Revert "Cherry pick getitem/setitem 0d (#53125)" (#53265)

This reverts commit a79c04f3.

paddle/fluid/framework/attribute_checker.h

@@ -73,10 +73,10 @@ class TypedAttrVarInfoChecker {
         platform::errors::InvalidArgument(
             "Required Attribute with Variable type shall not be nullptr."));
     auto shape = var_desc->GetShape();
-    PADDLE_ENFORCE_LE(shape.size(),
+    PADDLE_ENFORCE_EQ(shape.size(),
                       1U,
                       platform::errors::InvalidArgument(
-                          "Required shape rank of Attribute(%s) <= 1, "
+                          "Required shape rank of Attribute(%s) == 1, "
                           "but received rank == %s",
                           var_desc->Name(),
                           shape.size()));
@@ -105,18 +105,17 @@ class TypedAttrVarInfoChecker {
           platform::errors::InvalidArgument(
               "Required Attribute with Variable type shall not be nullptr."));
       auto shape = var_desc->GetShape();
-      PADDLE_ENFORCE_LE(shape.size(),
+      PADDLE_ENFORCE_EQ(shape.size(),
                         1U,
                         platform::errors::InvalidArgument(
-                            "Required shape rank of Attribute(%s) <= 1, "
+                            "Required shape rank of Attribute(%s) == 1, "
                             "but received rank == %s",
                             var_desc->Name(),
                             shape.size()));
-      PADDLE_ENFORCE_EQ(
-          shape.size() == 0U || shape[0] == 1U || shape[0] == -1,
+      PADDLE_ENFORCE_EQ(shape[0] == 1U || shape[0] == -1,
                         true,
                         platform::errors::InvalidArgument(
-              "Required shape is (), or shape[0] of Attribute(%s) == 1 or -1, "
+                            "Required shape[0] of Attribute(%s) == 1 or -1, "
                             "but received shape[0] == %s",
                             var_desc->Name(),
                             shape[0]));

paddle/fluid/inference/tensorrt/engine.h

@@ -86,10 +86,10 @@ template <typename T>
 nvinfer1::Dims Vec2TRT_Dims(const std::vector<T>& shape,
                             std::string input,
                             bool with_dynamic_shape = false) {
-  PADDLE_ENFORCE_GE(shape.size(),
+  PADDLE_ENFORCE_GT(shape.size(),
                     0UL,
                     platform::errors::InvalidArgument(
-                        "TensorRT's tensor input requires at least 0 "
+                        "TensorRT's tensor input requires at least 1 "
                         "dimensions, but input %s has %d dims.",
                         input,
                         shape.size()));

paddle/fluid/pybind/eager_method.cc

@@ -923,6 +923,17 @@ static PyObject* tensor__getitem_index_not_tensor(TensorObject* self,
   }
 
   if (!none_axes.empty()) {
+    // Deal with cases when all axes are decreased.
+    // After slice, the shape of out is [1], which should have been
+    // [], but Paddle doesn't support scalar.
+    // In order to ensure the correctness of the final shape of out,
+    // one dimension of out needs to be decreased.
+    // For example:
+    // # x.shape: (2,3,4)
+    // out = x[0, 1, 1, None] # out.shape : (1)
+    if (static_cast<int>(decrease_axis.size()) == tensor->dims().size()) {
+      none_axes.pop_back();
+    }
     if (!none_axes.empty()) {
       paddle::Tensor new_out;
       {

paddle/fluid/pybind/imperative.cc

@@ -1068,6 +1068,18 @@ void BindImperative(py::module *m_ptr) {
                tracer->TraceOp(op_type, ins, outs, std::move(attrs));
              }
              if (!none_axes.empty()) {
+               // Deal with cases when all axes are decreased.
+               // After slice, the shape of out is [1], which should have been
+               // [], but Paddle doesn't support scalar.
+               // In order to ensure the correctness of the final shape of out,
+               // one dimension of out needs to be decreased.
+               // For example:
+               // # x.shape: (2,3,4)
+               // out = x[0, 1, 1, None] # out.shape : (1)
+               if (static_cast<int>(decrease_axis.size()) ==
+                   tensor->dims().size()) {
+                 none_axes.pop_back();
+               }
                if (!none_axes.empty()) {
                  // Deal with cases that decrease_axes is not empty
                  // For example:

paddle/phi/infermeta/unary.cc

@@ -3927,6 +3927,9 @@ void StridedSliceRawInferMeta(const MetaTensor& x,
         new_out_shape.push_back(out_dims[i]);
       }
     }
+    if (new_out_shape.size() == 0) {
+      new_out_shape.push_back(1);
+    }
     out_dims = phi::make_ddim(new_out_shape);
   }
   VLOG(4) << "out_dims: " << out_dims;

paddle/phi/kernels/funcs/slice_utils.h

@@ -203,6 +203,12 @@ inline DDim GetDecreasedDims(const DDim slice_dims,
       }
     }
 
