remove eye (#48127)

python/paddle/fluid/layers/tensor.py

@@ -68,7 +68,6 @@ __all__ = [
     'zeros_like',
     'ones_like',
     'diag',
-    'eye',
     'triu',
 ]
 
@@ -1787,113 +1786,6 @@ def diag(diagonal):
     return out
 
 
-def eye(
-    num_rows, num_columns=None, batch_shape=None, dtype='float32', name=None
-):
-    """
-    This function constructs a or a batch of 2-D tensor with ones on the diagonal and zeros elsewhere.
-
-    Args:
-        num_rows(int): the number of rows in each batch tensor.
-        num_columns(int, optional): the number of columns in each batch tensor.
-            If None, default: num_rows.
-        batch_shape(list, optional): If provided, the returned tensor will have a leading
-            batch size of this shape, the data type of ``batch_shape`` is int. Default is None.
-        dtype(np.dtype|str, optional): The data type of the returned tensor.
-            It should be int32, int64, float16, float32, float64, default is 'float32'.
-        name(str, optional): The default value is None. Normally there is no
-            need for user to set this property. For more information, please
-            refer to :ref:`api_guide_Name`.
-
-    Returns:
-        Tensor: An identity Tensor or LoDTensor of shape batch_shape + [num_rows, num_columns].
-
-    Examples:
-        .. code-block:: python
-
-          import paddle.fluid as fluid
-          data = fluid.layers.eye(3, dtype='int32')
-          # [[1, 0, 0]
-          #  [0, 1, 0]
-          #  [0, 0, 1]]
-
-          data = fluid.layers.eye(2, 3, dtype='int32')
-          # [[1, 0, 0]
-          #  [0, 1, 0]]
-
-          data = fluid.layers.eye(2, batch_shape=[3])
-          # Construct a batch of 3 identity tensors, each 2 x 2.
-          # data[i, :, :] is a 2 x 2 identity tensor, i = 0, 1, 2.
-
-    """
-
-    def _check_attr(attr, message):
-        if isinstance(attr, ((Variable, core.VarBase, core.eager.Tensor))):
-            assert len(attr.shape) == 1 and attr.shape[0] in [1, -1]
-        elif not isinstance(attr, int) or attr < 0:
-            raise TypeError("{} should be a non-negative int.".format(message))
-
-    _check_attr(num_rows, "num_rows")
-    if not isinstance(dtype, core.VarDesc.VarType):
-        dtype = convert_np_dtype_to_dtype_(dtype)
-    if num_columns is not None:
-        _check_attr(num_columns, "num_columns")
-    else:
-        num_columns = num_rows
-
-    if in_dygraph_mode():
-        out = _C_ops.eye(
-            num_rows, num_columns, dtype, _current_expected_place()
-        )
-    elif _in_legacy_dygraph():
-        out = _legacy_C_ops.eye(
-            'dtype', dtype, 'num_rows', num_rows, 'num_columns', num_columns
-        )
-    else:
-        helper = LayerHelper("eye", **locals())
-        check_dtype(
-            dtype,
-            'dtype',
-            ['float16', 'float32', 'float64', 'int32', 'int64'],
-            'eye',
-        )
-        out = helper.create_variable_for_type_inference(dtype=dtype)
-        helper.append_op(
-            type='eye',
-            inputs={},
-            outputs={'Out': [out]},
-            attrs={
-                'num_rows': num_rows,
-                'num_columns': num_columns,
-                'dtype': dtype,
-            },
-            stop_gradient=True,
-        )
-
-    if batch_shape is not None:
-        re_shape = [1] * len(batch_shape)
-        re_shape = re_shape + [num_rows, num_columns]
-        expand_times = batch_shape + [1, 1]
-        if _non_static_mode():
-            out, _ = _legacy_C_ops.reshape2(out, None, 'shape', re_shape)
-            return _legacy_C_ops.expand(out, None, 'expand_times', expand_times)
-
-        if not isinstance(batch_shape, list):
-            raise TypeError("batch_shape should be a list")
-        for batch_val in batch_shape:
-            if batch_val <= 0:
-                raise TypeError("batch_shape should be a positive int list")
-
-        from .nn import expand
-        from paddle import reshape
-
-        out = reshape(x=out, shape=re_shape)
-        out = expand(x=out, expand_times=expand_times)
-
-    out.stop_gradient = True
-    return out
-
-
 def ones_like(x, out=None):
     """
     **ones_like**

python/paddle/fluid/tests/unittests/npu/test_eye_op_npu.py

@@ -139,39 +139,6 @@ class API_TestTensorEye(unittest.TestCase):
         paddle.enable_static()
         self.assertEqual((out.numpy() == expected_result).all(), True)
 
-        paddle.disable_static(paddle.NPUPlace(0))
-        batch_shape = [2]
-        out = fluid.layers.eye(10, 10, dtype="int32", batch_shape=batch_shape)
-        result = np.eye(10, dtype="int32")
-        expected_result = []
-        for index in reversed(batch_shape):
-            tmp_result = []
-            for i in range(index):
-                tmp_result.append(result)
-            result = tmp_result
-            expected_result = np.stack(result, axis=0)
-        paddle.enable_static()
-        self.assertEqual(
-            out.numpy().shape == np.array(expected_result).shape, True
-        )
-        self.assertEqual((out.numpy() == expected_result).all(), True)
-
-        paddle.disable_static(paddle.NPUPlace(0))
-        batch_shape = [3, 2]
-        out = fluid.layers.eye(10, 10, dtype="int32", batch_shape=batch_shape)
-        result = np.eye(10, dtype="int32")
-        expected_result = []
-        for index in reversed(batch_shape):
-            tmp_result = []
-            for i in range(index):
-                tmp_result.append(result)
-            result = tmp_result
-            expected_result = np.stack(result, axis=0)
-        paddle.enable_static()
-        self.assertEqual(
-            out.numpy().shape == np.array(expected_result).shape, True
-        )
-        self.assertEqual((out.numpy() == expected_result).all(), True)
 
     def test_errors(self):
         with paddle.static.program_guard(paddle.static.Program()):

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

@@ -109,40 +109,6 @@ class API_TestTensorEye(unittest.TestCase):
         paddle.enable_static()
         self.assertEqual((out.numpy() == expected_result).all(), True)
 
-        paddle.disable_static()
-        batch_shape = [2]
-        out = fluid.layers.eye(10, 10, dtype="int64", batch_shape=batch_shape)
-        result = np.eye(10, dtype="int64")
-        expected_result = []
-        for index in reversed(batch_shape):
-            tmp_result = []
-            for i in range(index):
-                tmp_result.append(result)
-            result = tmp_result
-            expected_result = np.stack(result, axis=0)
-        paddle.enable_static()
-        self.assertEqual(
-            out.numpy().shape == np.array(expected_result).shape, True
-        )
-        self.assertEqual((out.numpy() == expected_result).all(), True)
-
-        paddle.disable_static()
-        batch_shape = [3, 2]
-        out = fluid.layers.eye(10, 10, dtype="int64", batch_shape=batch_shape)
-        result = np.eye(10, dtype="int64")
-        expected_result = []
-        for index in reversed(batch_shape):
-            tmp_result = []
-            for i in range(index):
-                tmp_result.append(result)
-            result = tmp_result
-            expected_result = np.stack(result, axis=0)
-        paddle.enable_static()
-        self.assertEqual(
-            out.numpy().shape == np.array(expected_result).shape, True
-        )
-        self.assertEqual((out.numpy() == expected_result).all(), True)
-
     def test_errors(self):
         with paddle.static.program_guard(paddle.static.Program()):
 
@@ -212,18 +178,6 @@ class TestEyeRowsCol(UnittestBase):
             paddle.eye(-1)
 
