Fix/restore conversion script

.gitignore

@@ -206,6 +206,7 @@ vis/
 clt_test_pythia_70m_jumprelu/
 clt_smoke_output_local_wandb_batchtopk/
 clt_smoke_output_remote_wandb/
+wandb/
 
 # models
 *.pt

clt/activation_generation/generator.py

@@ -14,14 +14,13 @@
 from __future__ import annotations
 
 import os
-import time
 import json
 import queue
 import random
 import logging
 import threading
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple, Any
+from typing import Dict, List, Optional, Tuple, Any, DefaultDict
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
 import torch
@@ -36,7 +35,7 @@
 from clt.config.data_config import ActivationConfig  # noqa: E402
 
 # --- Profiling Imports ---
-import time  # Already imported, but good to note
+import time  # Keep this one
 from contextlib import contextmanager
 from collections import defaultdict
 import psutil
@@ -56,8 +55,8 @@
 # --- Performance Profiler Class ---
 class PerformanceProfiler:
     def __init__(self, chunk_tokens_threshold: int = 1_000_000):
-        self.timings = defaultdict(list)
-        self.memory_snapshots = []
+        self.timings: DefaultDict[str, List[float]] = defaultdict(list)
+        self.memory_snapshots: List[Dict[str, Any]] = []
         self.chunk_tokens_threshold = chunk_tokens_threshold
         self.system_metrics_log: List[Dict[str, Any]] = []
         self.layer_ids_ref: Optional[List[int]] = None
@@ -141,7 +140,7 @@ def log_system_metrics(self, interval_name: str = "interval"):
         return metrics
 
     def report(self):
-        print("\n=== Performance Report ===")
+        logger.info("\n=== Performance Report ===")
         # Sort by total time descending for timings
         sorted_timings = sorted(self.timings.items(), key=lambda item: sum(item[1]), reverse=True)
 
@@ -153,15 +152,17 @@ def report(self):
             min_time = min(times)
             max_time = max(times)
 
-            print(f"\n--- Operation: {name} ---")
-            print(f"  Count: {len(times)}")
-            print(f"  Total time: {total_time:.3f}s")
-            print(f"  Avg time: {avg_time:.4f}s")
-            print(f"  Min time: {min_time:.4f}s")
-            print(f"  Max time: {max_time:.4f}s")
+            logger.info(f"\n--- Operation: {name} ---")
+            logger.info(f"  Count: {len(times)}")
+            logger.info(f"  Total time: {total_time:.3f}s")
+            logger.info(f"  Avg time: {avg_time:.4f}s")
+            logger.info(f"  Min time: {min_time:.4f}s")
+            logger.info(f"  Max time: {max_time:.4f}s")
 
             if "chunk_write_total_idx" in name:  # New unique name per chunk
-                print(f"  Avg ms/k-tok (for this chunk): {avg_time / self.chunk_tokens_threshold * 1000 * 1000:.2f}")
+                logger.info(
+                    f"  Avg ms/k-tok (for this chunk): {avg_time / self.chunk_tokens_threshold * 1000 * 1000:.2f}"
+                )
             elif (
                 name == "batch_processing_total"
                 and self.batch_processing_total_calls > 0
@@ -171,29 +172,29 @@ def report(self):
                     self.total_tokens_processed_for_batch_profiling / self.batch_processing_total_calls
                 )
                 if avg_tok_per_batch_call > 0:
-                    print(
+                    logger.info(
                         f"  Avg ms/k-tok (estimated for batch_processing_total): {avg_time / avg_tok_per_batch_call * 1000 * 1000:.2f}"
                     )
 
-        print("\n=== Memory Snapshots (showing top 10 by RSS delta) ===")
+        logger.info("\n=== Memory Snapshots (showing top 10 by RSS delta) ===")
         interesting_mem_snapshots = sorted(
             self.memory_snapshots, key=lambda x: abs(x["rss_delta_bytes"]), reverse=True
         )[:10]
         for snap in interesting_mem_snapshots:
-            print(
+            logger.info(
                 f"  {snap['name']} (took {snap['duration_s']:.3f}s): Total RSS {snap['rss_total_bytes'] / (1024**3):.3f} GB (ΔRSS {snap['rss_delta_bytes'] / (1024**3):.3f} GB)"
             )
 