+    // NOTE(liym27): Paddle does not support that the rank of Tensor is 0, and
+    // uses [1] instead.
+    if (new_shape.size() == 0) {
+      new_shape.push_back(1);
+    }
+
     decreased_dims = phi::make_ddim(new_shape);
   }
   return decreased_dims;

paddle/phi/kernels/xpu/set_value_grad_kernel.cc

@@ -266,11 +266,6 @@ void SetValueGradImpl(const Context& dev_ctx,
                   {fake_value_grad_dims.Get(), fake_value_grad_dims.size()},
                   static_cast<T>(0));
       auto value_grad_dims_vec = phi::vectorize<int64_t>(value_grad_dims);
-      // for value is a 0-D Tensor
-      if (value_grad_dims.size() == 0) {
-        value_grad_dims_vec =
-            phi::vectorize<int64_t>(phi::make_ddim(std::vector<int>({1})));
-      }
       for (auto offset : offsets) {
         for (int i = 0; i < out_dims_size; i++) {
           slice_end[i] = offset[i] + fake_value_grad_dims[i];

python/paddle/distributed/auto_parallel/operators/dist_slice.py

@@ -70,7 +70,9 @@ class DistributedSliceImpl(DistributedOperatorImpl):
             if i not in decrease_axis:
                 ref_indices.append(i)
         if ref_indices == []:
-            assert len(out_dims_mapping) == 0
+            assert len(out_dims_mapping) == 1
+            if is_dim_shard(out_dims_mapping[0]):
+                return False
         else:
             for i in range(len(out_dims_mapping)):
                 ref_index = ref_indices[i]
@@ -140,7 +142,9 @@ class DistributedSliceImpl(DistributedOperatorImpl):
                 ref_indices.append(i)
 
         if ref_dims_mapping == []:
+            ref_dims_mapping = [-1]
             assert len(ref_dims_mapping) == len(out_dims_mapping)
+            assert ref_dims_mapping[0] == out_dims_mapping[0]
             changed = False
         else:
             assert len(ref_dims_mapping) == len(out_dims_mapping)

python/paddle/fft.py

@@ -1371,7 +1371,7 @@ def fftshift(x, axes=None, name=None):
     elif isinstance(axes, int):
         shifts = shape[axes] // 2
     else:
-        shifts = paddle.stack([shape[ax] // 2 for ax in axes])
+        shifts = paddle.concat([shape[ax] // 2 for ax in axes])
     return paddle.roll(x, shifts, axes, name=name)
 
 
@@ -1416,7 +1416,7 @@ def ifftshift(x, axes=None, name=None):
     elif isinstance(axes, int):
         shifts = -shape[axes] // 2
     else:
-        shifts = paddle.stack([-shape[ax] // 2 for ax in axes])
+        shifts = paddle.concat([-shape[ax] // 2 for ax in axes])
     return paddle.roll(x, shifts, axes, name=name)
 
 

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

@@ -43,7 +43,7 @@ class TestImperativeNumpyBridge(unittest.TestCase):
             np.testing.assert_array_equal(var2.numpy(), data_np)
             data_np[0][0] = -1
             self.assertEqual(data_np[0][0], -1)
-            self.assertNotEqual(var2[0][0].numpy(), -1)
+            self.assertNotEqual(var2[0][0].numpy()[0], -1)
             self.assertFalse(np.array_equal(var2.numpy(), data_np))
 
 

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

@@ -140,16 +140,16 @@ class TestKthvalueOpWithNaN(unittest.TestCase):
             nan_position = 100
             self.x[0, nan_position, 2] = float('nan')
             v, inds = self.x.kthvalue(k=200, axis=1)
-            self.assertTrue(np.isnan(v[0, 2].numpy()))
-            self.assertEqual(inds[0, 2].numpy(), nan_position)
+            self.assertTrue(np.isnan(v[0, 2].numpy()[0]))
+            self.assertEqual(inds[0, 2].numpy()[0], nan_position)
 
         def test_nan_in_gpu_kernel():
             paddle.set_device('gpu')
             nan_position = 100
             self.x[0, nan_position, 2] = float('nan')
             v, inds = self.x.kthvalue(k=200, axis=1)
-            self.assertTrue(np.isnan(v[0, 2].numpy()))
-            self.assertEqual(inds[0, 2].numpy(), nan_position)
+            self.assertTrue(np.isnan(v[0, 2].numpy()[0]))
+            self.assertEqual(inds[0, 2].numpy()[0], nan_position)
 
         test_nan_in_cpu_kernel()
         if fluid.core.is_compiled_with_cuda():

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

@@ -1590,7 +1590,7 @@ class TestSetValueInplace(unittest.TestCase):
             a.stop_gradient = False
             b = a[:]
             c = b
-            b[paddle.zeros([], dtype='int32')] = 1.0
+            b[paddle.to_tensor(0)] = 1.0
 
             self.assertTrue(id(b) == id(c))
             np.testing.assert_array_equal(b.numpy(), c.numpy())

