DOC Improve target modules description (#1290)

For LoRA and IA³, it is allowed to not specify a target module, in which
case the correct layers are derived from the model architecture. This
was not documented so far.

src/peft/tuners/ia3/config.py

@@ -27,7 +27,10 @@ class IA3Config(PeftConfig):
 
     Args:
         target_modules (`Union[List[str],str]`):
-            The names of the modules to apply (IA)^3 to.
+            The names of the modules to apply (IA)³ to. If this is specified, only the modules with the specified names
+            will be replaced. If this is not specified, modules will be chosen according to the model architecture. If
+            the architecture is not known, an error will be raised -- in this case, you should specify the target
+            modules manually.
         feedforward_modules (`Union[List[str],str]`):
             The names of the modules to be treated as feedforward modules, as in the original paper. These modules will
             have (IA)^3 vectors multiplied to the input, instead of the output. feedforward_modules must be a name or a
@@ -44,8 +47,12 @@ class IA3Config(PeftConfig):
     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 ia3."
-            "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
+            "help": (
+                "List of module names or regex expression of the module names to replace with (IA)³."
+                "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$'. "
+                "If not specified, modules will be chosen according to the model architecture, If the architecture is "
+                "not known, an error will be raised -- in this case, you shoud specify the target modules manually."
+            ),
         },
     )
     feedforward_modules: Optional[Union[List[str], str]] = field(

src/peft/tuners/lora/config.py

@@ -48,7 +48,10 @@ class LoraConfig(PeftConfig):
 
     Args:
         r (`int`): Lora attention dimension.
-        target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to.
+        target_modules (`Optional[Union[List[str], str]]`): The names of the modules to apply LoRA to. If this is
+            specified, only the modules with the specified names will be replaced. If this is not specified, modules
+            will be chosen according to the model architecture. If the architecture is not known, an error will be
+            raised -- in this case, you should specify the target modules manually.
         lora_alpha (`int`): The alpha parameter for Lora scaling.
         lora_dropout (`float`): The dropout probability for Lora layers.
         fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out).
@@ -82,8 +85,12 @@ class LoraConfig(PeftConfig):
     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."
-            "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
+            "help": (
+                "List of module names or regex expression of the module names to replace with LoRA."
+                "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$'. "
+                "If not specified, modules will be chosen according to the model architecture, If the architecture is "
+                "not known, an error will be raised -- in this case, you shoud specify the target modules manually."
+            ),
         },
     )
     lora_alpha: int = field(default=8, metadata={"help": "Lora alpha"})