])[:, :, 0]
# Ignores the tokens given in the input by overwriting their confidence.
selected_probs = torch.where(unknown_map, selected_probs, torch.finfo(selected_probs.dtype).max)
masking = mask_by_random_topk(mask_len, selected_probs, self.temperatures[step_idx].item())
# Masks tokens with lower confidence.
prev_sample = torch.where(masking, self.mask_token_id, pred_original_sample)
print("Unmasked:", (prev_sample != self.mask_token_id).sum(dim=1))
prev_sample = prev_sample.reshape(batch_size, height, width)
pred_original_sample = pred_original_sample.reshape(batch_size, height, width)
return SchedulerStepOutput(new_latents=prev_sample)
def step_with_approx_guidance(
self,
latents: torch.Tensor,
step: int,
logits: torch.Tensor,
approx_guidance: torch.Tensor,
) -> SchedulerApproxGuidanceOutput:
proposal_logits = logits + approx_guidance
sched_out = self.step(latents, step, proposal_logits)
new_latents = sched_out.new_latents
newly_filled_positions = (latents != new_latents)
print("Newly filled positions:", newly_filled_positions.sum(dim=(1, 2)))
log_prob_proposal = sum_masked_logits(
logits=proposal_logits.log_softmax(dim=-1),
preds=new_latents,
mask=newly_filled_positions,
)
log_prob_diffusion = sum_masked_logits(
logits=logits.log_softmax(dim=-1),
preds=new_latents,
mask=newly_filled_positions,
)
print("log prob proposal:", log_prob_proposal)
print("log prob diffusion:", log_prob_diffusion)
return SchedulerApproxGuidanceOutput(
new_latents,
log_prob_proposal,
log_prob_diffusion,
)
class ReMDMScheduler(BaseScheduler):
def __init__(
self,
schedule,
remask_strategy,
eta,
mask_token_id,
temperature=1.0,
):
self.schedule = schedule
self.remask_strategy = remask_strategy
self.eta = eta
self.temperature = temperature
self.mask_token_id = mask_token_id
def set_timesteps(self, num_inference_steps: int):
self.num_inference_steps = num_inference_steps
if self.schedule == "linear":
self.alphas = 1 - torch.linspace(0, 1, num_inference_steps + 1)
elif self.schedule == "cosine":
self.alphas = 1 - torch.cos((math.pi/2) * (1 - torch.linspace(0, 1, num_inference_steps + 1)))
else:
raise ValueError(f"unknown masking schedule {self.schedule}")
def step(
self,
latents: torch.Tensor,
step: int,
logits: torch.Tensor,
) -> SchedulerStepOutput:
B, H, W, C = logits.shape
assert latents.shape == (B, H, W)
latents = latents.reshape(B, H*W)
logits = logits.reshape(B, H*W, C)
t = self.num_inference_steps - step
s = t - 1
alpha_t = self.alphas[t]
alpha_s = self.alphas[s]
sigma_t_max = torch.clamp_max((1 - alpha_s) / alpha_t, 1.0)
if self.remask_strategy == "max_cap":
sigma_t = torch.clamp_max(sigma_t_max, self.eta)
elif self.remask_strategy == "rescale":
sigma_t = sigma_t_max * self.eta
else:
raise ValueError(f"unknown masking schedule {self.remask_strategy}")
# z_t != m
x_theta = F.one_hot(latents, num_classes=C).float()
logits_z_t_neq_m = (
torch.log(x_theta) +
torch.log(1 - sigma_t)
)
logits_z_t_neq_m[..., self.mask_token_id] = (
torch.log(sigma_t)
)
# z_t = m
log_x_theta = (logits / self.temperature).log_softmax(dim=-1)
logits_z_t_eq_m = (
log_x_theta +
torch.log((alpha_s - (1 - sigma_t) * alpha_t) / (1 - alpha_t))
)
logits_z_t_eq_m[..., self.mask_token_id] = (
torch.log((1 - alpha_s - sigma_t * alpha_t) / (1 - alpha_t))
)
z_t_neq_m = (latents != self.mask_token_id)
p_theta_logits = torch.where(
z_t_neq_m.unsqueeze(-1).expand(-1, -1, C),
logits_z_t_neq_m,
logits_z_t_eq_m,
)
assert torch.allclose(torch.exp(p_theta_logits).sum(dim=-1), torch.ones(B, H*W, device=logits.device)), (torch.exp(p_theta_logits).sum(dim=-1) - torch.ones(B, H*W, device=logits.device)).abs().max()
