[Lod][fluid_ops] Remove lod_level=0 in test/ (#69665)

test/collective/fleet/test_dgc_optimizer.py

@@ -45,7 +45,6 @@ def check_dgc_momentum_optimizer(
         mul_x = block.create_parameter(
             dtype="float32",
             shape=[dims[0], dims[1]],
-            lod_level=0,
             name="mul.x",
             optimize_attr={'learning_rate': 1.1},
             regularizer=(
@@ -55,12 +54,11 @@ def check_dgc_momentum_optimizer(
             ),
         )
         mul_y = block.create_var(
-            dtype="float32", shape=[dims[1], dims[2]], lod_level=0, name="mul.y"
+            dtype="float32", shape=[dims[1], dims[2]], name="mul.y"
         )
         mul_out = block.create_var(
             dtype="float32",
             shape=[dims[0], dims[2]],
-            lod_level=0,
             name="mul.out",
         )
         block.append_op(
@@ -94,9 +92,7 @@ def check_dgc_momentum_optimizer(
                 dgc_momentum_optimizer._optimizer.get_velocity_str
             )
 
-        mean_out = block.create_var(
-            dtype="float32", shape=[1], lod_level=0, name="mean.out"
-        )
+        mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out")
         block.append_op(
             type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
         )

test/deprecated/ir/test_ir_fc_fuse_pass_deprecated.py

@@ -31,7 +31,7 @@ class FCFusePassTest(PassTest):
     def setUp(self):
         with base.program_guard(self.main_program, self.startup_program):
             data = paddle.static.data(
-                name="data", shape=[32, 128], dtype="float32", lod_level=0
+                name="data", shape=[32, 128], dtype="float32"
             )
             tmp_0 = paddle.static.nn.fc(
                 x=data, size=128, num_flatten_dims=1, activation="relu"

test/deprecated/ir/test_ir_preln_residual_bias_fuse_pass_deprecated.py

@@ -27,13 +27,9 @@ def setUp(self):
         with paddle.static.program_guard(
             self.main_program, self.startup_program
         ):
-            x = paddle.static.data(
-                name="x", shape=[128, 768], dtype="float32", lod_level=0
-            )
+            x = paddle.static.data(name="x", shape=[128, 768], dtype="float32")
             bias = paddle.static.create_parameter(shape=[768], dtype='float32')
-            y = paddle.static.data(
-                name="y", shape=[128, 768], dtype="float32", lod_level=0
-            )
+            y = paddle.static.data(name="y", shape=[128, 768], dtype="float32")
             x = x + bias
             elementwise_out = x + y
             out = paddle.static.nn.layer_norm(input=elementwise_out)
@@ -63,12 +59,8 @@ def setUp(self):
         with paddle.static.program_guard(
             self.main_program, self.startup_program
         ):
-            x = paddle.static.data(
-                name="x", shape=[128, 768], dtype="float32", lod_level=0
-            )
-            y = paddle.static.data(
-                name="y", shape=[128, 768], dtype="float32", lod_level=0
-            )
+            x = paddle.static.data(name="x", shape=[128, 768], dtype="float32")
+            y = paddle.static.data(name="y", shape=[128, 768], dtype="float32")
             elementwise_out = x + y
             out = paddle.static.nn.layer_norm(input=elementwise_out)
 

test/deprecated/ir/test_ir_skip_layernorm_pass_deprecated.py

@@ -27,12 +27,8 @@ class SkipLayerNormFusePassTest(PassTest):
     def setUp(self):
         paddle.enable_static()
         with base.program_guard(self.main_program, self.startup_program):
-            x = paddle.static.data(
-                name="x", shape=[128, 768], dtype="float32", lod_level=0
-            )
-            y = paddle.static.data(
-                name="y", shape=[128, 768], dtype="float32", lod_level=0
-            )
+            x = paddle.static.data(name="x", shape=[128, 768], dtype="float32")
+            y = paddle.static.data(name="y", shape=[128, 768], dtype="float32")
             elementwise_out = paddle.add(x=x, y=y)
             out = paddle.static.nn.layer_norm(input=elementwise_out)
 

test/deprecated/legacy_test/dist_fleet_ctr.py

@@ -79,7 +79,6 @@ def net(self, args, is_train=True, batch_size=4, lr=0.01):
             name="click",
             shape=[-1, 1],
             dtype="int64",
-            lod_level=0,
         )
 
         datas = [dnn_data, lr_data, label]