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

@@ -541,8 +541,8 @@ class TestSliceAPI(unittest.TestCase):
     def test_1(self):
         with paddle_static_guard():
             input = np.random.random([3, 4, 5, 6]).astype("float64")
-            minus_1 = paddle.tensor.fill_constant([], "int32", -1)
-            minus_3 = paddle.tensor.fill_constant([], "int64", -3)
+            minus_1 = paddle.tensor.fill_constant([1], "int32", -1)
+            minus_3 = paddle.tensor.fill_constant([1], "int64", -3)
             starts = paddle.static.data(
                 name='starts', shape=[1, 3], dtype="float32"
             )

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

@@ -604,7 +604,8 @@ class TestVarBase(unittest.TestCase):
 
         nw = w[1, 1, 1]
 
-        self.assertEqual(len(nw.shape), 0)
+        self.assertEqual(len(nw.shape), 1)
+        self.assertEqual(nw.shape[0], 1)
 
         nw = w[:, :, :-1]
         self.assertEqual((784, 100, 99), tuple(nw.shape))
@@ -704,10 +705,10 @@ class TestVarBase(unittest.TestCase):
 
         var = paddle.to_tensor(tensor_array)
 
-        one = paddle.ones(shape=[], dtype="int32")
-        two = paddle.full(shape=[], fill_value=2, dtype="int32")
-        negative_one = paddle.full(shape=[], fill_value=-1, dtype="int32")
-        four = paddle.full(shape=[], fill_value=4, dtype="int32")
+        one = paddle.ones(shape=[1], dtype="int32")
+        two = paddle.full(shape=[1], fill_value=2, dtype="int32")
+        negative_one = paddle.full(shape=[1], fill_value=-1, dtype="int32")
+        four = paddle.full(shape=[1], fill_value=4, dtype="int32")
 
         var = fluid.dygraph.to_variable(tensor_array)
         var1 = var[0, one, one]

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

@@ -132,7 +132,8 @@ class TestVariable(unittest.TestCase):
 
         nw = w[1, 1, 1]
 
-        self.assertEqual(len(nw.shape), 0)
+        self.assertEqual(len(nw.shape), 1)
+        self.assertEqual(nw.shape[0], 1)
 
         nw = w[:, :, :-1]
         self.assertEqual((784, 100, 99), nw.shape)

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

@@ -192,9 +192,9 @@ class TestOutputsMustExistsInputs(unittest.TestCase):
         with fluid.program_guard(main_program, startup_program):
 
             def func(x):
-                s = paddle.zeros([])
-                i = paddle.ones([])
-                max_len = paddle.shape(x)
+                s = paddle.zeros([1])
+                i = paddle.ones([1])
+                max_len = paddle.shape(x)[0]
 
                 def cond(i, s, x):
                     return i < max_len

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

@@ -580,140 +580,6 @@ class TestSundryAPI(unittest.TestCase):
         self.assertEqual(zero_dim_var.shape, [])
         self.assertEqual(zero_dim_var.item(), 0.5)
 
