[CodeStyle][UP031] fix `/python/paddle/{hapi, geometric, jit, quantiz…

…ation, utils}*` - part 15 (PaddlePaddle#65576)

paddle/phi/kernels/fusion/cutlass/cutlass_kernels/fpA_intB_gemm/generic_mixed_gemm_kernelLauncher.py

@@ -135,7 +135,7 @@ def SubstituteTemplate(template, values):
     while changed:
         changed = False
         for key, value in values.items():
-            regex = "\\{%s\\}" % key
+            regex = f"\\{{{key}\\}}"
             newtext = re.sub(regex, value, text)
             if newtext != text:
                 changed = True
@@ -224,7 +224,7 @@ def generate_source_cu(
     header_name = "autogen_tmp/arch_define.h"
     if archs:
         for arch in archs:
-            define_line = "#define USE_FPAINTB_GEMM_WITH_SM%s\n" % str(arch)
+            define_line = f"#define USE_FPAINTB_GEMM_WITH_SM{arch}\n"
             header_all += define_line
     with open(header_name, "w") as f:
         f.write(header_all)

paddle/phi/kernels/fusion/cutlass/util.py

@@ -30,7 +30,7 @@ def SubstituteTemplate(template, values):
     while changed:
         changed = False
         for key, value in values.items():
-            regex = "\\$\\{%s\\}" % key
+            regex = f"\\$\\{{{key}\\}}"
             newtext = re.sub(regex, value, text)
             if newtext != text:
                 changed = True

python/paddle/geometric/message_passing/send_recv.py

@@ -121,8 +121,7 @@ def send_u_recv(
 
     if reduce_op not in ["sum", "mean", "max", "min"]:
         raise ValueError(
-            "reduce_op should be `sum`, `mean`, `max` or `min`, but received %s"
-            % reduce_op
+            f"reduce_op should be `sum`, `mean`, `max` or `min`, but received {reduce_op}"
         )
 
     # TODO(daisiming): Should we add judgement for out_size: max(dst_index) + 1.
@@ -291,14 +290,12 @@ def send_ue_recv(
 
     if message_op not in ["add", "sub", "mul", "div"]:
         raise ValueError(
-            "message_op should be `add`, `sub`, `mul`, `div`, but received %s"
-            % message_op
+            f"message_op should be `add`, `sub`, `mul`, `div`, but received {message_op}"
         )
 
     if reduce_op not in ["sum", "mean", "max", "min"]:
         raise ValueError(
-            "reduce_op should be `sum`, `mean`, `max` or `min`, but received %s"
-            % reduce_op
+            f"reduce_op should be `sum`, `mean`, `max` or `min`, but received {reduce_op}"
         )
 
     x, y = reshape_lhs_rhs(x, y)
@@ -459,8 +456,7 @@ def send_uv(x, y, src_index, dst_index, message_op="add", name=None):
 
     if message_op not in ['add', 'sub', 'mul', 'div']:
         raise ValueError(
-            "message_op should be `add`, `sub`, `mul`, `div`, but received %s"
-            % message_op
+            f"message_op should be `add`, `sub`, `mul`, `div`, but received {message_op}"
         )
 
     x, y = reshape_lhs_rhs(x, y)

python/paddle/hapi/callbacks.py

@@ -380,16 +380,16 @@ def on_epoch_begin(self, epoch=None, logs=None):
 
     def _updates(self, logs, mode):
         values = []
-        metrics = getattr(self, '%s_metrics' % (mode))
-        progbar = getattr(self, '%s_progbar' % (mode))
-        steps = getattr(self, '%s_step' % (mode))
+        metrics = getattr(self, f'{mode}_metrics')
+        progbar = getattr(self, f'{mode}_progbar')
+        steps = getattr(self, f'{mode}_step')
 
         for k in metrics:
             if k in logs:
                 values.append((k, logs[k]))
 
-        if self.verbose == 3 and hasattr(self, '_%s_timer' % (mode)):
-            timer = getattr(self, '_%s_timer' % (mode))
+        if self.verbose == 3 and hasattr(self, f'_{mode}_timer'):
+            timer = getattr(self, f'_{mode}_timer')
             cnt = timer['count'] if timer['count'] > 0 else 1.0
             samples = timer['samples'] if timer['samples'] > 0 else 1.0
             values.append(
@@ -814,8 +814,8 @@ def __init__(
         self.save_dir = None
         if mode not in ['auto', 'min', 'max']:
             warnings.warn(
-                'EarlyStopping mode %s is unknown, '
-                'fallback to auto mode.' % mode
+                f'EarlyStopping mode {mode} is unknown, '
+                'fallback to auto mode.'
             )
             mode = 'auto'
         if mode == 'min':
@@ -870,8 +870,7 @@ def on_eval_end(self, logs=None):
                 print('Epoch %d: Early stopping.' % (self.stopped_epoch + 1))
                 if self.save_best_model and self.save_dir is not None:
                     print(
-                        'Best checkpoint has been saved at %s'
-                        % (
+                        'Best checkpoint has been saved at {}'.format(
                             os.path.abspath(
                                 os.path.join(self.save_dir, 'best_model')
                             )
@@ -946,8 +945,8 @@ def _updates(self, logs, mode):
             visualdl = try_import('visualdl')
             self.writer = visualdl.LogWriter(self.log_dir)
 
-        metrics = getattr(self, '%s_metrics' % (mode))
-        current_step = getattr(self, '%s_step' % (mode))
+        metrics = getattr(self, f'{mode}_metrics')
+        current_step = getattr(self, f'{mode}_step')
 
         if mode == 'train':
             total_step = current_step
@@ -1118,8 +1117,8 @@ def _updates(self, logs, mode):
         if not self._is_write():
             return
 
-        metrics = getattr(self, '%s_metrics' % (mode))
-        current_step = getattr(self, '%s_step' % (mode))
+        metrics = getattr(self, f'{mode}_metrics')
+        current_step = getattr(self, f'{mode}_step')
 
         _metrics = {}
 
@@ -1271,8 +1270,8 @@ def _reset(self):
         """Resets wait counter and cooldown counter."""
         if self.mode not in ['auto', 'min', 'max']:
             warnings.warn(
-                'Learning rate reduction mode %s is unknown, '
-                'fallback to auto mode.' % self.mode
+                f'Learning rate reduction mode {self.mode} is unknown, '
+                'fallback to auto mode.'
             )
             self.mode = 'auto'
         if self.mode == 'min' or (

python/paddle/hapi/model.py

@@ -2242,8 +2242,7 @@ def _save_inference_model(self, path):
                     )
                 if self._is_shape_inferred:
                     warnings.warn(
-                        "'inputs' was not specified when Model initialization, so the input shape to be saved will be the shape derived from the user's actual inputs. The input shape to be saved is %s. For saving correct input shapes, please provide 'inputs' for Model initialization."
-                        % self._input_info[0]
+                        f"'inputs' was not specified when Model initialization, so the input shape to be saved will be the shape derived from the user's actual inputs. The input shape to be saved is {self._input_info[0]}. For saving correct input shapes, please provide 'inputs' for Model initialization."
                     )
 
                 paddle.jit.save(layer, path, input_spec=self._inputs)

python/paddle/hapi/model_summary.py

@@ -654,12 +654,12 @@ def _get_input_size(input_size, size):
         f"Non-trainable params: {total_params - trainable_params:,}" + "\n"
     )
     summary_str += "-" * table_width['table_width'] + "\n"
-    summary_str += "Input size (MB): %0.2f" % total_input_size + "\n"
+    summary_str += f"Input size (MB): {total_input_size:0.2f}" + "\n"
     summary_str += (
-        "Forward/backward pass size (MB): %0.2f" % total_output_size + "\n"
+        f"Forward/backward pass size (MB): {total_output_size:0.2f}" + "\n"
     )
-    summary_str += "Params size (MB): %0.2f" % total_params_size + "\n"
-    summary_str += "Estimated Total Size (MB): %0.2f" % total_size + "\n"
+    summary_str += f"Params size (MB): {total_params_size:0.2f}" + "\n"
+    summary_str += f"Estimated Total Size (MB): {total_size:0.2f}" + "\n"
     summary_str += "-" * table_width['table_width'] + "\n"
 
     # return summary

python/paddle/hapi/progressbar.py

@@ -135,16 +135,16 @@ def convert_uint16_to_float(in_list):
             sys.stdout.write(bar_chars)
 
             for k, val in values:
-                info += ' - %s:' % k
+                info += f' - {k}:'
                 val = val if isinstance(val, list) else [val]
                 for i, v in enumerate(val):
                     if isinstance(v, (float, np.float32, np.float64)):
                         if abs(v) > 1e-3:
-                            info += ' %.4f' % v
+                            info += f' {v:.4f}'
                         else:
-                            info += ' %.4e' % v
+                            info += f' {v:.4e}'
                     else:
-                        info += ' %s' % v
+                        info += f' {v}'
 
             if self._num is not None and current_num < self._num:
                 eta = time_per_unit * (self._num - current_num)
@@ -159,7 +159,7 @@ def convert_uint16_to_float(in_list):
                 else:
                     eta_format = '%ds' % eta
 
