WIP

optimization_algo/approaches/cma_approach_square_size.py

@@ -160,23 +160,36 @@ class cma_approach(object):
 
         return latencies, max_latency_idx
 
-
+    """
+    Decide partition sizes and evaluate.
+    """
     def eva_hybrid_sq(self, layer):
+        # res is a list of length # of partitions.  Step size is 3
         res = [self.res_step] * self.k
         latencies = []
+        
+        # TODO Change this to a binary search.
+
+        # max_res_unit = 1920*9*1 from sq_approach_faster
+        variable_max_res_unit = self.max_res_unit
 
-        while sum([r*r for r in res]) < self.max_res_unit:
+        # Generate initial solution
+        while sum([r*r for r in res]) < variable_max_res_unit:
             latencies, max_idx = self.find_max_latency(layer, res)
             res[max_idx] += self.res_step
-
-        # for i in range(0, int(self.max_res_unit/self.res_step - self.k*self.res_step)):
-        #     latencies, max_idx = self.find_max_latency(layer, res)
-        #     res[max_idx] += self.res_step
+        
+        while pp.packingPenalty(res, self) != 0:
+            while sum([r*r for r in res]) < variable_max_res_unit:
+                latencies, max_idx = self.find_max_latency(layer, res)
+                res[max_idx] += self.res_step
+            variable_max_res_unit -= 100
+
+        # TODO we want to penalize based on how much we had to decrease
+        # variable_max_res_unit.
+        max_res_unit_decrease = self.max_res_unit - variable_max_res_unit
         
         # If all layers couldn't be packed, packingPenalty returns 0.
         packing_penalty = pp.packingPenalty(res, self.max_pack_size)
-        if packing_penalty == 0:
-            return latencies[max_idx], latencies, res, layer
         return latencies[max_idx] + packing_penalty, latencies, res, layer
 
     def evaluation_top_level(self, in_val):
@@ -192,12 +205,9 @@ class cma_approach(object):
             else:
                 return pid, penalty*4
 
+        # regroup_layers assigns layers to the partitions.  Returns a list of
+        # partition lists which contain layers.
         layer = self.regroup_layers(layer)
-        # if self.is_hybrid:
-        #     return pid, self.eva_hybrid_sq(layer)[0]
-        # else:
-        #     score, _, res, _ = self.evaluate_full_relaxed(layer)
-        #     return pid, score, res
 
         return pid, self.eva_hybrid_sq(layer)[0]