Deprecate .mT,.T,.mH,.H on 0D tensors

lezcano · lezcano · commit 37c574f4e284 · 2023-01-15T19:35:15.000Z
As discussed with ngimel, this is not only not documented, but it's also an unnecessary edge case. See #90463 (comment) ghstack-source-id: b4e85ca Pull Request resolved: #92143
diff --git a/aten/src/ATen/native/TensorShape.cpp b/aten/src/ATen/native/TensorShape.cpp
@@ -3543,6 +3543,10 @@ Tensor numpy_T(const Tensor &self) {
         "or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor."
     );
   }
+  if (n == 0) {
+   // Added in PyTorch 2.0
+   TORCH_WARN_ONCE("Tensor.T is deprecated on 0-D tensors. This function is the identity in these cases.");
+  }
   DimVector transpose_dims;
   for (int64_t i = n - 1; i >= 0; --i) {
     transpose_dims.push_back(i);
@@ -3552,6 +3556,10 @@ Tensor numpy_T(const Tensor &self) {
 
 Tensor matrix_H(const Tensor &self) {
   const auto ndim = self.dim();
+  if (ndim == 0) {
+   // Added in PyTorch 2.0
+   TORCH_WARN_ONCE("Tensor.H is deprecated on 0-D tensors. Consider using x.conj().");
+  }
   TORCH_CHECK(ndim == 2 || ndim == 0,
       "tensor.H is only supported on matrices (2-D tensors). Got ", ndim, "-D tensor.",
       ndim > 2 ? " For batches of matrices, consider using tensor.mH" : "");
@@ -3576,14 +3584,25 @@ Tensor _adjoint(const Tensor &self, const bool transpose, const char* const name
 } // anonymous namespace
 
 Tensor mT(const Tensor &self) {
+  if (self.dim() == 0) {
+   // Added in PyTorch 2.0
+   TORCH_WARN_ONCE("Tensor.mT is deprecated on 0-D tensors. This function is the identity in these cases.");
+  }
   return _adjoint(self, /*transpose=*/true, "mT");
 }
 
 Tensor mH(const Tensor &self) {
+  if (self.dim() == 0) {
+    // Added in PyTorch 2.0
+   TORCH_WARN_ONCE("Tensor.mH is deprecated on 0-D tensors. Consider using x.conj().");
+  }
   return _adjoint(self, /*transpose=*/false, "mH");
 }
 
 Tensor adjoint(const Tensor &self) {
+  if (self.dim() == 0) {
+   TORCH_WARN_ONCE("adjoint() is deprecated on 0-D tensors. Consider using x.conj().");
+  }
   return _adjoint(self, /*transpose=*/false, "adjoint()");
 }
 
diff --git a/test/functorch/test_vmap.py b/test/functorch/test_vmap.py
@@ -2625,7 +2625,6 @@ def op(t):
         test = self._vmap_view_test
         B0, B1, B2 = 7, 11, 13
         test(op, (torch.rand(B0, 2, 3, 5),))
-        test(op, (torch.rand(B0),))
         test(op, (torch.rand(2, B0, 3, 5),), in_dims=1)
         test(vmap(op), (torch.rand(B1, 2, B0, 5),), in_dims=2)
         test(vmap(op), (torch.rand(B1, 2, B0, 3, 5),), in_dims=2)
diff --git a/test/onnx/test_pytorch_onnx_onnxruntime.py b/test/onnx/test_pytorch_onnx_onnxruntime.py
@@ -1210,7 +1210,6 @@ def forward(self, x):
                 return x.T
 
         self.run_test(NumpyTranspose(), torch.randn(4, 7))
-        self.run_test(NumpyTranspose(), torch.tensor(-42.0))
 
     # Conversion of Transpose depends on input shape to be known.
     # The following test only works when onnx shape inference is enabled.
diff --git a/test/test_legacy_vmap.py b/test/test_legacy_vmap.py
@@ -2040,7 +2040,6 @@ def op(t):
         test = self._vmap_view_test
         B0, B1, B2 = 7, 11, 13
         test(op, (torch.rand(B0, 2, 3, 5),))
-        test(op, (torch.rand(B0),))
         test(op, (torch.rand(2, B0, 3, 5),), in_dims=1)
         test(vmap(op), (torch.rand(B1, 2, B0, 5),), in_dims=2)
         test(vmap(op), (torch.rand(B1, 2, B0, 3, 5),), in_dims=2)
diff --git a/test/test_mps.py b/test/test_mps.py
@@ -6877,12 +6877,10 @@ def test_T(self, device="mps"):
         self.assertEqual(t2, t1)
         b = torch.randn(10, device=device)
         self.assertEqual(b, b.T)
-        scalar = torch.tensor(5, device=device)
-        self.assertEqual(scalar, scalar.T)
 
     def test_transposes(self, device="mps", dtype=torch.float32):
         for op in ("T", "H", "mT", "mH", "adjoint"):
-            shapes = ((), (2, 3), (2, 3, 4)) if op[0] == "m" or op == "adjoint" else ((), (2, 3),)
+            shapes = ((2, 3), (2, 3, 4)) if op[0] == "m" or op == "adjoint" else ((2, 3),)
             for shape in shapes:
                 a = make_tensor(shape, device=device, dtype=dtype)
                 t1 = getattr(a, op)
diff --git a/test/test_view_ops.py b/test/test_view_ops.py
@@ -1307,21 +1307,18 @@ def test_T(self, device):
         self.assertEqual(t2, t1)
         b = torch.randn(10, device=device)
         self.assertEqual(b, b.T)
-        scalar = torch.tensor(5, device=device)
-        self.assertEqual(scalar, scalar.T)
 
     @dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
     def test_transposes(self, device, dtype):
         for op in ("T", "H", "mT", "mH", "adjoint"):
-            shapes = ((), (2, 3), (2, 3, 4)) if op[0] == "m" or op == "adjoint" else ((), (2, 3),)
+            shapes = ((2, 3), (2, 3, 4)) if op[0] == "m" or op == "adjoint" else ((2, 3),)
             for shape in shapes:
                 a = make_tensor(shape, device=device, dtype=dtype)
                 t1 = getattr(a, op)
                 if op == "adjoint":
                     t1 = t1()
                 t2 = a
-                if a.ndim != 0:
-                    t2 = t2.transpose(-2, -1)
+                t2 = t2.transpose(-2, -1)
                 if op[-1] == "H" or op == "adjoint":
                     t2 = t2.conj()
                 self.assertEqual(t2, t1)
diff --git a/torch/testing/_internal/common_methods_invocations.py b/torch/testing/_internal/common_methods_invocations.py
@@ -1656,13 +1656,13 @@ def _numpy_ref_transpose(a, dim0, dim1):
 def sample_inputs_adjoint(self, device, dtype, requires_grad, **kwargs):
     make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad)
 
-    shapes = ((1, 2, 3), (), (M, M), (S, S, S), (S, M, S), (M, S, M, S))
+    shapes = ((1, 2, 3), (M, M), (S, S, S), (S, M, S), (M, S, M, S))
     return (SampleInput(make_arg(shape)) for shape in shapes)
 
 def sample_inputs_T(self, device, dtype, requires_grad, **kwargs):
     make_arg = partial(make_tensor, dtype=dtype, device=device, requires_grad=requires_grad)
 
-    shapes = ((), (M, M), (M, L))
+    shapes = ((M, M), (M, L))
     return (SampleInput(make_arg(shape)) for shape in shapes)
 
 def error_inputs_T(self, device, has_ndims_error=False):
