[CodeStyle][ruff] fix E226, NPY201 (PaddlePaddle#60245)

* fix ruff preview

* fix cast

python/paddle/distributed/launch/controllers/collective.py

@@ -92,7 +92,9 @@ def _build_pod_with_args(self):
         ips = self.ctx.args.ips.split(',')
 
         job_endpoints = [
-            f"{h}:{p+start_port}" for h in ips for p in range(self.pod.replicas)
+            f"{h}:{p + start_port}"
+            for h in ips
+            for p in range(self.pod.replicas)
         ]
 
         self.ctx.logger.debug(f"job endpoints: {job_endpoints}")

python/paddle/distributed/launch/main.py

@@ -576,10 +576,10 @@ def launch():
 
             end_time = time.time()
             ctx.logger.info(
-                f"AtuoTuner for GBS search ends in {end_time-start_time}s."
+                f"AtuoTuner for GBS search ends in {end_time - start_time}s."
             )
             logger.info(
-                f"AtuoTuner for GBS search ends in {end_time-start_time}s."
+                f"AtuoTuner for GBS search ends in {end_time - start_time}s."
             )
 
         # build AutoTuner to get new config
@@ -1118,8 +1118,8 @@ def launch():
         assert best_cfg and best_cfg["time"] != -1
 
         end_time = time.time()
-        ctx.logger.info(f"AutoTuner ended in {end_time-start_time}s.")
-        logger.info(f"AutoTuner ended in {end_time-start_time}s.")
+        ctx.logger.info(f"AutoTuner ended in {end_time - start_time}s.")
+        logger.info(f"AutoTuner ended in {end_time - start_time}s.")
         # launch best cfg
         # estimation search need not run best cfg
         if not tuner_cfg.get("run_best", True) or tuner_cfg["search_algo"].get(

python/paddle/hapi/callbacks.py

@@ -1279,10 +1279,10 @@ def _reset(self):
             self.mode == 'auto' and 'acc' not in self.monitor
         ):
             self.monitor_op = lambda a, b: np.less(a, b - self.min_delta)
-            self.best = np.Inf
+            self.best = np.inf
         else:
             self.monitor_op = lambda a, b: np.greater(a, b + self.min_delta)
-            self.best = -np.Inf
+            self.best = -np.inf
         self.cooldown_counter = 0
         self.wait = 0
 

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

@@ -497,7 +497,7 @@ def error_message_summary(original_error: Exception) -> str:
             )
             if current_line != -1:
                 message_lines.append(
-                    f"{indent}  {lines[current_line-start].rstrip()}"
+                    f"{indent}  {lines[current_line - start].rstrip()}"
                 )
         error_message = traceback.format_exception_only(
             type(original_error), original_error

python/paddle/static/quantization/quant2_int8_mkldnn_pass.py

@@ -203,7 +203,7 @@ def _gather_input_scales_from_fake(self, graph):
                 scale = np.array(
                     1.0 / self._load_param(self._scope, scale_name)[0]
                 ).astype(np.float64)
-                scale[scale == np.Inf] = 0.0
+                scale[scale == np.inf] = 0.0
                 lod_tensor = self._convert_scale2tensor(scale)
                 use_unsigned_int = False
                 self._add_scale_for_vars(
@@ -238,7 +238,7 @@ def _gather_output_scales_from_attr(self, graph):
                 if attr_scale == 0.0:
                     continue
                 scale = np.array(1.0 / attr_scale).astype(np.float64)
-                scale[scale == np.Inf] = 0.0
+                scale[scale == np.inf] = 0.0
                 scale_lod_tensor = self._convert_scale2tensor(scale)
                 use_unsigned_int = False
                 for output_name in op.op().outputs():
@@ -561,7 +561,7 @@ def _compute_var_scales(ops, w_name, axis):
                         ),
                         axis=axis,
                     )
-                    scales[scales == np.Inf] = 0.0
+                    scales[scales == np.inf] = 0.0
 
                     lod_tensor = self._convert_scale2tensor(scales)
                     use_unsigned_int = False

python/paddle/tensor/einsum.py

@@ -672,7 +672,7 @@ def plan_einsum(operands, g_view, g_shape, g_supports, g_count, n_bcast):
     if any(ax != dim for ax, dim in enumerate(view[:nout])):
         perm = [dim for dim in view if dim >= 0]
         if sorted(perm) != perm:
-            varname = f'op{nop-1}'
+            varname = f'op{nop - 1}'
             step = transpose, [varname], varname, perm
             plan.add_step(step)
         dim = 0
@@ -684,14 +684,14 @@ def plan_einsum(operands, g_view, g_shape, g_supports, g_count, n_bcast):
             if d == -1:
                 unsqueeze_dims.append(ax)
         if unsqueeze_dims:
-            varname = f'op{nop-1}'
+            varname = f'op{nop - 1}'
             step = unsqueeze, [varname], varname, unsqueeze_dims
             plan.add_step(step)
 
