[CodeStyle][F821] fix undefined variables due to missing imports, misspelled...

[CodeStyle][F821] fix undefined variables due to missing imports, misspelled variable names (#47899)

* `hann` -> `_hann`

* `false` -> `False`

* a missing passed argument `reduce_all`

* some missing imports

* `device_type` -> `heter_device_type`

* `PKVClient` -> `KVClient`

* fix some typos and missing imports

python/paddle/audio/functional/window.py

@@ -210,7 +210,7 @@ def _tukey(
     if alpha <= 0:
         return paddle.ones((M,), dtype=dtype)
     elif alpha >= 1.0:
-        return hann(M, sym=sym)
+        return _hann(M, sym=sym)
 
     M, needs_trunc = _extend(M, sym)
 

python/paddle/distributed/auto_parallel/cost/base_cost.py

@@ -833,7 +833,7 @@ class CommOpCost(OpCost):
             if self.op_desc is not None:
                 self._group_ranks = self.op_desc["group_ranks"]
             elif self.op is not None:
-                ring_id = op.attrs("ring_id")
+                ring_id = self.op.attrs("ring_id")
                 process_group = get_process_group(ring_id)
                 if process_group is None:
                     raise ValueError(

python/paddle/distributed/auto_parallel/operators/dist_reshape.py

@@ -23,6 +23,7 @@ from .dist_default import DistributedDefaultImpl0
 from ..cost import build_comp_desc_from_dist_op, build_comp_costs_from_descs
 from ..cost import Reshape2OpCost
 from ..cost import Reshape2GradOpCost
+from ..cost import build_dp_costs
 from paddle.distributed.fleet.meta_optimizers.common import OpRole
 
 

python/paddle/distributed/auto_parallel/planner.py

@@ -437,7 +437,7 @@ class SearchAlgorithm:
 
     @property
     def name(self):
-        self.name = name
+        self.name = self._name
 
     def search(self):
         raise NotImplementedError("Please Implement this method in subclass.")

python/paddle/distributed/fleet/base/orthogonal_strategy.py

@@ -129,7 +129,9 @@ class OrthogonalStrategy:
     def _check_valid_strategy(self):
         assert len(self._list_of_strategy_name) == len(
             set(self._list_of_strategy_name)
-        ), "Defined duplicated strategies: {}".format(list_of_strategy)
+        ), "Defined duplicated strategies: {}".format(
+            self._list_of_strategy_name
+        )
         num_of_ranks = functools.reduce(
             lambda x, y: x * y, self._list_of_degree
         )

python/paddle/distributed/fleet/data_generator/data_generator.py

@@ -342,9 +342,7 @@ class MultiSlotDataGenerator(DataGenerator):
                 for elem in elements:
                     if isinstance(elem, float):
                         self._proto_info[-1] = (name, "float")
-                    elif not isinstance(elem, int) and not isinstance(
-                        elem, long
-                    ):
+                    elif not isinstance(elem, int):
                         raise ValueError(
                             "the type of element%s must be in int or float"
                             % type(elem)
@@ -379,9 +377,7 @@ class MultiSlotDataGenerator(DataGenerator):
                     if self._proto_info[index][1] != "float":
                         if isinstance(elem, float):
                             self._proto_info[index] = (name, "float")
-                        elif not isinstance(elem, int) and not isinstance(
-                            elem, long
-                        ):
+                        elif not isinstance(elem, int):
                             raise ValueError(
                                 "the type of element%s must be in int or float"
                                 % type(elem)

python/paddle/distributed/fleet/layers/mpu/mp_ops.py

@@ -18,6 +18,7 @@ from paddle.fluid import core
 from paddle.fluid.framework import _non_static_mode
 from paddle.fluid.framework import _in_legacy_dygraph
 from paddle.fluid.framework import in_dygraph_mode
+from paddle.fluid.framework import _varbase_creator
 from paddle.fluid.layer_helper import LayerHelper
 from paddle.fluid.data_feeder import check_variable_and_dtype
 from paddle.fluid.dygraph import layers

python/paddle/distributed/fleet/runtime/the_one_ps.py

@@ -902,7 +902,9 @@ class TheOnePSRuntime(RuntimeBase):
                 heter_device_type = self.role_maker._heter_device_type().upper()
                 if heter_device_type not in ["GPU", "XPU", "CPU"]:
                     raise ValueError(
-                        "Heter Worker Not Support Device {}".format(device_type)
+                        "Heter Worker Not Support Device {}".format(
+                            heter_device_type
+                        )
                     )
                 if heter_device_type == "GPU":
                     executor = Executor(

python/paddle/distributed/fleet/utils/fs.py

@@ -19,6 +19,7 @@ import re
 import time
 import abc
 from paddle.fluid import core
+from .log_util import logger
 import functools
 
 import shutil

python/paddle/distributed/launch/job/pod.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 from .container import Container
 
 from .status import Status
@@ -27,8 +29,8 @@ class PodSepc:
         )
 
