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7e86b86 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | #!/usr/bin/env python3
"""Exact replication of PhysicsLLMEngine's evaluate_llm_mse.py protocol.
Same 6 scenes (identical generator + simulation code, same seeds/difficulties),
same prompt format (header + 10 context frames + "Predict next frame:"),
same autoregressive loop (5 steps, prompt re-rendered from the growing frame
list each step), same metric (np.mean((pred_pos - gt_pos)**2) per step).
Differences vs theirs (documented, favoring neither side):
- their LFM2 predicts positions only; fed-back frames carry forward the
previous velocity. Our model predicts the full state, so fed-back frames
contain our predicted velocities.
- our semantic latent refreshes every K frames (dual rate); with K=8 and
5 prediction steps the latent is computed once from the initial context.
--tick-k 1 refreshes it from the re-rendered prompt every step.
"""
import argparse
import json
import sys
from pathlib import Path
import numpy as np
import torch
sys.path.insert(0, str(Path(__file__).parent.parent))
from src.physics.scenario_generator import generate_scenario # noqa: E402
from src.data.formats import format_scene_header # noqa: E402
from src.training.data_loader import _format_header_text, _format_frame_text # noqa: E402
from src.dualrate.config import DualRateConfig # noqa: E402
from src.dualrate.model import DualRatePhysicsModel # noqa: E402
from src.dualrate.normalization import StateNormalizer # noqa: E402
from src.dualrate.predictor import DualRatePredictor # noqa: E402
# (scenario_type, difficulty, seed) exactly as in PhysicsLLMEngine/llm_mse_results.json
PLLE_SCENES = [
("pong", 3, 40723578),
("bowling", 3, 59859168),
("ramp_roll", 3, 95887616),
("angry_birds", 2, 12800155),
("hourglass", 2, 15862948),
("newtons_cradle", 3, 63938622),
]
PLLE_LLM_AVG = {"pong": 5027.39, "bowling": 25.95, "ramp_roll": 28.28,
"angry_birds": 30.21, "hourglass": 45.64, "newtons_cradle": 15.78}
def sim_get_frame_dict(sim):
"""Identical to PhysicsLLMEngine evaluate_llm_mse._sim_get_frame_dict."""
state = sim.get_state()
threshold = 1.0
moving = sum(1 for obj in state["objects"]
if abs(obj["velocity"]["x"]) > threshold
or abs(obj["velocity"]["y"]) > threshold)
total = len(state["objects"])
if moving == 0:
desc = "All objects are at rest"
elif moving == total:
desc = "All objects are in motion"
else:
desc = f"{moving} of {total} objects are moving"
state["description"] = f"Frame {state['frame']}: {desc}."
return state
def build_context_prompt(header_dict, frame_dicts):
header_text = _format_header_text(header_dict)
frame_texts = [_format_frame_text(f) for f in frame_dicts]
return header_text + "".join(frame_texts) + "Predict next frame:"
def frame_dict_state(frame_dict, id_order):
s = np.zeros((len(id_order), 6), dtype=np.float32)
by_id = {o["id"]: o for o in frame_dict["objects"]}
for i, oid in enumerate(id_order):
o = by_id[oid]
s[i] = [o["position"]["x"], o["position"]["y"],
o["velocity"]["x"], o["velocity"]["y"],
o.get("angle", 0.0), o.get("angular_velocity", 0.0)]
return s
def pred_frame_dict(state, id_order, prev_frame_dict, frame_num):
"""Our predicted full state as a frame dict for autoregressive re-rendering."""