     squeeze_dims = [dim for dim in view[nout:] if dim != -1]
     if squeeze_dims:
         # plan_reduce(plan, nop-1, reduce_dims, keepdim=False)
-        varname = f'op{nop-1}'
+        varname = f'op{nop - 1}'
         step = squeeze, [varname], varname, squeeze_dims
         plan.add_step(step)
 

test/custom_runtime/test_custom_cpu_to_static.py

@@ -42,7 +42,7 @@ def train_func_base(epoch_id, train_loader, model, cost, optimizer):
         )
     epoch_end = time.time()
     print(
-        f"Epoch ID: {epoch_id+1}, FP32 train epoch time: {(epoch_end - epoch_start) * 1000} ms"
+        f"Epoch ID: {epoch_id + 1}, FP32 train epoch time: {(epoch_end - epoch_start) * 1000} ms"
     )
 
 
@@ -75,7 +75,7 @@ def train_func_ampo1(epoch_id, train_loader, model, cost, optimizer, scaler):
         )
     epoch_end = time.time()
     print(
-        f"Epoch ID: {epoch_id+1}, AMPO1 train epoch time: {(epoch_end - epoch_start) * 1000} ms"
+        f"Epoch ID: {epoch_id + 1}, AMPO1 train epoch time: {(epoch_end - epoch_start) * 1000} ms"
     )
 
 
@@ -96,7 +96,7 @@ def test_func(epoch_id, test_loader, model, cost):
         avg_acc[1].append(acc_top5.numpy())
     model.train()
     print(
-        f"Epoch ID: {epoch_id+1}, Top1 accurary: {np.array(avg_acc[0]).mean()}, Top5 accurary: {np.array(avg_acc[1]).mean()}"
+        f"Epoch ID: {epoch_id + 1}, Top1 accurary: {np.array(avg_acc[0]).mean()}, Top5 accurary: {np.array(avg_acc[1]).mean()}"
     )
 
 

test/legacy_test/test_fill_constant_op.py

@@ -413,9 +413,9 @@ def test_inf(self):
 
     def test_ninf(self):
         with base.dygraph.guard():
-            res = paddle.tensor.fill_constant([1], 'float32', np.NINF)
+            res = paddle.tensor.fill_constant([1], 'float32', -np.inf)
             self.assertTrue(np.isinf(res.numpy().item(0)))
-            self.assertEqual(np.NINF, res.numpy().item(0))
+            self.assertEqual(-np.inf, res.numpy().item(0))
 
 
 class TestFillConstantOpError(unittest.TestCase):

test/legacy_test/test_seed_op.py

@@ -69,7 +69,10 @@ def check_static_result(self, place):
                 (out1,) = exe.run(
                     static.default_main_program(), fetch_list=res_list
                 )
-                self.assertEqual(out1, np.cast['int32'](self.rng1.random()))
+                self.assertEqual(
+                    out1,
+                    np.asarray(self.rng1.random()).astype(np.int32),
+                )
 
     def test_static(self):
         for place in self.places:

test/xpu/test_increment_op_xpu.py

@@ -41,7 +41,7 @@ def setUp(self):
             self.initTestCase()
 
             x = np.random.uniform(-100, 100, [1]).astype(self.dtype)
-            output = x + np.cast[self.dtype](self.step)
+            output = x + np.asarray(self.step).astype(self.dtype)
             output = output.astype(self.dtype)
 
             self.inputs = {'X': x}

tools/gen_ut_cmakelists.py

@@ -275,7 +275,7 @@ def _init_dist_ut_ports_from_cmakefile(self, cmake_file_name):
                 assert re.compile("^test_[0-9a-zA-Z_]+").search(
                     name
                 ), f'''we found a test for initial the latest dist_port but the test name '{name}' seems to be wrong
-                    at line {k-1}, in file {cmake_file_name}
+                    at line {k - 1}, in file {cmake_file_name}
                     '''
                 self.gset_port(name, port)
 
@@ -559,7 +559,7 @@ def _gen_cmakelists(self, current_work_dir, depth=0):
                     print("===============PARSE LINE ERRORS OCCUR==========")
                     print(e)
                     print(f"[ERROR FILE]: {current_work_dir}/testslist.csv")
-                    print(f"[ERROR LINE {i+1}]: {line.strip()}")
+                    print(f"[ERROR LINE {i + 1}]: {line.strip()}")
                     sys.exit(1)
 
         for sub in sub_dirs: