Removes dunder div (#39151)

Summary:
BC-breaking note:

If a user is using one of these dunders directly they will not longer be available. Users should update to Python3 compatible dunders.

Original PR note:

`__div__` (and `__idiv__` and `__rdiv__`) are no longer special dunders in Python3. This PR replaces them with the `__truediv__` (`__itrudediv__`, `__rtruediv__`) dunders, since we no longer support Python2.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39151

Differential Revision: D22075713

Pulled By: mruberry

fbshipit-source-id: d318b47b51f7cc4c3728b1606a34d81e49ba0fa1

docs/source/amp.rst

@@ -119,7 +119,6 @@ Ops that can autocast to ``float32``
 """"""""""""""""""""""""""""""""""""
 
 ``__pow__``,
-``__rdiv__``,
 ``__rpow__``,
 ``__rtruediv__``,
 ``acos``,

test/test_autograd.py

@@ -4325,7 +4325,7 @@ separate_complex_tests = ['log', 'log10', 'log1p', 'log2', 'reciprocal', 'tan']
 complex_list = ['t', 'view', 'reshape', 'reshape_as', 'view_as', 'zero_', 'clone',
                 'tril', 'triu', 'fill_', 'eq_', 'ne_', 'permute', 'squeeze', 'unsqueeze',
                 'chunk', 'split', 'split_with_sizes', 'resize', 'resize_as', 'sin', 'cos',
-                '__rmul__', '__rdiv__', 'sum', 'transpose', 'round', 'add', 'roll',
+                '__rmul__', '__rtruediv__', 'sum', 'transpose', 'round', 'add', 'roll',
                 '__radd__', 'repeat', 'expand', 'mul', 'tanh', 'flip', 'fliplr', 'flipud',
                 'rot90'] + separate_complex_tests
 

test/test_jit.py

@@ -137,8 +137,8 @@ def doAutodiffCheck(testname):
         'test_nn_batch_norm',
         'test_nn_max_pool2d_with_indices',
         # AutogradJitGenerated
-        'test___rdiv___constant',
-        'test___rdiv___scalar_constant',
+        'test___rtruediv___constant',
+        'test___rtruediv___scalar_constant',
         'test_split',
         'test_split_dim',
         'test_split_dim_neg0',
@@ -17840,8 +17840,8 @@ class TestJitGeneratedFunctional(JitTestCase):
 # UBSAN per-function exclusions don't seem to work with OpenMP pragmas,
 # and we have to disable the failing tests here instead.
 UBSAN_BLACKLISTED_TESTS = [
-    "test___rdiv___constant",
-    "test___rdiv___scalar_constant",
+    "test___rtruediv___constant",
+    "test___rtruediv___scalar_constant",
     "test_addcdiv",
     "test_addcdiv_broadcast_all",
     "test_addcdiv_broadcast_rhs",

tools/autograd/templates/python_variable_methods.cpp

@@ -929,10 +929,9 @@ PyMethodDef variable_methods[] = {
   {"__imul__", (PyCFunction)(void(*)(void))TypeError_to_NotImplemented_<THPVariable_mul_>, METH_VARARGS | METH_KEYWORDS, NULL},
   {"__sub__", (PyCFunction)(void(*)(void))TypeError_to_NotImplemented_<THPVariable_sub>, METH_VARARGS | METH_KEYWORDS, NULL},
   {"__isub__", (PyCFunction)(void(*)(void))TypeError_to_NotImplemented_<THPVariable_sub_>, METH_VARARGS | METH_KEYWORDS, NULL},
-  {"__div__", (PyCFunction)(void(*)(void))TypeError_to_NotImplemented_<THPVariable_div>, METH_VARARGS | METH_KEYWORDS, NULL},
   {"__truediv__", (PyCFunction)(void(*)(void))TypeError_to_NotImplemented_<THPVariable_div>, METH_VARARGS | METH_KEYWORDS, NULL},
+  {"__itruediv__", (PyCFunction)(void(*)(void))TypeError_to_NotImplemented_<THPVariable_div_>, METH_VARARGS | METH_KEYWORDS, NULL},
   {"__floordiv__", (PyCFunction)(void(*)(void))TypeError_to_NotImplemented_<THPVariable_floor_divide>, METH_VARARGS | METH_KEYWORDS, NULL},
-  {"__idiv__", (PyCFunction)(void(*)(void))TypeError_to_NotImplemented_<THPVariable_div_>, METH_VARARGS | METH_KEYWORDS, NULL},
   {"__ifloordiv__", (PyCFunction)(void(*)(void))TypeError_to_NotImplemented_<THPVariable_floor_divide_>, METH_VARARGS | METH_KEYWORDS, NULL},
   {"__mod__", (PyCFunction)(void(*)(void))TypeError_to_NotImplemented_<THPVariable_remainder>, METH_VARARGS | METH_KEYWORDS, NULL},
   {"__bool__", (PyCFunction)THPVariable_bool_scalar, METH_NOARGS, NULL},

torch/tensor.py

@@ -393,15 +393,12 @@ class Tensor(torch._C._TensorBase):
     def __rsub__(self, other):
         return _C._VariableFunctions.rsub(self, other)
 
-    def __rdiv__(self, other):
+    def __rtruediv__(self, other):
         if self.dtype.is_floating_point or self.dtype.is_complex:
             return self.reciprocal() * other
         else:
             return (self.double().reciprocal() * other).type_as(self)
 
-    __rtruediv__ = __rdiv__
-    __itruediv__ = _C._TensorBase.__idiv__
-
     __pow__ = _C._TensorBase.pow
 
     def __format__(self, format_spec):

torch/testing/_internal/autocast_test_lists.py

@@ -55,7 +55,7 @@ class AutocastTestLists(object):
             ("__lt__", pointwise0_fp32 + pointwise1_fp16, torch.bool),
             ("__ne__", pointwise0_fp32 + pointwise1_fp16, torch.bool),
             ("__add__", pointwise0_fp32 + pointwise1_fp16, torch.float32),
-            ("__div__", pointwise0_fp32 + pointwise1_fp16, torch.float32),
+            ("__truediv__", pointwise0_fp32 + pointwise1_fp16, torch.float32),
             ("__mul__", pointwise0_fp32 + pointwise1_fp16, torch.float32),
         ]
 

torch/testing/_internal/common_methods_invocations.py

@@ -163,10 +163,10 @@ def method_tests():
         ('div', (S, S, S), (uniform_scalar(0.1),), 'scalar_broadcast_rhs', (True,)),
         ('div', (), (uniform_scalar(0.1),), 'scalar_broadcast_lhs', (True,)),
         ('div', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant', (True,)),
-        ('__rdiv__', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant',
+        ('__rtruediv__', torch.rand(S, S, S) + 1e-1, (3.14,), 'constant',
             (True, [], ['aten::mul', 'aten::reciprocal'])),
         ('div', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant', (True,)),
-        ('__rdiv__', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant',
+        ('__rtruediv__', uniform_scalar(1e-1, requires_grad=True), (3.14,), 'scalar_constant',
             (True, [], ['aten::mul', 'aten::reciprocal'])),
         ('pow', torch.rand(S, S, S) + 1e-3, (torch.rand(S, S, S) + 0.1,), '', (True,)),
         ('pow', torch.rand(S, S, S) + 1e-3, (torch.rand(1,) + 0.1,), 'broadcast_rhs', (True,)),