【Zero-Dim】Support Zero dim for embedding and one-hot (#49562)

* zero-tensor

* remove unused

* zero_dim_xpu

* relocate

* add value test

* fix syntax

paddle/phi/infermeta/unary.cc

@@ -2257,8 +2257,8 @@ void OneHotRawInferMeta(const MetaTensor& x,
   auto x_dims = x.dims();
   PADDLE_ENFORCE_GE(
       x_dims.size(),
-      1,
-      phi::errors::InvalidArgument("Rank of Input(X) should be at least 1."));
+      0,
+      phi::errors::InvalidArgument("Rank of Input(X) should be at least 0."));
   auto out_dims_vec = phi::vectorize(x_dims);
   out_dims_vec.push_back(depth.to<int>());
   auto out_dims = phi::make_ddim(out_dims_vec);
@@ -2273,8 +2273,8 @@ void OneHotInferMeta(const MetaTensor& x,
   auto x_dims = x.dims();
   PADDLE_ENFORCE_GE(
       x_dims.size(),
-      1,
-      phi::errors::InvalidArgument("Rank of Input(X) should be at least 1."));
+      0,
+      phi::errors::InvalidArgument("Rank of Input(X) should be at least 0."));
 
   int depth = depth_t.to<int>();
   auto out_dims_vec = phi::vectorize(x_dims);

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

@@ -1421,6 +1421,24 @@ class TestNoBackwardAPI(unittest.TestCase):
         out = paddle.zeros(self.shape)
         self.assertEqual(out.shape, [2, 3, 4])
 
+    def test_embedding(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(
+            x=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)
+        self.assertEqual(one_hot_label.shape, [4])
+        self.assertEqual(one_hot_label.numpy()[2], 1)
+
 
 class TestNoBackwardAPIStatic(unittest.TestCase):
     def setUp(self):
@@ -1590,6 +1608,39 @@ class TestNoBackwardAPIStatic(unittest.TestCase):
         self.assertEqual(res[1].shape, ())
         self.assertEqual(res[2].shape, (2, 3, 4))
 
+    def test_embedding(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(
+            x=ids, weight=w, sparse=True, name="embedding"
+        )
+
+        prog = paddle.static.default_main_program()
+        res = self.exe.run(prog, fetch_list=[emb])
+        self.assertEqual(res[0].shape, (2,))
+        result = [5.0, 6.0]
+        for i in range(len(res)):
+            self.assertEqual(res[0][i], result[i])
+
+    def test_static_embedding(self):
+        ids = paddle.full(shape=[], fill_value=1, dtype='int64')
+        emb = paddle.static.nn.embedding(ids, (20, 3))
+        prog = paddle.static.default_main_program()
+        self.exe.run(paddle.fluid.default_startup_program())
+        res = self.exe.run(prog, fetch_list=[emb])
+        self.assertEqual(res[0].shape, (3,))
+
+    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)
+        prog = paddle.static.default_main_program()
+        self.exe.run(paddle.fluid.default_startup_program())
+        res = self.exe.run(prog, fetch_list=[one_hot_label])
+
+        self.assertEqual(res[0].shape, (4,))
+        self.assertEqual(res[0][2], 1)
+
 
 if __name__ == "__main__":
     unittest.main()

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

@@ -833,6 +833,24 @@ class TestNoBackwardAPI(unittest.TestCase):
         out = paddle.zeros(self.shape)
         self.assertEqual(out.shape, [2, 3, 4])
 
+    def test_embedding(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(
+            x=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)
+        self.assertEqual(one_hot_label.shape, [4])
+        self.assertEqual(one_hot_label.numpy()[2], 1)
+
 
 if __name__ == "__main__":
     unittest.main()