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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

63 lines
2.0 KiB
Python

# SPDX-License-Identifier: Apache-2.0
"""RL-specific dataclasses used by post-training and rollout paths."""
from dataclasses import dataclass, field
from typing import Any
import torch
@dataclass
class RolloutSessionData:
"""Per-batch rollout state created by prepare_rollout(), lives on the batch object.
Cleared by setting ``batch._rollout_session_data = None``.
"""
pipeline_config: Any = None
sigma_max: float = 0.0
latents_shape: tuple | None = None
noise_buffer: torch.Tensor | None = None
local_log_prob_sum: list[torch.Tensor] = field(default_factory=list)
local_log_prob_count: list[torch.Tensor] = field(default_factory=list)
local_variance_noises: list[torch.Tensor] = field(default_factory=list)
local_prev_sample_means: list[torch.Tensor] = field(default_factory=list)
local_noise_std_devs: list[torch.Tensor] = field(default_factory=list)
local_model_outputs: list[torch.Tensor] = field(default_factory=list)
@dataclass
class RolloutDebugTensors:
"""Container for rollout debug tensors collected during denoising."""
rollout_variance_noises: torch.Tensor | None = None
rollout_prev_sample_means: torch.Tensor | None = None
rollout_noise_std_devs: torch.Tensor | None = None
rollout_model_outputs: torch.Tensor | None = None
@dataclass
class RolloutDenoisingEnv:
image_kwargs: dict[str, Any] | None = None
pos_cond_kwargs: dict[str, Any] | None = None
neg_cond_kwargs: dict[str, Any] | None = None
guidance: torch.Tensor | None = None
@dataclass
class RolloutDitTrajectory:
# [B, T+1, ...]: per-step noisy latents x_{t_0..t_{T-1}} followed by the
# final denoised latent x_{t_T} (last scheduler.step output).
latents: torch.Tensor | None = None
timesteps: torch.Tensor | None = None # [T]
@dataclass
class RolloutTrajectoryData:
rollout_log_probs: torch.Tensor | None = None
rollout_debug_tensors: RolloutDebugTensors | None = None
denoising_env: RolloutDenoisingEnv | None = None
dit_trajectory: RolloutDitTrajectory | None = None