Add dump

Author: RattataKing

sharktuner/dispatch_tuner/dispatch_tuner.py

@@ -159,3 +159,11 @@ def main() -> None:
             print(path_config.run_log.resolve())
         print("Check the summary in:")
         print(summary_log_file.resolve())
+
+        output_csv_name = (
+            f"tuning_{args.dispatch_file.stem.removesuffix('_benchmark')}.csv"
+        )
+        csv_path = libtuner.candidate_tuning_records.export_record_to_csv(
+            dispatch_tuner.tuning_records, path_config.base_dir, output_csv_name
+        )
+        print(f"Wrote tuning records CSV: {csv_path}")

sharktuner/sharktuner/candidate_tuning_records.py

@@ -1,5 +1,8 @@
+import os
+import csv
 from typing import Optional
 from dataclasses import dataclass
+from pathlib import Path
 
 from . import common, candidate_tuning_records
 
@@ -20,9 +23,14 @@ class TuningRecord:
     benchmark_speedup: Optional[float] = None
     benchmark_rank_order: Optional[int] = None
 
-def init_tuning_records(knobs: list[Optional[common.KnobAssignment]], sorted_order: list[int]) -> list[TuningRecord]:
+
+def init_tuning_records(
+    knobs: list[Optional[common.KnobAssignment]], sorted_order: list[int]
+) -> list[TuningRecord]:
     tuning_records: list[TuningRecord] = []
-    tuning_records.append(TuningRecord(gen_id=0, candidate_id=0, to_compile=True, to_benchmark=True))
+    tuning_records.append(
+        TuningRecord(gen_id=0, candidate_id=0, to_compile=True, to_benchmark=True)
+    )
 
     for can_idx, gen_idx in enumerate(sorted_order, start=1):
         tr = TuningRecord(
@@ -32,4 +40,42 @@ def init_tuning_records(knobs: list[Optional[common.KnobAssignment]], sorted_ord
         )
         tuning_records.append(tr)
 
-    return tuning_records
+    return tuning_records
+
+
+def export_record_to_csv(
+    objects: list[TuningRecord], dest_dir: Path, filename: str = "export.csv"
+) -> Path:
+    if not objects:
+        return None
+
+    rows = []
+    headers = []
+
+    for obj in objects:
+        row = {}
+        for k, v in vars(obj).items():
+            if hasattr(v, "__dict__"):
+                nested = vars(v)
+                if nested:  # only if it has attrs
+                    for nk, nv in nested.items():
+                        key = f"{k}.{nk}"
+                        row[key] = nv
+                        if key not in headers:
+                            headers.append(key)
+                else:
+                    # skip empty nested object entirely
+                    continue
+            else:
+                row[k] = v
+                if k not in headers:
+                    headers.append(k)
+        rows.append(row)
+
+    path = os.path.join(dest_dir, filename)
+    with open(path, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=headers)
+        writer.writeheader()
+        writer.writerows(rows)
+
+    return path