Fix error of PEFT with disable adapters and FSDP

Makefile

@@ -68,3 +68,4 @@ tests_training:
 	accelerate launch --config_file tests/training/deepspeed_config.yaml tests/training/training.py --quant 8bit $(if $(IS_GITHUB_CI),--report-log "training_deepspeed_8bit.log",)
 	accelerate launch --config_file tests/training/fsdp_config.yaml tests/training/training.py $(if $(IS_GITHUB_CI),--report-log "training_fsdp.log",)
 	accelerate launch --config_file tests/training/fsdp_config.yaml tests/training/training.py --quant 4bit $(if $(IS_GITHUB_CI),--report-log "training_fsdp_4bit.log",)
+	accelerate launch --config_file tests/training/fsdp_config.yaml tests/training/adapters.py $(if $(IS_GITHUB_CI),--report-log "training_fsdp_adapters.log",)

src/peft/tuners/tuners_utils.py

@@ -1389,7 +1389,18 @@ def enable_adapters(self, enabled: bool) -> None:
             # disable grads on all adapter layers
             for layer_name in self.adapter_layer_names:
                 layer = getattr(self, layer_name)
-                layer.requires_grad_(False)
+                # Handle FSDP case where params may be non-leaf tensors by being wrapped in DTensors
+                # layer.parameters() returns an iterator, so we need to check if layer is a module
+                if hasattr(layer, "parameters"):
+                    for param in layer.parameters():
+                        if param.is_leaf:
+                            param.requires_grad_(False)
+                else:
+                    # layer is a parameter/tensor itself (e.g., from ParameterDict)
+                    # In this case we need to iterate through the dict
+                    for param in layer.values():
+                        if param.is_leaf:
+                            param.requires_grad_(False)
             self._disable_adapters = True
 
     def set_adapter(self, adapter_names: str | list[str], inference_mode: bool = False) -> None:
@@ -1411,12 +1422,20 @@ def set_adapter(self, adapter_names: str | list[str], inference_mode: bool = Fal
         for layer_name in self.adapter_layer_names:
             module_dict = getattr(self, layer_name)
             for key, layer in module_dict.items():
-                if (key in adapter_names) and (not inference_mode):
-                    # Note: It is possible that not a single layer is called with requires_grad_(True) here. This may
-                    # happen if a completely different adapter layer is being activated.
-                    layer.requires_grad_(True)
+                should_require_grad = (key in adapter_names) and (not inference_mode)
+                # Handle FSDP case where params may be non-leaf tensors
+                # Check if layer is a module or a parameter/tensor directly
+                # Note: It is possible that not a single layer is called with requires_grad_(True) here. This may
+                # happen if a completely different adapter layer is being activated.
+                if isinstance(layer, (torch.nn.Parameter, torch.Tensor)):
+                    # layer is a parameter/tensor itself (e.g., from ParameterDict)
+                    if layer.is_leaf:
+                        layer.requires_grad_(should_require_grad)
                 else:
-                    layer.requires_grad_(False)
+                    # layer is a module with parameters
+                    for param in layer.parameters():
+                        if param.is_leaf:
+                            param.requires_grad_(should_require_grad)
 
