[CodeStyle][Ruff][BUAA][G-[31-40]] Fix ruff RUF005 diagnostic for 10 files in python/paddle/

python/paddle/distributed/auto_parallel/static/callbacks.py

@@ -45,19 +45,19 @@ def config_callbacks(
     cbks = cbks if isinstance(cbks, (list, tuple)) else [cbks]
 
     if not any(isinstance(k, ProgBarLogger) for k in cbks) and verbose:
-        cbks = [ProgBarLoggerAuto(log_freq, verbose=verbose)] + cbks
+        cbks = [ProgBarLoggerAuto(log_freq, verbose=verbose), *cbks]
 
     if not any(isinstance(k, LRScheduler) for k in cbks):
-        cbks = [LRSchedulerAuto()] + cbks
+        cbks = [LRSchedulerAuto(), *cbks]
 
     if not any(isinstance(k, ModelCheckpoint) for k in cbks):
-        cbks = cbks + [ModelCheckpointAuto(save_freq, save_dir)]
+        cbks = [*cbks, ModelCheckpointAuto(save_freq, save_dir)]
 
     if not any(isinstance(k, Profiler) for k in cbks) and verbose == 3:
-        cbks = cbks + [Profiler(timer_only=True)]
+        cbks = [*cbks, Profiler(timer_only=True)]
 
     if not any(isinstance(k, History) for k in cbks):
-        cbks = cbks + [History()]
+        cbks = [*cbks, History()]
 
     for i, k in enumerate(cbks):
         if isinstance(k, ProgBarLogger):

python/paddle/distributed/ps/utils/public.py

@@ -1354,7 +1354,7 @@ def insert_communicate_op(
         outputs={"Out": []},
         attrs={
             "mode": "forward" if is_forward else "backward",
-            "send_var_name": entrance_var + ["microbatch_id"],
+            "send_var_name": [*entrance_var, "microbatch_id"],
             "recv_var_name": [],
             "message_name": comm_info["block_input_var_name"],
             "next_endpoints": next_heter_worker_endpoints,

python/paddle/distributed/transpiler/collective.py

@@ -814,7 +814,7 @@ def _insert_allgather_ops(self):
                     param = block.vars[op_role_var[i]]
                     new_grad_var = block.create_var(
                         name=op_role_var[i] + "_allgather",
-                        shape=[self.allgather_ranks] + list(param.shape),
+                        shape=[self.allgather_ranks, *list(param.shape)],
                         persistable=False,
                         dtype=core.VarDesc.VarType.FP32,
                         stop_gradient=True,

python/paddle/distributed/utils/launch_utils.py

@@ -466,7 +466,7 @@ def start_local_trainers(
 
         logger.debug(f"trainer proc env:{current_env}")
 
-        cmd = [sys.executable, "-u", training_script] + training_script_args
+        cmd = [sys.executable, "-u", training_script, *training_script_args]
 
         logger.info(f"start trainer proc:{cmd} env:{proc_env}")
 

python/paddle/distribution/lkj_cholesky.py

@@ -73,7 +73,8 @@ def vec_to_tril_matrix(
     # Calculate the dimension of the square matrix based on the last but one dimension of `p`
     # Define the output shape, which adds two dimensions for the square matrix
     shape0 = flatten_shape // last_dim
-    output_shape = sample_shape + (
+    output_shape = (
+        *sample_shape,
         shape0 // reduce(operator.mul, sample_shape),
         dim,
         dim,
@@ -251,7 +252,7 @@ def _onion(self, sample_shape: Sequence[int]) -> Tensor:
         # u_hypersphere[..., 0, :].fill_(0.0)
         # u_hypersphere[..., 0, :] = 0.0
         u_hypersphere_other = u_hypersphere[..., 1:, :]
-        zero_shape = tuple(u_hypersphere.shape[:-2]) + (1, self.dim)
+        zero_shape = (*tuple(u_hypersphere.shape[:-2]), 1, self.dim)
         zero_row = paddle.zeros(shape=zero_shape, dtype=u_hypersphere.dtype)
         u_hypersphere = paddle.concat([zero_row, u_hypersphere_other], axis=-2)
 

python/paddle/distribution/multinomial.py

@@ -159,12 +159,7 @@ def sample(self, shape: Iterable[int] = ()) -> Tensor:
         if not isinstance(shape, Iterable):
             raise TypeError('sample shape must be Iterable object.')
 
-        samples = self._categorical.sample(
-            [
-                self.total_count,
-            ]
-            + list(shape)
-        )
+        samples = self._categorical.sample([self.total_count, *list(shape)])
         return (
             paddle.nn.functional.one_hot(samples, self.probs.shape[-1])
             .cast(self.probs.dtype)

python/paddle/distribution/multivariate_normal.py

@@ -133,7 +133,7 @@ def __init__(
                 scale_tril.shape[:-2], loc.shape[:-1]
             )
             self.scale_tril = scale_tril.expand(
-                batch_shape + [scale_tril.shape[-2], scale_tril.shape[-1]]
+                [*batch_shape, scale_tril.shape[-2], scale_tril.shape[-1]]
             )
         elif covariance_matrix is not None:
             if covariance_matrix.dim() < 2:
@@ -146,8 +146,11 @@ def __init__(
                 covariance_matrix.shape[:-2], loc.shape[:-1]
             )
             self.covariance_matrix = covariance_matrix.expand(
-                batch_shape
-                + [covariance_matrix.shape[-2], covariance_matrix.shape[-1]]
+                [
+                    *batch_shape,
+                    covariance_matrix.shape[-2],
+                    covariance_matrix.shape[-1],
+                ]
             )
         else:
             if precision_matrix.dim() < 2:
@@ -160,15 +163,13 @@ def __init__(
                 precision_matrix.shape[:-2], loc.shape[:-1]
             )
             self.precision_matrix = precision_matrix.expand(
-                batch_shape
-                + [precision_matrix.shape[-2], precision_matrix.shape[-1]]
+                [
+                    *batch_shape,
+                    precision_matrix.shape[-2],
+                    precision_matrix.shape[-1],
+                ]
             )
-        self.loc = loc.expand(
-            batch_shape
-            + [
-                -1,
-            ]
-        )
+        self.loc = loc.expand([*batch_shape, -1])
         event_shape = self.loc.shape[-1:]
 
         if scale_tril is not None:

python/paddle/incubate/distributed/fleet/parameter_server/distribute_transpiler/__init__.py

@@ -554,7 +554,7 @@ def _save_dense_params(self, executor, dirname, context, main_program):
                 optimizer.type, varname
             )
 
-            for var_name in [varname] + reshaped_varnames + origin_varnames:
+            for var_name in [varname, *reshaped_varnames, *origin_varnames]:
                 var = self._origin_main_program.global_block().vars[var_name]
                 block.append_op(
                     type='recv_save',

python/paddle/incubate/distributed/fleet/parameter_server/ir/pserver_pass.py

@@ -904,7 +904,7 @@ def add_large_scale_op(
             names_str = ",".join(value_names)
             dims_str = ",".join([str(dim) for dim in value_dims])
             ids_name = f"kSparseIDs@{param}"
-            cached_str = ",".join(acture_names + [ids_name])
+            cached_str = ",".join([*acture_names, ids_name])
             init_attr_str = get_initializer_attrs(acture_names)
 
             meta_str = ":".join(

python/paddle/incubate/distributed/fleet/parameter_server/ir/trainer_pass.py

@@ -1264,7 +1264,7 @@ def insert_communicate_op(
         outputs={"Out": []},
         attrs={
             "mode": "forward" if is_forward else "backward",
-            "send_var_name": entrance_var + ["microbatch_id"],
+            "send_var_name": [*entrance_var, "microbatch_id"],
             "recv_var_name": [],
             "message_name": comm_info["block_input_var_name"],
             "next_endpoints": next_heter_worker_endpoints,
@@ -1339,7 +1339,7 @@ def replace_ops_by_communicate_op(
             outputs={"Out": []},
             attrs={
                 "mode": "forward",
-                "send_var_name": entrance_var + ["microbatch_id"],
+                "send_var_name": [*entrance_var, "microbatch_id"],
                 "recv_var_name": [],
                 "message_name": comm_info["block_input_var_name"],
                 "next_endpoints": next_heter_worker_endpoints,