SolitaryThinker
b3ddf6014d
Stress-tests the v2 Card/Loop/Program design with a UniRL/PromptRL-style joint RL recipe: a prompt-refiner LM expert and a flow generator expert, two separate experts driven by two loop types in one request, both updated simultaneously from a single RL reward. - loop/sampler.py: flow_sde_step_with_logprob — FlowGRPO SDE rollout sampler (per-step Gaussian log-prob), distinct from the deterministic ODE serve step. - request/params.py: gated sde_rollout/sde_noise_scale on DiffusionParams so the serve path stays byte-identical (default ODE). - models/wan21/loop.py: gated SDE-rollout capture in WanDenoiseLoop.advance. - models/backend.py: ToyPromptRefiner — a real REINFORCE categorical policy (the Qwen role), separate weights from the generator. - models/unified/: the unified card+program — two disjoint experts (llm + transformer) on ar_decode + diffusion_denoise; the topological opposite of the Cosmos3 MoT card, same vocabulary. - training/methods/unified_rl.py: joint GRPO — one reward -> group advantage -> LM token policy gradient + DiT FlowGRPO PPO; two LRs; prompt-only/joint flag; reuses the shared diffusion loop for rollout. - training/weight_sync.py: WeightSyncPlan gains a component scope so the two experts version + cache-invalidate independently (LM sync never flushes the frozen text-encoder feature cache). - tests/test_unified_rl.py: 9 tests incl. likelihood-based C2 identity, the two-loop interleave parity gate, joint vs prompt-only. Full suite 91 passed. designv4.md: unified design doc reflecting v2 as built+tested, with the joint RL stress test as the validating case study (the design held — new card + new method, no new runtime primitive).
69 lines
3.5 KiB
Python
69 lines
3.5 KiB
Python
"""Samplers as a small library (design_v3 §5.1: families use samplers as a library).
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Flow-match Euler is shared by the Wan and LTX-2 denoise step bodies. On a GPU box the real
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UniPC multistep / distilled schedulers are wrapped by the torch component adapters; this
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numpy form is the CPU-testable, bit-reproducible reference used by the loop tests.
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Flow-match interpolation: ``x_t = (1-σ_t)·x0 + σ_t·ε``; the model predicts velocity
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``v = ε - x0``. Then a deterministic Euler step to σ_next is simply ``x_next = x_t + (σ_next-σ_t)·v``
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(this is exactly LTX-2's ``velocity=(latents-denoised)/σ; latents += velocity·dt``).
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"""
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from __future__ import annotations
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import numpy as np
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def build_flow_sigmas(num_steps: int, shift: float = 1.0, terminal: float = 0.0) -> np.ndarray:
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"""σ schedule from 1→terminal over ``num_steps+1`` points, with flow-shift applied.
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Flow-shift: ``σ' = shift·σ / (1 + (shift-1)·σ)`` (Wan/LTX). shift=1 is the identity.
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"""
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base = np.linspace(1.0, terminal, num_steps + 1, dtype=np.float64)
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if shift != 1.0:
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base = shift * base / (1.0 + (shift - 1.0) * base)
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return base
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def x0_from_velocity(x_t: np.ndarray, velocity: np.ndarray, sigma_t: float) -> np.ndarray:
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"""x0 = x_t - σ_t·v (flow prediction → clean sample; Wan ``x0 = x_t - σ·model_output``)."""
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return x_t - sigma_t * velocity
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def flow_match_euler_step(x_t: np.ndarray, velocity: np.ndarray,
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sigma_t: float, sigma_next: float) -> np.ndarray:
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"""One deterministic Euler step along the flow ODE."""
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return x_t + (sigma_next - sigma_t) * velocity
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def add_noise(x0: np.ndarray, noise: np.ndarray, sigma: float) -> np.ndarray:
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"""Forward flow-match interpolation: x_σ = (1-σ)·x0 + σ·noise."""
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return (1.0 - sigma) * x0 + sigma * noise
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def flow_sde_step_with_logprob(x_t, velocity, sigma_t: float, sigma_next: float, *,
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noise=None, prev_sample=None, noise_scale: float = 0.7):
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"""FlowGRPO-style stochastic step + Gaussian log-prob (PromptRL/UniRL §6; FlowGRPO lineage).
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The RL *rollout* sampler (vs the deterministic ODE ``flow_match_euler_step`` used at serve time).
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Injects noise for exploration and returns the log-prob of the realized sample under the step's
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Gaussian — the quantity the FlowGRPO PPO ratio uses. ``prev_sample=None`` samples a new step;
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passing ``prev_sample`` (the rollout sample) recomputes its log-prob under the *current* velocity
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(the ratio's numerator at update time). Returns ``(prev_sample, log_prob, mean, eff_std)``.
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"""
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x_t = np.asarray(x_t, dtype=np.float64)
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velocity = np.asarray(velocity, dtype=np.float64)
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s = min(float(sigma_t), 0.9999)
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dt = float(sigma_next) - float(sigma_t) # negative (σ decreases)
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std = float(np.sqrt(s / (1.0 - s)) * noise_scale)
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denom = 2.0 * max(s, 1e-6)
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mean = x_t * (1.0 + std ** 2 / denom * dt) + velocity * (1.0 + std ** 2 * (1.0 - s) / denom) * dt
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eff_std = max(std * np.sqrt(max(-dt, 1e-12)), 1e-6)
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if prev_sample is None:
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n = noise if noise is not None else np.zeros_like(x_t)
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prev_sample = mean + eff_std * np.asarray(n, dtype=np.float64)
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prev_sample = np.asarray(prev_sample, dtype=np.float64)
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var = eff_std ** 2
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log_prob = float(np.mean(-((prev_sample - mean) ** 2) / (2.0 * var)
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- np.log(eff_std) - 0.5 * np.log(2.0 * np.pi)))
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return prev_sample.astype("float32"), log_prob, mean.astype("float32"), float(eff_std)
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