-    def test_getitem(self):
-        # case1: When all axis have a scalar indice, output should be a 0-d Tensor;
-        x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
-        x.stop_gradient = False
-        out = x[1, 2, 3, 4]
-        out.retain_grads()
-        out.backward()
-        self.assertEqual(out.shape, [])
-        np.testing.assert_allclose(out, np.array(119))
-        self.assertEqual(out.grad.shape, [])
-        np.testing.assert_allclose(out.grad, 1.0)
-        self.assertEqual(x.grad.shape, [2, 3, 4, 5])
-        x_grad_expected = np.zeros((2, 3, 4, 5))
-        x_grad_expected[1, 2, 3, 4] = 1.0
-        np.testing.assert_allclose(x.grad, x_grad_expected)
-
-        # case2: When one axis has a 0-d Tensor indice, the output should be same as int indice.
-        x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
-        out1 = x[1, 2]
-        out2 = x[
-            paddle.full([], 1, dtype='int32'), paddle.full([], 2, dtype='int32')
-        ]
-        np.testing.assert_allclose(out1, out2)
-
-        # case3: When all axis have a scalar indice (i.e. case1) and has None indice,
-        # ndim of output should be same with numbers of None.
-        x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
-        out1 = x[1, 2, None, 3, 4]
-        self.assertEqual(out1.shape, [1])
-        np.testing.assert_allclose(out1, np.array([119]))
-        out2 = x[1, None, 2, None, 3, 4]
-        self.assertEqual(out2.shape, [1, 1])
-        np.testing.assert_allclose(out2, np.array([[119]]))
-
-        # case4: 1-D Tensor will be treated as vector, no axis decrease will happen.
-        x = paddle.ones((2, 3, 4))
-        indice = paddle.ones([1], dtype='int32')
-        out1 = x[indice]
-        self.assertEqual(out1.shape, [1, 3, 4])
-        np.testing.assert_allclose(out1, np.ones((1, 3, 4)))
-        out2 = x[indice, indice]
-        self.assertEqual(out2.shape, [1, 4])
-        np.testing.assert_allclose(out2, np.ones((1, 4)))
-
-    def test_setitem(self):
-        # case1: all axis have a scalar indice
-        x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
-        x.stop_gradient = False
-        out = x * 2
-        out[1, 2, 3, 4] = 10
-        out.backward()
-
-        self.assertEqual(out.shape, x.shape)
-        np.testing.assert_allclose(out[1, 2, 3, 4], np.array(10))
-        self.assertEqual(x.grad.shape, [2, 3, 4, 5])
-        x_grad_expected = np.ones((2, 3, 4, 5)) * 2
-        x_grad_expected[1, 2, 3, 4] = 0
-        np.testing.assert_allclose(x.grad, x_grad_expected)
-
-        # case2: 0-D Tensor indice in some axis
-        # NOTE(zoooo0820): Now, int/slice with 0-D Tensor will still be
-        # treated as combined indexing, which is not support backward.
-        # There should have more test cases such as out[1, indice, :] = 0.5 when this
-        # problem is fixed.
-        x = paddle.randn((2, 3, 4, 5))
-        x.stop_gradient = False
-        indice = paddle.full([], 1, dtype='int32')
-        out = x * 1
-        out[indice, indice] = 0.5
-        out.backward()
-
-        self.assertEqual(out.shape, x.shape)
-        np.testing.assert_allclose(out[1, 1], np.ones((4, 5)) * 0.5)
-        x_grad_expected = np.ones((2, 3, 4, 5))
-        x_grad_expected[1, 1] = 0
-        np.testing.assert_allclose(x.grad, x_grad_expected)
-
-        # case3：0-D Tensor indice in some axis, value is a Tensor
-        # and there is broadcast
-        x = paddle.randn((2, 3, 4, 5))
-        x.stop_gradient = False
-        v = paddle.ones((4, 5), dtype='float32') * 5
-        v.stop_gradient = False
-        indice = paddle.full([], 1, dtype='int32')
-        out = x * 1
-        out[indice] = v
-        out.backward()
-
-        self.assertEqual(out.shape, x.shape)
-        np.testing.assert_allclose(out[1], np.ones((3, 4, 5)) * 5)
-        x_grad_expected = np.ones((2, 3, 4, 5))
-        x_grad_expected[1] = 0
-        np.testing.assert_allclose(x.grad, x_grad_expected)
-        value_grad_expected = np.ones((4, 5)) * 3
-        np.testing.assert_allclose(v.grad, value_grad_expected)
-
-        # case4: value is a 0-D tensor and there is broadcast
-        x = paddle.randn((2, 3, 4, 5))
-        x.stop_gradient = False
-        v = paddle.ones([], dtype='float32') * 5
-        v.stop_gradient = False
-        out = x * 1
-        indice = paddle.full([], 0, dtype='int32')
-        out[indice] = v
-        out.backward()
-
-        self.assertEqual(out.shape, x.shape)
-        self.assertEqual(v.grad.shape, [])
-        np.testing.assert_allclose(out[0], np.ones((3, 4, 5)) * 5)
-        x_grad_expected = np.ones((2, 3, 4, 5))
-        x_grad_expected[0] = 0
-        np.testing.assert_allclose(x.grad, x_grad_expected)
-        value_grad_expected = np.ones(()) * 3 * 4 * 5
-        np.testing.assert_allclose(v.grad, value_grad_expected)
-
-        # case5: indice / value is 0-D Tensor, and there is no broadcast
-        x = paddle.randn((2, 3, 4, 5))
-        x.stop_gradient = False
-        v = paddle.ones([], dtype='float32') * 2
-        v.stop_gradient = False
-        out = x * 1
-        indice = paddle.full([], 0, dtype='int32')
-        out[indice, indice, indice, indice] = v
-        out.backward()
-
-        self.assertEqual(out.shape, x.shape)
-        self.assertEqual(v.grad.shape, [])
-        np.testing.assert_allclose(out[0, 0, 0, 0], np.ones(()) * 2)
-        x_grad_expected = np.ones((2, 3, 4, 5))
-        x_grad_expected[0, 0, 0, 0] = 0
-        np.testing.assert_allclose(x.grad, x_grad_expected)
-        value_grad_expected = np.ones(())
-        np.testing.assert_allclose(v.grad, value_grad_expected)
-
     def test_expand(self):
         # case1
         x = paddle.full([], 1, 'float32')
@@ -2244,118 +2110,6 @@ class TestSundryAPIStatic(unittest.TestCase):
         self.assertEqual(res[0].shape, ())
         self.assertEqual(res[0], 0.5)
 
