SolitaryThinker
4e331eb7ff
Mechanical conversion of every relative import under v2/ to an absolute v2.* path (from .x / ..x / ...x -> from v2.<pkg>.x) so imports are unambiguous, grep-able, and stable when code is copied/moved between entrypoints (VideoGenerator, CLI, server). Surgical prefix-only rewrite: only the 'from <dots><module>' prefix changed — import names, parentheses, multi-line formatting, comments, and ordering are byte-for-byte preserved (no collapsing, no reorder, no unrelated reformatting). - 471 imports across 125 files; v2/tests/ was already absolute (untouched). - Validated: all 125 files compile, every 'from v2.* import' target resolves to a real module/package, zero relative imports remain (full sweep), CPU mini suite green (202 passed, 2 skipped).
150 lines
8.1 KiB
Python
150 lines
8.1 KiB
Python
"""DiffusionNFT — likelihood-free RL (design_v3 §10, §9.2 C2; repo: train/methods/rl/diffusion_nft).
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The landed RL method, the C2-split's reason for being: it is **likelihood-free** — no log-probs to
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match — so consistency is the C2 *behavioral* rung (seeded final sample + prediction-space identity:
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``old_deviate`` / ref-MSE), declared on the recipe. Sampling is from the **old** (decay-blended
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behavior) policy, NOT the student (the §8.4 caveat the WeightSyncPlan must carry). K samples of one
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prompt form a homogeneous group; group-relative advantages drive the update; the shared prompt
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encodes once via the feature cache (the §10 ``24×`` text-encode reduction).
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NFT objective (repo diffusion_nft.py):
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positive = β·forward + (1−β)·old ; implicit_neg = (1+β)·old − β·forward
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x0⁺ = xt − t·positive ; x0⁻ = xt − t·implicit_neg
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policy_loss = r·‖x0⁺−x0‖²/wf⁺/β + (1−r)·‖x0⁻−x0‖²/wf⁻/β (r = clamp(adv/clip/2+0.5, 0, 1))
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loss = policy_loss + kl_β·‖forward − ref‖² ; old_deviate = ‖forward − old‖²
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"""
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from __future__ import annotations
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import numpy as np
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from v2._enums import ConsistencyLevel
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from v2.models.common import cached_text_encode
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from v2.request import DiffusionParams, TaskType, make_request
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from v2.training.rewards import build_multi_reward_scorer
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from v2.training.rollout import Trajectory, TrajectoryBuffer
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from v2.training.weight_sync import WeightRole, WeightSyncPlan
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from v2.training.methods.base import TrainingMethod, new_instance
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class DiffusionNFTMethod(TrainingMethod):
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name = "diffusion_nft"
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consistency = ConsistencyLevel.C2 # likelihood-free behavioral identity
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def __init__(self, student_instance, old_instance, reference_instance, *,
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reward_fn: dict | None = None, num_video_per_prompt: int = 4, rollout_steps: int = 3,
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beta: float = 0.1, kl_beta: float = 1e-4, adv_clip_max: float = 5.0,
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num_inner_timesteps: int = 2, lr: float = 0.05, decay_type: int = 1, **kw):
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super().__init__(student_instance, lr=lr, **kw)
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self.old = old_instance # behavior policy (sampled from!)
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self.reference = reference_instance # frozen KL anchor
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self.K = num_video_per_prompt
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self.rollout_steps = rollout_steps
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self.beta = beta
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self.kl_beta = kl_beta
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self.adv_clip_max = adv_clip_max
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self.num_inner_timesteps = num_inner_timesteps
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self.decay_type = decay_type
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self.scorer = build_multi_reward_scorer(reward_fn or {"pickscore": 1.0, "clipscore": 1.0})
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self.old_sync = WeightSyncPlan(role=WeightRole.OLD_POLICY)
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# init old + reference from student (NFT inits old from student)
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self.old.component("transformer").copy_from(self.student_dit)
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self.reference.component("transformer").copy_from(self.student_dit)
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def manages_optimization(self) -> bool: # RL owns its sample→score→train cadence
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return True
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def get_grad_clip_targets(self, iteration: int = 0) -> dict:
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return {"student": self.student_dit} # NFT clips internally (matches repo)
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@staticmethod
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def _decay(step: int, decay_type: int) -> float:
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if decay_type == 1:
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return min(step * 0.001, 0.5)
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if decay_type == 2:
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return min(max(0, step - 75) * 0.0075, 0.999)
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return 0.0
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def _sample_group(self, prompt: str, base_seed: int) -> TrajectoryBuffer:
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"""K rollouts of one prompt FROM THE OLD POLICY via the shared loop (homogeneous group)."""