         self._active_adapter = adapter_names
 

tests/training/adapters.py

@@ -0,0 +1,165 @@
+# Copyright 2025-present the HuggingFace Inc. team.
+#
+# 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.
+
+"""
+Script to test FSDP adapter operations (disable_adapters, set_adapter, etc.) in a distributed environment.
+
+This script is designed to be run with `accelerate launch` to properly test FSDP behavior while running one pass with autograd and another with adapters being disabled.
+
+Usage:
+    accelerate launch --config_file tests/training/fsdp_config.yaml tests/training/adapters.py
+"""
+
+import argparse
+import tempfile
+
+import torch
+from accelerate import PartialState
+from datasets import load_dataset
+from torch import nn
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    DataCollatorForLanguageModeling,
+    Trainer,
+    TrainingArguments,
+)
+
+from peft import LoraConfig, get_peft_model
+
+
+def get_base_model_weights(peft_model):
+    """Extract base model weights (non-LoRA weights)."""
+    base_weights = {}
+    for name, param in peft_model.named_parameters():
+        if "lora" not in name.lower():
+            base_weights[name] = param.detach().clone()
+    return base_weights
+
+
+def get_adapter_weights(peft_model, adapter_name):
+    """Extract weights for a specific adapter."""
+    adapter_weights = {}
+    for name, param in peft_model.named_parameters():
+        if adapter_name in name:
+            adapter_weights[name] = param.detach().clone()
+    return adapter_weights
+
+
+def verify_weights_unchanged(initial_weights, final_weights, weight_type):
+    """Verify that weights have not changed during training."""
+    for name in initial_weights:
+        if name not in final_weights:
+            raise AssertionError(f"{weight_type} weight missing after training: {name}")
+        torch.testing.assert_close(
+            initial_weights[name].to(device=final_weights[name].device, dtype=final_weights[name].dtype),
+            final_weights[name],
+        )
+
+
+class Model(nn.Module):
+    def __init__(self, model_id):
+        super().__init__()
+        model = AutoModelForCausalLM.from_pretrained(
+            model_id,
+            torch_dtype=torch.bfloat16,
+        )
+        self.tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+        peft_config = LoraConfig(
+            r=16,
+            lora_alpha=32,
+            target_modules=["q_proj", "v_proj"],
+            lora_dropout=0.05,
+            bias="none",
+            task_type="CAUSAL_LM",
+        )
+        self.peft_model = get_peft_model(model, peft_config)
+
+        # Second adapter config (will remain disabled/unused throughout training)
+        peft_config_second = LoraConfig(
+            r=8,
+            lora_alpha=16,
+            target_modules=["q_proj", "v_proj"],
+            lora_dropout=0.05,
+            bias="none",
+            task_type="CAUSAL_LM",
+        )
+        self.peft_model.add_adapter("second_adapter", peft_config_second)
+
+        self.peft_model.set_adapter("default")
+        self.peft_model.to(torch.bfloat16)
+
+        for name, param in self.peft_model.named_parameters():
+            param.requires_grad = "lora_" in name.lower() and "second_adapter" not in name
+
+    def forward(self, input_ids=None, attention_mask=None, labels=None):
+        out1 = self.peft_model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
+        with self.peft_model.disable_adapter():
+            out2 = self.peft_model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
+        combined_loss = out1.loss + out2.loss
+        return (combined_loss,)
+
+
+def test_training(model_id: str):
+    state = PartialState()
+    torch.manual_seed(42)
+    model = Model(model_id)
+
+    initial_base_weights = get_base_model_weights(model.peft_model)
+    initial_second_adapter_weights = get_adapter_weights(model.peft_model, "second_adapter")
+
+    if state.is_main_process:
+        print(f"Number of base model weight tensors: {len(initial_base_weights)}")
+        print(f"Number of second_adapter weight tensors: {len(initial_second_adapter_weights)}")
+
+    data = load_dataset("ybelkada/english_quotes_copy")
+    data = data.map(lambda samples: model.tokenizer(samples["quote"]), batched=True)
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        trainer = Trainer(
+            model=model,
+            train_dataset=data["train"],
+            optimizer_cls_and_kwargs=(torch.optim.SGD, {"lr": 2e-4}),
+            args=TrainingArguments(
+                per_device_train_batch_size=4,
+                gradient_accumulation_steps=4,
+                warmup_steps=2,
+                max_steps=5,
+                learning_rate=2e-4,
+                bf16=True,
+                logging_steps=1,
+                output_dir=tmp_dir,
+            ),
+            data_collator=DataCollatorForLanguageModeling(model.tokenizer, mlm=False),
+        )
+        trainer.train()
+    with FSDP.summon_full_params(trainer.model):
+        final_base_weights = get_base_model_weights(model.peft_model)
+        final_second_adapter_weights = get_adapter_weights(model.peft_model, "second_adapter")
+
+    verify_weights_unchanged(initial_base_weights, final_base_weights, "Base model")
+    verify_weights_unchanged(initial_second_adapter_weights, final_second_adapter_weights, "second_adapter")
+
+
+def main(model_id: str):
+    test_training(model_id)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_id", type=str, required=False, default="Qwen/Qwen3-0.6B")
+    args = parser.parse_args()
+    main(model_id=args.model_id)