feat: persist Aegis-AI Physical Model Profiler and backend physical memory logger

- Injects C++ 'mach_task_basic_info' logging to parse real Apple Silicon wire memory limit
- Extracts 'OS_RAM' string output at prefill boundaries
- Integrates interactive --model parameter into profiling script matrix for ease-of-use.

Author: solderzzc

scripts/profiling/profile_runner.py

@@ -0,0 +1,140 @@
+import argparse
+import subprocess
+import time
+import urllib.request
+import urllib.error
+import json
+import re
+import sys
+import os
+
+CONFIGS = [
+    {"name": "Dense/Vanilla", "flags": []},
+    {"name": "SSD Stream", "flags": ["--stream-experts"]},
+    {"name": "TurboQuant", "flags": ["--turbo-kv"]},
+    {"name": "SSD + TurboQuant", "flags": ["--stream-experts", "--turbo-kv"]}
+]
+
+SWIFTLM_PATH = ".build/arm64-apple-macosx/release/SwiftLM"
+
+def poll_health(port=5413, timeout=30):
+    start = time.time()
+    url = f"http://127.0.0.1:{port}/health"
+    while time.time() - start < timeout:
+        try:
+            r = urllib.request.urlopen(url)
+            if r.getcode() == 200:
+                return True
+        except:
+            pass
+        time.sleep(1)
+    return False
+
+def make_request_stream(prompt_len, max_tokens, port=5413):
+    prompt = "apple " * int(prompt_len * 0.75)
+    data = json.dumps({
+        "messages": [{"role": "user", "content": prompt}],
+        "max_tokens": max_tokens,
+        "temperature": 0.0,
+        "stream": True
+    }).encode('utf-8')
+    
+    req = urllib.request.Request(
+        f"http://127.0.0.1:{port}/v1/chat/completions",
+        data=data,
+        headers={'Content-Type': 'application/json'}
+    )
+    
+    ttft = None
+    start = time.time()
+    tokens = 0
+    try:
+        with urllib.request.urlopen(req, timeout=120) as response:
+            for line in response:
+                line = line.decode('utf-8').strip()
+                if line.startswith("data: ") and line != "data: [DONE]":
+                    if ttft is None:
+                        ttft = time.time() - start
+                    tokens += 1
+            total_time = time.time() - start
+            gen_time = total_time - ttft if ttft else 0
+            tps = (tokens - 1) / gen_time if gen_time > 0 and tokens > 1 else 0
+            return True, ttft, tps
+    except Exception as e:
+        print(f"Request failed: {e}")
+        return False, 0, 0
+
+def extract_base_memory(log_path):
+    try:
+        with open(log_path, 'r') as f:
+            for line in f:
+                if "Memory strategy: FULL GPU" in line:
+                    m = re.search(r"\(([0-9.]+)GB model", line)
+                    if m: return f"{m.group(1)} GB"
+    except: pass
+    return "N/A"
+
+def extract_real_memory(log_path):
+    try:
+        with open(log_path, 'r') as f:
+            log_data = f.read()
+            m = re.findall(r"OS_RAM=([0-9.]+)", log_data)
+            if m: return f"{m[-1]} GB"
+    except: pass
+    return "N/A"
+
+def main():
+    parser = argparse.ArgumentParser(description="Aegis-AI Physical Model Profiler")
+    parser.add_argument("--model", required=True, help="Model ID (e.g. gemma-4-26b-a4b-it-4bit)")
+    parser.add_argument("--out", default="./profiling_results.md", help="Output markdown file path")
+    args = parser.parse_args()
+    
+    results = []
+    subprocess.run(["killall", "SwiftLM"], stderr=subprocess.DEVNULL)
+    
+    for config in CONFIGS:
+        print(f"\n--- Profiling {args.model} [{config['name']}] ---")
+        model_path = f"/Users/simba/.aegis-ai/models/mlx_models/mlx-community/{args.model}"
+        
+        log_path = "./tmp/profile_server.log"
+        cmd = [SWIFTLM_PATH, "--model", model_path] + config["flags"]
+        
+        with open(log_path, "w") as root_log:
+            server_proc = subprocess.Popen(cmd, stdout=root_log, stderr=subprocess.STDOUT)
+        
+        if not poll_health():
+            print("Server failed to start.")
+            server_proc.terminate()
+            continue
+            
+        static_mem = extract_base_memory(log_path)
+        
+        print("Running 20-token test (prefill ~512, max ~20)...")
+        ok, ttft, tps = make_request_stream(prompt_len=512, max_tokens=20)
+        
+        server_proc.send_signal(subprocess.signal.SIGTERM)
+        server_proc.wait(timeout=10)
+        
+        real_mem = extract_real_memory(log_path)
+        
+        if ok:
+            results.append({
+                "config": config["name"],
+                "ttft_20": f"{ttft:.2f}",
+                "tps_20": f"{tps:.2f}",
+                "static_mem": static_mem,
+                "real_mem": real_mem
+            })
+            print(f"Result [{config['name']}]: TTFT={ttft:.2f}s TPS={tps:.2f} BaseRAM={static_mem} PhysRAM={real_mem}")
+        
+    with open(args.out, "w") as f:
+        f.write(f"### `{args.model}` - Throughput & OS Memory Profile\n\n")
+        f.write("| Configuration | Time To First Token | Generation Speed | Theoretical Reservation | Physical OS Footprint (RAM) |\n")
+        f.write("|---|---|---|---|---|\n")
+        for r in results:
+            f.write(f"| {r['config']} | {r['ttft_20']}s | {r['tps_20']} tok/s | {r['static_mem']} | {r['real_mem']} |\n")
+            
+    print(f"\nDone. Results saved to {args.out}")
+
+if __name__ == "__main__":
+    main()