-                info += ' - ETA: %s' % eta_format
+                info += f' - ETA: {eta_format}'
 
             info += fps
             self._total_width += len(info)
@@ -187,25 +187,25 @@ def convert_uint16_to_float(in_list):
             info = count + info
 
             for k, val in values:
-                info += ' - %s:' % k
+                info += f' - {k}:'
                 val = val if isinstance(val, list) else [val]
                 for v in val:
                     if isinstance(v, (float, np.float32, np.float64)):
                         if abs(v) > 1e-3:
-                            info += ' %.4f' % v
+                            info += f' {v:.4f}'
                         else:
-                            info += ' %.4e' % v
+                            info += f' {v:.4e}'
                     elif (
                         isinstance(v, np.ndarray)
                         and v.size == 1
                         and v.dtype in [np.float32, np.float64]
                     ):
                         if abs(v.item()) > 1e-3:
-                            info += ' %.4f' % v.item()
+                            info += f' {v.item():.4f}'
                         else:
-                            info += ' %.4e' % v.item()
+                            info += f' {v.item():.4e}'
                     else:
-                        info += ' %s' % v
+                        info += f' {v}'
 
             info += fps
             info += '\n'

python/paddle/jit/dy2static/convert_operators.py

@@ -493,11 +493,11 @@ def new_false_fn():
             "Unsupported return type of true_fn and false_fn in cond", str(e)
         ):
             raise Dygraph2StaticException(
-                f"Your if/else have different return type. TODO: add link to modifty. {str(e)}"
+                f"Your if/else have different return type. TODO: add link to modifty. {e}"
             )
         if re.search("Incompatible return values of", str(e)):
             raise Dygraph2StaticException(
-                f"Your if/else have different number of return value. TODO: add link to modifty. {str(e)}"
+                f"Your if/else have different number of return value. TODO: add link to modifty. {e}"
             )
         raise e
     get_args = lambda: helper.get(union_name)
@@ -644,7 +644,7 @@ def convert_zip(*args):
         if isinstance(arg, (Variable, Value)) and arg.shape[0] == -1:
             raise RuntimeError(
                 "Not support zip(tensor, ...) when tensor.shape[0] == -1, "
-                f"but found args[{str(i)}].shape[0] == -1 in 'zip'"
+                f"but found args[{i}].shape[0] == -1 in 'zip'"
             )
     return zip(*args)
 

python/paddle/jit/dy2static/error.py

@@ -339,7 +339,7 @@ def _create_revise_suggestion(self, bottom_error_message):
                 for suggestion in self.suggestion_dict[keywords]:
                     suggestion_msg = (
                         ' ' * BLANK_COUNT_BEFORE_FILE_STR * 2
-                        + f'{str(len(revise_suggestions) - 1)}. {suggestion}'
+                        + f'{len(revise_suggestions) - 1}. {suggestion}'
                     )
                     revise_suggestions.append(suggestion_msg)
         return revise_suggestions if len(revise_suggestions) > 2 else []

python/paddle/jit/dy2static/export_subgraph.py

@@ -240,5 +240,5 @@ def pir_exporter(pp_layer, program, role, shared_inputs=None, inter_outs=None):
             )
     except Exception as e:
         _logger.error(
-            f"Export subgraph failed: {e}\n. Received original program: {str(program)}"
+            f"Export subgraph failed: {e}\n. Received original program: {program}"
         )

python/paddle/jit/dy2static/function_spec.py

@@ -403,7 +403,7 @@ def _replace_to_input_spec_with_new_name(args, arg_names):
                     paddle.base.framework.Variable,
                 ),
             ):
-                input_var.name = f"_jst.{str(order).zfill(order_digit)}.{name_prefix}.{str(index)}"
+                input_var.name = f"_jst.{str(order).zfill(order_digit)}.{name_prefix}.{index}"
                 index += 1
             args_with_spec.append(input_var)
     args_with_spec = paddle.utils.pack_sequence_as(args, args_with_spec)

