peft module documentation completion

docs/en/api/peft/ADAPTERS/AdaLoRA.md

@@ -0,0 +1,2 @@
+:::mindnlp.peft.tuners.adalora.config.AdaLoraConfig
+::: mindnlp.peft.tuners.adalora.model.AdaLoraModel

docs/en/api/peft/ADAPTERS/Adaption_Prompt.md

@@ -0,0 +1,2 @@
+:::mindnlp.peft.tuners.adaption_prompt.config.AdaptionPromptConfig
+::: mindnlp.peft.tuners.adaption_prompt.model.AdaptionPromptModel

docs/en/api/peft/ADAPTERS/IA3.md

@@ -0,0 +1,2 @@
+:::mindnlp.peft.tuners.ia3.config
+::: mindnlp.peft.tuners.ia3.model

docs/en/api/peft/ADAPTERS/LoKr.md

@@ -0,0 +1,2 @@
+:::mindnlp.peft.tuners.lokr.config
+::: mindnlp.peft.tuners.lokr.model

docs/en/api/peft/ADAPTERS/LoRA.md

@@ -0,0 +1,2 @@
+:::mindnlp.peft.tuners.lora.config
+::: mindnlp.peft.tuners.lora.model

docs/en/api/peft/MAIN_CLASSES/PEFT_TYPE.md

@@ -0,0 +1,2 @@
+::: mindnlp.peft.utils.peft_types.PeftType
+::: mindnlp.peft.utils.peft_types.TaskType

docs/en/api/peft/MAIN_CLASSES/Tuner.md

@@ -0,0 +1,2 @@
+::: mindnlp.peft.tuners.tuners_utils.BaseTuner
+::: mindnlp.peft.tuners.tuners_utils.BaseTunerLayer

docs/en/api/peft/MAIN_CLASSES/config.md

@@ -0,0 +1 @@
+:::mindnlp.peft.config

docs/en/api/peft/MAIN_CLASSES/mapping.md

@@ -0,0 +1 @@
+:::mindnlp.peft.mapping

docs/en/api/peft/MAIN_CLASSES/peft_model.md

@@ -0,0 +1 @@
+:::mindnlp.peft.peft_model

docs/en/api/peft/UTILITIES/Model_merge.md

@@ -0,0 +1 @@
+:::mindnlp.peft.utils.merge_utils

mindnlp/peft/peft_model.py

@@ -417,6 +417,9 @@ def construct(
         return_dict=None,
         **kwargs,
     ):
+        """
+        Forward pass of the model.
+        """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
         peft_config = self.active_peft_config
         if not peft_config.is_prompt_learning:
@@ -492,6 +495,9 @@ def construct(
         return_dict=None,
         **kwargs,
     ):
+        """
+        Forward pass of the model.
+        """
         peft_config = self.active_peft_config
         if not isinstance(peft_config, PromptLearningConfig):
             if self.base_model.config.model_type == "mpt":
@@ -636,6 +642,9 @@ def construct(
         return_dict=None,
         **kwargs,
     ):
+        """
+        Forward pass of the model.
+        """
         peft_config = self.active_peft_config
         if not isinstance(peft_config, PromptLearningConfig):
             return self.base_model(
@@ -845,6 +854,9 @@ def construct(
         return_dict=None,
         **kwargs,
     ):
+        """
+        Forward pass of the model.
+        """
         peft_config = self.active_peft_config
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 

mindnlp/peft/tuners/adalora/model.py

@@ -41,12 +41,12 @@ class AdaLoraModel(LoraModel):
     https://openreview.net/forum?id=lq62uWRJjiY
 
     Args:
-        model ([`transformers.PreTrainedModel`]): The model to be adapted.
+        model ([`mindspore.nn.Cell`]): The model to be adapted.
         config ([`AdaLoraConfig`]): The configuration of the AdaLora model.
         adapter_name (`str`): The name of the adapter, defaults to `"default"`.
 
     Returns:
-        `torch.nn.Module`: The AdaLora model.
+        AdaLoraModel ([`mindspore.nn.Cell`]): The AdaLora model.
 
