Bring back removed lines of codes, minor fixes

pytorch · krshrimali · May 10, 2021 · May 10, 2021 · May 10, 2021 · May 10, 2021
commit c445b0ba1e3ca25d86b3d9a9562d56f3b3358a64
diff --git a/torch/testing/_internal/common_methods_invocations.py b/torch/testing/_internal/common_methods_invocations.py
@@ -2528,38 +2528,6 @@ def sample_inputs_pow(op_info, device, dtype, requires_grad, **kwargs):
                                                      requires_grad=requires_grad),)))
     return tuple(samples)
 
-def sample_inputs_fmod_remainder(op_info, device, dtype, requires_grad, **kwargs):
-    make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad)
-
-    cases = [
-                ((S, S, S), (), 1.5, False), # Scalar
-                ((), 1.5, (), False), # Scalar
-                ((S, S, S), (S, S, S), 1.5, False),
-            ]
-
-    # Sample inputs with broadcasting
-    cases_with_broadcasting = [
-            ((S,), (S, S, S), 1.5, True),
-            ((S, S, S), (S,), 1.5, True),
-            ((S, 1, S), (S, S), 1.5, True),
-            ((), (S, S, S), 1.5, True),
-            ((S, S, S), 1.5, (), True) # Scalar
-            ]
-
-    cases.extend(cases_with_broadcasting)
-
-    def generator():
-        for shape, shape_other, add_other, broadcasts_input in cases:
-            if isinstance(shape_other, tuple):
-                arg = make_arg(shape_other, requires_grad=False, include_zero=False) + add_other
-            else:
-                # shape_other is scalar
-                arg = shape_other
-            yield(SampleInput(make_arg(shape), args=(arg,),
-                broadcasts_input=broadcasts_input))
-
-    return list(generator())
-
 def sample_inputs_svd(op_info, device, dtype, requires_grad=False, **kwargs):
     return _sample_inputs_svd(op_info, device, dtype, requires_grad, is_linalg_svd=False)
 
@@ -2692,6 +2660,34 @@ def sample_inputs_fliplr_flipud(op_info, device, dtype, requires_grad, **kwargs)
     )
     return [SampleInput(tensor) for tensor in tensors]
 
+def sample_inputs_fmod_remainder(op_info, device, dtype, requires_grad, **kwargs):
+    make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad)
+
+    cases = [((S, S, S), (), 1.5, False),
+            ((), 1.5, (), False), # Scalar
+            ((S, S, S), (S, S, S), 1.5, False)]
+
+    # Sample inputs with broadcasting
+    cases_with_broadcasting = [((S,), (S, S, S), 1.5, True),
+            ((S, S, S), (S,), 1.5, True),
+            ((S, 1, S), (S, S), 1.5, True),
+            ((), (S, S, S), 1.5, True),
+            ((S, S, S), 1.5, (), True)]
+
+    cases.extend(cases_with_broadcasting)
+
+    def generator():
+        for shape, shape_other, add_other, broadcasts_input in cases:
+            if isinstance(shape_other, tuple):
+                arg = make_arg(shape_other, requires_grad=False, include_zero=False) + add_other
+            else:
+                # shape_other is scalar
+                arg = shape_other
+            yield(SampleInput(make_arg(shape), args=(arg,),
+                broadcasts_input=broadcasts_input))
+
+    return list(generator())
+
 # TODO: clamp shares tensors among its sample inputs --- we should prohibit this!
 def sample_inputs_clamp(op_info, device, dtype, requires_grad, **kwargs):
     x = make_tensor((S, M, S), device, dtype, low=None, high=None, requires_grad=requires_grad)
@@ -6268,6 +6264,22 @@ def method_tests():
         ('reshape_as', (S, S, S), (non_differentiable(torch.rand(S * S, S)),)),
         ('reshape_as', (), (non_differentiable(torch.tensor(42.)),), 'scalar'),
         ('reshape_as', (), (non_differentiable(torch.rand(1, 1)),), 'scalar_to_dims'),
+        ('fmod', (S, S, S), (1.5,), '', (True,)),
+        ('fmod', (), (1.5,), 'scalar', (True,)),
+        ('fmod', (S, S, S), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor'),
+        ('fmod', (S,), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor_broadcast_lhs'),
+        ('fmod', (S, S, S), (non_differentiable(torch.rand(S) + 1.5),), 'tensor_broadcast_rhs'),
+        ('fmod', (S, 1, S), (non_differentiable(torch.rand(S, S) + 1.5),), 'tensor_broadcast_all'),
+        ('fmod', (), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor'),
+        ('fmod', (), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'scalar_tensor_broadcast_lhs'),
+        ('fmod', (S, S, S), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor_broadcast_rhs'),
+        ('remainder', (S, S, S), (1.5,), '', (True,)),
+        ('remainder', (), (1.5,), 'scalar', (True,)),
+        ('remainder', (S, S, S), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor'),
+        ('remainder', (S,), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'tensor_broadcast_lhs'),
+        ('remainder', (S, 1, S), (non_differentiable(torch.rand(S, S) + 1.5),), 'tensor_broadcast_all'),
+        ('remainder', (), (non_differentiable(uniform_scalar(1.5)),), 'scalar_tensor'),
+        ('remainder', (), (non_differentiable(torch.rand(S, S, S) + 1.5),), 'scalar_tensor_broadcast_lhs'),
         ('kthvalue', (S, S, S), (2,)),
         ('kthvalue', (S, S, S), (2, 1,), 'dim', (), [1]),
         ('kthvalue', (S, S, S), (2, 1, True,), 'keepdim_dim', (), [1]),
@@ -6606,25 +6618,53 @@ def unpack_variables(args):
         return args
 
