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477b01d | 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 | from __future__ import annotations
import json
from pathlib import Path
import pandas as pd
import torch
import trackio
from data import irregular_batch
from model import (
LiquidTimeConstantRNN,
MatchedGRU,
MatchedRNN,
parameter_count,
)
from safetensors.torch import save_file
from torch.nn import functional as F
PROJECT_DIR = Path(__file__).resolve().parent
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "liquid-time-pocket"
DATA_DIR = PROJECT_DIR / "data"
SEED = 2203
@torch.inference_mode()
def evaluate(model: torch.nn.Module, *, large_gaps: bool) -> dict:
inputs, targets, _ = irregular_batch(
2_000,
128,
SEED + 10_000 + int(large_gaps),
large_gaps=large_gaps,
)
prediction = model(inputs)
error = prediction - targets
return {
"rmse": float(error.square().mean().sqrt()),
"mae": float(error.abs().mean()),
"examples": len(inputs),
"sequence_length": inputs.shape[1],
"delta_time_range": [0.12, 0.40] if large_gaps else [0.02, 0.12],
}
def train_variant(name: str, model: torch.nn.Module) -> tuple[torch.nn.Module, int]:
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=1e-5)
best = float("inf")
best_step = 0
best_state = None
for step in range(1, 2_501):
inputs, targets, _ = irregular_batch(
96, 64, SEED + step, large_gaps=False
)
prediction = model(inputs)
loss = F.mse_loss(prediction, targets)
optimizer.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5)
optimizer.step()
if step % 125 == 0:
validation = evaluate(model, large_gaps=False)
trackio.log(
{
"variant": name,
"training_step": step,
"training_mse": float(loss.detach()),
"validation_rmse": validation["rmse"],
}
)
if validation["rmse"] < best:
best = validation["rmse"]
best_step = step
best_state = {
key: value.detach().cpu().clone()
for key, value in model.state_dict().items()
}
assert best_state is not None
model.load_state_dict(best_state)
return model, best_step
def main() -> None:
torch.manual_seed(SEED)
torch.set_num_threads(1)
models = {
"liquid_time_constant": LiquidTimeConstantRNN(),
"matched_gru": MatchedGRU(),
"matched_rnn": MatchedRNN(),
}
assert {parameter_count(model) for model in models.values()} == {1_887}
trackio.init(
project="liquid-time-pocket",
name="irregular-gap-ltc-v1",
config={
"parameters_per_model": 1_887,
"training_steps": 2_500,
"training_delta_time": [0.02, 0.12],
"unseen_delta_time": [0.12, 0.40],
},
)
results = {}
ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
DATA_DIR.mkdir(parents=True, exist_ok=True)
for name, model in models.items():
model, best_step = train_variant(name, model)
results[name] = {
"parameters": parameter_count(model),
"best_step": best_step,
"normal_gaps": evaluate(model, large_gaps=False),
"unseen_large_gaps": evaluate(model, large_gaps=True),
}
save_file(model.state_dict(), ARTIFACT_DIR / f"{name}.safetensors")
inputs, targets, time = irregular_batch(
100, 128, SEED + 30_000, large_gaps=True
)
frame = {
"task": [],
"time": [],
"target": [],
**{name: [] for name in models},
}
with torch.inference_mode():
predictions = {name: model(inputs) for name, model in models.items()}
for task in range(len(inputs)):
for step in range(inputs.shape[1]):
frame["task"].append(task)
frame["time"].append(float(time[task, step]))
frame["target"].append(float(targets[task, step, 0]))
for name in models:
frame[name].append(float(predictions[name][task, step, 0]))
report = {
"experiment": "Liquid time-constant irregular forecasting",
"learned_time_constants": {
"minimum": float(
(F.softplus(models["liquid_time_constant"].log_time_constant) + 0.03)
.min()
),
"mean": float(
(F.softplus(models["liquid_time_constant"].log_time_constant) + 0.03)
.mean()
),
"maximum": float(
(F.softplus(models["liquid_time_constant"].log_time_constant) + 0.03)
.max()
),
},
"results": results,
}
(ARTIFACT_DIR / "evaluation.json").write_text(
json.dumps(report, indent=2), encoding="utf-8"
)
pd.DataFrame(frame).to_parquet(DATA_DIR / "large_gap_forecasts.parquet", index=False)
trackio.log(
{
f"{name}_large_gap_rmse": result["unseen_large_gaps"]["rmse"]
for name, result in results.items()
}
)
trackio.finish()
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
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