[API compatibility] add Alias : paddle.unique_consecutive, paddle.embedding, paddle. ones_like, paddle.repeat_interleave, paddle.var, paddle. take_along_axis (#74490)

* add alias : unique_consecutive, embedding

* add alias : ones_like, repeat_interleave, var

Author: zhengshengning

python/paddle/nn/functional/input.py

@@ -17,6 +17,7 @@
 
 import paddle
 from paddle import _C_ops
+from paddle.utils.decorator_utils import ParamAliasDecorator
 
 from ...base.data_feeder import check_variable_and_dtype
 from ...base.layer_helper import LayerHelper
@@ -161,6 +162,7 @@ def embedding_renorm_(
         return weight
 
 
+@ParamAliasDecorator({"x": ["input"]})
 def embedding(
     x: Tensor,
     weight: Tensor,

python/paddle/tensor/creation.py

@@ -1327,6 +1327,7 @@ def ones(
     )
 
 
+@ParamAliasDecorator({"x": ["input"]})
 def ones_like(
     x: paddle.Tensor,
     dtype: DTypeLike | None = None,

python/paddle/tensor/manipulation.py

@@ -3463,6 +3463,7 @@ def squeeze_(
         return _C_ops.squeeze_(input, axes)
 
 
+@ParamAliasDecorator({"x": ["input"], "axis": ["dim"]})
 def unique_consecutive(
     x: Tensor,
     return_inverse: bool = False,
@@ -6288,6 +6289,7 @@ def as_real(x: Tensor, name: str | None = None) -> Tensor:
         return out
 
 
+@ParamAliasDecorator({"x": ["input"], "axis": ["dim"]})
 def repeat_interleave(
     x: Tensor,
     repeats: int | Tensor,
@@ -6690,6 +6692,7 @@ def infer_broadcast_shape(
     return broadcast_shape
 
 
+@ParamAliasDecorator({"arr": ["input"], "axis": ["dim"]})
 def take_along_axis(
     arr: Tensor, indices: Tensor, axis: int, broadcast: bool = True
 ) -> Tensor:

python/paddle/tensor/stat.py

@@ -25,6 +25,7 @@
     in_dynamic_mode,
     in_dynamic_or_pir_mode,
 )
+from paddle.utils.decorator_utils import ParamAliasDecorator
 
 from ..base.data_feeder import check_type, check_variable_and_dtype
 from ..common_ops_import import Variable
@@ -149,6 +150,7 @@ def mean(
         return out
 
 
+@ParamAliasDecorator({"x": ["input"], "axis": ["dim"]})
 def var(
     x: Tensor,
     axis: int | Sequence[int] | None = None,

test/auto_parallel/semi_auto_parallel_for_embedding.py

@@ -57,6 +57,14 @@ def test_body(self, x_shape, w_shape, x_placements, w_placements):
         dist_out.backward()
         self.check_tensor_eq(w.grad, dist_w.grad)
 
+        out = paddle.nn.functional.embedding(input=x, weight=w)
+        dist_out = paddle.nn.functional.embedding(input=dist_x, weight=dist_w)
+        self.check_tensor_eq(out, dist_out)
+
+        out.backward()
+        dist_out.backward()
+        self.check_tensor_eq(w.grad, dist_w.grad)
+
         return dist_out, dist_w.grad
 
     def test_non_shard(self):

test/legacy_test/test_unique_consecutive_op.py

@@ -232,6 +232,13 @@ def test_dygraph(self):
                 x = paddle.to_tensor(input_x)
                 result = paddle.unique_consecutive(x)
 
+    def test_dygraph_alias(self):
+        for place in self.places:
+            with base.dygraph.guard(place):
+                input_x = np.random.randint(20, size=100).astype("float64")
+                x = paddle.to_tensor(input_x)
+                result = paddle.unique_consecutive(input=x)
+
 
 class TestUniqueConsecutiveCase2API(unittest.TestCase):
     def setUp(self):
@@ -299,9 +306,32 @@ def check_static_result(self, place):
                 fetch_list=[result],
             )
 
+    def check_static_result_alias(self, place):
+        with paddle.static.program_guard(
+            paddle.static.Program(), paddle.static.Program()
+        ):
+            paddle.enable_static()
+            input_x = paddle.static.data(
+                name="input_x",
+                shape=[
+                    100,
+                ],
+                dtype="float32",
+            )
+            result, inverse, counts = paddle.unique_consecutive(
+                input=input_x, return_inverse=True, return_counts=True, axis=-1
+            )
+            x_np = np.random.randint(20, size=100).astype("float32")
+            exe = base.Executor(place)
+            fetches = exe.run(
+                feed={"input_x": x_np},
+                fetch_list=[result],
+            )
+
     def test_static(self):
         for place in self.places:
             self.check_static_result(place=place)
+            self.check_static_result_alias(place=place)
 
     def test_dygraph(self):
         for place in self.places:

test/legacy_test/test_zero_dim_no_backward_api.py

@@ -175,6 +175,18 @@ def test_embedding(self):
         for i in range(len(res)):
             self.assertEqual(emb.numpy()[i], res[i])
 
+    def test_embedding_alias(self):
+        ids = paddle.full(shape=[], fill_value=1, dtype='int64')
+        w0 = paddle.arange(3, 9).reshape((3, 2)).astype(paddle.float32)
+        w = paddle.to_tensor(w0, stop_gradient=False)
+        emb = paddle.nn.functional.embedding(
+            input=ids, weight=w, sparse=True, name="embedding"
+        )
+        self.assertEqual(emb.shape, [2])
+        res = [5.0, 6.0]
+        for i in range(len(res)):
+            self.assertEqual(emb.numpy()[i], res[i])
+
     def test_one_hot_label(self):
         label = paddle.full(shape=[], fill_value=2, dtype='int64')
         one_hot_label = paddle.nn.functional.one_hot(label, num_classes=4)

test/xpu/test_zero_dim_tensor_xpu.py

@@ -2673,6 +2673,18 @@ def test_embedding(self):
         for i in range(len(res)):
             self.assertEqual(emb.numpy()[i], res[i])
 
+    def test_embedding_alias(self):
+        ids = paddle.full(shape=[], fill_value=1, dtype='int64')
+        w0 = paddle.arange(3, 9).reshape((3, 2)).astype(paddle.float32)
+        w = paddle.to_tensor(w0, stop_gradient=False)
+        emb = paddle.nn.functional.embedding(
+            input=ids, weight=w, sparse=True, name="embedding"
+        )
+        self.assertEqual(emb.shape, [2])
+        res = [5.0, 6.0]
+        for i in range(len(res)):
+            self.assertEqual(emb.numpy()[i], res[i])
+
     def test_one_hot_label(self):
         label = paddle.full(shape=[], fill_value=2, dtype='int64')
         one_hot_label = paddle.nn.functional.one_hot(label, num_classes=4)