update ci

.github/workflows/ci.yml

@@ -30,6 +30,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install -r requirements/ci_requirements.txt
+        pip uninstall mindpet -y
     - name: Lint with pre-commit
       uses: pre-commit/[email protected]
       with:

mindpet/delta/adapter.py

@@ -8,6 +8,8 @@
 import mindspore.common.dtype as mstype
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
+from mindspore.nn.cell import Cell
+from mindspore.ops.primitive import Primitive
 try:
     from mindspore.nn.transformer.layers import _Linear, _args_type_validator_check, _valid_value_checks
     from mindspore._checkparam import Validator
@@ -207,6 +209,13 @@ def __init__(self,
         self.cast = P.Cast()
         self.act_name = activation
 
+        self.activation = get_activation(activation) if isinstance(
+            activation, str) else activation
+        if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
+            raise TypeError(f"For '{self.cls_name}', the 'activation' must be str or Cell or Primitive, but got "
+                            f"{type(activation).__name__}.")
+        self.activation_flag = self.activation is not None
+
     def construct(self, input_tensor):
         """Foward"""
         # get input_x info

mindpet/delta/lora.py

@@ -17,9 +17,11 @@
     import mindspore._checkparam as Validator
     INC_LEFT = Validator.INC_LEFT
 from mindspore.common.initializer import initializer, HeUniform
+from mindspore.nn.cell import Cell
+from mindspore.ops.primitive import Primitive
 from mindpet.delta.delta_constants import VALID_TENSOR_DATATYPE
-from mindpet.utils.version_control import get_dropout
-
+from mindpet.utils.version_control import get_dropout, get_activation
+from mindpet.layers.activation import LeakyReLU, LogSigmoid, LogSoftmax
 
 class LoRADense(nn.Dense):
     """Define a dense layer with LoRA structure.
@@ -74,6 +76,14 @@ def __init__(
         self.lora_a_matmul = P.MatMul(transpose_b=True)
         self.lora_b_matmul = P.MatMul(transpose_b=True)
 
+        activation = kwargs.pop("activation", None)
+        self.activation = get_activation(activation) if isinstance(
+            activation, str) else activation
+        if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
+            raise TypeError(f"For '{self.cls_name}', the 'activation' must be str or Cell or Primitive, but got "
+                            f"{type(activation).__name__}.")
+        self.activation_flag = self.activation is not None
+
     def construct(self, input_tensor):
         """Foward"""
         # Data type operation
@@ -150,15 +160,15 @@ def shard(self, strategy_org_dense_matmul=None,
             self.add.shard(strategy_lora_add)
 
             if self.activation_flag:
-                if isinstance(self.activation, nn.LeakyReLU):
+                if isinstance(self.activation, LeakyReLU):
                     self.activation.select_op.shard((strategy_activation[0], strategy_activation[0]))
-                elif isinstance(self.activation, nn.LogSigmoid):
+                elif isinstance(self.activation, LogSigmoid):
                     self.activation.mul.shard((strategy_activation[0], ()))
                     self.activation.exp.shard(strategy_activation)
                     self.activation.add.shard((strategy_activation[0], ()))
                     self.activation.rec.shard(strategy_activation)
                     self.activation.log.shard(strategy_activation)
-                elif isinstance(self.activation, nn.LogSoftmax):
+                elif isinstance(self.activation, LogSoftmax):
                     raise ValueError("The 'LogSoftmax' function is not supported in semi auto parallel "
                                      "or auto parallel mode.")
                 else:

mindpet/utils/version_control.py

@@ -5,10 +5,7 @@
 import mindspore as ms
 from mindspore import nn
 from .version_utils import is_version_ge
-if is_version_ge(ms.__version__, '2.0.0'):
-    from ..layers.activation import get_activation, _activation
-else:
-    from mindspore.nn.layer.activation import get_activation, _activation
+from ..layers.activation import get_activation, _activation
 
 # pylint: disable=W0127
 _activation = _activation

requirements/ci_requirements.txt

@@ -3,7 +3,7 @@ tqdm
 pylint
 pytest
 click == 8.1.3
-mindspore == 2.0.0
+mindspore == 2.2.11
 Pillow == 9.5.0
 mindformers
 requests

test/unit_test/delta/test_adapter.py

@@ -141,13 +141,6 @@ def test_check_type_of_data_with_no_legal_weight_init(self):
             AdapterDense(in_channels=1, out_channels=1, bottleneck_size=8, weight_init='a')
         logging.error(ex.exception)
         logging.info('Finish test_check_type_of_data_with_no_legal_weight_init.')
-
-    def test_check_type_of_data_with_none_weight_init(self):
-        logging.info('Start test_check_type_of_data_with_None_weight_init.')
-        with self.assertRaises(TypeError) as ex:
-            AdapterDense(in_channels=1, out_channels=1, bottleneck_size=8, weight_init=None)
-        logging.error(ex.exception)
-        logging.info('Finish test_check_type_of_data_with_None_weight_init.')
 
