Code and stored results for the IEEE Communications Letters submission

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.
2026-08-26 22:02:59 +09:00
commit 248e637f55
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+# cl_maml_extra.py — MAML-trained K sweep (K=1,2,8) and DistilBERT
+# replication, completing the unified MAML protocol.
+import argparse, os, json, csv
+import torch
+import torch.nn.functional as F
+
+from cl_experiments import (
+    load_agnews_labeled, Extractor, SplitCache, final_eval, set_seed,
+    EVAL_SNRS
+)
+from cl_maml_all import train_maml
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--save-dir", default="fig_cl")
+    ap.add_argument("--epochs", type=int, default=200)
+    ap.add_argument("--steps", type=int, default=300)
+    ap.add_argument("--trials", type=int, default=500)
+    args = ap.parse_args()
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    os.makedirs(args.save_dir, exist_ok=True)
+    print(f"[Device: {device}]", flush=True)
+    train_items, test_items = load_agnews_labeled()
+    bert = Extractor("bert-base-uncased", device)
+    d_bert = bert.embed_dim
+    cache = SplitCache(bert, train_items, test_items)
+    del bert
+    torch.cuda.empty_cache()
+
+    R = {}
+    conv_rows = []
+    for name, K in [("mamlK_U4_K1", 1), ("mamlK_U4_K2", 2),
+                    ("mamlK_U4_K8", 8)]:
+        print(f"\n=== {name} ===", flush=True)
+        model, conv, ttime = train_maml(cache, 4, d_bert, K, device,
+                                        epochs=args.epochs,
+                                        steps=args.steps, label=name)
+        sweep, _ = final_eval(model, cache, 4, device, trials=args.trials)
+        R[name] = {"U": 4, "K": K, "train_s": ttime,
+                   "snr": {str(s): sweep[s] for s in EVAL_SNRS}}
+        for c in conv:
+            conv_rows.append([name, c["epoch"], c["loss"], c["cos10"]])
+        with open(os.path.join(args.save_dir,
+                               "cl_results_maml2.json"), "w") as f:
+            json.dump(R, f, indent=1)
+
+    print("\n=== mamlD_U4_K4 (DistilBERT) ===", flush=True)
+    distil = Extractor("distilbert-base-uncased", device)
+    dcache = SplitCache(distil, train_items, test_items)
+    d_d = distil.embed_dim
+    del distil
+    torch.cuda.empty_cache()
+    model, conv, ttime = train_maml(dcache, 4, d_d, 4, device,
+                                    epochs=args.epochs,
+                                    steps=args.steps, label="mamlD")
+    sweep, _ = final_eval(model, dcache, 4, device, trials=args.trials)
+    R["mamlD_U4_K4"] = {"U": 4, "K": 4, "train_s": ttime,
+                        "snr": {str(s): sweep[s] for s in EVAL_SNRS}}
+    for c in conv:
+        conv_rows.append(["mamlD_U4_K4", c["epoch"], c["loss"],
+                          c["cos10"]])
+
+    with open(os.path.join(args.save_dir, "cl_results_maml2.json"),
+              "w") as f:
+        json.dump(R, f, indent=1)
+    with open(os.path.join(args.save_dir, "cl_convergence_maml2.csv"),
+              "w", newline="") as f:
+        w = csv.writer(f)
+        w.writerow(["config", "epoch", "loss", "cos10_test"])
+        w.writerows(conv_rows)
+    print("\nExtra MAML experiments complete.", flush=True)
+
+
+if __name__ == "__main__":
+    main()