 
-    EXCLUDE_FUNCTIONAL = {
-        'addmm',
-        'addmm_',
-        'reshape',
-        'where'  # argument order
-    }
-    EXCLUDE_GRADCHECK: Dict[str, Any] = {
-    }
-    EXCLUDE_GRADGRADCHECK: Dict[str, Any] = {
+EXCLUDE_FUNCTIONAL = {
+    'addmm',
+    'addmm_',
+    'reshape',
+    'where'  # argument order
+}
+EXCLUDE_GRADCHECK: Dict[str, Any] = {
+}
+EXCLUDE_GRADGRADCHECK: Dict[str, Any] = {
+}
+EXCLUDE_GRADGRADCHECK_BY_TEST_NAME = {
+    # `other` expand_as(self, other) is not used in autograd.
+    'test_expand_as',
+    'test_cdist',
+}
+
+
+def exclude_tensor_method(name, test_name):
+    # there are no tensor equivalents for these (inplace or out)
+    exclude_all_tensor_method_by_test_name = {
+        'test_slice',
+        'test_where',
+        'test_where_broadcast_all',
+        'test_where_scalar',
+        'test_where_scalar_broadcast_mask',
+        'test_where_scalar_broadcast_non_mask',
+        'test_var_mean_keepdim_dim_1d',
+        'test_var_mean_keepdim_dim',
+        'test_var_mean_dim_1d',
+        'test_var_mean_dim',
+        'test_var_mean',
+        'test_std_mean_keepdim_dim_1d',
+        'test_std_mean_keepdim_dim',
+        'test_std_mean_dim_1d',
+        'test_std_mean_dim',
+        'test_std_mean',
     }
-    EXCLUDE_GRADGRADCHECK_BY_TEST_NAME = {
-        # `other` expand_as(self, other) is not used in autograd.
-        'test_expand_as',
-        'test_cdist',
+    # there are no out-of-place tensor equivalents for these
+    exclude_outplace_tensor_method = {
+        'index_fill',
+        'scatter',
+        'scatter_add',
     }
     if test_name in exclude_all_tensor_method_by_test_name:
         return True
     is_magic_method = name[:2] == '__' and name[-2:] == '__'
     is_inplace = name[-1] == "_" and not is_magic_method
     if not is_inplace and name in exclude_outplace_tensor_method:
         return True
-    return False
+    return False