Add implementations for the PeftModel class

Add the implementations for the PeftModel class

Author: solopku

examples/singa_peft/src/singa_peft/peft_model.py

@@ -0,0 +1,86 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+
+from singa import model
+from singa_peft.peft_config import PeftConfig
+from singa_peft.peft_registry import PeftRegistry
+
+
+class PeftModel(model.Model):
+    """
+    PeftModel: modify the base model based on the peft config. A Wrapper of model and tuner.
+    """
+    def __init__(self, base_model: model.Model, peft_config: PeftConfig):
+        r"""
+        Args:
+            base_model: the base model
+            peft_config: the config of peft
+        """
+        super().__init__()
+        self.base_model = base_model
+        self.peft_config = peft_config
+        self.peft_type = peft_config.peft_type
+        self.dimension = self.base_model.dimension
+        # Get the injected tuner class based on peft_type
+        cls = PeftRegistry.get_tuner(self.peft_type)
+        self.tuner = cls(peft_config)
+        # Inject adapter into base_model
+        self.base_model = self.tuner.inject(base_model)
+
+    def forward(self, inputs):
+        return self.base_model.forward(inputs)
+
+    def train_one_batch(self, x, y, dist_option, spars):
+        return self.base_model.train_one_batch(x, y, dist_option, spars)
+
+    def set_optimizer(self, optimizer):
+        self.base_model.set_optimizer(optimizer)
+
+    def compile(self, inputs, is_train=True, use_graph=False, sequential=False):
+        self.base_model.compile(inputs, is_train, use_graph, sequential)
+
+    def train(self, mode=True):
+        super().train(mode)
+        self.base_model.train(mode)
+
+    def eval(self):
+        super().eval()
+        self.base_model.eval()
+
+    def merge_weights(self, mode=True):
+        self.tuner.merge_weights(self.base_model, mode)
+
+    def get_params(self):
+        params =  self.base_model.get_params()
+        return params
+
+    def set_params(self, params):
+        self.base_model.set_params(params)
+
+
+def get_peft_model(base_model: model.Model, peft_config: PeftConfig):
+    r"""
+    Args:
+        base_model: the base model
+        peft_config: the config of peft
+
+    Returns: a peft model based on peft config
+    """
+    peft_model = PeftModel(base_model, peft_config)
+    return peft_model