Fix profiler.py: return 3 values (macs=0, memory, params) for backward compat with solver_registry.py

own-solver/neurogolf_solver/profiler.py

@@ -8,6 +8,8 @@ ORT with profiling enabled, which is too heavy for local model generation.
 Strategy: Use static fallback for local scoring during model generation.
 Real scoring happens on Kaggle at submission time via the official utils.
 Models are NOT rejected locally — they're validated via inference correctness.
+
+Returns (macs=0, memory, params) for backward compatibility with solver_registry.py.
 """
 
 import math
@@ -17,21 +19,25 @@ from .constants import BANNED_OPS, GH, GW
 
 
 def score_network(path):
-    """Score network locally. Returns (memory, params) or (None, None, None).
+    """Score network locally. Returns (macs, memory, params) or (None, None, None).
     
-    Uses static estimation (sum of tensor sizes + param count).
-    This is APPROXIMATE but sufficient for local development.
+    macs is always 0 (no longer used in Kaggle scoring since May 4 2026).
+    memory and params are static estimates sufficient for local development.
     Real scoring uses ORT profiler on Kaggle.
     """
-    return _static_profile(path)
+    result = _static_profile(path)
+    if result is None:
+        return None, None, None
+    memory, params = result
+    return 0, memory, params
 
 
 def estimate_score(path):
     """Estimate score under new formula: 25 - ln(memory + params)."""
     result = _static_profile(path)
-    if result is None or result[0] is None:
+    if result is None:
         return None
-    memory, params = result[0], result[1]  # memory in bytes, params in elements
+    memory, params = result
     cost = memory + params
     if cost <= 0:
         return 25.0
@@ -44,12 +50,12 @@ def _static_profile(path):
     memory = sum of all initializer bytes + estimated intermediate tensor bytes
     params = sum of all initializer element counts + Constant node values
     
-    Returns (memory, params) or (None, None, None) if model is invalid.
+    Returns (memory, params) or None if model is invalid.
     """
     try:
         model = onnx.load(path)
     except Exception:
-        return None, None, None
+        return None
     
     params = 0
     memory = 0  # bytes
@@ -84,11 +90,9 @@ def _static_profile(path):
         # Banned op check
         if nd.op_type.upper() in {op.upper() for op in BANNED_OPS}:
             print(f"WARNING: Banned op '{nd.op_type}' found in {path}")
-            return None, None, None
+            return None
     
     # Estimate intermediate tensor memory (node outputs that aren't 'output')
-    # Each intermediate tensor is approximately (1,10,30,30) float32 = 36,000 bytes
-    # This is rough but gives directional guidance
     n_intermediates = 0
     for nd in model.graph.node:
         for out_name in nd.output: