fix undefined-variable (#33355)

python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/hybrid_parallel_optimizer.py

@@ -14,6 +14,7 @@
 
 from __future__ import print_function
 import sys
+import paddle
 from paddle.optimizer import Optimizer
 from paddle.fluid.clip import ClipGradByGlobalNorm
 from ...utils.hybrid_parallel_util import fused_allreduce_gradients
@@ -22,6 +23,8 @@ from paddle.fluid.dygraph import base as imperative_base
 from paddle.fluid import framework
 from paddle.fluid.framework import Variable
 from ...utils.log_util import logger
+from paddle.fluid import core
+from paddle.fluid import layers
 
 __all__ = []
 

python/paddle/fluid/contrib/model_stat.py

@@ -150,6 +150,7 @@ def _format_summary(collected_ops_list):
     '''
     _verify_dependent_package()
 
+    from prettytable import PrettyTable
     summary_table = PrettyTable(
         ["No.", "TYPE", "INPUT", "OUTPUT", "PARAMs", "FLOPs"])
     summary_table.align = 'r'

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

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import os
-
+import sys
 import six
 import unittest
 import time

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

@@ -268,7 +268,7 @@ class AutoCheckpointTest(AutoCheckPointACLBase):
     def test_checker(self):
         os.environ.pop("PADDLE_JOB_ID", None)
         try:
-            checker = AutoCheckpointChecker()
+            checker = acp.AutoCheckpointChecker()
             self.assertFalse(True)
         except Exception as e:
             pass

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

@@ -333,7 +333,7 @@ class TestDynamicRNNErrors(unittest.TestCase):
                     hidden = fluid.layers.fc(input=[word, memory],
                                              size=10,
                                              act='tanh')
-                    out = np.ones(1).astype('float32')
+                    out = numpy.ones(1).astype('float32')
                     drnn.update_memory(ex_mem=memory, new_mem=hidden)
                     drnn.output(hidden, out)
 

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

@@ -47,7 +47,7 @@ class TestExportWithTensor(unittest.TestCase):
         self.x_spec = paddle.static.InputSpec(
             shape=[None, 128], dtype='float32')
 
-    def test_with_tensor():
+    def test_with_tensor(self):
         model = LinearNet()
         paddle.onnx.export(model, 'linear_net', input_spec=[self.x_spec])
 

python/paddle/hapi/model.py

@@ -163,7 +163,7 @@ def init_communicator(program, rank, nranks, wait_port, current_endpoint,
             })
     elif core.is_compiled_with_npu():
         hccl_id_var = block.create_var(
-            name=unique_name.generate('hccl_id'),
+            name=fluid.unique_name.generate('hccl_id'),
             persistable=True,
             type=core.VarDesc.VarType.RAW)
         endpoint_to_index_map = {e: idx for idx, e in enumerate(endpoints)}
@@ -710,10 +710,10 @@ class DynamicGraphAdapter(object):
                 enable=self._amp_level != 'O0', **self._amp_custom_lists):
             if self._nranks > 1:
                 outputs = self.ddp_model.forward(
-                    * [to_variable(x) for x in inputs])
+                    *[to_variable(x) for x in inputs])
             else:
                 outputs = self.model.network.forward(
-                    * [to_variable(x) for x in inputs])
+                    *[to_variable(x) for x in inputs])
 
             losses = self.model._loss(*(to_list(outputs) + labels))
             losses = to_list(losses)
@@ -732,7 +732,7 @@ class DynamicGraphAdapter(object):
         metrics = []
         for metric in self.model._metrics:
             metric_outs = metric.compute(*(to_list(outputs) + labels))
-            m = metric.update(* [to_numpy(m) for m in to_list(metric_outs)])
+            m = metric.update(*[to_numpy(m) for m in to_list(metric_outs)])
             metrics.append(m)
 
         return ([to_numpy(l) for l in losses], metrics) \
@@ -746,7 +746,7 @@ class DynamicGraphAdapter(object):
         labels = labels or []
         labels = [to_variable(l) for l in to_list(labels)]
 
-        outputs = self.model.network.forward(* [to_variable(x) for x in inputs])
+        outputs = self.model.network.forward(*[to_variable(x) for x in inputs])
         if self.model._loss:
             losses = self.model._loss(*(to_list(outputs) + labels))
             losses = to_list(losses)
@@ -777,7 +777,7 @@ class DynamicGraphAdapter(object):
                     self._merge_count[self.mode + '_batch'] = samples
 
             metric_outs = metric.compute(*(to_list(outputs) + labels))
-            m = metric.update(* [to_numpy(m) for m in to_list(metric_outs)])
+            m = metric.update(*[to_numpy(m) for m in to_list(metric_outs)])
             metrics.append(m)
 
         if self.model._loss and len(metrics):