[Zero-Dim] reshape/reshape_/reverse 0D support (PaddlePaddle#49357)

* [Zero-Dim] reshape/reshape_/reverse 0D support

* rm comment

* change paddle.to_tensor to paddle.full

* fix docs

* update paddle.full

paddle/fluid/operators/reshape_op.cc

@@ -114,11 +114,6 @@ class ReshapeOp : public framework::OperatorWithKernel {
       return;
     }
 
-    PADDLE_ENFORCE_EQ(!shape.empty(),
-                      true,
-                      platform::errors::InvalidArgument(
-                          "The parameter 'shape' in ReshapeOp must be set. "
-                          "But received 'shape' is empty."));
     auto x_dims = ctx->GetInputDim("X");
     auto out_dims = ValidateShape(shape, x_dims);
     ctx->SetOutputDim("Out", out_dims);

python/paddle/fluid/tests/unittests/test_reshape_op.py

@@ -49,20 +49,20 @@ def test_check_grad(self):
 class TestReshapeOp_ZeroDim1(OpTest):
     def init_data(self):
         self.ori_shape = ()
-        self.new_shape = 1
-        self.infered_shape = 1
+        self.new_shape = (1,)
+        self.infered_shape = (1,)
 
 
 class TestReshapeOp_ZeroDim2(OpTest):
     def init_data(self):
         self.ori_shape = ()
-        self.new_shape = -1
-        self.infered_shape = 1
+        self.new_shape = (-1,)
+        self.infered_shape = (1,)
 
 
 class TestReshapeOp_ZeroDim3(OpTest):
     def init_data(self):
-        self.ori_shape = 1
+        self.ori_shape = (1,)
         self.new_shape = ()
         self.infered_shape = ()
 

python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py

@@ -756,6 +756,105 @@ def test_floor_divide(self):
         np.testing.assert_array_equal(out3_1.numpy(), out3_2.numpy())
         np.testing.assert_array_equal(out3_2.numpy(), np.asarray(1))
 
+    def test_reshape_list(self):
+        x = paddle.rand([])
+        x.stop_gradient = False
+
+        out = paddle.reshape(x, [])
+        out.backward()
+        self.assertEqual(x.grad.shape, [])
+        self.assertEqual(out.shape, [])
+        self.assertEqual(out.grad.shape, [])
+
+        out = paddle.reshape(x, [1])
+        out.backward()
+        self.assertEqual(x.grad.shape, [])
+        self.assertEqual(out.shape, [1])
+        self.assertEqual(out.grad.shape, [1])
+
+        out = paddle.reshape(x, [-1])
+        out.backward()
+        self.assertEqual(x.grad.shape, [])
+        self.assertEqual(out.shape, [1])
+        self.assertEqual(out.grad.shape, [1])
+
+        out = paddle.reshape(x, [-1, 1])
+        out.backward()
+        self.assertEqual(x.grad.shape, [])
+        self.assertEqual(out.shape, [1, 1])
+        self.assertEqual(out.grad.shape, [1, 1])
+
+    def test_reshape_tensor(self):
+        x = paddle.rand([1, 1])
+        x.stop_gradient = False
+
+        out = paddle.reshape(x, [])
+        out.backward()
+        self.assertEqual(x.grad.shape, [1, 1])
+        self.assertEqual(out.shape, [])
+        self.assertEqual(out.grad.shape, [])
+
+        new_shape = paddle.full([1], 1, "int32")
+        out = paddle.reshape(x, new_shape)
+        out.backward()
+        self.assertEqual(x.grad.shape, [1, 1])
+        self.assertEqual(out.shape, [1])
+        self.assertEqual(out.grad.shape, [1])
+
+        new_shape = paddle.full([1], -1, "int32")
+        out = paddle.reshape(x, new_shape)
+        out.backward()
+        self.assertEqual(x.grad.shape, [1, 1])
+        self.assertEqual(out.shape, [1])
+        self.assertEqual(out.grad.shape, [1])
+
+        new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")]
+        out = paddle.reshape(x, new_shape)
+        out.backward()
+        self.assertEqual(x.grad.shape, [1, 1])
+        self.assertEqual(out.shape, [1, 1])
+        self.assertEqual(out.grad.shape, [1, 1])
+
+    def test_reshape__list(self):
+        x = paddle.rand([])
+        out = paddle.reshape_(x, [])
+        self.assertEqual(out.shape, [])
+
+        out = paddle.reshape_(x, [1])
+        self.assertEqual(out.shape, [1])
+
+        out = paddle.reshape_(x, [-1])
+        self.assertEqual(out.shape, [1])
+
+        out = paddle.reshape_(x, [-1, 1])
+        self.assertEqual(out.shape, [1, 1])
+
+    def test_reshape__tensor(self):
+        x = paddle.rand([1, 1])
+        out = paddle.reshape_(x, [])
+        self.assertEqual(out.shape, [])
+
+        new_shape = paddle.full([1], 1, "int32")
+        out = paddle.reshape_(x, new_shape)
+        self.assertEqual(out.shape, [1])
+
+        new_shape = paddle.full([1], -1, "int32")
+        out = paddle.reshape_(x, new_shape)
+        self.assertEqual(out.shape, [1])
+
+        new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")]
+        out = paddle.reshape_(x, new_shape)
+        self.assertEqual(out.shape, [1, 1])
+
+    def test_reverse(self):
+        x = paddle.rand([])
+        x.stop_gradient = False
+        out = paddle.reverse(x, axis=[])
+        out.backward()
+        self.assertEqual(x.shape, [])
+        self.assertEqual(out.shape, [])
+        self.assertEqual(out.grad.shape, [])
+
 
