Adding LoKrModel Class to paddle.peft library

llm/config/llama/lokr_argument.json

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+{
+    "model_name_or_path": "meta-llama/Meta-Llama-3-8B",
+    "dataset_name_or_path": "./data",
+    "output_dir": "./checkpoints/lokr_ckpts",
+    "lokr": true,
+    "per_device_train_batch_size": 4,
+    "gradient_accumulation_steps": 4,
+    "num_train_epochs": 1,
+    "learning_rate": 2e-05,
+    "lr_scheduler_type": "linear",
+    "attention_probs_dropout_prob": 0,
+    "hidden_dropout_prob": 0,
+    "warmup_steps": 30,
+    "logging_steps": 1,
+    "evaluation_strategy": "no",
+    "save_strategy": "steps",
+    "save_steps": 500,
+    "src_length": 512,
+    "max_length": 512,
+    "bf16": true,
+    "do_train": true,
+    "do_eval": false,
+    "disable_tqdm": false,
+    "load_best_model_at_end": false,
+    "eval_with_do_generation": false,
+    "metric_for_best_model": "accuracy",
+    "recompute": false,
+    "save_total_limit": 100,
+    "fp16_opt_level": "O2",
+    "sharding": "stage2",
+    "zero_padding": false,
+    "use_flash_attention": false,
+    "unified_checkpoint": true
+  }

paddlenlp/peft/lokr/__init__.py

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+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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 .lokr_config import LoKrConfig
+from .lokr_layers import LoKrLinear
+from .lokr_model import LoKrModel
+
+__all__ = ["LoKrConfig", "LoKrModel", "LoKrLinear"]

paddlenlp/peft/lokr/lokr_config.py

@@ -0,0 +1,141 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+import json
+import os
+from dataclasses import asdict, dataclass, field
+from typing import List, Optional, Union
+
+from ...utils.env import LOKR_CONFIG_NAME
+
+
+@dataclass
+class LoKrConfig:
+    """
+    This is the configuration class to store the configuration of a [`LoKrModel`].
+    Convention of LoKrModel: W1 can be named as scaling matrix, W2 can be named as adapter matrix.
+    Args:
+        target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to.
+        trainable_modules (`List[str]`): The names of the modules to train when applying Lora.
+        lokr_alpha (`float`): The alpha parameter for Lora scaling.
+        merge_weights (`bool`):
+            Whether to merge the weights of the Lora layers with the base transformer model in `eval` mode.
+    """
+
+    base_model_name_or_path: Optional[str] = field(
+        default=None, metadata={"help": "The name of the base model to use."}
+    )
+    target_modules: Optional[Union[List[str], str]] = field(
+        default=None,
+        metadata={
+            "help": "List of module names or regex expression of the module names to replace with Lora and Lora Variant."
+            "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
+        },
+    )
+    trainable_modules: Optional[List[str]] = field(
+        default=None,
+        metadata={
+            "help": "List of module names or regex expression of the module names to train when applying with Lora and Lora Variant."
+            "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
+        },
+    )
+    trainable_bias: Optional[str] = field(
+        default=None, metadata={"help": "Define trainable bias parameters for the Lora model."}
+    )
+    lora_dim: int = field(default=8, metadata={"help": "Lora dimention in LoKr dimension, for adapter matrix"})
+    factor: int = field(default=-1, metadata={"help": "Determine the decomposition size of LoKr matrices"})
+    decompose_both: bool = field(
+        default=False,
+        metadata={"help": "Determine whether to decomposed both Scaling Matrix and adapter matrix together"},
+    )
+    lokr_alpha: float = field(
+        default=0.0, metadata={"help": "Determine the scaling of adapter weight, follow lokr convention"}
+    )
+    merge_weight: bool = field(
+        default=False, metadata={"help": "Merge weights of the original model and the Lokr model"}
+    )
+    tensor_parallel_degree: int = field(default=-1, metadata={"help": "-1 for not use tensor parallel"})
+    dtype: Optional[str] = field(default=None, metadata={"help": "The data type of tensor"})
+
+    @property
+    def __dict__(self):
+        return asdict(self)
+
+    def to_dict(self):
+        return self.__dict__
+
+    @property
+    def scaling(self):
+        if not (self.lokr_alpha or self.lora_dim):
+            return 1.0
+        return self.lokr_alpha / self.lora_dim
+
+    def save_pretrained(self, save_directory):
+        r"""
+        This method saves the configuration of your adapter model in a directory.
+        Args:
+            save_directory (`str`):
+                The directory where the configuration will be saved.
+        """
+        if os.path.isfile(save_directory):
+            raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
+
+        os.makedirs(save_directory, exist_ok=True)
+
+        output_dict = self.__dict__
+        output_dict["scaling"] = self.scaling
+        output_path = os.path.join(save_directory, LOKR_CONFIG_NAME)
+
+        # save it
+        with open(output_path, "w") as writer:
+            writer.write(json.dumps(output_dict, indent=2, sort_keys=True))
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        r"""
+        This method loads the configuration of your adapter model from a directory.
+        Args:
+            pretrained_model_name_or_path (`str`):
+                The directory or the hub-id where the configuration is saved.
+            **kwargs:
+                Additional keyword arguments passed along to the child class initialization.
+        """
+        if os.path.isfile(os.path.join(pretrained_model_name_or_path, LOKR_CONFIG_NAME)):
+            config_file = os.path.join(pretrained_model_name_or_path, LOKR_CONFIG_NAME)
+        else:
+            raise ValueError(f"Can't find lokr_config.json at '{pretrained_model_name_or_path}'")
+
+        loaded_attributes = cls.from_json_file(config_file)
+        loaded_attributes.pop("scaling", None)
+
+        config = cls(**kwargs)
+
+        for key, value in loaded_attributes.items():
+            if hasattr(config, key):
+                setattr(config, key, value)
+
+        return config
+
+    @classmethod
+    def from_json_file(cls, path_json_file):
+        r"""
+        Loads a configuration file from a json file.
+        Args:
+            path_json_file (`str`):
+                The path to the json file.
+        """
+        with open(path_json_file, "r") as file:
+            json_object = json.load(file)
+
+        return json_object