-    @prog_scope()
-    def test_getitem(self):
-        # case1: When all axis have a scalar indice, output should be a 0-d Tensor;
-        x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
-        x.stop_gradient = False
-        out = x[1, 2, 3, 4]
-        paddle.static.append_backward(out.sum())
-        prog = paddle.static.default_main_program()
-        res = self.exe.run(prog, fetch_list=[out, x.grad_name, out.grad_name])
-
-        self.assertEqual(res[0].shape, ())
-        np.testing.assert_allclose(res[0], np.array(119))
-        self.assertEqual(res[2].shape, ())
-        np.testing.assert_allclose(res[2], 1.0)
-        self.assertEqual(res[1].shape, (2, 3, 4, 5))
-        x_grad_expected = np.zeros((2, 3, 4, 5))
-        x_grad_expected[1, 2, 3, 4] = 1.0
-        np.testing.assert_allclose(res[1], x_grad_expected)
-
-        # case2: When one axis has a 0-d Tensor indice, the output should be same as int indice.
-        x2 = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
-        out1 = x2[1, 2]
-        out2 = x2[
-            paddle.full([], 1, dtype='int32'), paddle.full([], 2, dtype='int32')
-        ]
-        res = self.exe.run(prog, fetch_list=[out1, out2])
-        np.testing.assert_allclose(res[0], res[1])
-
-        # case3: When all axis have a scalar indice (i.e. case1) and has None indice,
-        # ndim of output should be same with numbers of None.
-        x3 = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
-        out3 = x3[1, 2, None, 3, 4]
-        out4 = x3[1, None, 2, None, 3, 4]
-        res = self.exe.run(prog, fetch_list=[out3, out4])
-        self.assertEqual(res[0].shape, (1,))
-        np.testing.assert_allclose(res[0], np.array([119]))
-        self.assertEqual(res[1].shape, (1, 1))
-        np.testing.assert_allclose(res[1], np.array([[119]]))
-
-        # case4: 1-D Tensor will be treated as vector, no axis decrease will happen.
-        x4 = paddle.ones((2, 3, 4))
-        indice = paddle.ones([1], dtype='int32')
-        out5 = x4[indice]
-        out6 = x4[indice, indice]
-        res = self.exe.run(prog, fetch_list=[out5, out6])
-
-        self.assertEqual(res[0].shape, (1, 3, 4))
-        np.testing.assert_allclose(res[0], np.ones((1, 3, 4)))
-        self.assertEqual(res[1].shape, (1, 4))
-        np.testing.assert_allclose(res[1], np.ones((1, 4)))
-
-    @prog_scope()
-    def test_setitem(self):
-        # NOTE(zoooo0820): __setitem__ has gradient problem in static graph.
-        # To solve this, we may not support __setitem__ in static graph.
-        # These unit tests will delete soon.
-
-        # case1: all axis have a scalar indice
-        x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
-        x.stop_gradient = False
-        out = x * 2
-        out[1, 2, 3, 4] = 10
-        paddle.static.append_backward(out.sum())
-        prog = paddle.static.default_main_program()
-        res = self.exe.run(prog, fetch_list=[out, x.grad_name])
-
-        self.assertEqual(out.shape, x.shape)
-        np.testing.assert_allclose(res[0][1, 2, 3, 4], np.array(10))
-        self.assertEqual(res[1].shape, (2, 3, 4, 5))
-        x_grad_expected = np.ones((2, 3, 4, 5)) * 2
-        x_grad_expected[1, 2, 3, 4] = 0
-        np.testing.assert_allclose(res[1], x_grad_expected)
-
-        # case2: 0-D Tensor indice in some axis
-        # NOTE(zoooo0820): Now, int/slice with 0-D Tensor will still be
-        # treated as combined indexing, which is not support backward.
-        # There should have more test cases such as out[1, indice, :] = 0.5 when this
-        # problem is fixed.
-        x = paddle.randn((2, 3, 4, 5))
-        x.stop_gradient = False
-        indice = paddle.full([], 1, dtype='int32')
-        out = x * 1
-        out[indice, indice] = 0.5
-        paddle.static.append_backward(out.sum())
-        prog = paddle.static.default_main_program()
-        res = self.exe.run(prog, fetch_list=[out, x.grad_name])
-
-        self.assertEqual(out.shape, x.shape)
-        np.testing.assert_allclose(res[0][1, 1], np.ones((4, 5)) * 0.5)
-        x_grad_expected = np.ones((2, 3, 4, 5))
-        x_grad_expected[1, 1] = 0
-        np.testing.assert_allclose(res[1], x_grad_expected)
-
-        # case3：0-D Tensor indice in some axis, value is a Tensor
-        # and there is broadcast
-        x = paddle.randn((2, 3, 4, 5))
-        x.stop_gradient = False
-        v = paddle.ones((4, 5), dtype='float32') * 5
-        v.stop_gradient = False
-        indice = paddle.full([], 1, dtype='int32')
-        out = x * 1
-        out[indice] = v
-        paddle.static.append_backward(out.sum())
-        prog = paddle.static.default_main_program()
-        res = self.exe.run(prog, fetch_list=[out, x.grad_name, v.grad_name])
-
-        self.assertEqual(out.shape, x.shape)
-        np.testing.assert_allclose(res[0][1], np.ones((3, 4, 5)) * 5)
-        x_grad_expected = np.ones((2, 3, 4, 5))
-        x_grad_expected[1] = 0
-        np.testing.assert_allclose(res[1], x_grad_expected)
-
     @prog_scope()
     def test_expand(self):
         x = paddle.full([], 1, 'float32')

python/paddle/fluid/variable_index.py

@@ -282,7 +282,7 @@ def is_integer_or_scalar_tensor(ele):
     if isinstance(ele, int):
         return True
     elif isinstance(ele, Variable):
-        if len(ele.shape) == 0:
+        if len(ele.shape) == 1 and ele.shape[0] == 1:
             return True
     return False
 
