GEOPY-2182: c

simpeg/dask/inverse_problem.py

@@ -46,9 +46,9 @@ def get_dpred(self, m, f=None, return_residuals=False):
         )
 
     if return_residuals:
-        return np.hstack(results[0]), np.hstack(results[1])
+        return results[0], results[1]
 
-    return np.hstack(results)
+    return results
 
 
 BaseInvProblem.get_dpred = get_dpred
@@ -61,7 +61,7 @@ def dask_evalFunction(self, m, return_g=True, return_H=True):
         self.model = m
         self.dpred, self.residuals = self.get_dpred(m, return_residuals=True)
 
-    phi_d = np.vdot(self.residuals, self.residuals)
+    phi_d = (np.hstack(self.residuals) ** 2.0).sum()
 
     reg2Deriv = []
     if isinstance(self.reg, ComboObjectiveFunction):

simpeg/dask/objective_function.py

@@ -149,39 +149,49 @@ def _validate_type_or_future_of_type(
     objects = validate_list_of_types(
         property_name, objects, obj_type, ensure_unique=True
     )
-    workload = [[]]
+    workloads = {}
+    for worker in workers:
+        workloads[worker] = []
 
     count = 0
-    for obj in objects:
-        if count == len(workers):
-            count = 0
-            workload.append([])
+    for ii, obj in enumerate(objects):
+        count = ii % len(workers)
 
         if isinstance(obj, Future):
             future = obj
+            count = workers.index(client.who_has(obj)[obj.key])
         else:
             future = client.scatter([obj], workers=workers[count])[0]
 
-        workload[-1].append(future)
-        count += 1
+        workloads[workers[count]].append(future)
 
     futures = []
     assignments = []
-    for work in workload:
-        for obj, worker in zip(work, workers):
+    for worker, work in workloads.items():
+        for future in work:
             futures.append(
                 client.submit(
-                    lambda v: not isinstance(v, obj_type), obj, workers=worker
+                    lambda v: not isinstance(v, obj_type), future, workers=worker
                 )
             )
-            assignments.append(client.submit(_set_worker, obj, worker, workers=worker))
+            assignments.append(
+                client.submit(_set_worker, future, worker, workers=worker)
+            )
 
     client.gather(assignments)
 
     is_not_obj = np.array(client.gather(futures))
     if np.any(is_not_obj):
         raise TypeError(f"{property_name} futures must be an instance of {obj_type}")
 
+    # Re-distribute the workload to ensure all workers are equally loaded
+    workload = []
+    for work in workloads.values():
+        for ii, future in enumerate(work):
+            if len(workload) <= ii:
+                workload.append([])
+            workload[ii].append(future)
+
     if return_workers:
         return workload, workers
     else:
@@ -382,9 +392,9 @@ def get_dpred(self, m, f=None, return_residuals=False):
                     dpred += [result]
 
         if return_residuals:
-            return np.hstack(dpred), np.hstack(residuals)
+            return dpred, residuals
 
-        return np.hstack(dpred)
+        return dpred
 
     def getJtJdiag(self, m, f=None):
         """

simpeg/directives/_regularization.py

@@ -218,7 +218,7 @@ def misfit_from_chi_factor(self, chi_factor: float) -> float:
         chi_factor : float
             Chi factor to compute the target misfit from.
         """
-        return self.invProb.dpred.shape[0] * chi_factor
+        return np.hstack(self.invProb.dpred).shape[0] * chi_factor
 
     def adjust_cooling_schedule(self):
         """

simpeg/directives/directives.py

@@ -3058,10 +3058,9 @@ def initialize(self):
     def endIter(self):
         ratio = self.invProb.beta / self.last_beta
         chi_factors = []
-        for objfct, pred in zip(self.invProb.dmisfit.objfcts, self.invProb.dpred):
-            residual = objfct.W * (objfct.data.dobs - pred)
+        for residual in self.invProb.residuals:
             phi_d = np.vdot(residual, residual)
-            chi_factors.append(phi_d / objfct.nD)
+            chi_factors.append(phi_d / len(residual))
 
         self.chi_factors = np.asarray(chi_factors)