feat: implement parasitic QLoRA adapter extraction and unit tests

benchmark_fces_vs_adam.py

@@ -14,6 +14,7 @@ import dspy  # noqa: E402
 import torch.nn.functional as F  # noqa: E402
 from send_telemetry import push_to_mariadb, push_to_surrealdb  # noqa: E402
 from transformers import AutoModelForCausalLM, AutoTokenizer  # noqa: E402
+from parasitic_qlora import ParasiticQLoRAExtractor, QLoRAConfig  # noqa: E402
 
 # ==============================================================================
 # 1. DSPY SIGNATURE & SYSTEM DESIGN
@@ -162,6 +163,18 @@ def train_run(
 
     model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
 
+    # Initialize Parasitic QLoRA Extractor
+    extractor = ParasiticQLoRAExtractor(
+        QLoRAConfig(
+            min_rank=8,
+            max_rank=32,
+            explained_variance_threshold=0.95,
+            extraction_interval=5,
+            interesting_point_detection=True,
+        )
+    )
+    extractor.snapshot_base(model)
+
     # 1. Pre-Training Evaluation
     print(f"[{optimizer_name}] Running Pre-Training Evaluation...")
     pre_eval = evaluate_model(model, tokenizer, device)
@@ -211,6 +224,16 @@ def train_run(
         if optimizer_name == "FCES":
             optimizer.update_fitness(float(loss.item()))
 
+        # Call parasitic extractor
+        if extractor.should_extract(step, float(loss.item())):
+            metrics = {
+                "loss": float(loss.item()),
+                "sft_loss": float(sft_loss.item()),
+                "optimizer": optimizer_name,
+                "spectral_rank": getattr(optimizer, "last_spectral_rank_", 0.0),
+            }
+            extractor.extract_adapters(model, step, metrics)
+
         # Tracking metrics
         elapsed = time.perf_counter() - start_time
         batch_tokens = int((input_win != tokenizer.pad_token_id).sum().item())
@@ -232,6 +255,10 @@ def train_run(
                 f"Time: {elapsed:.2f}s | Tokens: {tokens_processed}"
             )
 
+    # Save the extracted adapter library
+    library_path = f"parasitic_adapters_{optimizer_name.lower()}_step{steps}.pt"
+    extractor.save_library(library_path)
+
     # 4. Post-Training Evaluation
     print(f"[{optimizer_name}] Running Post-Training Evaluation...")
     post_eval = evaluate_model(model, tokenizer, device)