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

@@ -1002,10 +1002,10 @@ def make_stringify_guard(self) -> list[StringifyExpression]:
             frame_value_tracer = self.tracker.trace_value_from_frame()
 
             def format_dtype(dtype: np.dtype):
-                return f"np.{str(dtype)}"
+                return f"np.{dtype}"
 
             def format_number(number: np.number):
-                return f"{format_dtype(number.dtype)}({str(number.item())})"
+                return f"{format_dtype(number.dtype)}({number.item()})"
 
             return [
                 StringifyExpression(

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

@@ -193,7 +193,7 @@ def create_layer(self):
             if inp in self.SIR.non_param_symbol:
                 meta = self.SIR.symbol_meta_map[inp]
                 forward_definition.append(
-                    f"    {self.name_gener(inp)},    # {str(meta)}"
+                    f"    {self.name_gener(inp)},    # {meta}"
                 )
         forward_definition.append("):")
 

python/paddle/quantization/imperative/qat.py

@@ -338,7 +338,7 @@ def __init__(
             assert (
                 not isinstance(layer, str)
                 and layer in self.fake_quant_input_layers
-            ), ("%s is unsupported to be quantized." % layer)
+            ), f"{layer} is unsupported to be quantized."
 
         quantize_type = {
             'abs_max',
@@ -352,14 +352,13 @@ def __init__(
             weight_quantize_type != 'moving_average_abs_max'
             and weight_quantize_type in quantize_type
         ), (
-            "Unsupported weight_quantize_type: %s. It can only "
-            "be abs_max or channel_wise_abs_max." % weight_quantize_type
+            f"Unsupported weight_quantize_type: {weight_quantize_type}. It can only "
+            "be abs_max or channel_wise_abs_max."
         )
         # TODO (jc): activation_quantize_type supports range_abs_max
         assert activation_quantize_type in act_quantize_type, (
-            "Unsupported activation_quantize_type: %s. It can "
+            f"Unsupported activation_quantize_type: {activation_quantize_type}. It can "
             "only be moving_average_abs_max or lsq_act now."
-            % activation_quantize_type
         )
 
         bits_check = (
@@ -436,9 +435,9 @@ def _get_input_quantized_layer(self, layer):
             if isinstance(layer, value):
                 quant_layer_name = 'Quantized' + key
                 break
-        assert quant_layer_name is not None, (
-            "The layer %s is unsupported to be quantized." % layer.full_name()
-        )
+        assert (
+            quant_layer_name is not None
+        ), f"The layer {layer.full_name()} is unsupported to be quantized."
 
         return quant_layers.__dict__[quant_layer_name](layer, **self._kwargs)
 

python/paddle/utils/cpp_extension/cpp_extension.py

@@ -751,7 +751,7 @@ def run(self, *args, **kwargs):
             if will_rename:
                 new_so_path = filename + "_pd_" + ext
                 if not os.path.exists(new_so_path):
-                    os.rename(r'%s' % egg_file, r'%s' % new_so_path)
+                    os.rename(rf'{egg_file}', rf'{new_so_path}')
                 assert os.path.exists(new_so_path)
 
 

python/paddle/utils/cpp_extension/extension_utils.py

@@ -1209,21 +1209,25 @@ def _get_api_inputs_str(op_name):
     # input name by `@`, and only use first substr as argument
     params_list = ','.join([p.split("@")[0].lower() for p in param_names])
     # e.g: {'X': x, 'Y': y, 'Z': z}
-    ins_map = "{%s}" % ','.join(
-        [
-            "'{}' : {}".format(in_name, in_name.split("@")[0].lower())
-            for in_name in in_names
-        ]
+    ins_map = "{{{}}}".format(
+        ','.join(
+            [
+                "'{}' : {}".format(in_name, in_name.split("@")[0].lower())
+                for in_name in in_names
+            ]
+        )
     )
     # e.g: {'num': n}
-    attrs_map = "{%s}" % ",".join(
-        [
-            "'{}' : {}".format(attr_name, attr_name.split("@")[0].lower())
-            for attr_name in attr_names
-        ]
+    attrs_map = "{{{}}}".format(
+        ",".join(
+            [
+                "'{}' : {}".format(attr_name, attr_name.split("@")[0].lower())
+                for attr_name in attr_names
+            ]
+        )
     )
     # e.g: ['Out', 'Index']
-    outs_list = "[%s]" % ','.join([f"'{name}'" for name in out_names])
+    outs_list = "[{}]".format(','.join([f"'{name}'" for name in out_names]))
 
     inplace_reverse_idx = core.eager._get_custom_operator_inplace_map(op_name)
 

python/paddle/utils/download.py

@@ -189,9 +189,7 @@ def _get_download(url, fullname):
             return fullname
 
     except Exception as e:  # requests.exceptions.ConnectionError
-        logger.info(
-            f"Downloading {fname} from {url} failed with exception {str(e)}"
-        )
+        logger.info(f"Downloading {fname} from {url} failed with exception {e}")
         return False