[CodeStyle] Cleanup useless conditions (part1) (PaddlePaddle#66004)

test/auto_parallel/semi_auto_parallel_shard_optimizer.py

@@ -109,14 +109,9 @@ def test_adamw_shard_optimizer(self, stage1=False):
                         v, self._mesh, [dist.Replicate()]
                     )
                 else:
-                    if 'w' in k:
-                        opt._accumulators[key][k] = dist.shard_tensor(
-                            v, self._mesh, [dist.Shard(0)]
-                        )
-                    else:
-                        opt._accumulators[key][k] = dist.shard_tensor(
-                            v, self._mesh, [dist.Shard(0)]
-                        )
+                    opt._accumulators[key][k] = dist.shard_tensor(
+                        v, self._mesh, [dist.Shard(0)]
+                    )
         for _ in range(5):
             loss = linear(batch)
             loss.backward()

test/auto_parallel/test_shard_layer_api.py

@@ -48,14 +48,9 @@ def forward(self, x):
 def shard_fn(layer_name, layer, process_mesh):
     if isinstance(layer, nn.Linear):
         for name, param in layer.named_parameters():
-            if 'weight' in name:
-                dist_param = dist.shard_tensor(
-                    param, process_mesh, [dist.Replicate()]
-                )
-            else:
-                dist_param = dist.shard_tensor(
-                    param, process_mesh, [dist.Replicate()]
-                )
+            dist_param = dist.shard_tensor(
+                param, process_mesh, [dist.Replicate()]
+            )
             layer.add_parameter(name, dist_param)
 
 

test/cinn/conv2d_utils.py

@@ -93,7 +93,4 @@ def conv2d_native(inputs_data, input_shape, filter_size, attrs, is_depthwise):
 
     res_shape = output.shape[1:]
 
-    if is_depthwise:
-        return output, [res_shape]
-    else:
-        return output, [res_shape]
+    return output, [res_shape]

test/deprecated/legacy_test/test_dataloader_keep_order_deprecated.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 import unittest
 
 import numpy as np
@@ -85,17 +84,14 @@ def assertInputData(self, batch_id, input_data, dev_cnt):
                 start_val += 1
 
     def get_places(self):
-        place_list = [base.cpu_places(1)]
-        if base.is_compiled_with_cuda():
-            if os.name == "nt":
-                place_list.extend([base.cuda_places(0)])
-            else:
-                place_list.extend([base.cuda_places(0)])
-        return place_list
+        if paddle.is_compiled_with_cuda():
+            places = base.cuda_places(0)
+        else:
+            places = base.cpu_places(1)
+        return places
 
     def test_main(self):
-        for p in self.get_places():
-            self.run_main_with_place(p)
+        self.run_main_with_place(self.get_places())
 
     def run_main_with_place(self, places):
         with base.scope_guard(base.Scope()):

test/deprecated/legacy_test/test_dataloader_unkeep_order_deprecated.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 import unittest
 
 import numpy as np
@@ -105,17 +104,14 @@ def assertInputData(
                 start_val += 1
 
     def get_places(self):
-        place_list = [base.cpu_places(1)]
-        if base.is_compiled_with_cuda():
-            if os.name == "nt":
-                place_list.extend([base.cuda_places(0)])
-            else:
-                place_list.extend([base.cuda_places(0)])
-        return place_list
+        if paddle.is_compiled_with_cuda():
+            places = base.cuda_places(0)
+        else:
+            places = base.cpu_places(1)
+        return places
 
     def test_main(self):
-        for p in self.get_places():
-            self.run_main_with_place(p)
+        self.run_main_with_place(self.get_places())
 
     def run_main_with_place(self, places):
         with base.scope_guard(base.Scope()):

test/deprecated/legacy_test/test_model.py

@@ -590,14 +590,9 @@ def test_dynamic_load(self):
             net = LeNet()
             inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
             labels = [InputSpec([None, 1], 'int64', 'label')]
-            if new_optimizer:
-                optim = paddle.optimizer.Adam(
-                    learning_rate=0.001, parameters=net.parameters()
-                )
-            else:
-                optim = paddle.optimizer.Adam(
-                    learning_rate=0.001, parameters=net.parameters()
-                )
+            optim = paddle.optimizer.Adam(
+                learning_rate=0.001, parameters=net.parameters()
+            )
             model = Model(net, inputs, labels)
             model.prepare(
                 optimizer=optim, loss=CrossEntropyLoss(reduction="sum")

