rudimentary joint pdf

src/pyhf/pdf.py

@@ -221,10 +221,7 @@ def logpdf(self, auxdata, pars):
         tensorlib, _ = get_backend()
         normal = self.constraints_gaussian.logpdf(auxdata, pars)
         poisson = self.constraints_poisson.logpdf(auxdata, pars)
-        if self.batch_size is None:
-            return normal + poisson
-        terms = tensorlib.stack([normal, poisson])
-        return tensorlib.sum(terms, axis=0)
+        return prob.joint_logpdf([normal, poisson])
 
 
 class _MainModel(object):
@@ -548,10 +545,13 @@ def logpdf(self, pars, data):
             mainpdf = self.main_model.logpdf(actual_data, pars)
             constraint = self.constraint_model.logpdf(aux_data, pars)
 
-            result = tensorlib.sum(tensorlib.stack([mainpdf, constraint]), axis=0)
-            if not self.batch_size:
+            result = prob.joint_logpdf([mainpdf, constraint])
+
+            if (
+                not self.batch_size
+            ):  # force to be not scalar, should we changed with #522
                 return tensorlib.reshape(result, (1,))
-            return tensorlib.astensor(result)
+            return result
         except:
             log.error(
                 'eval failed for data {} pars: {}'.format(

src/pyhf/probability.py

@@ -39,3 +39,10 @@ def log_prob(self, value):
         result = self._pdf.log_prob(value)
         result = tensorlib.sum(result, axis=-1)
         return result
+
+
+def joint_logpdf(terms):
+    tensorlib, _ = get_backend()
+    terms = tensorlib.stack(terms)
+    result = tensorlib.sum(terms, axis=0)
+    return result