prev_by_id = {o["id"]: o for o in prev_frame_dict["objects"]}
objects = []
for i, oid in enumerate(id_order):
px, py, vx, vy, a, av = (float(x) for x in state[i])
prev = prev_by_id[oid]
objects.append({"id": oid, "type": prev.get("type", "unknown"),
"position": {"x": round(px, 4), "y": round(py, 4)},
"velocity": {"x": round(vx, 4), "y": round(vy, 4)},
"angle": round(a, 4), "angular_velocity": round(av, 4),
"material": prev.get("material", {})})
threshold = 1.0
moving = sum(1 for o in objects
if abs(o["velocity"]["x"]) > threshold or abs(o["velocity"]["y"]) > threshold)
if moving == 0:
desc = "All objects are at rest"
elif moving == len(objects):
desc = "All objects are in motion"
else:
desc = f"{moving} of {len(objects)} objects are moving"
return {"frame": frame_num, "description": f"Frame {frame_num}: {desc}.",
"objects": objects}
def evaluate_scene(predictor, scenario_type, difficulty, seed,
context_frames=10, predict_frames=5):
sim, metadata = generate_scenario(seed=seed, scenario_type=scenario_type,
difficulty=difficulty)
header_dict = format_scene_header(sim, seed, metadata=metadata)
all_frames = []
for _ in range(context_frames + predict_frames):
sim.step()
all_frames.append(sim_get_frame_dict(sim))
context_dicts = list(all_frames[:context_frames])
gt_dicts = all_frames[context_frames:]
id_order = [o["id"] for o in header_dict["objects"]]
init_state = frame_dict_state(context_dicts[-1], id_order)
predictor.reset(header_dict, init_state,
start_frame=context_dicts[-1]["frame"] + 1)
per_step_mse = []
for step_idx in range(predict_frames):
prompt = build_context_prompt(header_dict, context_dicts)
predictor.step(prompt)
pred_pos = predictor.state[:, :2].copy()
gt_pos = frame_dict_state(gt_dicts[step_idx], id_order)[:, :2]
per_step_mse.append(float(np.mean((pred_pos - gt_pos) ** 2)))
context_dicts.append(pred_frame_dict(predictor.state, id_order,
context_dicts[-1],
gt_dicts[step_idx]["frame"]))
return {"scenario_type": scenario_type, "difficulty": difficulty, "seed": seed,
"num_objects": len(id_order), "per_step_mse": per_step_mse,
"avg_mse": float(np.mean(per_step_mse)), "success": True}
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--ckpt", required=True)
ap.add_argument("--tick-k", type=int, default=None)
ap.add_argument("--max-ctx-tokens", type=int, default=4096)
ap.add_argument("--out", default=None)
args = ap.parse_args()
ck = torch.load(args.ckpt, map_location="cpu", weights_only=False)
cfg = DualRateConfig.from_dict(ck["config"])
if args.tick_k:
cfg.tick_k = args.tick_k
cfg.max_ctx_tokens = args.max_ctx_tokens
norm = StateNormalizer(**{k: ck["normalizer"][k]
for k in ("state_offset", "state_scale", "delta_scale")})
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(cfg.model_name)
model = DualRatePhysicsModel(cfg, readout_token_id=tokenizer.pad_token_id).cuda()
model.load_state_dict(ck["model"], strict=False)
model.eval()
predictor = DualRatePredictor(model, tokenizer, cfg, norm, device="cuda")
results = []
for scenario_type, difficulty, seed in PLLE_SCENES:
r = evaluate_scene(predictor, scenario_type, difficulty, seed)
theirs = PLLE_LLM_AVG[scenario_type]
print(f"{scenario_type:15s} ours {r['avg_mse']:10.3f} theirs {theirs:8.2f} "
f"per-step {[round(x, 2) for x in r['per_step_mse']]}", flush=True)
results.append(r)
ours_all = float(np.mean([r["avg_mse"] for r in results]))
per_step = np.array([r["per_step_mse"] for r in results]).mean(0)
print(f"\nOVERALL ours {ours_all:.3f} vs PhysicsLLMEngine 862.21")
print("per-step ours ", [round(x, 2) for x in per_step])
print("per-step theirs [80.32, 308.6, 732.58, 1274.23, 1915.31]")
out = args.out or str(Path(args.ckpt).parent /
f"plle_exact_k{cfg.tick_k}_{Path(args.ckpt).stem}.json")
Path(out).write_text(json.dumps({
"protocol": "PhysicsLLMEngine evaluate_llm_mse exact (ctx=10, predict=5)",
"ckpt": args.ckpt, "tick_k": cfg.tick_k,
"results": results, "overall_avg_mse": ours_all,
"per_step_mean": per_step.tolist(),
"plle_reference": {"overall": 862.21,
"per_step": [80.32, 308.6, 732.58, 1274.23, 1915.31],
"per_type": PLLE_LLM_AVG},
}, indent=2))
print("saved", out)
if __name__ == "__main__":
main()
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