5555
5656 # --- Parameters ---
5757 MAX_VALID_REVISIONS : 5
58- MAX_FETCH_REVISIONS : 50
58+ MAX_FETCH_REVISIONS : 40
5959 RUNTIME_REGRESSION_TOLERANCE_PCT : 10
6060 COMPILE_REGRESSION_TOLERANCE_PCT : 10
6161
@@ -224,8 +224,8 @@ jobs:
224224 # ----- build TWO tables -----
225225
226226 # Parse benchmark IDs into key-value dicts
227- id2kv = {bid: parse_norm_id(bid) for bid in current_bm.keys()}
228- params_name = sorted(set(e for kv in id2kv .values() for e in kv.keys()))
227+ params_map_by_bid = {bid: parse_norm_id(bid) for bid in current_bm.keys()}
228+ params_name = sorted(set(e for kv in params_map_by_bid .values() for e in kv.keys()))
229229
230230 reg_found = False
231231 tables = {}
@@ -249,8 +249,10 @@ jobs:
249249 is_int = isinstance(value_cur, int) or value_cur.is_integer()
250250 value_repr = fmt_num(value_cur, is_int)
251251
252+ params_map = params_map_by_bid[bid]
253+ params_repr = [params_map.get(k, "-") for k in params_name]
252254 info = {
253- **{k: kv.get(k, "-") for k in params_name} ,
255+ **dict(zip(params_name, params_repr)) ,
254256 "current": value_cur,
255257 "baseline_last": None,
256258 "baseline_min": None,
@@ -295,10 +297,7 @@ jobs:
295297 else:
296298 picto, stats_repr, delta_repr = "ℹ️", "---", "---"
297299
298- kv = id2kv[bid]
299- key_cells = [kv.get(k, "-") for k in params_name]
300-
301- rows_md.append("| " + " | ".join([picto] + key_cells + [value_repr, stats_repr, delta_repr]) + " |")
300+ rows_md.append("| " + " | ".join((picto, *params_repr, value_repr, stats_repr, delta_repr)) + " |")
302301 rows_for_csv[metric].append(info)
303302
304303 tables[metric] = [header, align] + rows_md
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