[CodeStyle] black -> ruff format migration - part 3

python/paddle/incubate/cc/ap/facade_op.py

@@ -20,7 +20,6 @@
 
 
 class FacadeOp:
-
     def __init__(self):
         self.custom_op_name_ = self.custom_op_name()
         self.infer_meta_ = self._check_to_str_pair(self.infer_meta())

python/paddle/incubate/cc/ap/pir_attrs_serializer.py

@@ -24,7 +24,6 @@
 
 
 class PirAttrsSerializer:
-
     def __init__(self, func):
         self.attributes_schema = self._get_attributes_schema(func)
         self._check_attributes_schema(self.attributes_schema)
@@ -107,7 +106,6 @@ def _get_schema_item_as_key(self, schema_item):
 
 
 class PirAttributeSerializer:
-
     def __init__(self, attr_name):
         self.attr_name = attr_name
 
@@ -117,7 +115,6 @@ def __call__(self, value):
 
 
 class BoolAttributeSerializer(PirAttributeSerializer):
-
     def __init__(self, attr_name):
         self.attr_name = attr_name
 
@@ -127,7 +124,6 @@ def __call__(self, value):
 
 
 class IntAttributeSerializer(PirAttributeSerializer):
-
     def __init__(self, attr_name):
         self.attr_name = attr_name
 
@@ -137,7 +133,6 @@ def __call__(self, value):
 
 
 class FloatAttributeSerializer(PirAttributeSerializer):
-
     def __init__(self, attr_name):
         self.attr_name = attr_name
 
@@ -147,7 +142,6 @@ def __call__(self, value):
 
 
 class StrAttributeSerializer(PirAttributeSerializer):
-
     def __init__(self, attr_name):
         self.attr_name = attr_name
 
@@ -157,7 +151,6 @@ def __call__(self, value):
 
 
 class DTypeAttributeSerializer(PirAttributeSerializer):
-
     def __init__(self, attr_name):
         self.attr_name = attr_name
 
@@ -168,7 +161,6 @@ def __call__(self, value):
 
 
 class BoolArrayAttributeSerializer(PirAttributeSerializer):
-
     def __init__(self, attr_name):
         self.attr_name = attr_name
 
@@ -180,7 +172,6 @@ def __call__(self, value):
 
 
 class IntArrayAttributeSerializer(PirAttributeSerializer):
-
     def __init__(self, attr_name):
         self.attr_name = attr_name
 
@@ -192,7 +183,6 @@ def __call__(self, value):
 
 
 class FloatArrayAttributeSerializer(PirAttributeSerializer):
-
     def __init__(self, attr_name):
         self.attr_name = attr_name
 
@@ -204,7 +194,6 @@ def __call__(self, value):
 
 
 class StrArrayAttributeSerializer(PirAttributeSerializer):
-
     def __init__(self, attr_name):
         self.attr_name = attr_name
 
@@ -216,7 +205,6 @@ def __call__(self, value):
 
 
 class DTypeArrayAttributeSerializer(PirAttributeSerializer):
-
     def __init__(self, attr_name):
         self.attr_name = attr_name
 

python/paddle/incubate/nn/functional/fp8.py

@@ -237,7 +237,6 @@ def fused_transpose_split_quant(
 def fused_transpose_wlch_split_quant(
     x: Tensor, tokens_per_expert: Sequence[int], pow_2_scales: bool = False
 ) -> tuple[list[Tensor], list[Tensor]]:
-
     tokens_per_expert = [int(t) for t in tokens_per_expert]
 
     if in_dynamic_or_pir_mode():
@@ -323,7 +322,6 @@ def fp8_gemm_blockwise(
     is_a_1d_scaled: bool = True,
     is_b_1d_scaled: bool = True,
 ):
-
     assert bias is None, "Bias is not supported"
 
     if bias is None:

python/paddle/incubate/nn/functional/fused_bias_act.py

@@ -71,7 +71,6 @@ def fused_bias_act(
             [3, 5]
     """
     if in_dynamic_or_pir_mode():
-
         return _C_ops.fused_bias_act(
             x,
             bias,

python/paddle/jit/dy2static/convert_operators.py

@@ -93,7 +93,6 @@ def convert_load(x):
 
         # get the new output of the var
         if isinstance(x, Value):
-
             from paddle.jit.pir_dy2static.parameter_recorder import (
                 _global_inplace_map,
             )

python/paddle/jit/dy2static/program_translator.py

@@ -1642,7 +1642,6 @@ def __init__(self):
         self._recent_cache_key = None
 
     def _build_once(self, cache_key):
-
         if use_pir_api():
             concrete_program = ConcreteProgram.pir_from_func_spec(
                 func_spec=cache_key.function_spec,

python/paddle/jit/dy2static/utils.py

@@ -130,7 +130,6 @@ class CUDAGraphState(IntEnum):
 
 
 class TransformOptions:
-
     class ToStaticMode(Flag):
         SOT = auto()
         AST = auto()

python/paddle/jit/sot/opcode_translator/executor/variable_dispatch.py

@@ -1071,7 +1071,6 @@ def is_not_func(var: VariableBase, other: VariableBase):
 def apply_op_with_zero_division_check(
     op: BinaryOp, lhs: VariableBase, rhs: VariableBase
 ):
-
     graph = lhs.graph
     if op in NEED_GUARD_ZERO_DIVISION_ERROR_OPS:
         call_eq = BuiltinVariable(operator.eq, graph, DanglingTracker())

python/paddle/jit/sot/opcode_translator/executor/variables/base.py

@@ -343,7 +343,6 @@ class VariableBase:
     mutable_attrs = []
 
     def __init__(self, graph: FunctionGraph, tracker: Tracker):
-
         self.graph = graph
         self.tracker = tracker
         self.id = VariableBase.name_generator.next()

python/paddle/jit/sot/opcode_translator/instruction_utils/instruction_utils.py

@@ -99,7 +99,6 @@ def convert_instruction(instr: dis.Instruction) -> Instruction:
 
 
 def expand_super_instrs(instructions: list[Instruction]) -> list[Instruction]:
-
     expanded_instrs = []
 
     def replace_jump_target(instrs, old_target, new_target):

python/paddle/jit/sot/symbolic/statement_ir.py

@@ -106,7 +106,6 @@ class StatementContext: ...
 
 
 class StatementContextRegistry:
-
     _ctx_map: dict[
         type[Any],
         Callable[[Any], AbstractContextManager[None]],

python/paddle/jit/sot/utils/info_collector.py

@@ -131,7 +131,6 @@ def summary(cls, history: list[Self]) -> str: ...
 
     @classmethod
     def serialize(cls, obj: dict[str:Any]) -> str:
-
         json_data = json.dumps(obj)
         b64_bytes = base64.b64encode(json_data.encode(ENCODING))
 
@@ -334,7 +333,6 @@ def classify(cls, history: list[Self]) -> str:
 
     @classmethod
     def summary(cls, history: list[Self]) -> str:
-
         reason_dict, reason_list = cls.classify(history)
 
         return "\n".join(
@@ -346,7 +344,6 @@ def summary(cls, history: list[Self]) -> str:
 
     @classmethod
     def json_report(cls, history: list[Self]) -> str:
-
         reason_dict, sorted_reasons = cls.classify(history)
         reason_dict["count"] = {k: len(v) for k, v in sorted_reasons}
         serialized = cls.serialize({cls.SHORT_NAME: reason_dict})
@@ -364,7 +361,6 @@ def restore_from_string(cls, serialized: str) -> list[Self]:
         obj.pop("count")
 
         for classname in obj:
-
             ReasonClass = getattr(exceptions, classname, None)
             for reason in obj[classname]:
                 history.append(cls(ReasonClass(reason_str=reason)))
@@ -446,7 +442,6 @@ def restore_from_string(cls, serialized: str) -> list[Self]:
         obj = cls.deserialize(serialized)[cls.SHORT_NAME]
 
         for entry in obj:
-
             history.append(
                 SubGraphInfo(
                     graph=entry["Graph"],
@@ -458,7 +453,6 @@ def restore_from_string(cls, serialized: str) -> list[Self]:
         return history
 
     def __eq__(self, other):
-
         need_graph_equal = "details" in ENV_SOT_COLLECT_INFO.get().get(
             self.SHORT_NAME, []
         )

python/paddle/nn/functional/flash_attention.py

@@ -260,7 +260,6 @@ def _math_attention(
 
 
 def _select_sdp_cuda(head_dim: int) -> str:
-
     if head_dim <= 256:
         return "flash_attn"
     else:

python/paddle/nn/layer/layers.py

@@ -1516,7 +1516,6 @@ def _build_once(self, *args: Any, **kwargs: Any) -> None:
         pass
 
     def _dygraph_call_func(self, *inputs: Any, **kwargs: Any) -> Any:
-
         for hook_id, forward_pre_hook in self._forward_pre_hooks.items():
             if hook_id in self._forward_pre_hooks_with_kwargs_flag:
                 args_kwargs_result = forward_pre_hook(self, inputs, kwargs)

python/paddle/nn/quant/format.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Define some layers used to export quantization model with ONNX style."""
+
 from __future__ import annotations
 
 import abc

python/paddle/nn/quant/qat/conv.py

@@ -14,6 +14,7 @@
 """
 Layers used for QAT.
 """
+
 from paddle.nn import functional as F
 
 from ...layer.layers import Layer

python/paddle/quantization/quanters/abs_max.py

@@ -217,7 +217,6 @@ def static_forward(self, input):
         return quant_out
 
     def pir_forward(self, input):
-
         state = self._state if self.training else None
         accum = self._accum if self.training else None
 

python/paddle/static/nn/metric.py

@@ -14,6 +14,7 @@
 """
 All layers just related to metric.
 """
+
 import numpy as np
 
 import paddle

python/paddle/tensorrt/impls/math.py

@@ -204,7 +204,6 @@ def clip_converter(network, paddle_op, inputs):
     def _get_constant_or_expand_tensor(
         value, constant_inputs, input_shape_tensor, rank, name=None
     ):
-
         if value is not None:
             return fill_constant_layer(
                 network,

test/ap/test_matmul_add_relu.py

@@ -72,7 +72,6 @@ def foo(
             w: pct.Tensor([K, N], DType),
             b: pct.Tensor([B, M, N], DType),
         ):
-
             y = paddle.matmul(x, w)
             tmp = paddle.nn.functional.relu(y)
             tmp2 = tmp + b