         # by controller
-        self._init_containers: List[Container] = []
-        self._containers: List[Container] = []
+        self._init_containers: list[Container] = []
+        self._containers: list[Container] = []
 
         # self.resource: Resource = None
         # self.status: Status = None

python/paddle/distributed/launch/utils/kv_client.py

@@ -79,7 +79,7 @@ class KVClient:
 
 
 if __name__ == '__main__':
-    cli = PKVClient("http://localhost:8090")
+    cli = KVClient("http://localhost:8090")
     data = {"/workers/1": "rank1", "/workers/2": "rank2"}
     for k, v in data.items():
         cli.put(k, v)

python/paddle/distributed/passes/auto_parallel_data_parallel_optimization.py

@@ -494,7 +494,9 @@ class DataParallelOptimizationPass(PassBase):
             for idx in sorted(remove_op_indices, reverse=True):
                 assert (
                     block.ops[idx].type in remove_op_types
-                ), "Unexception: try to remove op {}".format(str(op))
+                ), "Unexception: try to remove op {}".format(
+                    str(block.ops[idx])
+                )
                 block._remove_op(idx)
 
             # insert coalecse op

python/paddle/distributed/ps/utils/public.py

@@ -482,7 +482,7 @@ def get_geo_trainer_send_context(attrs):
     if attrs['ps_mode'] != DistributedMode.GEO:
         raise ValueError(
             "ps mode: {} not matched {}",
-            format(ps_mode, "get_geo_trainer_send_context"),
+            format(attrs['ps_mode'], "get_geo_trainer_send_context"),
         )
     send_ctx = {}
     trainer_id = get_role_id(attrs['role_maker'])

python/paddle/distribution/variable.py

@@ -14,6 +14,8 @@
 
 from paddle.distribution import constraint
 
+import paddle
+
 
 class Variable:
     """Random variable of probability distribution.

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_equal.py

@@ -27,7 +27,7 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest):
             program_config.ops[i].attrs for i in range(len(program_config.ops))
         ]
         if attrs[0]['axis'] == 0:
-            return false
+            return False
         ver = paddle_infer.get_trt_compile_version()
         if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8415:
             return False

python/paddle/fluid/tests/unittests/test_dropout_nd_op.py

@@ -22,6 +22,7 @@ from paddle.fluid.layer_helper import LayerHelper
 from paddle.fluid.framework import _non_static_mode
 from paddle import _legacy_C_ops
 from paddle.static import default_main_program
+from paddle.fluid.data_feeder import check_variable_and_dtype
 
 
 def dropout_nd(

python/paddle/fluid/tests/unittests/test_mean_op.py

@@ -140,7 +140,7 @@ def ref_reduce_mean(x, axis=None, keepdim=False, reduce_all=False):
     return np.mean(x, axis=axis, keepdims=keepdim)
 
 
-def ref_reduce_mean_grad(x, axis, dtype):
+def ref_reduce_mean_grad(x, axis, dtype, reduce_all):
     if reduce_all:
         axis = list(range(x.ndim))
 
@@ -191,7 +191,6 @@ class TestReduceMeanOp(OpTest):
         if self.dtype != 'float16':
             self.check_grad(['X'], ['Out'], check_eager=True)
         else:
-            return
             if not core.is_compiled_with_cuda():
                 return
             place = paddle.CUDAPlace(0)
@@ -204,7 +203,10 @@ class TestReduceMeanOp(OpTest):
                 )
                 dx = paddle.grad(y, x)[0].numpy()
                 dx_expected = ref_reduce_mean_grad(
-                    self.inputs['X'], self.attrs['dim'], self.dtype
+                    self.inputs['X'],
+                    self.attrs['dim'],
+                    self.dtype,
+                    self.attrs['reduce_all'],
                 )
                 np.testing.assert_array_equal(dx, dx_expected)