[CodeStyle][SIM117] Combine multiple with statements (part3)

pyproject.toml

@@ -85,6 +85,7 @@ select = [
 
  # Flake8-simplify
  "SIM101",
+ "SIM117",
 
  # Pygrep-hooks
  "PGH004",

python/paddle/base/dygraph/base.py

@@ -650,11 +650,13 @@ def guard(place: PlaceLike | None = None) -> Generator[None, None, None]:
  else:
  expected_place = framework._current_expected_place_()
 
- with framework.program_guard(train, startup):
- with framework.unique_name.guard():
- with framework._dygraph_guard(tracer):
- with framework._dygraph_place_guard(expected_place):
- yield
+ with (
+ framework.program_guard(train, startup),
+ framework.unique_name.guard(),
+ framework._dygraph_guard(tracer),
+ framework._dygraph_place_guard(expected_place),
+ ):
+ yield
 
 
 @framework.non_static_only

python/paddle/cinn/compiler/compute_code_generator.py

@@ -221,20 +221,22 @@ def visit_If(self, node):
  self.visit_compound_statement(node.body)
 
  def visit_With(self, node):
- with self.variables_table:
- with contextlib.ExitStack() as context_stack:
- for item in node.items:
- cur_ctx = ExprExecutor(self.variables_table.get()).exec(
- item.context_expr
+ with (
+ self.variables_table,
+ contextlib.ExitStack() as context_stack,
+ ):
+ for item in node.items:
+ cur_ctx = ExprExecutor(self.variables_table.get()).exec(
+ item.context_expr
+ )
+ cur_ctx = context_stack.enter_context(cur_ctx)
+ if item.optional_vars is not None:
+ local_var_table = exec_assign(
+ target=item.optional_vars, source=cur_ctx
  )
- cur_ctx = context_stack.enter_context(cur_ctx)
- if item.optional_vars is not None:
- local_var_table = exec_assign(
- target=item.optional_vars, source=cur_ctx
- )
- for k, v in local_var_table.items():
- self.variables_table.add(k, v)
- body = self.visit_compound_statement(node.body)
+ for k, v in local_var_table.items():
+ self.variables_table.add(k, v)
+ body = self.visit_compound_statement(node.body)
 
  def visit_Expr(self, node):
  if is_node_parsed_in_schedule(node.value):

python/paddle/dataset/movielens.py

@@ -148,19 +148,21 @@ def __initialize_meta_info__():
 def __reader__(rand_seed=0, test_ratio=0.1, is_test=False):
  fn = __initialize_meta_info__()
  np.random.seed(rand_seed)
- with zipfile.ZipFile(file=fn) as package:
- with package.open('ml-1m/ratings.dat') as rating:
- for line in rating:
- line = line.decode(encoding='latin')
- if (np.random.random() < test_ratio) == is_test:
- uid, mov_id, rating, _ = line.strip().split("::")
- uid = int(uid)
- mov_id = int(mov_id)
- rating = float(rating) * 2 - 5.0
-
- mov = MOVIE_INFO[mov_id]
- usr = USER_INFO[uid]
- yield usr.value() + mov.value() + [[rating]]
+ with (
+ zipfile.ZipFile(file=fn) as package,
+ package.open('ml-1m/ratings.dat') as rating,
+ ):
+ for line in rating:
+ line = line.decode(encoding='latin')
+ if (np.random.random() < test_ratio) == is_test:
+ uid, mov_id, rating, _ = line.strip().split("::")
+ uid = int(uid)
+ mov_id = int(mov_id)
+ rating = float(rating) * 2 - 5.0
+
+ mov = MOVIE_INFO[mov_id]
+ usr = USER_INFO[uid]
+ yield usr.value() + mov.value() + [[rating]]
 
 
 @deprecated(

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

@@ -564,21 +564,25 @@ def init(self, main_program, place, dist_context):
  param.get_tensor()._clear()
  with paddle.base.dygraph.guard():
  if amp_dtype == "float16":
- with paddle.no_grad():
- with paddle.base.framework._dygraph_place_guard(
+ with (
+ paddle.no_grad(),
+ paddle.base.framework._dygraph_place_guard(
  place=place
- ):
- t_casted = param_used.cast(
- dtype=core.VarDesc.VarType.FP16
- )
+ ),
+ ):
+ t_casted = param_used.cast(
+ dtype=core.VarDesc.VarType.FP16
+ )
  elif amp_dtype == "bfloat16":
- with paddle.no_grad():
- with paddle.base.framework._dygraph_place_guard(
+ with (
+ paddle.no_grad(),
+ paddle.base.framework._dygraph_place_guard(
  place=place
- ):
- t_casted = param_used.cast(
- dtype=core.VarDesc.VarType.BF16
- )
+ ),
+ ):
+ t_casted = param_used.cast(
+ dtype=core.VarDesc.VarType.BF16
+ )
  # NOTE(lizhiyu): Clear the origin param. Don't use `param_used.get_tensor().get_tensor()._clear()` to
  # clear the `DistTensor`, because it can't clear the `_holder`,
  # which `param_used.get_tensor().get_tensor()` will copy one `DenseTensor`.

python/paddle/distributed/auto_parallel/static/parallelizer_v2.py

@@ -244,9 +244,11 @@ def _generate_optimizer(
  self._dist_context._serial_optimizer = optimizer
  self._dist_context._serial_optimizer._learning_rate = learning_rate
 
- with program_guard(main_program, startup_program):
- with main_program.switch_name_generator_guard("opt_"):
- optimizer_ops = new_optimizer.apply_gradients(params_grads)
+ with (
+ program_guard(main_program, startup_program),
+ main_program.switch_name_generator_guard("opt_"),
+ ):
+ optimizer_ops = new_optimizer.apply_gradients(params_grads)
  self._completer.complete_update_annotation(main_program)
  return optimizer_ops
 

python/paddle/distributed/auto_parallel/static/tuner/optimization_tuner.py

@@ -342,11 +342,13 @@ def _apply_optimization(self, trial):
 
  # Generate optimizer
  # FIXME should be remove from apply pass after pass support optimizers
- with program_guard(dist_main_prog, dist_startup_prog):
- with dist_main_prog.switch_name_generator_guard("opt_"):
- optimizer_ops = dist_context.serial_optimizer.apply_gradients(
- dist_params_grads
- )
+ with (
+ program_guard(dist_main_prog, dist_startup_prog),
+ dist_main_prog.switch_name_generator_guard("opt_"),
+ ):
+ optimizer_ops = dist_context.serial_optimizer.apply_gradients(
+ dist_params_grads
+ )
  completer.complete_update_annotation(dist_main_prog)
 
  resharder = Resharder(

python/paddle/distributed/passes/auto_parallel_master_grad.py

@@ -273,9 +273,11 @@ def _regenerate_optimizer(
  dist_context._serial_optimizer._learning_rate
  )
  serial_optimizer._sorted = False
- with program_guard(main_program, startup_program):
- with main_program.switch_name_generator_guard("opt_"):
- _ = serial_optimizer.apply_gradients(params_grads)
+ with (
+ program_guard(main_program, startup_program),
+ main_program.switch_name_generator_guard("opt_"),
+ ):
+ _ = serial_optimizer.apply_gradients(params_grads)
  self._completer.complete_update_annotation(main_program)
 
  def _add_master_grad(self, main_program, params_grads, dist_context):

python/paddle/distributed/passes/auto_parallel_sync_shared_params.py

@@ -113,20 +113,20 @@ def sync_shared_parameters(self, main_program, startup_program):
  )
 
  # Add shared parameter builtin.parameter with "shared_" prefix.
- with auto_complete_op_role(main_program, OpRole.Forward):
- with paddle.static.program_guard(
+ with (
+ auto_complete_op_role(main_program, OpRole.Forward),
+ paddle.static.program_guard(
  main_program, startup_program
- ):
- shared_param = paddle.pir.core.create_parameter(
- dtype=param.dtype,
- shape=param.shape,
- name="shared_" + param_name,
- process_mesh=dst_mesh,
- placements=src_dist_attr.placements,
- initializer=paddle.nn.initializer.Constant(
- value=0
- ),
- )
+ ),
+ ):
+ shared_param = paddle.pir.core.create_parameter(
+ dtype=param.dtype,
+ shape=param.shape,
+ name="shared_" + param_name,
+ process_mesh=dst_mesh,
+ placements=src_dist_attr.placements,
+ initializer=paddle.nn.initializer.Constant(value=0),
+ )
  main_program.set_parameters_from(startup_program)
 
  # Record new shared parameter.

python/paddle/framework/io_utils.py

@@ -171,11 +171,13 @@ def _load_program_scope(main=None, startup=None, scope=None):
  prog = main if main else paddle.base.Program()
  startup_prog = startup if startup else paddle.base.Program()
  scope = scope if scope else paddle.base.core.Scope()
- with paddle.base.scope_guard(scope):
- with paddle.base.program_guard(prog, startup_prog):
- with paddle.base.unique_name.guard():
- with paddle.base.framework._dygraph_guard(None):
- yield
+ with (
+ paddle.base.scope_guard(scope),
+ paddle.base.program_guard(prog, startup_prog),
+ paddle.base.unique_name.guard(),
+ paddle.base.framework._dygraph_guard(None),
+ ):
+ yield
 
 
 @static_only