From 70d2ac8d368fb2ef8ab94d530d6feb3ce062e9d9bc4f65f7c6ae08dcdd5947b8 Mon Sep 17 00:00:00 2001
From: mstoeck3 <mstoeck3@hs-mittweida.de>
Date: Mon, 19 Jan 2026 12:23:10 +0100
Subject: [PATCH] fix context optimizer to search dowwards when baseline uses
 offload

---
 scripts/context-optimizer.py | 71 ++++++++++++++++++++++++++----------
 1 file changed, 52 insertions(+), 19 deletions(-)

diff --git a/scripts/context-optimizer.py b/scripts/context-optimizer.py
index 9866abd..84ddd6f 100755
--- a/scripts/context-optimizer.py
+++ b/scripts/context-optimizer.py
@@ -332,37 +332,63 @@ def find_optimal_context(model_name: str, max_turns: Optional[int], overhead_gb:
             'error': 'Unknown failure during baseline test'
         }
     
-    # Turn 2: Test a higher context to calculate VRAM/context ratio
-    # Try doubling the context or 32K, whichever is smaller
-    test_ctx_2 = min(baseline_ctx * 2, 32768, max_context)
-    if test_ctx_2 <= baseline_ctx:
-        test_ctx_2 = min(baseline_ctx + 16384, max_context)
-    # Round to multiple of 2048
-    test_ctx_2 = (test_ctx_2 // 2048) * 2048
+    # Turn 2: Test a different context to calculate VRAM/context ratio
+    # If baseline already shows offloading OR is at max_context, test LOWER
+    # Otherwise, test HIGHER
+    baseline_has_offload = results[0]['offload_pct'] > 0
+    
+    if baseline_has_offload or baseline_ctx >= max_context:
+        # Test lower context to find where it fits
+        test_ctx_2 = max(8192, baseline_ctx // 2)
+        # Round to multiple of 2048
+        test_ctx_2 = (test_ctx_2 // 2048) * 2048
+        calibration_label = "lower bound"
+    else:
+        # Try doubling the context or 32K, whichever is smaller
+        test_ctx_2 = min(baseline_ctx * 2, 32768, max_context)
+        if test_ctx_2 <= baseline_ctx:
+            test_ctx_2 = min(baseline_ctx + 16384, max_context)
+        # Round to multiple of 2048
+        test_ctx_2 = (test_ctx_2 // 2048) * 2048
+        calibration_label = "upper bound"
     
     turn_label = f"Turn 2/{max_turns}" if max_turns else "Turn 2"
-    print(f"{turn_label}: Testing num_ctx={test_ctx_2:,} (calibration)...", end=' ', flush=True)
+    print(f"{turn_label}: Testing num_ctx={test_ctx_2:,} ({calibration_label})...", end=' ', flush=True)
     result = test_context_size(model_name, test_ctx_2)
     
     if result and 'error' not in result:
         results.append(result)
         print(f"✓ VRAM: {result['vram_gb']:.2f} GB, Offload: {result['offload_pct']:.1f}% CPU" if result['offload_pct'] > 0 else f"✓ VRAM: {result['vram_gb']:.2f} GB, Offload: GPU only")
         
-        # Calculate VRAM per 1K context tokens
+        # Calculate VRAM per 1K context tokens (works for both higher and lower tests)
         vram_diff = result['vram_gb'] - baseline_vram
         ctx_diff = test_ctx_2 - baseline_ctx
-        if ctx_diff > 0:
-            vram_per_1k_ctx = (vram_diff / ctx_diff) * 1000
+        
+        if ctx_diff != 0:  # Can be positive or negative
+            vram_per_1k_ctx = abs(vram_diff / ctx_diff) * 1000
             print(f"         → Estimated VRAM usage: {vram_per_1k_ctx:.4f} GB per 1K context")
             
-            # Predict optimal context size
+            # Predict optimal context size based on available VRAM
             if target_vram and vram_per_1k_ctx > 0:
-                available_for_ctx = target_vram - baseline_vram
-                estimated_additional_ctx = (available_for_ctx / vram_per_1k_ctx) * 1000
-                predicted_optimal = baseline_ctx + int(estimated_additional_ctx)
-                # Round to multiple of 2048
+                # Find which result has no offload (if any) to use as reference
+                ref_result = result if result['offload_pct'] == 0 else (results[0] if results[0]['offload_pct'] == 0 else None)
+                
+                if ref_result:
+                    # We have a point that fits - extrapolate from there
+                    available_for_ctx = target_vram - ref_result['vram_gb']
+                    estimated_additional_ctx = (available_for_ctx / vram_per_1k_ctx) * 1000
+                    predicted_optimal = ref_result['num_ctx'] + int(estimated_additional_ctx)
+                else:
+                    # Neither fits - need to find what would fit
+                    # Start from the smaller test and work backwards
+                    smaller_result = results[0] if results[0]['num_ctx'] < result['num_ctx'] else result
+                    vram_needed_reduction = smaller_result['vram_gb'] - target_vram
+                    ctx_reduction_needed = (vram_needed_reduction / vram_per_1k_ctx) * 1000
+                    predicted_optimal = smaller_result['num_ctx'] - int(ctx_reduction_needed)
+                
+                # Round to multiple of 2048 and clamp to valid range
                 predicted_optimal = (predicted_optimal // 2048) * 2048
-                predicted_optimal = max(baseline_ctx, min(predicted_optimal, max_context))
+                predicted_optimal = max(2048, min(predicted_optimal, max_context))
                 
                 print(f"         → Predicted optimal context: {predicted_optimal:,}")
             else:
@@ -384,8 +410,15 @@ def find_optimal_context(model_name: str, max_turns: Optional[int], overhead_gb:
         predicted_optimal = None
     
     # Remaining turns: Test predicted optimal or use VRAM-based refinement
-    min_ctx = baseline_ctx
-    max_ctx = max_context
+    # Initialize search bounds based on whether baseline has offload
+    if baseline_has_offload:
+        # Search downward from baseline to find what fits
+        min_ctx = 2048  # Minimum practical context
+        max_ctx = baseline_ctx
+    else:
+        # Search upward from baseline to find max that fits
+        min_ctx = baseline_ctx
+        max_ctx = max_context
     
     turn = 2
     while True: