Introduce a (non-public) autograd scalar method and improve printing (#4586)

* Specialize Variable pinting and always print device for GPU tensors/Variables.

* Introduce a (non-public) _scalar_sum() method for autograd scalar testing.

4 files changed, 32 insertions, 11 deletions

diff --git a/torch/_tensor_str.py b/torch/_tensor_str.py
index 1d93fd10e5..83113b2cdd 100644
--- a/torch/_tensor_str.py
+++ b/torch/_tensor_str.py
@@ -288,9 +288,9 @@ def _vector_str(self):
                 '\n')
 
 
-def _str(self):
+def _str(self, include_footer=True):
     if self.ndimension() == 0:
-        return '[{} with no dimension]\n'.format(torch.typename(self))
+        strt = ''
     elif self.ndimension() == 1:
         strt = _vector_str(self)
     elif self.ndimension() == 2:
@@ -298,9 +298,11 @@ def _str(self):
     else:
         strt = _tensor_str(self)
 
-    size_str = 'x'.join(str(size) for size in self.size())
-    device_str = '' if not self.is_cuda else \
-        ' (GPU {})'.format(self.get_device())
-    strt += '[{} of size {}{}]\n'.format(torch.typename(self),
-                                         size_str, device_str)
+    if include_footer:
+        size_str = 'x'.join(str(size) for size in self.size())
+        size_str_prefix = 'of size ' if self.ndimension() > 0 else 'with no dimension'
+        device_str = '' if not self.is_cuda else \
+            ' (GPU {})'.format(self.get_device())
+        strt += '[{} {}{}{}]\n'.format(torch.typename(self), size_str_prefix,
+                                      size_str, device_str)
     return '\n' + strt
diff --git a/torch/autograd/variable.py b/torch/autograd/variable.py
index 4c95bc8760..ce11c38cc2 100644
--- a/torch/autograd/variable.py
+++ b/torch/autograd/variable.py
@@ -70,7 +70,25 @@ class Variable(_C._VariableBase):
         self.requires_grad, _, self._backward_hooks = state
 
     def __repr__(self):
-        return 'Variable containing:' + self.data.__repr__()
+        strt = 'Variable containing:' + torch._tensor_str._str(self.data, False)
+        # let's make our own Variable-specific footer
+        size_str = '(' + ','.join(str(size) for size in self.size()) + (',)' if len(self.size()) == 1 else ')')
+        device_str = '' if not self.is_cuda else \
+            ' (GPU {})'.format(self.get_device())
+        strt += '[{} of size {}{}]\n'.format(torch.typename(self.data),
+                                             size_str, device_str)
+
+        # All strings are unicode in Python 3, while we have to encode unicode
+        # strings in Python2. If we can't, let python decide the best
+        # characters to replace unicode characters with.
+        if sys.version_info > (3,):
+            return strt
+        else:
+            if hasattr(sys.stdout, 'encoding'):
+                return strt.encode(
+                    sys.stdout.encoding or 'UTF-8', 'replace')
+            else:
+                return strt.encode('UTF-8', 'replace')
 
     def backward(self, gradient=None, retain_graph=None, create_graph=False):
         """Computes the gradient of current variable w.r.t. graph leaves.
diff --git a/torch/csrc/autograd/python_variable.cpp b/torch/csrc/autograd/python_variable.cpp
index f541e1343f..9fa3bc9040 100644
--- a/torch/csrc/autograd/python_variable.cpp
+++ b/torch/csrc/autograd/python_variable.cpp
@@ -44,13 +44,13 @@ static PyObject* THPVariable_NewWithVar(PyTypeObject* type, Variable var)
   return obj;
 }
 
-PyObject * THPVariable_Wrap(Variable var)
+PyObject * THPVariable_Wrap(Variable var, bool allow_scalar)
 {
   if (!var.defined()) {
     Py_RETURN_NONE;
   }
 
-  if (var.dim() == 0) {
+  if (!allow_scalar && var.dim() == 0) {
     throw std::runtime_error("Variable API does not support Scalars");
   }
 
diff --git a/torch/csrc/autograd/python_variable.h b/torch/csrc/autograd/python_variable.h
index 328d0a6e33..91f6acc3bd 100644
--- a/torch/csrc/autograd/python_variable.h
+++ b/torch/csrc/autograd/python_variable.h
@@ -24,7 +24,8 @@ struct THPVariable {
 extern PyObject *THPVariableClass;
 
 bool THPVariable_initModule(PyObject *module);
-PyObject * THPVariable_Wrap(torch::autograd::Variable var);
+// FixMe: remove allow_scalar when scalars are fully supported.
+PyObject * THPVariable_Wrap(torch::autograd::Variable var, bool allow_scalar=false);
 PyObject * THPVariable_get_data(THPVariable *self);
 
 inline bool THPVariable_Check(PyObject *obj)