fix named_modules (#1877)

Author: lvyufeng

mindnlp/peft/peft_model.py

@@ -25,6 +25,7 @@
 from mindspore.train.serialization import _exec_save
 
 from mindnlp.core import nn, ops
+from mindnlp.core.nn import functional as F
 from .config import PeftConfig, PromptLearningConfig
 from ..transformers import PreTrainedModel
 
@@ -473,7 +474,7 @@ def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
         else:
             self.modules_to_save.update({"classifier", "score"})
 
-        for name, _ in self.base_model.modules_and_names():
+        for name, _ in self.base_model.named_modules():
             if any(module_name in name for module_name in self.modules_to_save):
                 self.cls_layer_name = name
                 break
@@ -955,7 +956,7 @@ def __init__(self, model, peft_config: PeftConfig = None, adapter_name="default"
         else:
             self.modules_to_save.update({"classifier", "score"})
 
-        for name, _ in self.base_model.modules_and_names():
+        for name, _ in self.base_model.named_modules():
             if any(module_name in name for module_name in self.modules_to_save):
                 self.cls_layer_name = name
                 break
@@ -1085,13 +1086,13 @@ def _prefix_tuning_forward(
                 raise ValueError("Model does not support past key values which are required for prefix tuning.")
             outputs = transformer_backbone_name(**kwargs)
             sequence_output = outputs[0]
-            if "dropout" in [name for name, _ in list(self.base_model.modules_and_names())]:
+            if "dropout" in [name for name, _ in list(self.base_model.named_modules())]:
                 sequence_output = self.base_model.dropout(sequence_output)
             logits = self.base_model.get_submodule(self.cls_layer_name)(sequence_output)
 
             loss = None
             if labels is not None:
-                loss = ops.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))
+                loss = F.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))
 
             output = (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output

mindnlp/peft/tuners/lora/model.py

@@ -611,7 +611,7 @@ def _unload_and_optionally_merge(
         if merge:
             self._check_merge_allowed()
 
-        key_list = [key for key, _ in self.model.modules_and_names() if self.prefix not in key]
+        key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key]
         desc = "Unloading " + ("and merging " if merge else "") + "model"
         for key in tqdm(key_list, disable=not progressbar, desc=desc):
             try:
@@ -773,7 +773,7 @@ def add_weighted_adapter(
         # Do we really need that?
         _freeze_adapter(self.model, adapter_name)
 
-        key_list = [key for key, _ in self.model.modules_and_names() if self.prefix not in key]
+        key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key]
         for key in key_list:
             _, target, _ = _get_submodules(self.model, key)
             if isinstance(target, LoraLayer):
@@ -1015,7 +1015,7 @@ def delete_adapter(self, adapter_name: str) -> None:
             raise ValueError(f"Adapter {adapter_name} does not exist")
         del self.peft_config[adapter_name]
 
-        key_list = [key for key, _ in self.model.modules_and_names() if self.prefix not in key]
+        key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key]
         new_adapter = None
         for key in key_list:
             _, target, _ = _get_submodules(self.model, key)

mindnlp/peft/tuners/tuners_utils.py

@@ -47,7 +47,7 @@ def onload_layer(layer):
             layer with tuners to be merged
     """
     offloaded_modules = []
-    for name, cell in layer.modules_and_names():
+    for name, module in layer.named_modules():
         if name in ["", "base_layer"]:
             continue
         # if hasattr(cell, "_hf_hook") and isinstance(cell._hf_hook, AlignDevicesHook) and cell._hf_hook.offload:
@@ -328,7 +328,7 @@ def inject_adapter(self, model: nn.Module, adapter_name: str):
         self._check_new_adapter_config(peft_config)
 
         is_target_modules_in_base_model = False
-        key_list = [key for key, _ in model.modules_and_names()]  # named_modules
+        key_list = [key for key, _ in model.named_modules()]  # named_modules
 
         model_config = getattr(model, "config", {"model_type": "custom"})
         if hasattr(model_config, "to_dict"):

mindnlp/peft/utils/other.py

@@ -292,7 +292,7 @@ def _set_trainable(model, adapter_name):
     """
     set trainable
     """
-    key_list = [key for key, _ in model.modules_and_names()]  # named_modules modules_and_names
+    key_list = [key for key, _ in model.named_modules()]  # named_modules
     for key in key_list:
         target_module_found = any(key.endswith(target_key) for target_key in model.modules_to_save)
         if target_module_found: