Handle float64 downstream

predtuner/approxapp.py

@@ -241,7 +241,8 @@ class TunerInterface(MeasurementInterface):
         self.pbar = tqdm(total=test_limit, leave=False)
         self.app_kwargs = app_kwargs
 
-        objective = ThresholdAccuracyMinimizeTime(tuner_thres)
+        # tune_thres can come in as np.float64 and opentuner doesn't like that
+        objective = ThresholdAccuracyMinimizeTime(float(tuner_thres))
         input_manager = FixedInputManager(size=len(self.app.op_knobs))
         super(TunerInterface, self).__init__(
             args,
@@ -263,7 +264,8 @@ class TunerInterface(MeasurementInterface):
         from opentuner.resultsdb.models import Result
 
         cfg = desired_result.configuration.data
-        qos, perf = self.app.measure_qos_perf(cfg, False)
+        qos, perf = self.app.measure_qos_perf(cfg, False, **self.app_kwargs)
+        qos, perf = float(qos), float(perf)
         # Print a debug message for each config in tuning and keep threshold
         self.print_debug_config(qos, perf)
         self.pbar.update()

predtuner/torchapp.py

@@ -110,8 +110,7 @@ class TorchApp(ModeledApp, abc.ABC):
                 end = begin + len(target)
                 qos = self.tensor_to_qos(tensor_output[begin:end], target)
                 qoses.append(qos)
-            # float64 -> float
-            return float(self.combine_qos(np.array(qoses)))
+            return self.combine_qos(np.array(qoses))
 
         return [
             LinearPerfModel(self._op_costs, self._knob_speedups),
@@ -137,7 +136,7 @@ class TorchApp(ModeledApp, abc.ABC):
             qoses.append(self.tensor_to_qos(outputs, targets))
         time_end = time_ns() / (10 ** 9)
         qos = self.combine_qos(np.array(qoses))
-        return float(qos), time_end - time_begin  # float64->float
+        return qos, time_end - time_begin
 
     def __repr__(self) -> str:
         class_name = self.__class__.__name__