 
-class TestEyeRowsCol2(TestEyeRowsCol):
-    def call_func(self, x):
-        rows = paddle.assign(3)
-        cols = paddle.assign(10)
-        out = paddle.fluid.layers.eye(rows, cols)
-        return out
-
-    def test_error(self):
-        with self.assertRaises(TypeError):
-            paddle.fluid.layers.eye(-1)
-
-
 if __name__ == "__main__":
     paddle.enable_static()
     unittest.main()

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

@@ -2407,70 +2407,6 @@ class TestLayer(LayerTest):
                 conv3d1.bias.numpy(), conv3d2.bias.numpy()
             )
 
-    def test_eye_op(self):
-        np_eye = np.eye(3, 2)
-        array_rlt1 = [np_eye for _ in range(3)]
-        stack_rlt1 = np.stack(array_rlt1, axis=0)
-        array_rlt2 = [stack_rlt1 for _ in range(4)]
-        stack_rlt2 = np.stack(array_rlt2, axis=0)
-
-        with self.dynamic_graph():
-            with _test_eager_guard():
-                eager_eye_tensor = layers.eye(num_rows=3, num_columns=2)
-                eager_eye_tensor_rlt1 = layers.eye(
-                    num_rows=3, num_columns=2, batch_shape=[3]
-                )
-                eager_eye_tensor_rlt2 = layers.eye(
-                    num_rows=3, num_columns=2, batch_shape=[4, 3]
-                )
-                eager_diag_tensor = layers.eye(20)
-                eager_eye_tensor_value = eager_eye_tensor.numpy()
-                eager_eye_tensor_rlt1_value = eager_eye_tensor_rlt1.numpy()
-                eager_eye_tensor_rlt2_value = eager_eye_tensor_rlt2.numpy()
-                eager_diag_tensor_value = eager_diag_tensor.numpy()
-
-            eye_tensor = layers.eye(num_rows=3, num_columns=2)
-            eye_tensor_rlt1 = layers.eye(
-                num_rows=3, num_columns=2, batch_shape=[3]
-            )
-            eye_tensor_rlt2 = layers.eye(
-                num_rows=3, num_columns=2, batch_shape=[4, 3]
-            )
-            diag_tensor = layers.eye(20)
-            eye_tensor_value = eye_tensor.numpy()
-            eye_tensor_rlt1_value = eye_tensor_rlt1.numpy()
-            eye_tensor_rlt2_value = eye_tensor_rlt2.numpy()
-            diag_tensor_value = diag_tensor.numpy()
-
-        np.testing.assert_allclose(eager_eye_tensor_value, np_eye, rtol=1e-05)
-        np.testing.assert_allclose(
-            eager_eye_tensor_rlt1_value, stack_rlt1, rtol=1e-05
-        )
-        np.testing.assert_allclose(
-            eager_eye_tensor_rlt2_value, stack_rlt2, rtol=1e-05
-        )
-        np.testing.assert_allclose(
-            eager_diag_tensor_value, np.eye(20), rtol=1e-05
-        )
-
-        np.testing.assert_allclose(eye_tensor_value, np_eye, rtol=1e-05)
-        np.testing.assert_allclose(
-            eye_tensor_rlt1_value, stack_rlt1, rtol=1e-05
-        )
-        np.testing.assert_allclose(
-            eye_tensor_rlt2_value, stack_rlt2, rtol=1e-05
-        )
-        np.testing.assert_allclose(diag_tensor_value, np.eye(20), rtol=1e-05)
-
-        with self.assertRaises(TypeError):
-            layers.eye(num_rows=3.1)
-        with self.assertRaises(TypeError):
-            layers.eye(num_rows=3, num_columns=2.2)
-        with self.assertRaises(TypeError):
-            layers.eye(num_rows=3, batch_shape=2)
-        with self.assertRaises(TypeError):
-            layers.eye(num_rows=3, batch_shape=[-1])
-
     def func_while_loop(self):
         with self.static_graph():
             i = layers.fill_constant(shape=[1], dtype='int64', value=0)