 class TestSundryAPIStatic(unittest.TestCase):
     def setUp(self):
@@ -1011,6 +1110,78 @@ def test_floor_divide(self):
         np.testing.assert_array_equal(out3_1, out3_2)
         np.testing.assert_array_equal(out3_2, np.asarray(1))
 
+    @prog_scope()
+    def test_reshape_list(self):
+        x1 = paddle.rand([])
+        x2 = paddle.rand([])
+        x3 = paddle.rand([])
+        x4 = paddle.rand([])
+        x1.stop_gradient = False
+        x2.stop_gradient = False
+        x3.stop_gradient = False
+        x4.stop_gradient = False
+
+        out1 = paddle.reshape(x1, [])
+        paddle.static.append_backward(out1)
+
+        out2 = paddle.reshape(x2, [1])
+        paddle.static.append_backward(out2)
+
+        out3 = paddle.reshape(x3, [-1])
+        paddle.static.append_backward(out3)
+
+        out4 = paddle.reshape(x4, [-1, 1])
+        paddle.static.append_backward(out4)
+
+        program = paddle.static.default_main_program()
+        res1, res2, res3, res4 = self.exe.run(
+            program, fetch_list=[out1, out2, out3, out4]
+        )
+        self.assertEqual(res1.shape, ())
+        self.assertEqual(res2.shape, (1,))
+        self.assertEqual(res3.shape, (1,))
+        self.assertEqual(res4.shape, (1, 1))
+
+    @prog_scope()
+    def test_reshape_tensor(self):
+        x1 = paddle.rand([])
+        x2 = paddle.rand([])
+        x3 = paddle.rand([])
+        x1.stop_gradient = False
+        x2.stop_gradient = False
+        x3.stop_gradient = False
+
+        new_shape = paddle.full([1], 1, "int32")
+        out1 = paddle.reshape(x1, new_shape)
+        paddle.static.append_backward(out1)
+
+        new_shape = paddle.full([1], -1, "int32")
+        out2 = paddle.reshape(x2, new_shape)
+        paddle.static.append_backward(out2)
+
+        new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")]
+        out3 = paddle.reshape(x3, new_shape)
+        paddle.static.append_backward(out3)
+
+        program = paddle.static.default_main_program()
+        res1, res2, res3 = self.exe.run(program, fetch_list=[out1, out2, out3])
+        self.assertEqual(res1.shape, (1,))
+        self.assertEqual(res2.shape, (1,))
+        self.assertEqual(res3.shape, (1, 1))
+
+    @prog_scope()
+    def test_reverse(self):
+        x = paddle.rand([])
+        x.stop_gradient = False
+
+        out = paddle.reverse(x, axis=[])
+        paddle.static.append_backward(out)
+
+        program = paddle.static.default_main_program()
+        res1, res2 = self.exe.run(program, fetch_list=[x, out])
+        self.assertEqual(res1.shape, ())
+        self.assertEqual(res2.shape, ())
+
 
 # Use to test API whose zero-dim input tensors don't have grad and not need to test backward in OpTest.
 class TestNoBackwardAPI(unittest.TestCase):

python/paddle/fluid/tests/unittests/xpu/test_zero_dim_tensor_xpu.py

@@ -556,6 +556,96 @@ def test_floor_divide(self):
         np.testing.assert_array_equal(out3_1.numpy(), out3_2.numpy())
         np.testing.assert_array_equal(out3_2.numpy(), np.asarray(1))
 