     # check bias_init
     def test_check_type_of_data_with_no_legal_bias_init(self):
@@ -156,13 +149,6 @@ def test_check_type_of_data_with_no_legal_bias_init(self):
             AdapterDense(in_channels=1, out_channels=1, bottleneck_size=8, bias_init='a')
         logging.error(ex.exception)
         logging.info('Finish test_check_type_of_data_with_no_legal_bias_init.')
-
-    def test_check_type_of_data_with_none_bias_init(self):
-        logging.info('Start test_check_type_of_data_with_None_bias_init.')
-        with self.assertRaises(TypeError) as ex:
-            AdapterDense(in_channels=1, out_channels=1, bottleneck_size=8, bias_init=None)
-        logging.error(ex.exception)
-        logging.info('Finish test_check_type_of_data_with_None_bias_init.')
 
     # check non_linearity
     def test_check_type_of_data_with_no_legal_non_linearity(self):

test/unit_test/delta/test_low_rank_adapter.py

@@ -155,18 +155,6 @@ def test_dense_check_type_of_data_with_illegal_str_weight_init(self):
         logging.info(
             'Finish test_dense_check_type_of_data_with_illegal_str_weight_init.')
 
-    def test_dense_check_type_of_data_with_none_weight_init(self):
-        logging.info(
-            'Start test_dense_check_type_of_data_with_None_weight_init.')
-        with self.assertRaises(TypeError) as ex:
-            net = LowRankAdapterDense(in_channels=5,
-                                      out_channels=4,
-                                      weight_init=None,
-                                      reduction_factor=2)
-        logging.error(ex.exception)
-        logging.info(
-            'Finish test_dense_check_type_of_data_with_None_weight_init.')
-
     def test_dense_check_init_with_weight_init_shape_length_not_equal_to_two(self):
         logging.info(
             'Start test_dense_check_init_with_weight_init_shape_length_not_equal_to_two.')
@@ -219,18 +207,6 @@ def test_dense_check_type_of_data_with_illegal_str_bias_init(self):
         logging.info(
             'Finish test_dense_check_type_of_data_with_illegal_str_bias_init.')
 
-    def test_dense_check_type_of_data_with_none_bias_init(self):
-        logging.info(
-            'Start test_dense_check_type_of_data_with_none_bias_init.')
-        with self.assertRaises(TypeError) as ex:
-            net = LowRankAdapterDense(in_channels=5,
-                                      out_channels=4,
-                                      bias_init=None,
-                                      reduction_factor=2)
-        logging.error(ex.exception)
-        logging.info(
-            'Finish test_dense_check_type_of_data_with_none_bias_init.')
-
     def test_dense_check_init_with_bias_init_shape_length_not_equal_to_one(self):
         logging.info(
             'Start test_dense_check_init_with_bias_init_shape_length_not_equal_to_one.')

test/unit_test/graph/test_save_ckpt.py

@@ -403,26 +403,6 @@ def test_enable_ge(self, mock_api, mock_func):
 
         logging.info('Finish test_enable_ge.')
 
-    def test_saved_network(self):
-        logging.info('Start test_saved_network.')
-
-        class TestCheckpoint(TrainableParamsCheckPoint):
-            def step_end(self, run_context):
-                super(TestCheckpoint, self).step_end(run_context)
-                cb_params = run_context.original_args()
-                cb_params.train_network = dict()
-
-        ckpt_path = os.path.join(cur_dir, "per_min")
-        config_ck = CheckpointConfig(save_checkpoint_steps=1, keep_checkpoint_max=1)
-        params_check_point = TestCheckpoint(prefix='checkpoint_delta',
-                                            directory=ckpt_path, config=config_ck)
-        with self.assertRaises(TypeError):
-            train(params_check_point, enable=True)
-
-        shutil.rmtree(ckpt_path, ignore_errors=True)
-
-        logging.info('Start test_saved_network.')
-
 
 if __name__ == '__main__':
     pytest.main(['-s', os.path.abspath(__file__)])