bump black to 2023 style

.cmake-format.py

@@ -16,7 +16,6 @@
 # Options affecting formatting.
 # -----------------------------
 with section("format"):
-
     # How wide to allow formatted cmake files
     line_width = 80
 

.pre-commit-config.yaml

@@ -53,7 +53,7 @@ repos:
             )$
 # For Python files
 -   repo: https://github.com/psf/black.git
-    rev: 22.8.0
+    rev: 23.3.0
     hooks:
     -   id: black
         files: (.*\.(py|pyi|bzl)|BUILD|.*\.BUILD|WORKSPACE)$

paddle/fluid/eager/auto_code_generator/generator/eager_gen.py

@@ -547,7 +547,6 @@ def GenerateCoreOpInfoDeclaration():
 
 
 def GenerateCoreOpInfoDefinition():
-
     op_args_info_list = []
     for op_name, arg_list in core_ops_args_info.items():
         arg_str = ",".join(["\"" + v + "\"" for v in arg_list])
@@ -803,7 +802,6 @@ def CollectBackwardInfo(self):
             self.backward_returns_list = backward_returns_list_new
 
     def CollectForwardInfoFromBackwardContents(self):
-
         backward_forward_str = self.backward_forward_str
 
         (
@@ -1910,7 +1908,6 @@ def GenerateHigherOrderNodeCreationCode(self):
                     self.grad_api_contents["backward_op"] in prim_white_list
                     or is_invoke_forward_api
                 ):
-
                     next_grad_node_creation_str = f"""
  if (!paddle::prim::PrimCommonUtils::IsEagerPrimEnabled()) {{
     if(trace_backward) {{
@@ -2274,7 +2271,6 @@ def GenerateNodeDefinition(
       egr::EagerUtils::HandleViewBetweenInputAndOutput({inplace_grad_input_str}, api_output_{out_index});
     }}"""
             if IsPlainTensorType(ttype):
-
                 if (
                     backward_inplace_map
                     and name in backward_inplace_map.values()

paddle/fluid/eager/auto_code_generator/generator/python_c_gen.py

@@ -604,7 +604,6 @@ def GenerateCoreOpsInfoMap():
 
 
 def GeneratePythonCWrappers(python_c_function_str, python_c_function_reg_str):
-
     (
         core_ops_infos_definition,
         core_ops_infos_registry,

paddle/fluid/operators/generator/generate_op.py

@@ -500,7 +500,7 @@ def parse_get_expected_kerneltype(
             fw_name = op_comp_map['op'].split('(')[0].strip()
             # deal the last underline of function name in op_comp_map['get_expected_kernel_type']
             new_get_expected_kernel_type_func_map = {}
-            for (key, value) in op_comp_map['get_expected_kernel_type'].items():
+            for key, value in op_comp_map['get_expected_kernel_type'].items():
                 new_get_expected_kernel_type_func_map[
                     delete_last_underline(key)
                 ] = value

paddle/fluid/operators/generator/parse_utils.py

@@ -615,15 +615,15 @@ def cross_validate(ops):
                 assert len(fw_call["inputs"]) <= len(
                     fw_op["inputs"]
                 ), f"{name}: forward call has more inputs than the op "
-                for (input, input_) in zip(fw_call["inputs"], fw_op["inputs"]):
+                for input, input_ in zip(fw_call["inputs"], fw_op["inputs"]):
                     assert (
                         input["typename"] == input_["typename"]
                     ), f"type mismatch in {name} and {fw_name}"
 
                 assert len(fw_call["attrs"]) <= len(
                     fw_op["attrs"]
                 ), f"{name}: forward call has more attrs than the op "
-                for (attr, attr_) in zip(fw_call["attrs"], fw_op["attrs"]):
+                for attr, attr_ in zip(fw_call["attrs"], fw_op["attrs"]):
                     if attr["typename"] == "Scalar":
                         # special case for Scalar, fw_call can omit the type
                         assert re.match(
@@ -637,7 +637,7 @@ def cross_validate(ops):
                 assert len(fw_call["outputs"]) == len(
                     fw_op["outputs"]
                 ), f"{name}: forward call has more outputs than the op "
-                for (output, output_) in zip(
+                for output, output_ in zip(
                     fw_call["outputs"], fw_op["outputs"]
                 ):
                     assert (

paddle/phi/api/yaml/generator/backward_api_gen.py

@@ -316,7 +316,6 @@ def generate_backward_api(
     header_file_path,
     source_file_path,
 ):
-
     bw_apis = []
     for each_api_yaml in backward_yaml_path:
         with open(each_api_yaml, 'r') as f:

paddle/phi/api/yaml/generator/intermediate_api_gen.py

@@ -92,7 +92,6 @@ def generate_intermediate_api(
     dygraph_header_file_path,
     dygraph_source_file_path,
 ):
-
     dygraph_header_file = open(dygraph_header_file_path, 'w')
     dygraph_source_file = open(dygraph_source_file_path, 'w')
 

paddle/phi/api/yaml/generator/sparse_api_gen.py

@@ -351,7 +351,6 @@ def api_namespace():
 
 
 def generate_api(api_yaml_path, header_file_path, source_file_path):
-
     with open(api_yaml_path, 'r') as f:
         apis = yaml.load(f, Loader=yaml.FullLoader)
     header_file = open(header_file_path, 'w')

paddle/phi/api/yaml/generator/sparse_bw_api_gen.py

@@ -158,7 +158,6 @@ def api_namespace():
 
 
 def generate_api(api_yaml_path, header_file_path, source_file_path):
-
     with open(api_yaml_path, 'r') as f:
         apis = yaml.load(f, Loader=yaml.FullLoader)
     header_file = open(header_file_path, 'w')

paddle/phi/api/yaml/generator/strings_api_gen.py

@@ -362,7 +362,6 @@ def api_namespace():
 
 
 def generate_api(api_yaml_path, header_file_path, source_file_path):
-
     with open(api_yaml_path, 'r') as f:
         apis = yaml.load(f, Loader=yaml.FullLoader)
     header_file = open(header_file_path, 'w')

paddle/phi/kernels/sparse/gpu/cutlass_generator/gather_gemm_scatter_generator.py

@@ -68,7 +68,6 @@ def CreateGatherGemmScatterOperator(
         for tile_description in tile_descriptions:
             for alignment in alignment_constraints:
                 for complex_transform in complex_transforms:
-
                     alignment_c = min(8, alignment)
 
                     A = TensorDescription(
@@ -98,7 +97,6 @@ def CreateGatherGemmScatterOperator(
 
 
 def GenerateSM80_TensorOp_16816(manifest, cuda_version, debug=False):
-
     if not CudaToolkitVersionSatisfies(cuda_version, 11, 0):
         return
 
@@ -211,7 +209,6 @@ def GenerateSM80_TensorOp_16816(manifest, cuda_version, debug=False):
 
         # Avoid emitting two kernels if the accumulator type does not differ from the input type (e.g. F16 accumulation)
         if math_inst.element_a != math_inst.element_accumulator:
-
             data_type_mixed = [
                 math_inst.element_a,
                 math_inst.element_b,
@@ -225,7 +222,6 @@ def GenerateSM80_TensorOp_16816(manifest, cuda_version, debug=False):
 
 
 def GenerateSM80_TensorOp_1688(manifest, cuda_version, debug=False):
-
     if not CudaToolkitVersionSatisfies(cuda_version, 11, 0):
         return
 
@@ -341,7 +337,6 @@ def GenerateSM80_TensorOp_1688(manifest, cuda_version, debug=False):
 
 
 def GenerateSM80_TensorOp_1688_fast_math(manifest, cuda_version, debug=False):
-
     if not CudaToolkitVersionSatisfies(cuda_version, 11, 0):
         return
 
@@ -443,7 +438,6 @@ def GenerateSM80_TensorOp_1688_fast_math(manifest, cuda_version, debug=False):
 def GenerateSM80_TensorOp_1688_fast_fp32_math(
     manifest, cuda_version, debug=False
 ):
-
     if not CudaToolkitVersionSatisfies(cuda_version, 11, 0):
         return
 
@@ -525,7 +519,6 @@ def GenerateSM80_TensorOp_1688_fast_fp32_math(
 
 
 def GenerateSM75_TensorOp_1688(manifest, cuda_version, debug=False):
-
     if not CudaToolkitVersionSatisfies(cuda_version, 10, 2):
         return
 
@@ -649,7 +642,6 @@ def __init__(
 
 
 if __name__ == "__main__":
-
     args = KernelCfg(
         architectures='80',
         build_dir=sys.argv[2],

paddle/phi/kernels/sparse/gpu/cutlass_generator/gather_gemm_scatter_manifest.py

@@ -156,7 +156,6 @@ def __exit__(self, exception_type, exception_value, traceback):
 
 class GatherGemmScatterManifest(Manifest):
     def emit(self, target=GeneratorTarget.Library):
-
         operation_emitters = {
             GeneratorTarget.Library: GatherGemmScatterEmitOperationKindLibrary
         }

paddle/phi/kernels/sparse/gpu/cutlass_generator/gather_gemm_scatter_operation.py

@@ -89,7 +89,6 @@ def instance_template(self):
         return ""
 
     def emit(self, operation):
-
         threadblock_shape = operation.tile_description.threadblock_shape
         warp_count = operation.tile_description.warp_count
 
@@ -107,7 +106,6 @@ def emit(self, operation):
             and operation.B.layout in transpose_layouts.keys()
             and operation.C.layout in transpose_layouts.keys()
         ):
-
             instance_layout_A = transpose_layouts[operation.A.layout]
             instance_layout_B = transpose_layouts[operation.B.layout]
             instance_layout_C = transpose_layouts[operation.C.layout]
@@ -124,7 +122,6 @@ def emit(self, operation):
 
         # Support built-in epilogue functors or user-defined functions
         if isinstance(operation.epilogue_functor, enum.Enum):
-
             epilogue_vector_length = (
                 min(
                     operation.C.alignment * DataTypeSize[operation.C.element],
@@ -256,7 +253,6 @@ def __enter__(self):
         return self
 
     def __exit__(self, exception_type, exception_value, traceback):
-
         # Write instance definitions in top-level namespace
         for instance_definition in self.instance_definitions:
             self.configuration_file.write(instance_definition)
@@ -278,7 +274,6 @@ def __init__(
         epilogue_functor=EpilogueFunctor.LinearCombination,
         swizzling_functor=SwizzlingFunctor.Identity8,
     ):
-
         super().__init__(
             gemm_kind,
             arch,

python/paddle/amp/accuracy_compare.py

@@ -458,7 +458,6 @@ def _write_titles(self, worksheet, loss_scale, row):
     def add_worksheet(
         self, mp_tensor_info_list, sheetname, loss_scale, skip_normal_tensors
     ):
-
         assert self.workbook is not None
 
         worksheet = self.workbook.add_worksheet(sheetname)

python/paddle/amp/debugging.py

@@ -137,7 +137,6 @@ def __init__(
         debug_step=None,
         stack_height_limit=1,
     ):
-
         self.enable = enable
         self.debug_mode = debug_mode
         self.output_dir = output_dir

python/paddle/amp/grad_scaler.py

@@ -98,7 +98,6 @@ def __init__(
         decr_every_n_nan_or_inf=1,
         use_dynamic_loss_scaling=True,
     ):
-
         tracer = _dygraph_tracer()
         if not tracer:
             raise ValueError(

python/paddle/cost_model/cost_model.py

@@ -52,7 +52,6 @@ def profile_measure(
         device='gpu',
         fetch_cost_list=['time'],
     ):
-
         place = paddle.set_device('gpu')
         x = np.random.random(size=(10, 1)).astype('float32')
         exe = paddle.static.Executor(place)

python/paddle/dataset/conll05.py

@@ -151,7 +151,6 @@ def reader_creator(
 ):
     def reader():
         for sentence, predicate, labels in corpus_reader():
-
             sen_len = len(sentence)
 
             verb_index = labels.index('B-V')

python/paddle/dataset/voc2012.py

@@ -42,7 +42,6 @@
 
 
 def reader_creator(filename, sub_name):
-
     tarobject = tarfile.open(filename)
     name2mem = {}
     for ele in tarobject.getmembers():

python/paddle/distributed/auto_parallel/random.py

@@ -69,7 +69,6 @@ def parallel_manual_seed(seed):
 
 
 def determinate_rng(rank, dims_mapping, process_mesh):
-
     # TODO(JZ-LIANG) Support Mesh with any high rank
     # use a string to unique integer hashing algorithm for seed computation.
     # instead of using offsets to coodinate seed across devices.
@@ -119,7 +118,6 @@ def determinate_rng(rank, dims_mapping, process_mesh):
 
 
 def init_auto_parallel_rng():
-
     if not is_enable_auto_rand_ctrl():
         return
 

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

@@ -319,7 +319,7 @@ def load(save_dir):
             assert os.path.isfile(filepath)
             if "vars" in filename:
                 assert filename.endswith("pkl")
-                with (open(filepath, "rb")) as f:
+                with open(filepath, "rb") as f:
                     vars_list.append(pickle.load(f))
             elif "program" in filename:
                 assert filename.endswith("pdmodel")
@@ -328,7 +328,7 @@ def load(save_dir):
                 program_list.append(deserialize_program(program_string))
             elif "dist_attr" in filename:
                 assert filename.endswith("pkl")
-                with (open(filepath, "rb")) as f:
+                with open(filepath, "rb") as f:
                     dist_attr_list.append(pickle.load(f))
 
         dist_attr_map = {}

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

@@ -147,7 +147,6 @@ def __repr__(self):
 
 
 class Link:
-
     default_hop = 1
     default_nic_bandwidth = 24
 

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

@@ -1257,7 +1257,6 @@ def _get_op_by_id(ops, id):
 
             # grad ops that have not a corresponding mapping in grad_op_id_to_op_id
             else:
-
                 if grad_op.type == 'sum':
                     assert all(map(_is_grad_var_name, grad_op.input_arg_names))
                     output_name = grad_op.output_arg_names[0]
@@ -1382,7 +1381,6 @@ def _get_op_by_id(ops, id):
         ]
 
         for idx in range(first_backward_op_idx, len(ops)):
-
             # complete the initial grad loss op
             if idx == first_backward_op_idx:
                 assert ops[idx].type == "fill_constant"
@@ -1656,7 +1654,6 @@ def complete_update_annotation(self, serial_main_program):
         learning_rate_completed = False
 
         for idx in range(len(ops)):
-
             # complete the annotation of the optimizer op.
             # TODO to add attribute for moment var
             op = ops[idx]
@@ -1823,7 +1820,6 @@ def complete_update_annotation(self, serial_main_program):
                         )
 
                     for input_name in op.desc.input_names():
-
                         if input_name in [
                             'Param',
                             'Grad',

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

@@ -316,7 +316,6 @@ def _parse_sub_program(self, program, nodes, graph, cost_data, sub_idx):
                     if pred.type == CostNodeType.COMPUTATION and (
                         pred_id in graph[node_id][SUCC]
                     ):
-
                         graph[pred_id][SUCC].remove(node_id)
                         graph[node_id][PRED].remove(pred_id)