+    def test_reshape_list(self):
+        x = paddle.rand([])
+        x.stop_gradient = False
+
+        out = paddle.reshape(x, [])
+        out.backward()
+        self.assertEqual(x.grad.shape, [])
+        self.assertEqual(out.shape, [])
+        self.assertEqual(out.grad.shape, [])
+
+        out = paddle.reshape(x, [1])
+        out.backward()
+        self.assertEqual(x.grad.shape, [])
+        self.assertEqual(out.shape, [1])
+        self.assertEqual(out.grad.shape, [1])
+
+        out = paddle.reshape(x, [-1])
+        out.backward()
+        self.assertEqual(x.grad.shape, [])
+        self.assertEqual(out.shape, [1])
+        self.assertEqual(out.grad.shape, [1])
+
+        out = paddle.reshape(x, [-1, 1])
+        out.backward()
+        self.assertEqual(x.grad.shape, [])
+        self.assertEqual(out.shape, [1, 1])
+        self.assertEqual(out.grad.shape, [1, 1])
+
+    def test_reshape_tensor(self):
+        x = paddle.rand([1, 1])
+        x.stop_gradient = False
+
+        out = paddle.reshape(x, [])
+        out.backward()
+        self.assertEqual(x.grad.shape, [1, 1])
+        self.assertEqual(out.shape, [])
+        self.assertEqual(out.grad.shape, [])
+
+        new_shape = paddle.full([], 1, "int32")
+        out = paddle.reshape(x, new_shape)
+        out.backward()
+        self.assertEqual(x.grad.shape, [1, 1])
+        self.assertEqual(out.shape, [1])
+        self.assertEqual(out.grad.shape, [1])
+
+        new_shape = paddle.full([], -1, "int32")
+        out = paddle.reshape(x, new_shape)
+        out.backward()
+        self.assertEqual(x.grad.shape, [1, 1])
+        self.assertEqual(out.shape, [1])
+        self.assertEqual(out.grad.shape, [1])
+
+        new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")]
+        out = paddle.reshape(x, new_shape)
+        out.backward()
+        self.assertEqual(x.grad.shape, [1, 1])
+        self.assertEqual(out.shape, [1, 1])
+        self.assertEqual(out.grad.shape, [1, 1])
+
+    def test_reshape__list(self):
+        x = paddle.rand([])
+        out = paddle.reshape_(x, [])
+        self.assertEqual(out.shape, [])
+
+        out = paddle.reshape_(x, [1])
+        self.assertEqual(out.shape, [1])
+
+        out = paddle.reshape_(x, [-1])
+        self.assertEqual(out.shape, [1])
+
+        out = paddle.reshape_(x, [-1, 1])
+        self.assertEqual(out.shape, [1, 1])
+
+    def test_reshape__tensor(self):
+        x = paddle.rand([1, 1])
+        out = paddle.reshape_(x, [])
+        self.assertEqual(out.shape, [])
+
+        new_shape = paddle.full([1], 1, "int32")
+        out = paddle.reshape_(x, new_shape)
+        self.assertEqual(out.shape, [1])
+
+        new_shape = paddle.full([1], -1, "int32")
+        out = paddle.reshape_(x, new_shape)
+        self.assertEqual(out.shape, [1])
+
+        new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")]
+        out = paddle.reshape_(x, new_shape)
+        self.assertEqual(out.shape, [1, 1])
+
 
 # Use to test API whose zero-dim input tensors don't have grad and not need to test backward in OpTest.
 class TestNoBackwardAPI(unittest.TestCase):

python/paddle/tensor/manipulation.py

@@ -3450,7 +3450,7 @@ def reshape(x, shape, name=None):
     Args:
         x (Tensor): An N-D Tensor. The data type is ``float32``, ``float64``, ``int32``, ``int64`` or ``bool``
         shape (list|tuple|Tensor): Define the target shape. At most one dimension of the target shape can be -1.
-                        The data type is ``int32`` . If ``shape`` is a list or tuple, the elements of it should be integers or Tensors with shape [1].
+                        The data type is ``int32`` . If ``shape`` is a list or tuple, the elements of it should be integers or Tensors with shape [].
                         If ``shape`` is an Tensor, it should be an 1-D Tensor .
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
@@ -3574,10 +3574,6 @@ def get_attr_shape(list_shape):
             shape.stop_gradient = True
             inputs["Shape"] = shape
         elif isinstance(shape, (list, tuple)):
-            assert len(shape) > 0, (
-                "The size of 'shape' in reshape can't be zero, "
-                "but received %s." % len(shape)
-            )
             attrs["shape"] = get_attr_shape(shape)
             if utils._contain_var(shape):
                 inputs['ShapeTensor'] = utils._convert_to_tensor_list(shape)