Ki-Ho Lee
248e637f55
Semantic Multiplexing Gain in Wireless Systems via Expanded Embeddings: A BERT Case Study. Includes the shared library, all experiment scripts (training with and without SNR-aware MAML, the token-domain comparison, the K sweep, and DistilBERT), the replot script that regenerates every figure from the stored results, the supplementary probe-versus-cosine analysis, and the raw results behind every figure in the letter.
78 lines
3.0 KiB
Python
Executable File
78 lines
3.0 KiB
Python
Executable File
# cl_maml_extra.py — MAML-trained K sweep (K=1,2,8) and DistilBERT
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# replication, completing the unified MAML protocol.
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import argparse, os, json, csv
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import torch
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import torch.nn.functional as F
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from cl_experiments import (
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load_agnews_labeled, Extractor, SplitCache, final_eval, set_seed,
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EVAL_SNRS
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)
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from cl_maml_all import train_maml
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--save-dir", default="fig_cl")
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ap.add_argument("--epochs", type=int, default=200)
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ap.add_argument("--steps", type=int, default=300)
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ap.add_argument("--trials", type=int, default=500)
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args = ap.parse_args()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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os.makedirs(args.save_dir, exist_ok=True)
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print(f"[Device: {device}]", flush=True)
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train_items, test_items = load_agnews_labeled()
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bert = Extractor("bert-base-uncased", device)
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d_bert = bert.embed_dim
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cache = SplitCache(bert, train_items, test_items)
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del bert
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torch.cuda.empty_cache()
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R = {}
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conv_rows = []
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for name, K in [("mamlK_U4_K1", 1), ("mamlK_U4_K2", 2),
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("mamlK_U4_K8", 8)]:
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print(f"\n=== {name} ===", flush=True)
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model, conv, ttime = train_maml(cache, 4, d_bert, K, device,
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epochs=args.epochs,
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steps=args.steps, label=name)
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sweep, _ = final_eval(model, cache, 4, device, trials=args.trials)
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R[name] = {"U": 4, "K": K, "train_s": ttime,
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"snr": {str(s): sweep[s] for s in EVAL_SNRS}}
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for c in conv:
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conv_rows.append([name, c["epoch"], c["loss"], c["cos10"]])
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with open(os.path.join(args.save_dir,
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"cl_results_maml2.json"), "w") as f:
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json.dump(R, f, indent=1)
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print("\n=== mamlD_U4_K4 (DistilBERT) ===", flush=True)
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distil = Extractor("distilbert-base-uncased", device)
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dcache = SplitCache(distil, train_items, test_items)
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d_d = distil.embed_dim
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del distil
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torch.cuda.empty_cache()
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model, conv, ttime = train_maml(dcache, 4, d_d, 4, device,
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epochs=args.epochs,
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steps=args.steps, label="mamlD")
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sweep, _ = final_eval(model, dcache, 4, device, trials=args.trials)
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R["mamlD_U4_K4"] = {"U": 4, "K": 4, "train_s": ttime,
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"snr": {str(s): sweep[s] for s in EVAL_SNRS}}
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for c in conv:
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conv_rows.append(["mamlD_U4_K4", c["epoch"], c["loss"],
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c["cos10"]])
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with open(os.path.join(args.save_dir, "cl_results_maml2.json"),
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"w") as f:
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json.dump(R, f, indent=1)
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with open(os.path.join(args.save_dir, "cl_convergence_maml2.csv"),
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"w", newline="") as f:
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w = csv.writer(f)
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w.writerow(["config", "epoch", "loss", "cos10_test"])
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w.writerows(conv_rows)
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print("\nExtra MAML experiments complete.", flush=True)
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if __name__ == "__main__":
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main()
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