test/deprecated/legacy_test/test_prune_deprecated.py

@@ -498,10 +498,7 @@ def test_prune_with_cache_program(self):
                             fetch_list=[loss1.name],
                             use_prune=True,
                         )
-                        if i == 0:
-                            self.assertEqual(exe.prune_called_times, 1)
-                        else:
-                            self.assertEqual(exe.prune_called_times, 1)
+                        self.assertEqual(exe.prune_called_times, 1)
 
     def test_prune_with_cache_program2(self):
         '''
@@ -601,10 +598,7 @@ def test_prune_with_cache_compiled_program(self):
                             fetch_list=[loss1.name],
                             use_prune=True,
                         )
-                        if i == 0:
-                            self.assertEqual(exe.prune_called_times, 1)
-                        else:
-                            self.assertEqual(exe.prune_called_times, 1)
+                        self.assertEqual(exe.prune_called_times, 1)
 
     def test_prune_with_multi_optimizers(self):
         '''

test/deprecated/legacy_test/test_warprnnt_op.py

@@ -19,7 +19,6 @@
 
 import paddle
 from paddle import _C_ops
-from paddle.base import core
 from paddle.pir_utils import test_with_pir_api
 
 paddle.enable_static()
@@ -232,14 +231,9 @@ def test_check_output(self):
 
     def test_check_grad(self):
         self.outputs["warprnntgrad"] = self.gradient
-        if core.is_compiled_with_rocm():
-            self.check_grad(
-                ["input"], "loss", numeric_grad_delta=0.009, check_pir=True
-            )
-        else:
-            self.check_grad(
-                ["input"], "loss", numeric_grad_delta=0.009, check_pir=True
-            )
+        self.check_grad(
+            ["input"], "loss", numeric_grad_delta=0.009, check_pir=True
+        )
 
 
 class TestWarpRNNTFP64Op(TestWarpRNNTOp):
@@ -250,14 +244,9 @@ def test_check_output(self):
     def test_check_grad(self):
         self.acts.astype(np.float64)
         self.outputs["warprnntgrad"] = self.gradient
-        if core.is_compiled_with_rocm():
-            self.check_grad(
-                ["input"], "loss", numeric_grad_delta=0.009, check_pir=True
-            )
-        else:
-            self.check_grad(
-                ["input"], "loss", numeric_grad_delta=0.009, check_pir=True
-            )
+        self.check_grad(
+            ["input"], "loss", numeric_grad_delta=0.009, check_pir=True
+        )
 
 
 class TestWarpRNNTOpError(unittest.TestCase):

test/ir/pir/test_special_op_translator.py

@@ -51,14 +51,9 @@ def cond_with_inplace():
                 running_variance = paddle.to_tensor([1], dtype="float32")
                 weight = paddle.to_tensor([2], dtype="float32")
                 bias = paddle.to_tensor([1], dtype="float32")
-                if x > y:
-                    y = paddle.nn.functional.batch_norm(
-                        x, running_mean, running_variance, weight, bias
-                    )
-                else:
-                    y = paddle.nn.functional.batch_norm(
-                        x, running_mean, running_variance, weight, bias
-                    )
+                y = paddle.nn.functional.batch_norm(
+                    x, running_mean, running_variance, weight, bias
+                )
 
             legacy_program = paddle.jit.to_static(
                 cond_with_inplace,
@@ -80,24 +75,14 @@ def cond_with_inplace():
                 running_variance = paddle.to_tensor([1], dtype="float32")
                 weight = paddle.to_tensor([2], dtype="float32")
                 bias = paddle.to_tensor([1], dtype="float32")
-                if x > y:
-                    if y > z:
-                        z = paddle.nn.functional.batch_norm(
-                            z, running_mean, running_variance, weight, bias
-                        )
-                    else:
-                        y = paddle.nn.functional.batch_norm(
-                            x, running_mean, running_variance, weight, bias
-                        )
+                if y > z:
+                    z = paddle.nn.functional.batch_norm(
+                        z, running_mean, running_variance, weight, bias
+                    )
                 else:
-                    if y > z:
-                        z = paddle.nn.functional.batch_norm(
-                            z, running_mean, running_variance, weight, bias
-                        )
-                    else:
-                        y = paddle.nn.functional.batch_norm(
-                            x, running_mean, running_variance, weight, bias
-                        )
+                    y = paddle.nn.functional.batch_norm(
+                        x, running_mean, running_variance, weight, bias
+                    )
 
