[Cleanup][B-13]Replace to_variable part2 (#61603)

co63oc · web-flow · commit aba5e383f090 · 2024-02-05T14:28:45.000+08:00
diff --git a/test/dygraph_to_static/test_lac.py b/test/dygraph_to_static/test_lac.py
@@ -26,7 +26,6 @@
 
 import paddle
 from paddle import _legacy_C_ops, base
-from paddle.base.dygraph import to_variable
 from paddle.framework import in_dynamic_mode
 from paddle.jit.api import to_static
 from paddle.jit.translated_layer import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
@@ -66,9 +65,9 @@ def __init__(
         self.is_reverse = is_reverse
 
     def forward(self, inputs):
-        # Use `to_variable` to create a copy of global h_0 created not in `DynamicGRU`,
+        # Use `paddle.assign` to create a copy of global h_0 created not in `DynamicGRU`,
         # to avoid modify it because `h_0` is both used in other `DynamicGRU`.
-        hidden = to_variable(self.h_0)
+        hidden = paddle.assign(self.h_0)
         hidden.stop_gradient = True
 
         res = []
@@ -375,7 +374,7 @@ def __init__(self, args, length=None):
         )
 
         h_0 = np.zeros((args.batch_size, self.grnn_hidden_dim), dtype="float32")
-        h_0 = to_variable(h_0)
+        h_0 = paddle.to_tensor(h_0)
 
         self.bigru_units = []
         for i in range(self.bigru_num):
@@ -659,9 +658,9 @@ def predict_dygraph(self, batch):
                 model.eval()
 
                 _, pred_res = model(
-                    to_variable(words),
-                    to_variable(targets),
-                    to_variable(length),
+                    paddle.to_tensor(words),
+                    paddle.to_tensor(targets),
+                    paddle.to_tensor(length),
                 )
 
                 return pred_res.numpy()
@@ -699,7 +698,7 @@ def predict_dygraph_jit(self, batch):
             model = paddle.jit.load(self.model_save_prefix)
             model.eval()
 
-            pred_res = model(to_variable(words), to_variable(length))
+            pred_res = model(paddle.to_tensor(words), paddle.to_tensor(length))
 
             return pred_res.numpy()
 
diff --git a/test/legacy_test/test_jit_save_load.py b/test/legacy_test/test_jit_save_load.py
@@ -389,9 +389,7 @@ def load_and_inference(self, train_layer, infer_layer):
         train_layer.eval()
         infer_layer.eval()
         # inference & compare
-        x = base.dygraph.to_variable(
-            np.random.random((1, 784)).astype('float32')
-        )
+        x = paddle.to_tensor(np.random.random((1, 784)).astype('float32'))
         np.testing.assert_array_equal(
             train_layer(x).numpy(), infer_layer(x).numpy()
         )
@@ -417,9 +415,7 @@ def load_dygraph_state_dict(self, train_layer):
         new_layer.set_state_dict(load_state_dict)
         new_layer.eval()
         # inference & compare
-        x = base.dygraph.to_variable(
-            np.random.random((1, 784)).astype('float32')
-        )
+        x = paddle.to_tensor(np.random.random((1, 784)).astype('float32'))
         np.testing.assert_array_equal(
             train_layer(x).numpy(), new_layer(x).numpy()
         )
@@ -449,7 +445,7 @@ def tearDown(self):
         self.temp_dir.cleanup()
 
     def test_nest_output(self):
-        x = base.dygraph.to_variable(np.random.random((4, 8)).astype('float32'))
+        x = paddle.to_tensor(np.random.random((4, 8)).astype('float32'))
 
         net = LinearNetWithNestOut(8, 8)
         dy_outs = paddle.utils.flatten(net(x))
@@ -565,7 +561,7 @@ def test_with_input_spec(self):
 
         # 2. load to infer
         infer_layer = paddle.jit.load(model_path)
-        x = base.dygraph.to_variable(np.random.random((4, 8)).astype('float32'))
+        x = paddle.to_tensor(np.random.random((4, 8)).astype('float32'))
         pred = infer_layer(x)
 
     def test_multi_in_out(self):
@@ -587,8 +583,8 @@ def test_multi_in_out(self):
 
         # 3. load to infer
         infer_layer = paddle.jit.load(model_path)
-        x = base.dygraph.to_variable(np.random.random((4, 8)).astype('float32'))
-        y = base.dygraph.to_variable(np.random.random((4, 8)).astype('float32'))
+        x = paddle.to_tensor(np.random.random((4, 8)).astype('float32'))
+        y = paddle.to_tensor(np.random.random((4, 8)).astype('float32'))
         # 4. predict
         pred_x, pred_y = infer_layer(x, y)
 
@@ -625,8 +621,8 @@ def test_multi_in_out1(self):
 
         # 3. load to infer
         infer_layer = paddle.jit.load(model_path)
-        x = base.dygraph.to_variable(np.random.random((4, 8)).astype('float32'))
-        y = base.dygraph.to_variable(np.random.random((4, 8)).astype('float32'))
+        x = paddle.to_tensor(np.random.random((4, 8)).astype('float32'))
+        y = paddle.to_tensor(np.random.random((4, 8)).astype('float32'))
         # 4. predict
         pred_x, pred_y = infer_layer(x, y)
 
@@ -668,7 +664,7 @@ def test_output_spec(self):
         adam = paddle.optimizer.Adam(
             learning_rate=0.1, parameters=train_layer.parameters()
         )
-        x = base.dygraph.to_variable(np.random.random((4, 8)).astype('float32'))
+        x = paddle.to_tensor(np.random.random((4, 8)).astype('float32'))
         for i in range(10):
             out, loss = train_layer(x)
             loss.backward()
@@ -688,7 +684,7 @@ def test_output_spec(self):
 
         train_layer.eval()
         infer_layer = paddle.jit.load(model_path)
-        x = base.dygraph.to_variable(np.random.random((4, 8)).astype('float32'))
+        x = paddle.to_tensor(np.random.random((4, 8)).astype('float32'))
         np.testing.assert_array_equal(
             train_layer(x)[0].numpy(), infer_layer(x).numpy()
         )
@@ -772,7 +768,7 @@ def train_and_save(self):
         adam = paddle.optimizer.Adam(
             learning_rate=0.1, parameters=train_layer.parameters()
         )
-        x = base.dygraph.to_variable(np.random.random((4, 8)).astype('float32'))
+        x = paddle.to_tensor(np.random.random((4, 8)).astype('float32'))
         for i in range(10):
             hidden, loss = train_layer(x)
             loss.backward()
@@ -795,7 +791,7 @@ def test_load_pruned_model(self):
 
         infer_layer = paddle.jit.load(self.model_path)
 
-        x = base.dygraph.to_variable(np.random.random((4, 8)).astype('float32'))
+        x = paddle.to_tensor(np.random.random((4, 8)).astype('float32'))
         np.testing.assert_array_equal(
             train_layer(x)[0].numpy(), infer_layer(x).numpy()
         )
