[CodeStyle] black -> ruff format migration - part 16

python/paddle/incubate/fp8/deep_gemm/jit/compiler.py

@@ -45,7 +45,7 @@ def get_jit_include_dir() -> str:
 @functools.cache
 def get_deep_gemm_version() -> str:
     # Update include directories
-    include_dir = f"{get_jit_include_dir()+'/../../../../include/paddle/fluid/fp8/deep_gemm/include'}"
+    include_dir = f"{get_jit_include_dir()}/../../../../include/paddle/fluid/fp8/deep_gemm/include"
     assert os.path.exists(
         include_dir
     ), f"Cannot find GEMM include directory {include_dir}"

python/paddle/incubate/fp8/deep_gemm/jit/interleave_ffma.py

@@ -104,9 +104,10 @@ def modify_segment(m, name, ffma_lines):
     for i in range(num_lines // 2):
         dst_reg = parse_registers(ffma_lines[i * 2])[-2]
         low_line, high_line = ffma_lines[i * 2], ffma_lines[i * 2 + 1]
-        low_hex, high_hex = extract_hex_from_line(
-            low_line
-        ), extract_hex_from_line(high_line)
+        low_hex, high_hex = (
+            extract_hex_from_line(low_line),
+            extract_hex_from_line(high_line),
+        )
         le_bytes.append(
             low_hex.to_bytes(8, "little") + high_hex.to_bytes(8, "little")
         )

python/paddle/incubate/fp8/deep_gemm/jit_kernels/gemm.py

@@ -118,9 +118,10 @@ def get_best_configs(
     for block_m in block_ms:
         for block_n in block_ns:
             success = False
-            num_waves, best_num_waves = get_num_waves(
-                block_m, block_n
-            ), get_num_waves(best_block_m, best_block_n)
+            num_waves, best_num_waves = (
+                get_num_waves(block_m, block_n),
+                get_num_waves(best_block_m, best_block_n),
+            )
             if best_block_m is None or best_block_n is None:
                 success = True
             elif num_waves < best_num_waves:

python/paddle/incubate/nn/layer/fused_transformer.py

@@ -147,10 +147,9 @@ def __init__(
         name: str | None = None,
     ) -> None:
         super().__init__()
-        assert embed_dim > 0, (
-            "Expected embed_dim to be greater than 0, "
-            f"but received {embed_dim}"
-        )
+        assert (
+            embed_dim > 0
+        ), f"Expected embed_dim to be greater than 0, but received {embed_dim}"
         self._dtype = self._helper.get_default_dtype()
         self._bias_attr = bias_attr
         self._weight_attr = weight_attr
@@ -338,13 +337,12 @@ def __init__(
     ) -> None:
         super().__init__()
 
-        assert embed_dim > 0, (
-            "Expected embed_dim to be greater than 0, "
-            f"but received {embed_dim}"
-        )
-        assert num_heads > 0, (
-            "Expected nhead to be greater than 0, " f"but received {num_heads}"
-        )
+        assert (
+            embed_dim > 0
+        ), f"Expected embed_dim to be greater than 0, but received {embed_dim}"
+        assert (
+            num_heads > 0
+        ), f"Expected nhead to be greater than 0, but received {num_heads}"
 
         self.normalize_before = normalize_before
         self._dtype = self._helper.get_default_dtype()
@@ -830,12 +828,12 @@ def __init__(
         self._config.pop("__class__", None)  # py3
 
         super().__init__()
-        assert d_model > 0, (
-            "Expected d_model to be greater than 0, " f"but received {d_model}"
-        )
-        assert nhead > 0, (
-            "Expected nhead to be greater than 0, " f"but received {nhead}"
-        )
+        assert (
+            d_model > 0
+        ), f"Expected d_model to be greater than 0, but received {d_model}"
+        assert (
+            nhead > 0
+        ), f"Expected nhead to be greater than 0, but received {nhead}"
         assert dim_feedforward > 0, (
             "Expected dim_feedforward to be greater than 0, "
             f"but received {dim_feedforward}"
@@ -1306,13 +1304,12 @@ def __init__(
     ) -> None:
         super().__init__()
 
-        assert embed_dim > 0, (
-            "Expected embed_dim to be greater than 0, "
-            f"but received {embed_dim}"
-        )
-        assert num_heads > 0, (
-            "Expected nhead to be greater than 0, " f"but received {num_heads}"
-        )
+        assert (
+            embed_dim > 0
+        ), f"Expected embed_dim to be greater than 0, but received {embed_dim}"
+        assert (
+            num_heads > 0
+        ), f"Expected nhead to be greater than 0, but received {num_heads}"
         assert (
             dim_feedforward > 0
         ), f"Expected dim_feedforward to be greater than 0, but received {dim_feedforward}"

