Make loaded peft adapters optionally trainable

llmfoundry/models/hf/hf_base.py

@@ -392,6 +392,7 @@ def build_inner_model(
             model = PeftModelForCausalLM.from_pretrained(
                 model,
                 pretrained_lora_id_or_path,
+                is_trainable=True,
             )
 
         if prepare_for_fsdp:

tests/models/hf/test_hf_base.py

@@ -1,6 +1,8 @@
 # Copyright 2024 MosaicML LLM Foundry authors
 # SPDX-License-Identifier: Apache-2.0
 
+from peft import PeftModel
+
 from llmfoundry.models.hf.hf_base import BaseHuggingFaceModel
 
 
@@ -23,3 +25,24 @@ def test_build_inner_model_fsdp():
     )
 
     assert model.fsdp_wrap_fn(model.model.layers[0])
+
+
+def test_pretrained_peft_trainable():
+    model = BaseHuggingFaceModel.build_inner_model(
+        pretrained_model_name_or_path='facebook/opt-350m',
+        pretrained_lora_id_or_path='ybelkada/opt-350m-lora',
+        trust_remote_code=False,
+        init_device='cpu',
+        use_flash_attention_2=False,
+        use_auth_token=False,
+        config_overrides={},
+        load_in_8bit=False,
+        pretrained=True,
+        prepare_for_fsdp=True,
+    )
+
+    assert isinstance(model, PeftModel)
+
+    n_trainable, n_all = model.get_nb_trainable_parameters()
+    assert n_all > 0
+    assert n_trainable > 0