Create prompt_morpher.py

prompt_morpher.py

@@ -0,0 +1,142 @@
+import os
+import argparse
+import glob
+import shutil
+import time
+import numpy as np
+from tensorboard.backend.event_processing import event_accumulator
+
+def get_latest_event_file(log_root):
+    """Recursively find the latest TensorBoard event file under log_root."""
+    event_files = glob.glob(os.path.join(log_root, "**", "events.out.tfevents.*"), recursive=True)
+    if not event_files:
+        raise RuntimeError(f"No TensorBoard event files found under {log_root}")
+    return max(event_files, key=os.path.getmtime)
+
+def load_scalar(event_file, tag="loss"):
+    ea = event_accumulator.EventAccumulator(event_file)
+    ea.Reload()
+    if tag not in ea.Tags()["scalars"]:
+        raise RuntimeError(f"Tag '{tag}' not found in {event_file}. Available: {ea.Tags()['scalars']}")
+    events = ea.Scalars(tag)
+    steps = [e.step for e in events]
+    values = [e.value for e in events]
+    return np.array(steps), np.array(values)
+
+def smooth(values, weight=0.9):
+    smoothed = []
+    last = values[0]
+    for val in values:
+        last = last * weight + (1 - weight) * val
+        smoothed.append(last)
+    return np.array(smoothed)
+
+def preview_file_lines(file_path, max_chars=50):
+    """Return number of lines and a list of truncated lines for preview."""
+    with open(file_path, "r", encoding="utf-8") as f:
+        lines = [line.rstrip("\n") for line in f.readlines()]
+    n_lines = len(lines)
+    truncated_lines = [line if len(line) <= max_chars else line[:max_chars] + "…" for line in lines]
+    if n_lines == 0:
+        truncated_lines = ["<empty>"]
+    return n_lines, truncated_lines
+
+def main():
+    parser = argparse.ArgumentParser(description="Monitor TensorBoard logs and update prompt.txt when loss threshold is reached")
+    parser.add_argument("--log_root", type=str, required=True, help="Root folder containing TensorBoard logs (nested allowed)")
+    parser.add_argument("--sample_folder", type=str, required=True, help="Folder with prompt.txt and payload file")
+    parser.add_argument("--tag", type=str, default="loss/average", help="Scalar tag to monitor (default: loss)")
+    parser.add_argument("--threshold", type=float, required=True, help="Loss threshold to trigger prompt update")
+    parser.add_argument("--smoothing", type=float, default=0.9, help="Exponential smoothing weight (0-1, default 0.9)")
+    parser.add_argument("--poll_interval", type=int, default=10, help="Seconds between polling for new logs (default 10)")
+    parser.add_argument("--archive", action="store_true",
+                        help="Pre-step: move contents of prompt.txt → prompt_payload.txt and empty prompt.txt. Must be only .txt file.")
+    args = parser.parse_args()
+
+    # Ensure sample folder exists
+    if not os.path.isdir(args.sample_folder):
+        raise RuntimeError(f"Sample folder does not exist: {args.sample_folder}")
+
+    # Ensure prompt.txt exists
+    prompt_file = os.path.join(args.sample_folder, "prompt.txt")
+    if not os.path.exists(prompt_file):
+        with open(prompt_file, "w", encoding="utf-8") as f:
+            f.write("")
+
+    # --- Pre-step: Archive prompt.txt contents if flag is set ---
+    if args.archive:
+        txt_files = glob.glob(os.path.join(args.sample_folder, "*.txt"))
+        if len(txt_files) != 1 or not txt_files[0].endswith("prompt.txt"):
+            raise RuntimeError("Archive mode requires prompt.txt to be the ONLY .txt file in the sample folder.")
+
+        archive_file = os.path.join(args.sample_folder, "prompt_payload.txt")
+
+        with open(prompt_file, "r", encoding="utf-8") as f:
+            contents = f.read()
+
+        with open(archive_file, "w", encoding="utf-8") as f:
+            f.write(contents)
+
+        # Empty out prompt.txt
+        with open(prompt_file, "w", encoding="utf-8") as f:
+            f.write("")
+
+        print(f"[ARCHIVE] Moved contents of {prompt_file} → {archive_file} and emptied {prompt_file}")
+
+    # Find payload file (any txt besides prompt.txt)
+    txt_files = [f for f in glob.glob(os.path.join(args.sample_folder, "*.txt")) if not f.endswith("prompt.txt")]
+    if not txt_files:
+        raise RuntimeError("No payload file found in sample folder (need one .txt besides prompt.txt).")
+    payload_file = txt_files[0]
+
+    # Startup preview
+    prompt_lines, prompt_preview_lines = preview_file_lines(prompt_file)
+    payload_lines, payload_preview_lines = preview_file_lines(payload_file)
+
+    print("=== Startup Info ===")
+    print(f"Prompt file: {prompt_file}")
+    print(f"  Lines: {prompt_lines}")
+    print(f"  Preview:")
+    for line in prompt_preview_lines:
+        print(f"    {line}")
+    print(f"Payload file: {payload_file}")
+    print(f"  Lines: {payload_lines}")
+    print(f"  Preview:")
+    for line in payload_preview_lines:
+        print(f"    {line}")
+    print("===================")
+
+    # Find latest event file (nested)
+    event_file = get_latest_event_file(args.log_root)
+    print(f"[INFO] Monitoring event file: {event_file}")
+
+    last_reported_step = -1
+
+    while True:
+        try:
+            steps, values = load_scalar(event_file, args.tag)
+            if len(values) == 0:
+                time.sleep(args.poll_interval)
+                continue
+
+            smoothed_values = smooth(values, args.smoothing)
+
+            # Check every 50 steps
+            for step, loss in zip(steps, smoothed_values):
+                if step % 50 == 0 and step > last_reported_step:
+                    last_reported_step = step
+                    triggered = loss < args.threshold
+                    status = "TRIGGERED" if triggered else "not triggered"
+                    print(f"[Step {step}] Smoothed loss={loss:.4f} -> {status}")
+                    if triggered:
+                        shutil.copy(payload_file, prompt_file)
+                        print(f"[EXIT] Copied payload ({os.path.basename(payload_file)}) → prompt.txt")
+                        return
+
+        except Exception as e:
+            print(f"[WARN] Error reading log: {e}")
+
+        time.sleep(args.poll_interval)
+
+if __name__ == "__main__":
+    main()