【UnitTestFix No.4】Fix unittest test_dropout_op (PaddlePaddle#75729)

* fix: using latest API

* switch check_prim_pir ON

* fix: Code Style Issue

* remove: useless whitelist.

* fix: code-style issue.

* Update test/legacy_test/test_dropout_op.py

Co-authored-by: Nyakku Shigure <[email protected]>

* fix: code-style issue.

---------

Co-authored-by: Nyakku Shigure <[email protected]>

Author: aztice

test/legacy_test/test_dropout_op.py

@@ -34,7 +34,6 @@
 from paddle.base import Program, Scope, core, program_guard
 from paddle.base.executor import scope_guard
 from paddle.decomposition import decompose
-from paddle.incubate.autograd import primapi
 
 
 def dropout_wrapper(
@@ -478,71 +477,63 @@ def test_seed_cpu_place(self):
         paddle.enable_static()
         main_program = Program()
         with program_guard(main_program):
+            paddle.seed(1)
             seed_input_name = "tensor@SeedInput"
             x_var_name = "tensor@X"
             x_out_var = "tensor@XOut"
 
             mask_var_name = "tensor@Mask"
-            seed_input_var = main_program.global_block().create_var(
+            seed_input_var = paddle.static.data(
                 name=seed_input_name,
                 shape=[1],
                 dtype='int32',
-                persistable=False,
-                stop_gradient=True,
             )
-            x_out_var = main_program.global_block().create_var(
+            seed_input_var.persistable = False
+            seed_input_var.stop_gradient = True
+            x_out_var = paddle.static.data(
                 name=x_out_var,
                 shape=[40, 40],
                 dtype='float32',
-                persistable=False,
-                stop_gradient=True,
             )
-            x_var = main_program.global_block().create_var(
+            x_out_var.persistable = False
+            x_out_var.stop_gradient = True
+            x_var = paddle.static.data(
                 name=x_var_name,
                 shape=[40, 40],
                 dtype='float32',
-                persistable=False,
-                stop_gradient=True,
             )
-            mask_var = main_program.global_block().create_var(
+            x_var.persistable = False
+            x_var.stop_gradient = True
+            mask_var = paddle.static.data(
                 name=mask_var_name,
                 shape=[1],
                 dtype='int',
-                persistable=False,
-                stop_gradient=True,
             )
+            mask_var.persistable = False
+            mask_var.stop_gradient = True
 
-            main_program.global_block().append_op(
-                type="fill_constant",
-                outputs={"Out": x_var_name},
-                attrs={
-                    "shape": [40, 40],
-                    "dtype": x_var.dtype,
-                    "value": 1.0,
-                    "place_type": 0,
-                },
-            )
-            main_program.global_block().append_op(
-                type='seed',
-                inputs={},
-                outputs={'Out': seed_input_var},
-                attrs={'seed': 1, 'force_cpu': True},
-            )
-            main_program.global_block().append_op(
-                type='dropout',
-                inputs={'X': x_var, 'Seed': seed_input_var},
-                attrs={'dropout_prob': 0.0},
-                outputs={'Out': x_out_var, 'Mask': mask_var},
+            x_var = paddle.full(shape=[40, 40], dtype='float32', fill_value=1.0)
+            x_out_var = paddle.static.data(
+                name='x_out', shape=[40, 40], dtype='float32'
             )
+            x_out_var.persistable = True
+            tmp = paddle.nn.functional.dropout(x_var, p=0.0, training=False)
+            paddle.assign(tmp, output=x_out_var)
+
             place = base.CPUPlace()
             if core.is_compiled_with_cuda() or is_custom_device():
                 place = get_device_place()
             exe = base.Executor(place)
-            x_out, mask_out = exe.run(
+            x_out = exe.run(
                 main_program,
-                feed={},
-                fetch_list=[x_out_var.name, mask_var.name],
-            )
+                feed={
+                    'tensor@X': np.ones([40, 40], dtype=np.float32),
+                    'tensor@XOut': np.ones([40, 40], dtype=np.float32),
+                    'tensor@SeedInput': np.array([123], dtype=np.int32),
+                    'tensor@Mask': np.array([123], dtype=np.int64),
+                },
+                fetch_list=[x_out_var],
+            )[0]
             x_in_np = np.ones([40, 40]).astype("float32")
             np.testing.assert_allclose(x_out, x_in_np, rtol=1e-05)
 
@@ -1423,27 +1414,23 @@ def setUp(self):
         self.places = get_places()
 
     def check_static_result(self, place):
-        from paddle.distributed.fleet.meta_parallel.parallel_layers.random import (
-            dropout,
-        )
-
         with static.program_guard(static.Program(), static.Program()):
+            paddle.seed(0)
             input = static.data(name="input", shape=[40, 40], dtype="float32")
-            res1 = dropout(
+            res1 = paddle.nn.functional.dropout(
                 input,
                 p=0.3,
                 training=True,
                 mode='upscale_in_train',
-                rng_name='seed0',
             )
-            res2 = dropout(
+
+            res2 = paddle.nn.functional.dropout(
                 input,
                 p=0.3,
                 training=True,
                 mode='upscale_in_train',
-                rng_name='seed1',
             )
-            res3 = dropout(input, p=0.3)
+            res3 = paddle.nn.functional.dropout(input, p=0.3)
 
             in_np = np.random.random([40, 40]).astype("float32")
 
@@ -1489,8 +1476,8 @@ def init_info(self):
         self.api = paddle.nn.functional.dropout
 
     def api_case(self, x):
-        p = paddle.assign([0.5])
-        out = self.api(x=x, p=p, training=True)
+        p = 0.5
+        out = self.api(x, p, training=True)
         return out
 
     def run_static(self, x):
@@ -1859,7 +1846,8 @@ def test_static_comp(self):
                         mode=self.mode,
                     )
                     if core._is_fwd_prim_enabled():
-                        primapi.to_prim(mp.blocks)
+                        # primapi.to_prim(mp.blocks)
+                        [output] = decompose(mp, [output])
                     grad = paddle.static.gradients(output, input_)[0]
                     if self.dtype == "bfloat16":
                         output = paddle.cast(output, "float32")
@@ -1874,7 +1862,8 @@ def test_static_comp(self):
                 mps.append(mp)
         for i in range(len(self.places)):
             self.assertTrue(
-                'dropout' not in [op.type for op in mps[i].block(0).ops]
+                'pd_op.dropout'
+                not in [op.name() for op in mps[i].global_block().ops]
             )
             np.testing.assert_allclose(
                 self.fwd_desire[i].sum(),