     Example::
 
@@ -57,9 +57,11 @@ class AdaLoraModel(LoraModel):
             )
         >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> model = AdaLoraModel(model, config, "default")
 
-    **Attributes**:
-        - **model** ([`transformers.PreTrainedModel`]) -- The model to be adapted.
-        - **peft_config** ([`AdaLoraConfig`]): The configuration of the AdaLora model.
+    > **Attributes**:  
+
+    >   - **model** ([`transformers.PreTrainedModel`])— The model to be adapted. 
+
+    >   - **peft_config** ([`AdaLoraConfig`]): The configuration of the AdaLora model. 
     """
 
     # Note: don't redefine prefix here, it should be inherited from LoraModel
@@ -266,6 +268,7 @@ def __getattr__(self, name: str):
             return getattr(self.model, name)
 
     def construct(self, *args, **kwargs):
+        """The construct method of the model"""
         outputs = self.model(*args, **kwargs)
 
         if (getattr(outputs, "loss", None) is not None) and isinstance(outputs.loss, Tensor):

mindnlp/peft/tuners/adaption_prompt/model.py

@@ -53,6 +53,7 @@ def __init__(self, model, configs: Dict, adapter_name: str):
         self._mark_only_adaption_prompts_as_trainable(self.model)
 
     def add_adapter(self, adapter_name: str, config: AdaptionPromptConfig) -> None:
+        """Add an adapter with the given name and config."""
         config = prepare_config(config, self.model)
         if adapter_name in self.peft_config:
             raise ValueError(f"Adapter named '{adapter_name}' already exists.")

mindnlp/peft/tuners/ia3/model.py

@@ -52,7 +52,7 @@ class IA3Model(BaseTuner):
         adapter_name (`str`): The name of the adapter, defaults to `"default"`.
 
     Returns:
-        `torch.nn.Module`: The (IA)^3 model.
+        IA3Model ([`mindspore.nn.Cell`]): The IA3Lora model.
 
     Example:
 
@@ -70,10 +70,11 @@ class IA3Model(BaseTuner):
         >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
         >>> ia3_model = IA3Model(config, model)
         ```
+    > **Attributes**:  
 
-    **Attributes**:
-        - **model** ([`~transformers.PreTrainedModel`]) -- The model to be adapted.
-        - **peft_config** ([`ia3Config`]): The configuration of the (IA)^3 model.
+    >   - **model** ([`transformers.PreTrainedModel`])— The model to be adapted. 
+
+    >   - **peft_config** ([`IA3Config`]): The configuration of the (IA)^3  model. 
     """
 
     prefix: str = "ia3_"

mindnlp/peft/tuners/lokr/config.py

@@ -53,10 +53,10 @@ class LoKrConfig(PeftConfig):
             pattern is not in the common layers pattern.
         rank_pattern (`dict`):
             The mapping from layer names or regexp expression to ranks which are different from the default rank
-            specified by `r`.（新）
+            specified by `r`.
         alpha_pattern (`dict`):
             The mapping from layer names or regexp expression to alphas which are different from the default alpha
-            specified by `alpha`.（新）
+            specified by `alpha`.
     """
 
     r: int = field(default=8, metadata={"help": "lokr attention dimension"})

mindnlp/peft/tuners/lokr/model.py

@@ -43,11 +43,11 @@ class LoKrModel(BaseTuner):
 
     Args:
         model (`mindspore.nn.Cell`): The model to which the adapter tuner layers will be attached.
-        config ([`LoKrConfig`]): The configuration of the LoKr model.
+        peft_config ([`LoKrConfig`]): The configuration of the LoKr model.
         adapter_name (`str`): The name of the adapter, defaults to `"default"`.
 
     Returns:
-        `mindspore.nn.Cell`: The LoKr model.
+        LoKrModel ([`mindspore.nn.Cell`]): The LoKr model.
 