-        print("\n=== System Metrics Log (sample) ===")
+        logger.info("\n=== System Metrics Log (sample) ===")
         for i, metrics in enumerate(self.system_metrics_log[:5]):  # Print first 5 samples
-            print(
+            logger.info(
                 f"  Sample {i} ({metrics['interval_name']}): CPU {metrics['cpu_percent']:.1f}%, Mem {metrics['memory_percent']:.1f}%, GPU {metrics['gpu_util_percent']:.1f}% (Mem {metrics['gpu_memory_percent']:.1f}%)"
             )
         if len(self.system_metrics_log) > 5:
-            print("  ...")
+            logger.info("  ...")
             if self.system_metrics_log:  # Check if not empty before accessing last element
                 metrics = self.system_metrics_log[-1]
-                print(
+                logger.info(
                     f"  Sample End ({metrics['interval_name']}): CPU {metrics['cpu_percent']:.1f}%, Mem {metrics['memory_percent']:.1f}%, GPU {metrics['gpu_util_percent']:.1f}% (Mem {metrics['gpu_memory_percent']:.1f}%)"
                 )
 
@@ -286,7 +287,7 @@ def _async_uploader(upload_q: "queue.Queue[Optional[Path]]", cfg: ActivationConf
         # --> ADDED: Retry Loop <--
         for attempt in range(max_retries_per_chunk):
             try:
-                print(
+                logger.info(
                     f"[Uploader Thread Attempt {attempt + 1}/{max_retries_per_chunk}] Uploading chunk: {p.name} to {url}"
                 )
                 with open(p, "rb") as f:
@@ -970,7 +971,7 @@ def _upload_binary_file(self, path: Path, endpoint: str):
     try:
         activation_config_instance = ActivationConfig(**loaded_config)
     except TypeError as e:
-        print(f"Error creating ActivationConfig from YAML. Ensure all keys are correct: {e}")
+        logger.error(f"Error creating ActivationConfig from YAML. Ensure all keys are correct: {e}")
         import sys
 
         sys.exit(1)

clt/config/data_config.py

@@ -1,5 +1,8 @@
 from dataclasses import dataclass, field
 from typing import Literal, Optional, Dict, Any
+import logging
+
+logger = logging.getLogger(__name__)
 
 
 @dataclass
@@ -74,7 +77,23 @@ def __post_init__(self):
             except ImportError:
                 raise ImportError("h5py is required for HDF5 output format. Install with: pip install h5py")
         if self.compression not in ["lz4", "gzip", None, False]:
-            print(
+            logger.warning(
                 f"Warning: Unsupported compression '{self.compression}'. Will attempt without compression for {self.output_format}."
             )
             # Allow generator to handle disabling if format doesn't support it.
+
+        # Example: Print a summary or key values
+        # This is more for user feedback than programmatic use.
+        logger.info(
+            "ActivationConfig Summary:\n"
+            f"  Model: {self.model_name}\n"
+            f"  Dataset: {self.dataset_path} (Split: {self.dataset_split})\n"
+            f"  Target Tokens: {self.target_total_tokens}\n"
+            f"  Chunk Threshold: {self.chunk_token_threshold}\n"
+            f"  Activation Dtype: {self.activation_dtype}\n"
+            f"  Output Dir: {self.activation_dir}"
+        )
+        if self.remote_server_url:
+            logger.info(f"  Remote Server URL: {self.remote_server_url}")
+        if self.delete_after_upload:
+            logger.info("  Delete after upload: Enabled")

clt/models/clt.py

@@ -275,3 +275,30 @@ def convert_to_jumprelu_inplace(self, default_theta_value: float = 1e6) -> None:
         logger.info(
             f"Rank {self.rank}: CLT model config updated by ThetaManager. New activation_fn='{self.config.activation_fn}'."
         )
+
+    # --- Back-compat: expose ThetaManager.log_threshold at model level ---
+    @property
+    def log_threshold(self) -> Optional[torch.nn.Parameter]:
+        """Proxy to ``theta_manager.log_threshold`` for backward compatibility.
+
+        Older training scripts, conversion utilities and tests referenced
+        ``model.log_threshold`` directly.  After the Step-5 refactor the
+        parameter migrated into the dedicated ``ThetaManager`` module.  We
+        now expose a read-only view that always returns the *current* parameter
+        held by ``self.theta_manager``.  Modifying the returned tensor (e.g.
+        in-place updates to ``.data``) therefore continues to work as before.
+        Assigning a brand-new ``nn.Parameter`` to ``model.log_threshold`` will
+        forward the assignment to ``theta_manager`` to preserve the linkage.
+        """
+        if hasattr(self, "theta_manager") and self.theta_manager is not None:
+            return self.theta_manager.log_threshold
+        return None
+
+    @log_threshold.setter
+    def log_threshold(self, new_param: Optional[torch.nn.Parameter]) -> None:
+        # Keep property writable so callers that used to assign a fresh
+        # parameter (rare) do not break.  We delegate the storage to
+        # ``ThetaManager`` so there is a single source of truth.
+        if not hasattr(self, "theta_manager") or self.theta_manager is None:
+            raise AttributeError("ThetaManager is not initialised; cannot set log_threshold.")
+        self.theta_manager.log_threshold = new_param