test/deprecated/legacy_test/test_dataset.py

@@ -444,16 +444,16 @@ def test_in_memory_dataset_masterpatch1(self):
             startup_program = base.Program()
             with base.program_guard(train_program, startup_program):
                 var1 = paddle.static.data(
-                    name="slot1", shape=[-1, 1], dtype="int64", lod_level=0
+                    name="slot1", shape=[-1, 1], dtype="int64"
                 )
                 var2 = paddle.static.data(
-                    name="slot2", shape=[-1, 1], dtype="int64", lod_level=0
+                    name="slot2", shape=[-1, 1], dtype="int64"
                 )
                 var3 = paddle.static.data(
-                    name="slot3", shape=[-1, 1], dtype="float32", lod_level=0
+                    name="slot3", shape=[-1, 1], dtype="float32"
                 )
                 var4 = paddle.static.data(
-                    name="slot4", shape=[-1, 1], dtype="float32", lod_level=0
+                    name="slot4", shape=[-1, 1], dtype="float32"
                 )
                 slots_vars = [var1, var2, var3, var4]
 

test/deprecated/legacy_test/test_infer_no_need_buffer_slots_deprecated.py

@@ -26,12 +26,12 @@ def net(self):
         x1 = (
             base.default_main_program()
             .global_block()
-            .create_var(dtype="float32", shape=[1], lod_level=0, name="x1")
+            .create_var(dtype="float32", shape=[1], name="x1")
         )
         x2 = (
             base.default_main_program()
             .global_block()
-            .create_var(dtype="float32", shape=[1], lod_level=0, name="x2")
+            .create_var(dtype="float32", shape=[1], name="x2")
         )
         x = paddle.add(x1, x2)
         return x

test/deprecated/legacy_test/test_layers_deprecated.py

@@ -1400,9 +1400,7 @@ def test_simple_conv2d(self):
     def test_shuffle_batch(self):
         # TODO(minqiyang): dygraph do not support lod now
         with self.static_graph():
-            x = paddle.static.data(
-                name='X', shape=[-1, 4, 50], dtype='float32', lod_level=0
-            )
+            x = paddle.static.data(name='X', shape=[-1, 4, 50], dtype='float32')
             out1 = shuffle_batch(x)
             paddle.seed(1000)
             out2 = shuffle_batch(x)

test/deprecated/legacy_test/test_optimizer_deprecated.py

@@ -37,18 +37,17 @@ def check_sgd_optimizer(optimizer_attr):
             mul_x = block.create_parameter(
                 dtype="float32",
                 shape=[5, 10],
-                lod_level=0,
                 name="mul.x",
                 optimize_attr=optimizer_attr,
             )
             mul_y = block.create_var(
-                dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
+                dtype="float32", shape=[10, 8], name="mul.y"
             )
             mul_out = block.create_var(
-                dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
+                dtype="float32", shape=[5, 8], name="mul.out"
             )
             mean_out = block.create_var(
-                dtype="float32", shape=[1], lod_level=0, name="mean.out"
+                dtype="float32", shape=[1], name="mean.out"
             )
             block.append_op(
                 type="mul",
@@ -81,18 +80,17 @@ def check_sgd_optimizer(optimizer_attr):
             mul_x = block.create_parameter(
                 dtype="float32",
                 shape=[5, 10],
-                lod_level=0,
                 name="mul.x",
                 optimize_attr=optimizer_attr,
             )
             mul_y = block.create_var(
-                dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
+                dtype="float32", shape=[10, 8], name="mul.y"
             )
             mul_out = block.create_var(
-                dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
+                dtype="float32", shape=[5, 8], name="mul.out"
             )
             mean_out = block.create_var(
-                dtype="float32", shape=[1], lod_level=0, name="mean.out"
+                dtype="float32", shape=[1], name="mean.out"
             )
             block.append_op(
                 type="mul",
@@ -133,15 +131,12 @@ def test_vanilla_momentum_optimizer(self):
         mul_x = block.create_parameter(
             dtype="float32",
             shape=[5, 10],
-            lod_level=0,
             name="mul.x",
             optimize_attr={'learning_rate': 1.1},
         )
-        mul_y = block.create_var(
-            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
-        )
+        mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y")
         mul_out = block.create_var(
-            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
+            dtype="float32", shape=[5, 8], name="mul.out"
         )
         block.append_op(
             type="mul",
@@ -153,9 +148,7 @@ def test_vanilla_momentum_optimizer(self):
         momentum_optimizer = self.MockMomentum(
             learning_rate=learning_rate, momentum=0.2
         )
-        mean_out = block.create_var(
-            dtype="float32", shape=[1], lod_level=0, name="mean.out"
-        )
+        mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out")
         block.append_op(
             type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
         )
@@ -192,25 +185,20 @@ def test_nesterov_momentum_optimizer(self):
         mul_x = block.create_parameter(
             dtype="float32",
             shape=[5, 10],
-            lod_level=0,
             name="mul.x",
             optimize_attr={'learning_rate': 1.1},
         )
-        mul_y = block.create_var(
-            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
-        )
+        mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y")
         mul_out = block.create_var(
-            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
+            dtype="float32", shape=[5, 8], name="mul.out"
         )
         block.append_op(
             type="mul",
             inputs={"X": mul_x, "Y": mul_y},
             outputs={"Out": mul_out},
             attrs={"x_num_col_dims": 1},
         )
-        mean_out = block.create_var(
-            dtype="float32", shape=[1], lod_level=0, name="mean.out"
-        )
+        mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out")
         block.append_op(
             type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
         )
@@ -263,25 +251,20 @@ def test_adam_optimizer(self):
         mul_x = block.create_parameter(
             dtype="float32",
             shape=[5, 10],
-            lod_level=0,
             name="mul.x",
             optimize_attr={'learning_rate': 1.1},
         )
-        mul_y = block.create_var(
-            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
-        )
+        mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y")
         mul_out = block.create_var(
-            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
+            dtype="float32", shape=[5, 8], name="mul.out"
         )
         block.append_op(
             type="mul",
             inputs={"X": mul_x, "Y": mul_y},
             outputs={"Out": mul_out},
             attrs={"x_num_col_dims": 1},
         )
-        mean_out = block.create_var(
-            dtype="float32", shape=[1], lod_level=0, name="mean.out"
-        )
+        mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out")
         block.append_op(
             type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out}
         )
@@ -321,45 +304,32 @@ def net(self, return_input=False, with_dropout=False, with_seed=False):
         program = framework.Program()
         block = program.global_block()
         mul_x = block.create_parameter(
-            dtype="float32", shape=[5, 10], lod_level=0, name="mul.x"
-        )
-        mul_y = block.create_var(
-            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
+            dtype="float32", shape=[5, 10], name="mul.x"
         )
+        mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y")
         mul_out = block.create_var(
-            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
+            dtype="float32", shape=[5, 8], name="mul.out"
         )
 