     Example:
         ```py
@@ -86,9 +86,12 @@ class LoKrModel(BaseTuner):
         >>> model.unet = LoKrModel(model.unet, config_unet, "default")
         ```
 
-    **Attributes**:
-        - **model** ([`~nn.Cell`]) -- The model to be adapted.
-        - **peft_config** ([`LoKrConfig`]): The configuration of the LoKr model.
+    > **Attributes**:  
+
+    >   - **model** ([`~nn.Cell`])— The model to be adapted. 
+
+    >   - **peft_config** ([`LoKrConfig`]): The configuration of the LoKr  model. 
+
     """
 
     prefix: str = "lokr_"

mindnlp/peft/tuners/lora/model.py

@@ -66,7 +66,7 @@ class LoraModel(BaseTuner):
         adapter_name (`str`): The name of the adapter, defaults to `"default"`.
 
     Returns:
-        `nn.Cell`: The Lora model.
+        LoraModel ([`mindspore.nn.Cell`]): The Lora model.
 
     Example:
 
@@ -120,9 +120,11 @@ class LoraModel(BaseTuner):
         >>> lora_model = get_peft_model(model, config)
         ```
 
-    **Attributes**:
-        - **model** ([`~transformers.PreTrainedModel`]) -- The model to be adapted.
-        - **peft_config** ([`LoraConfig`]): The configuration of the Lora model.
+    > **Attributes**:  
+
+    >   - **model** ([`transformers.PreTrainedModel`])— The model to be adapted. 
+
+    >   - **peft_config** ([`LoraConfig`]): The configuration of the Lora model.
     """
 
     prefix: str = "lora_"
@@ -153,7 +155,7 @@ def _prepare_model(self, peft_config: LoraConfig, model: nn.Module):
         Args:
             peft_config (`PeftConfig`):
                 The prepared adapter config.
-            model (`nn.Module`):
+            model (`nn.Cell`):
                 The model that is going to be adapted.
         """
         if peft_config.layer_replication:

mindnlp/peft/tuners/prompt_tuning/model.py

@@ -25,7 +25,7 @@ class PromptEmbedding(nn.Cell):
 
     Args:
         config ([`PromptTuningConfig`]): The configuration of the prompt embedding.
-        word_embeddings (`nn.Module`): The word embeddings of the base transformer model.
+        word_embeddings (`nn.Cell`): The word embeddings of the base transformer model.
 
     **Attributes**:
         - **embedding** (`nn.Embedding`) -- The embedding layer of the prompt embedding.

mindnlp/peft/utils/peft_types.py

@@ -21,21 +21,21 @@ class PeftType(str, enum.Enum):
     """
     Enum class for the different types of adapters in PEFT.
 
-    Supported PEFT types:
-    - PROMPT_TUNING
-    - MULTITASK_PROMPT_TUNING
-    - P_TUNING
-    - PREFIX_TUNING
-    - LORA
-    - ADALORA
-    - BOFT
-    - ADAPTION_PROMPT
-    - IA3
-    - LOHA
-    - LOKR
-    - OFT
-    - POLY
-    - LN_TUNING
+    Supported PEFT types: 
+    - PROMPT_TUNING 
+    - MULTITASK_PROMPT_TUNING 
+    - P_TUNING 
+    - PREFIX_TUNING 
+    - LORA 
+    - ADALORA 
+    - BOFT 
+    - ADAPTION_PROMPT 
+    - IA3 
+    - LOHA 
+    - LOKR 
+    - OFT 
+    - POLY 
+    - LN_TUNING 
     """
 
     PROMPT_TUNING = "PROMPT_TUNING"

mkdocs.yml

@@ -16,7 +16,17 @@ nav:
       - engine: api/engine.md
       - modules: api/modules.md
       - parallel: api/parallel.md
-      - peft: api/peft.md
+      - peft: 
+          MAIN CLASSES: 
+              PEFT model: api/peft/MAIN_CLASSES/peft_model.md
+              PEFT mapping: api/peft/MAIN_CLASSES/mapping.md
+              Configuration: api/peft/MAIN_CLASSES/config.md
+          ADAPTERS: 
+              AdaLoRA: api/peft/ADAPTERS/AdaLoRA.md
+              Adaption_Prompt: api/peft/ADAPTERS/Adaption_Prompt.md
+              IA3: api/peft/ADAPTERS/IA3.md
+              LoKr: api/peft/ADAPTERS/LoKr.md
+              LoRA: api/peft/ADAPTERS/LoRA.md
       - sentence: api/sentence.md
       - transformers: api/transformers.md
       - trl: api/trl.md