             legacy_program = paddle.jit.to_static(
                 cond_with_inplace,

test/legacy_test/test_activation_op.py

@@ -5601,10 +5601,7 @@ def test_check_grad(self):
 create_test_act_fp16_class(TestCELU, check_pir=True)
 create_test_act_fp16_class(TestReciprocal, check_pir=True)
 create_test_act_fp16_class(TestLog, check_prim=True, check_pir=True)
-if core.is_compiled_with_rocm():
-    create_test_act_fp16_class(TestLog2, check_pir=True)
-else:
-    create_test_act_fp16_class(TestLog2, check_pir=True)
+create_test_act_fp16_class(TestLog2, check_pir=True)
 create_test_act_fp16_class(TestLog10, check_pir=True)
 create_test_act_fp16_class(TestLog1p, check_pir=True)
 create_test_act_fp16_class(TestSquare, check_pir=True, check_prim_pir=True)
@@ -5773,10 +5770,7 @@ def test_check_grad(self):
 create_test_act_bf16_class(TestCELU, check_pir=True)
 create_test_act_bf16_class(TestReciprocal, check_pir=True)
 create_test_act_bf16_class(TestLog, check_prim=True, check_pir=True)
-if core.is_compiled_with_rocm():
-    create_test_act_bf16_class(TestLog2, check_pir=True)
-else:
-    create_test_act_bf16_class(TestLog2, check_pir=True)
+create_test_act_bf16_class(TestLog2, check_pir=True)
 create_test_act_bf16_class(TestLog10, check_pir=True)
 create_test_act_bf16_class(TestLog1p, check_pir=True)
 create_test_act_bf16_class(TestSquare, check_pir=True, check_prim_pir=True)

test/legacy_test/test_dist_base.py

@@ -1697,22 +1697,13 @@ def check_with_place(
         need_envs={},
         log_name="",
     ):
-        if self._dygraph and (self._gloo_mode or self._nccl2_mode):
-            self.check_with_place_func(
-                model_file=model_file,
-                delta=delta,
-                check_error_log=check_error_log,
-                need_envs=need_envs,
-                log_name=log_name,
-            )
-        else:
-            self.check_with_place_func(
-                model_file=model_file,
-                delta=delta,
-                check_error_log=check_error_log,
-                need_envs=need_envs,
-                log_name=log_name,
-            )
+        self.check_with_place_func(
+            model_file=model_file,
+            delta=delta,
+            check_error_log=check_error_log,
+            need_envs=need_envs,
+            log_name=log_name,
+        )
 
     def check_with_place_func(
         self,

test/legacy_test/test_elementwise_mod_op.py

@@ -45,10 +45,7 @@ def setUp(self):
         self.outputs = {'Out': self.out}
 
     def test_check_output(self):
-        if self.attrs['axis'] == -1:
-            self.check_output(check_pir=True)
-        else:
-            self.check_output(check_pir=True)
+        self.check_output(check_pir=True)
 
     def init_input_output(self):
         self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
@@ -102,10 +99,7 @@ def init_input_output(self):
         self.out = np.fmod(self.y + np.fmod(self.x, self.y), self.y)
 
     def test_check_output(self):
-        if self.attrs['axis'] == -1:
-            self.check_output(check_pir=True)
-        else:
-            self.check_output(check_pir=True)
+        self.check_output(check_pir=True)
 
 
 @unittest.skipIf(
@@ -121,10 +115,7 @@ def init_input_output(self):
         self.out = np.fmod(self.y + np.fmod(self.x, self.y), self.y)
 
     def test_check_output(self):
-        if self.attrs['axis'] == -1:
-            self.check_output(check_pir=True)
-        else:
-            self.check_output(check_pir=True)
+        self.check_output(check_pir=True)
 
 
 class TestElementwiseModFP16Op_ZeroDim1(TestElementwiseModFP16Op):