@@ -573,6 +573,15 @@ def _getitem_impl_(var, item):
 
         out = reverse(out, axis=reverse_axes)
 
+    # Deal with cases when all axes are decreased.
+    # After slice, the shape of out is [1], which should have been [], but Paddle doesn't support scalar.
+    # In order to ensure the correctness of the final shape of out, one dimension of out needs to be decreased.
+    # For example:
+    # # x.shape: (2,3,4)
+    # out = x[0, 1, 1, None] # out.shape : (1)
+    if len(decrease_axes) == len(var.shape):
+        none_axes = none_axes[1:]
+
     if len(none_axes) > 0:
         # Deal with cases that decrease_axes is not empty
         # For example:
@@ -583,6 +592,13 @@ def _getitem_impl_(var, item):
             new_axis = axis - l
             none_axes[idx] = new_axis
 
+        # Deal with cases when all axes are decreased.
+        # After slice, the shape of out is [1], which should have been [], but Paddle doesn't support scalar.
+        # In order to ensure the correctness of the final shape of out, one dimension of out needs to be decreased.
+        # For example:
+        # # x.shape: (2,3,4)
+        # out = x[0, 1, 1, None] # out.shape : (1)
+
         from ..tensor import unsqueeze
 
         out = unsqueeze(out, axis=none_axes)

python/paddle/incubate/optimizer/functional/lbfgs.py

@@ -125,7 +125,9 @@ def minimize_lbfgs(
     is_converge = paddle.full(shape=[1], fill_value=False, dtype='bool')
     num_func_calls = paddle.full(shape=[1], fill_value=1, dtype='int64')
 
-    history_size = paddle.full(shape=[], fill_value=history_size, dtype='int64')
+    history_size = paddle.full(
+        shape=[1], fill_value=history_size, dtype='int64'
+    )
     head = paddle.full(shape=[1], fill_value=1, dtype='int64')
     tail = paddle.full(shape=[1], fill_value=0, dtype='int64')
 
@@ -175,7 +177,7 @@ def minimize_lbfgs(
         q = paddle.assign(g1)
         # In a array circle, the index may out of range, so must use mod.
         i = paddle.full(
-            shape=[], fill_value=(head - 1).mod(history_size), dtype='int64'
+            shape=[1], fill_value=(head - 1).mod(history_size), dtype='int64'
         )
 
         def cond(i, q):
@@ -191,7 +193,7 @@ def minimize_lbfgs(
 
         r = paddle.matmul(H0, q)
 
-        i = paddle.full(shape=[], fill_value=tail + 1, dtype='int64')
+        i = paddle.full(shape=[1], fill_value=tail + 1, dtype='int64')
 
         def cond(i, r):
             return i != head

python/paddle/jit/dy2static/variable_trans_func.py

@@ -51,11 +51,11 @@ def to_static_variable(x):
     Translate a Python Tensor to PaddlePaddle static graph Tensor
     '''
     if isinstance(x, bool):
-        return paddle.full(shape=[], dtype='bool', fill_value=x)
+        return paddle.full(shape=[1], dtype='bool', fill_value=x)
     if isinstance(x, float):
-        return paddle.full(shape=[], dtype='float64', fill_value=x)
+        return paddle.full(shape=[1], dtype='float64', fill_value=x)
     if isinstance(x, int):
-        return paddle.full(shape=[], dtype='int64', fill_value=x)
+        return paddle.full(shape=[1], dtype='int64', fill_value=x)
     if isinstance(x, UndefinedVar) or x is None:
         """
         for early return case, we need a variable to represent None, current we use data_layer_not_check.

python/paddle/nn/layer/rnn.py

@@ -271,7 +271,7 @@ def _rnn_static_graph(
         mask = paddle.reverse(mask, axis=[0]) if sequence_length else None
 
     with paddle.fluid.framework.device_guard("cpu"):
-        start_i = paddle.zeros([], dtype="int64")
+        start_i = paddle.zeros([1], dtype="int64")
         end = max_seq_len
 
         end = paddle.cast(end, "int64")

python/paddle/tensor/manipulation.py

@@ -3160,19 +3160,19 @@ def tile(x, repeat_times, name=None):
         )
         if isinstance(repeat_times, Variable):
             assert (
-                repeat_times.numel() == 1
-            ), 'repeat_times must be a Tensor with one element.'
+                len(repeat_times.shape) == 1
+            ), 'repeat_times must be an 1-D Tensor.'
         else:
             for elem in repeat_times:
                 if isinstance(elem, Variable):
                     assert (
-                        elem.numel() == 1
-                    ), 'Elements in repeat_times must be Tensor with one element or integers.'
+                        len(elem.shape) == 1
+                    ), 'Elements in repeat_times must be 1-D Tensors or integers.'
                 else:
                     type_tuple = (int, np.int32, np.int64)
                     assert isinstance(
                         elem, type_tuple
-                    ), 'Elements in repeat_times must be Tensor with one element or integers.'
+                    ), 'Elements in repeat_times must be 1-D Tensors or integers.'
 