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buf = TrajectoryBuffer()
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for k in range(self.K):
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seed = base_seed * 1000 + k
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req = make_request(TaskType.T2V, self.old.card.model_id, prompt,
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diffusion=DiffusionParams(num_steps=self.rollout_steps, seed=seed))
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res = self._rollout(req, instance=self.old) # behavior policy = OLD
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buf.add(Trajectory(request_id=req.request_id, prompt=prompt, seed=seed,
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latents=np.asarray(res.outputs["latents"], dtype="float32"),
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behavior=res.behavior, weights_version=self.old.weights_version))
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return buf
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def managed_train_step(self, batch: dict, iteration: int) -> tuple[dict, dict]:
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self.iteration = iteration
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prompts = batch["prompts"]
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seeds = batch.get("seeds", list(range(len(prompts))))
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policy_losses, kl_losses, old_devs, gnorms, all_adv, all_reward = [], [], [], [], [], []
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for pi, prompt in enumerate(prompts):
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group = self._sample_group(prompt, seeds[pi])
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media = [t.latents for t in group]
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scored = self.scorer.score(media, [prompt] * len(group))
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rewards = scored["avg"]
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adv = (rewards - rewards.mean()) / (rewards.std() + 1e-4) # group-relative
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adv = np.clip(adv, -self.adv_clip_max, self.adv_clip_max)
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all_reward.extend(rewards.tolist())
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all_adv.extend(adv.tolist())
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emb = cached_text_encode(self.student, prompt) # encoded ONCE for the group
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old_dit, ref_dit = self.old.component("transformer"), self.reference.component("transformer")
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for k, traj in enumerate(group):
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x0 = traj.latents
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r = float(np.clip(adv[k] / self.adv_clip_max / 2.0 + 0.5, 0.0, 1.0))
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rng = np.random.default_rng(traj.seed ^ 0x4F7)
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for _ in range(self.num_inner_timesteps):
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t = float(rng.uniform(0.05, 0.95))
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noise = rng.standard_normal(x0.shape).astype("float32")
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xt = ((1.0 - t) * x0 + t * noise).astype("float32")
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fwd = self.student_dit(xt, emb, t)
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old_p = old_dit(xt, emb, t)
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ref_p = ref_dit(xt, emb, t)
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positive = self.beta * fwd + (1 - self.beta) * old_p
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neg = (1.0 + self.beta) * old_p - self.beta * fwd
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x0p = xt - t * positive
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x0n = xt - t * neg
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wfp = max(float(np.mean(np.abs(x0p - x0))), 1e-5)
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wfn = max(float(np.mean(np.abs(x0n - x0))), 1e-5)
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pos_loss = float(np.mean((x0p - x0) ** 2) / wfp)
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neg_loss = float(np.mean((x0n - x0) ** 2) / wfn)
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policy_loss = (r * pos_loss / self.beta + (1 - r) * neg_loss / self.beta) * self.adv_clip_max
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kl = float(np.mean((fwd - ref_p) ** 2))
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policy_losses.append(policy_loss)
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kl_losses.append(kl)
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old_devs.append(float(np.mean((fwd - old_p) ** 2)))
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# update student toward x0 along the (reward-weighted) positive direction
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v_target = ((xt - x0) / max(t, 1e-3)).astype("float32")
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_, gn = self.student_dit.mse_grad_step(xt, emb, t, v_target, self.lr * max(r, 0.05))
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gnorms.append(gn)
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# update OLD ← decay-blend student (the behavior-policy role); bump version + invalidate caches
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self.old_sync.decay = self._decay(iteration, self.decay_type)
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new_ver = self.old_sync.apply(self.student_dit, self.old.component("transformer"), self.old)
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metrics = {
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"policy_loss": float(np.mean(policy_losses)),
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"kl_div_loss": float(np.mean(kl_losses)),
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"old_deviate": float(np.mean(old_devs)),
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"grad_norm/student": float(np.mean(gnorms)),
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"reward_mean": float(np.mean(all_reward)),
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"advantage_std": float(np.std(all_adv)),
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"old_decay": self.old_sync.decay,
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"old_weights_version": new_ver,
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}
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loss_map = {"policy_loss": metrics["policy_loss"], "kl_div_loss": metrics["kl_div_loss"]}
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return loss_map, metrics
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def train_step(self, batch: dict, iteration: int) -> tuple[dict, dict]:
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return self.managed_train_step(batch, iteration)
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def build_diffusion_nft(card, **kw) -> DiffusionNFTMethod:
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return DiffusionNFTMethod(new_instance(card), new_instance(card), new_instance(card), **kw)
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