python/paddle/incubate/operators/graph_khop_sampler.py

@@ -130,7 +130,7 @@ def graph_khop_sampler(
         if return_eids:
             if sorted_eids is None:
                 raise ValueError(
-                    "`sorted_eid` should not be None " "if return_eids is True."
+                    "`sorted_eid` should not be None if return_eids is True."
                 )
             (
                 edge_src,
@@ -171,7 +171,7 @@ def graph_khop_sampler(
     if return_eids:
         if sorted_eids is None:
             raise ValueError(
-                "`sorted_eid` should not be None " "if return_eids is True."
+                "`sorted_eid` should not be None if return_eids is True."
             )
         check_variable_and_dtype(
             sorted_eids, "Eids", ("int32", "int64"), "graph_khop_sampler"

python/paddle/incubate/operators/graph_sample_neighbors.py

@@ -157,8 +157,7 @@ def graph_sample_neighbors(
     if flag_perm_buffer:
         if perm_buffer is None:
             raise ValueError(
-                "`perm_buffer` should not be None if `flag_perm_buffer`"
-                "is True."
+                "`perm_buffer` should not be None if `flag_perm_buffer` is True."
             )
 
     if in_dynamic_or_pir_mode():

python/paddle/incubate/optimizer/pipeline.py

@@ -481,10 +481,9 @@ def _get_op_device_attr(self, op):
             else None
         )
         if device:
-            assert device[0:3] == 'gpu', (
-                "Now, only gpu devices are "
-                "supported in pipeline parallelism."
-            )
+            assert (
+                device[0:3] == 'gpu'
+            ), "Now, only gpu devices are supported in pipeline parallelism."
         return device
 
     def _add_op_device_attr_for_op(self, op, idx, block):
@@ -669,17 +668,16 @@ def _check_validation(self, block):
             ), f"op ({op.type}) has no {self._op_device_key} attribute."
 
             device = op.attr(self._op_device_key)
-            assert device, (
-                "op_device attribute for op " f"{op.type} has not been set."
-            )
+            assert (
+                device
+            ), f"op_device attribute for op {op.type} has not been set."
             if device == f"{self._device}:all":
                 continue
 
             dev_type = device.split(':')[0]
-            assert dev_type == "gpu", (
-                "Now only gpu devices are supported "
-                "for pipeline parallelism."
-            )
+            assert (
+                dev_type == "gpu"
+            ), "Now only gpu devices are supported for pipeline parallelism."
 
             if device not in device_list:
                 device_list.append(device)

python/paddle/io/dataloader/dataloader_iter.py

@@ -376,10 +376,9 @@ def __init__(self, loader):
         self._persistent_workers = loader._persistent_workers
         self._resume_worker_cnt = 0
 
-        assert self._num_workers > 0, (
-            "Multi-process DataLoader "
-            f"invalid num_workers({self._num_workers})"
-        )
+        assert (
+            self._num_workers > 0
+        ), f"Multi-process DataLoader invalid num_workers({self._num_workers})"
 
         # subprocess wrokers' result queue
         self._data_queue = None

python/paddle/io/dataloader/dataset.py

@@ -87,14 +87,16 @@ def __init__(self) -> None:
 
     def __getitem__(self, idx: int) -> _T:
         raise NotImplementedError(
-            "'{}' not implement in class "
-            "{}".format('__getitem__', self.__class__.__name__)
+            "'{}' not implement in class {}".format(
+                '__getitem__', self.__class__.__name__
+            )
         )
 
     def __len__(self) -> int:
         raise NotImplementedError(
-            "'{}' not implement in class "
-            "{}".format('__len__', self.__class__.__name__)
+            "'{}' not implement in class {}".format(
+                '__len__', self.__class__.__name__
+            )
         )
 
     if TYPE_CHECKING:
@@ -268,20 +270,23 @@ def __init__(self) -> None:
 
     def __iter__(self) -> Iterator[_T]:
         raise NotImplementedError(
-            "'{}' not implement in class "
-            "{}".format('__iter__', self.__class__.__name__)
+            "'{}' not implement in class {}".format(
+                '__iter__', self.__class__.__name__
+            )
         )
 
     def __getitem__(self, idx: int) -> Never:
         raise RuntimeError(
-            "'{}' should not be called for IterableDataset"
-            "{}".format('__getitem__', self.__class__.__name__)
+            "'{}' should not be called for IterableDataset{}".format(
+                '__getitem__', self.__class__.__name__
+            )
         )
 
     def __len__(self) -> Never:
         raise RuntimeError(
-            "'{}' should not be called for IterableDataset"
-            "{}".format('__len__', self.__class__.__name__)
+            "'{}' should not be called for IterableDataset{}".format(
+                '__len__', self.__class__.__name__
+            )
         )
 