         check_variable_and_dtype(
             x,
@@ -3416,18 +3416,18 @@ def expand(x, shape, name=None):
         return _C_ops.expand(x, shape)
     else:
         if isinstance(shape, Variable):
-            assert shape.numel() == 1, 'shape must be a Tensor with one element'
+            assert len(shape.shape) == 1, 'shape must be an 1-D Tensor.'
         else:
             for elem in shape:
                 if isinstance(elem, Variable):
                     assert (
-                        elem.numel() == 1
-                    ), 'Elements in shape must be Tensor with one element or integers.'
+                        len(elem.shape) == 1
+                    ), 'Elements in shape must be 1-D Tensors or integers.'
                 else:
                     type_tuple = (int, np.int32, np.int64)
                     assert isinstance(
                         elem, type_tuple
-                    ), 'Elements in shape must be Tensor with one element or integers.'
+                    ), 'Elements in shape must be 1-D Tensors or integers.'
 
         check_variable_and_dtype(
             x,

test/dygraph_to_static/test_list.py

@@ -364,7 +364,7 @@ class TestListWithCondGradInferVarType(unittest.TestCase):
         x = paddle.to_tensor([2, 3, 4], dtype='float32')
         index = paddle.to_tensor([1])
         res = net(x, index)
-        self.assertEqual(res, 48.0)
+        self.assertEqual(res[0], 48.0)
 
 
 if __name__ == '__main__':

test/xpu/test_set_value_op_xpu.py

@@ -1432,7 +1432,7 @@ class XPUTestSetValueOp(XPUOpTestWrapper):
                 a.stop_gradient = False
                 b = a[:]
                 c = b
-                b[paddle.zeros([], dtype='int32')] = 1.0
+                b[paddle.to_tensor(0)] = 1.0
 
                 self.assertTrue(id(b) == id(c))
                 np.testing.assert_array_equal(b.numpy(), c.numpy())

test/xpu/test_slice_op_xpu.py

@@ -166,7 +166,7 @@ class XPUTestSliceOp_decs_dim(XPUOpTestWrapper):
             self.starts = [0, 1, 2, 3]
             self.ends = [1, 2, 3, 4]
             self.axes = [0, 1, 2, 3]
-            self.decrease_axis = [0, 1, 2]
+            self.decrease_axis = [0, 1, 2, 3]
             self.infer_flags = [1, 1, 1]
             self.out = self.input[0, 1, 2, 3:4]
 
@@ -188,7 +188,7 @@ class XPUTestSliceOp_decs_dim(XPUOpTestWrapper):
             self.axes = [0, 1, 2, 3]
             self.decrease_axis = [0, 1, 2, 3]
             self.infer_flags = [1, 1, 1]
-            self.out = self.input[0, 1, 2, 3]
+            self.out = self.input[0, 1, 2, 3:4]
 
 
 support_types = get_xpu_op_support_types('slice')

test/xpu/test_zero_dim_tensor_xpu.py

@@ -344,140 +344,6 @@ class TestSundryAPI(unittest.TestCase):
         paddle.disable_static()
         self.x = paddle.rand([])
 