diffusion_dist = torch.distributions.Categorical(logits=p_theta_logits) # type: ignore
new_latents = diffusion_dist.sample()
print("Unmasked:", (new_latents != self.mask_token_id).sum(dim=1))
return SchedulerStepOutput(new_latents.reshape(B, H, W))
def step_with_approx_guidance(
self,
latents: torch.Tensor,
step: int,
logits: torch.Tensor,
approx_guidance: torch.Tensor,
) -> SchedulerApproxGuidanceOutput:
B, H, W, C = logits.shape
assert latents.shape == (B, H, W)
assert approx_guidance.shape == (B, H, W, C)
latents = latents.reshape(B, H*W)
logits = logits.reshape(B, H*W, C)
approx_guidance = approx_guidance.reshape(B, H*W, C)
t = self.num_inference_steps - step
s = t - 1
alpha_t = self.alphas[t]
alpha_s = self.alphas[s]
sigma_t_max = torch.clamp_max((1 - alpha_s) / alpha_t, 1.0)
if self.remask_strategy == "max_cap":
sigma_t = torch.clamp_max(sigma_t_max, self.eta)
elif self.remask_strategy == "rescale":
sigma_t = sigma_t_max * self.eta
else:
raise ValueError(f"unknown masking schedule {self.remask_strategy}")
# z_t != m
x_theta = F.one_hot(latents, num_classes=C).float()
logits_z_t_neq_m = (
torch.log(x_theta) +
torch.log(1 - sigma_t)
)
logits_z_t_neq_m[..., self.mask_token_id] = (
torch.log(sigma_t)
)
# z_t = m
log_x_theta = (logits / self.temperature).log_softmax(dim=-1)
logits_z_t_eq_m = (
log_x_theta +
torch.log((alpha_s - (1 - sigma_t) * alpha_t) / (1 - alpha_t))
)
logits_z_t_eq_m[..., self.mask_token_id] = (
torch.log((1 - alpha_s - sigma_t * alpha_t) / (1 - alpha_t))
)
z_t_neq_m = (latents != self.mask_token_id)
p_theta_logits = torch.where(
z_t_neq_m.unsqueeze(-1).expand(-1, -1, C),
logits_z_t_neq_m,
logits_z_t_eq_m,
)
assert torch.allclose(torch.exp(p_theta_logits).sum(dim=-1), torch.ones(B, H*W, device=logits.device))
proposal_logits = (p_theta_logits + approx_guidance).log_softmax(dim=-1)
assert torch.allclose(torch.exp(proposal_logits).sum(dim=-1), torch.ones(B, H*W, device=logits.device))
# modify proposal logits to have the same mask schedule as the original logits
proposal_logits[..., :self.mask_token_id] += (
torch.logsumexp(p_theta_logits[..., :self.mask_token_id], dim=(1, 2), keepdim=True) -
torch.logsumexp(proposal_logits[..., :self.mask_token_id], dim=(1, 2), keepdim=True)
)
proposal_logits[..., :self.mask_token_id] = torch.where(
proposal_logits[..., :self.mask_token_id].logsumexp(dim=-1, keepdim=True) >= 0,
proposal_logits[..., :self.mask_token_id].log_softmax(dim=-1),
proposal_logits[..., :self.mask_token_id]
)
assert not (proposal_logits[..., :self.mask_token_id].logsumexp(dim=-1) > 1e-6).any(), proposal_logits[..., :self.mask_token_id].logsumexp(dim=-1).max()
proposal_logits[..., self.mask_token_id] = (
log1mexp(proposal_logits[..., :self.mask_token_id].logsumexp(dim=-1).clamp_max(0))
)
assert torch.allclose(torch.exp(proposal_logits).sum(dim=-1), torch.ones(B, H*W, device=logits.device)), (torch.exp(proposal_logits).sum(dim=-1) - torch.ones(B, H*W, device=logits.device)).abs().max()
# modify proposal logits to have the same mask schedule as the original logits
proposal_dist = torch.distributions.Categorical(logits=proposal_logits) # type: ignore
diffusion_dist = torch.distributions.Categorical(logits=p_theta_logits) # type: ignore
new_latents = proposal_dist.sample()
log_prob_proposal = proposal_dist.log_prob(new_latents).sum(dim=1)
log_prob_diffusion = diffusion_dist.log_prob(new_latents).sum(dim=1)
print("Unmasked:", (new_latents != self.mask_token_id).sum(dim=1))
return SchedulerApproxGuidanceOutput(
new_latents.reshape(B, H, W),
log_prob_proposal,
log_prob_diffusion,
)
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