 

python/paddle/jit/dy2static/error.py

@@ -211,7 +211,7 @@ def numpy_api_check(self, format_exception, error_line):
         func_str = None
         for frame in tb:
             searched_name = re.search(
-                fr'({RE_PYMODULE})*{frame.name}',
+                rf'({RE_PYMODULE})*{frame.name}',
                 error_line,
             )
             if searched_name:

python/paddle/jit/dy2static/pir_partial_program.py

@@ -315,10 +315,13 @@ def split_forward_backward(self):
         ), "Please ensure only split once! don't call split_forward_backward manually."
         self.has_splited = True
         self.update_op_range()
-        [
-            fwd_prog,
-            bwd_prog,
-        ], prog_attr = paddle.base.libpaddle.pir.split_program(
+        (
+            [
+                fwd_prog,
+                bwd_prog,
+            ],
+            prog_attr,
+        ) = paddle.base.libpaddle.pir.split_program(
             self.program,
             self.x_values,
             self.param_values,
@@ -622,7 +625,10 @@ def __call__(self, program):
         )
         names = paddle.utils.map_structure(
             lambda value: ValuePreservePass.attach_preserved_name(
-                value, program, value2name, name_generator  # noqa: F821
+                value,
+                program,
+                value2name,  # noqa: F821
+                name_generator,
             ),
             self.values,
         )

python/paddle/jit/dy2static/transformers/decorator_transformer.py

@@ -78,7 +78,7 @@ def visit_FunctionDef(self, node):
                 # match case like:
                 # @a.d.g.deco
                 re_tmp = re.match(
-                    fr'({RE_PYMODULE})*({RE_PYNAME})$',
+                    rf'({RE_PYMODULE})*({RE_PYNAME})$',
                     deco_full_name,
                 )
                 deco_name = re_tmp.group(2)

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

@@ -523,11 +523,14 @@ def compile_function(
         from ..breakpoint import BreakpointManager
 
         BreakpointManager().on_event("compile_function")
-        graph_fn, (
-            statement_ir,
-            symbolic_inputs,
-            _,
-            symbolic_outputs,
+        (
+            graph_fn,
+            (
+                statement_ir,
+                symbolic_inputs,
+                _,
+                symbolic_outputs,
+            ),
         ) = compile_graph_result
         compiled_fn_name = f"___graph_fn_{statement_ir.name}"
         # prepare function and inputs

python/paddle/nn/functional/common.py

@@ -657,7 +657,7 @@ def _is_list_or_tuple_(data):
             if len(x.shape) == 5:
                 if len(out_shape) != 3:
                     raise ValueError(
-                        "size length should be 3 for " "input 5-D tensor."
+                        "size length should be 3 for input 5-D tensor."
                     )
                 if contain_var:
                     attrs['out_d'] = size_list[0]

python/paddle/nn/functional/conv.py

@@ -963,8 +963,7 @@ def conv1d_transpose(
     else:
         if output_padding != 0:
             raise ValueError(
-                'output_padding option is mutually exclusive with '
-                'output_size'
+                'output_padding option is mutually exclusive with output_size'
             )
         if isinstance(output_size, (list, tuple, int)):
             output_size = [*convert_to_list(output_size, 1, 'output_size'), 1]
@@ -1236,8 +1235,7 @@ def conv2d_transpose(
     else:
         if output_padding != 0:
             raise ValueError(
-                'output_padding option is mutually exclusive with '
-                'output_size'
+                'output_padding option is mutually exclusive with output_size'
             )
         if isinstance(output_size, (list, tuple)):
             if _contain_var(output_size):
@@ -1710,8 +1708,7 @@ def conv3d_transpose(
     else:
         if output_padding != 0:
             raise ValueError(
-                'output_padding option is mutually exclusive with '
-                'output_size'
+                'output_padding option is mutually exclusive with output_size'
             )
         if isinstance(output_size, (list, tuple, int)):
             output_size = convert_to_list(output_size, 3, 'output_size')

python/paddle/nn/functional/loss.py

@@ -3917,9 +3917,7 @@ def triplet_margin_with_distance_loss(
 
     if not (input.shape == positive.shape == negative.shape):
         raise ValueError(
-            "input's shape must equal to "
-            "positive's shape and  "
-            "negative's shape"
+            "input's shape must equal to positive's shape and negative's shape"
         )
 
     distance_function = (
@@ -4064,9 +4062,7 @@ def triplet_margin_loss(
 
     if not (input.shape == positive.shape == negative.shape):
         raise ValueError(
-            "input's shape must equal to "
-            "positive's shape and  "
-            "negative's shape"
+            "input's shape must equal to positive's shape and negative's shape"
         )
 
     distance_function = paddle.nn.PairwiseDistance(p, epsilon=epsilon)
@@ -4420,7 +4416,7 @@ def soft_margin_loss(
         )
 
     if not (input.shape == label.shape):
-        raise ValueError("input's shape must equal to " "label's shape")
+        raise ValueError("input's shape must equal to label's shape")
 
     label = paddle.cast(label, input.dtype)
     out = paddle.log(1 + paddle.exp(-label * input))
@@ -4678,7 +4674,7 @@ def adaptive_log_softmax_with_loss(
             )
     else:
         raise ValueError(
-            '0D or 1D label tensor expected, ' 'multi-label not supported'
+            '0D or 1D label tensor expected, multi-label not supported'
         )
 
     is_batched = target_dim > 0