-    def test_getitem(self):
-        # case1: When all axis have a scalar indice, output should be a 0-d Tensor;
-        x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
-        x.stop_gradient = False
-        out = x[1, 2, 3, 4]
-        out.retain_grads()
-        out.backward()
-        self.assertEqual(out.shape, [])
-        np.testing.assert_allclose(out, np.array(119))
-        self.assertEqual(out.grad.shape, [])
-        np.testing.assert_allclose(out.grad, 1.0)
-        self.assertEqual(x.grad.shape, [2, 3, 4, 5])
-        x_grad_expected = np.zeros((2, 3, 4, 5))
-        x_grad_expected[1, 2, 3, 4] = 1.0
-        np.testing.assert_allclose(x.grad, x_grad_expected)
-
-        # case2: When one axis has a 0-d Tensor indice, the output should be same as int indice.
-        x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
-        out1 = x[1, 2]
-        out2 = x[
-            paddle.full([], 1, dtype='int32'), paddle.full([], 2, dtype='int32')
-        ]
-        np.testing.assert_allclose(out1, out2)
-
-        # case3: When all axis have a scalar indice (i.e. case1) and has None indice,
-        # ndim of output should be same with numbers of None.
-        x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
-        out1 = x[1, 2, None, 3, 4]
-        self.assertEqual(out1.shape, [1])
-        np.testing.assert_allclose(out1, np.array([119]))
-        out2 = x[1, None, 2, None, 3, 4]
-        self.assertEqual(out2.shape, [1, 1])
-        np.testing.assert_allclose(out2, np.array([[119]]))
-
-        # case4: 1-D Tensor will be treated as vector, no axis decrease will happen.
-        x = paddle.ones((2, 3, 4))
-        indice = paddle.ones([1], dtype='int32')
-        out1 = x[indice]
-        self.assertEqual(out1.shape, [1, 3, 4])
-        np.testing.assert_allclose(out1, np.ones((1, 3, 4)))
-        out2 = x[indice, indice]
-        self.assertEqual(out2.shape, [1, 4])
-        np.testing.assert_allclose(out2, np.ones((1, 4)))
-
-    def test_setitem(self):
-        # case1: all axis have a scalar indice
-        x = paddle.arange(2 * 3 * 4 * 5).reshape((2, 3, 4, 5))
-        x.stop_gradient = False
-        out = x * 2
-        out[1, 2, 3, 4] = 10
-        out.backward()
-
-        self.assertEqual(out.shape, x.shape)
-        np.testing.assert_allclose(out[1, 2, 3, 4], np.array(10))
-        self.assertEqual(x.grad.shape, [2, 3, 4, 5])
-        x_grad_expected = np.ones((2, 3, 4, 5)) * 2
-        x_grad_expected[1, 2, 3, 4] = 0
-        np.testing.assert_allclose(x.grad, x_grad_expected)
-
-        # case2: 0-D Tensor indice in some axis
-        # NOTE(zoooo0820): Now, int/slice with 0-D Tensor will still be
-        # treated as combined indexing, which is not support backward.
-        # There should have more test cases such as out[1, indice, :] = 0.5 when this
-        # problem is fixed.
-        x = paddle.randn((2, 3, 4, 5))
-        x.stop_gradient = False
-        indice = paddle.full([], 1, dtype='int32')
-        out = x * 1
-        out[indice, indice] = 0.5
-        out.backward()
-
-        self.assertEqual(out.shape, x.shape)
-        np.testing.assert_allclose(out[1, 1], np.ones((4, 5)) * 0.5)
-        x_grad_expected = np.ones((2, 3, 4, 5))
-        x_grad_expected[1, 1] = 0
-        np.testing.assert_allclose(x.grad, x_grad_expected)
-
-        # case3：0-D Tensor indice in some axis, value is a Tensor
-        # and there is broadcast
-        x = paddle.randn((2, 3, 4, 5))
-        x.stop_gradient = False
-        v = paddle.ones((4, 5), dtype='float32') * 5
-        v.stop_gradient = False
-        indice = paddle.full([], 1, dtype='int32')
-        out = x * 1
-        out[indice] = v
-        out.backward()
-
-        self.assertEqual(out.shape, x.shape)
-        np.testing.assert_allclose(out[1], np.ones((3, 4, 5)) * 5)
-        x_grad_expected = np.ones((2, 3, 4, 5))
-        x_grad_expected[1] = 0
-        np.testing.assert_allclose(x.grad, x_grad_expected)
-        value_grad_expected = np.ones((4, 5)) * 3
-        np.testing.assert_allclose(v.grad, value_grad_expected)
-
-        # case4: value is a 0-D tensor and there is broadcast
-        x = paddle.randn((2, 3, 4, 5))
-        x.stop_gradient = False
-        v = paddle.ones([], dtype='float32') * 5
-        v.stop_gradient = False
-        out = x * 1
-        indice = paddle.full([], 0, dtype='int32')
-        out[indice] = v
-        out.backward()
-
-        self.assertEqual(out.shape, x.shape)
-        self.assertEqual(v.grad.shape, [])
-        np.testing.assert_allclose(out[0], np.ones((3, 4, 5)) * 5)
-        x_grad_expected = np.ones((2, 3, 4, 5))
-        x_grad_expected[0] = 0
-        np.testing.assert_allclose(x.grad, x_grad_expected)
-        value_grad_expected = np.ones(()) * 3 * 4 * 5
-        np.testing.assert_allclose(v.grad, value_grad_expected)
-
-        # case5: indice / value is 0-D Tensor, and there is no broadcast
-        x = paddle.randn((2, 3, 4, 5))
-        x.stop_gradient = False
-        v = paddle.ones([], dtype='float32') * 2
-        v.stop_gradient = False
-        out = x * 1
-        indice = paddle.full([], 0, dtype='int32')
-        out[indice, indice, indice, indice] = v
-        out.backward()
-
-        self.assertEqual(out.shape, x.shape)
-        self.assertEqual(v.grad.shape, [])
-        np.testing.assert_allclose(out[0, 0, 0, 0], np.ones(()) * 2)
-        x_grad_expected = np.ones((2, 3, 4, 5))
-        x_grad_expected[0, 0, 0, 0] = 0
-        np.testing.assert_allclose(x.grad, x_grad_expected)
-        value_grad_expected = np.ones(())
-        np.testing.assert_allclose(v.grad, value_grad_expected)
-
     def test_expand(self):
         # case1
         x = paddle.full([], 1, 'float32')