         if with_dropout is True:
             mul_out_drop = block.create_var(
                 dtype="float32",
                 shape=[5, 8],
-                lod_level=0,
                 name="mul.out.dropout",
             )
             mul_out_mask = block.create_var(
-                dtype="uint8", shape=[5, 8], lod_level=0, name="mul.out.mask"
+                dtype="uint8", shape=[5, 8], name="mul.out.mask"
             )
             if with_seed is True:
                 seed_out = block.create_var(
                     dtype="int32", shape=[1], name="seed.out"
                 )
 
-        b1 = block.create_parameter(
-            dtype="float32", shape=[5, 8], lod_level=0, name="b1"
-        )
-        b1_out = block.create_var(
-            dtype="float32", shape=[5, 8], lod_level=0, name="b1_out"
-        )
-        b2 = block.create_parameter(
-            dtype="float32", shape=[5, 8], lod_level=0, name="b2"
-        )
-        b2_out = block.create_var(
-            dtype="float32", shape=[5, 8], lod_level=0, name="b2_out"
-        )
-        mean_out = block.create_var(
-            dtype="float32", shape=[1], lod_level=0, name="mean.out"
-        )
+        b1 = block.create_parameter(dtype="float32", shape=[5, 8], name="b1")
+        b1_out = block.create_var(dtype="float32", shape=[5, 8], name="b1_out")
+        b2 = block.create_parameter(dtype="float32", shape=[5, 8], name="b2")
+        b2_out = block.create_var(dtype="float32", shape=[5, 8], name="b2_out")
+        mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out")
         block.append_op(
             type="mul",
             inputs={"X": mul_x, "Y": mul_y},
@@ -927,23 +897,15 @@ def net(self):
         program = framework.Program()
         block = program.global_block()
         mul_x = block.create_parameter(
-            dtype="float32", shape=[5, 10], lod_level=0, name="mul.x"
-        )
-        mul_y = block.create_var(
-            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y"
+            dtype="float32", shape=[5, 10], name="mul.x"
         )
+        mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y")
         mul_out = block.create_var(
-            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out"
-        )
-        b1 = block.create_parameter(
-            dtype="float32", shape=[5, 8], lod_level=0, name="b1"
-        )
-        b1_out = block.create_var(
-            dtype="float32", shape=[5, 8], lod_level=0, name="b1_out"
-        )
-        mean_out = block.create_var(
-            dtype="float32", shape=[1], lod_level=0, name="mean.out"
+            dtype="float32", shape=[5, 8], name="mul.out"
         )
+        b1 = block.create_parameter(dtype="float32", shape=[5, 8], name="b1")
+        b1_out = block.create_var(dtype="float32", shape=[5, 8], name="b1_out")
+        mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out")
         block.append_op(
             type="mul",
             inputs={"X": mul_x, "Y": mul_y},