Trainer._load_from_checkpoint - support loading multiple Peft adapters (#30505)

* Trainer: load checkpoint model with multiple adapters

* Trainer._load_from_checkpoint support multiple active adapters

* PeftModel.set_adapter does not support multiple adapters yet

* Trainer._load_from_checkpoint test multiple adapters

---------
Co-authored-by: Clara Luise Pohland <clara-luise.pohland@telekom.de>

src/transformers/trainer.py

@@ -2413,6 +2413,20 @@ class Trainer:
             # this checks the FSDP state dict when `FULL_STATE_DICT` is used
             or os.path.isfile(os.path.join(resume_from_checkpoint, f"{FSDP_MODEL_NAME}.bin"))
         )
+        # if multiple adapters exist, they get saved in sub directories
+        adapter_subdirs = (
+            [
+                folder_name
+                for folder_name in os.listdir(resume_from_checkpoint)
+                if os.path.isdir(os.path.join(resume_from_checkpoint, folder_name))
+                and (
+                    os.path.isfile(os.path.join(resume_from_checkpoint, folder_name, ADAPTER_WEIGHTS_NAME))
+                    or os.path.isfile(os.path.join(resume_from_checkpoint, folder_name, ADAPTER_SAFE_WEIGHTS_NAME))
+                )
+            ]
+            if os.path.isdir(resume_from_checkpoint)
+            else []
+        )
 
         if is_fsdp_ckpt and not self.is_fsdp_enabled:
             raise ValueError(f"Checkpoint found at {resume_from_checkpoint} is only supported when using PyTorch FSDP")
@@ -2430,6 +2444,7 @@ class Trainer:
                 ]
             )
             or is_fsdp_ckpt
+            or adapter_subdirs
         ):
             raise ValueError(f"Can't find a valid checkpoint at {resume_from_checkpoint}")
 
@@ -2503,6 +2518,13 @@ class Trainer:
             # If train a model using PEFT & LoRA, assume that adapter have been saved properly.
             if hasattr(model, "active_adapter") and hasattr(model, "load_adapter"):
                 if os.path.exists(resume_from_checkpoint):
+                    if adapter_subdirs:
+                        active_adapter = model.active_adapter
+                        for subdir_name in adapter_subdirs:
+                            peft_id = os.path.join(resume_from_checkpoint, subdir_name)
+                            model.load_adapter(peft_id, subdir_name, is_trainable=(subdir_name == active_adapter))
+                        model.set_adapter(active_adapter)
+                    else:
                         model.load_adapter(resume_from_checkpoint, model.active_adapter, is_trainable=True)
                 else:
                     logger.warning(

tests/trainer/test_trainer.py

@@ -964,6 +964,63 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
             with self.assertRaises(ValueError):
                 _ = Trainer(tiny_model, args, train_dataset=train_dataset)  # noqa
 
+    @require_peft
+    def test_multiple_peft_adapters(self):
+        from peft import LoraConfig, get_peft_model
+
+        # Tests if resuming from checkpoint works if the model has multiple adapters
+
+        MODEL_ID = "hf-internal-testing/tiny-random-LlamaForCausalLM"
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+        tiny_model = AutoModelForCausalLM.from_pretrained(MODEL_ID)
+
+        peft_config = LoraConfig(
+            r=4,
+            lora_alpha=16,
+            lora_dropout=0.05,
+            bias="none",
+            task_type="CAUSAL_LM",
+        )
+        tiny_model = get_peft_model(tiny_model, peft_config, "adapter1")
+        tiny_model.add_adapter("adapter2", peft_config)
+
+        train_dataset = LineByLineTextDataset(
+            tokenizer=tokenizer,
+            file_path=PATH_SAMPLE_TEXT,
+            block_size=tokenizer.max_len_single_sentence,
+        )
+        for example in train_dataset.examples:
+            example["labels"] = example["input_ids"]
+
+        tokenizer.pad_token = tokenizer.eos_token
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            args = TrainingArguments(
+                tmpdir,
+                per_device_train_batch_size=1,
+                learning_rate=1e-9,
+                save_steps=5,
+                logging_steps=5,
+                max_steps=10,
+                use_cpu=True,
+            )
+            trainer = Trainer(tiny_model, args, tokenizer=tokenizer, train_dataset=train_dataset)
+
+            trainer.train()
+            parameters = dict(tiny_model.named_parameters())
+            state = dataclasses.asdict(trainer.state)
+
+            # Reinitialize trainer
+            trainer = Trainer(tiny_model, args, tokenizer=tokenizer, train_dataset=train_dataset)
+
+            checkpoint = os.path.join(tmpdir, "checkpoint-5")
+
+            trainer.train(resume_from_checkpoint=checkpoint)
+            parameters1 = dict(tiny_model.named_parameters())
+            state1 = dataclasses.asdict(trainer.state)
+            self.assertEqual(parameters, parameters1)
+            self.check_trainer_state_are_the_same(state, state1)
+
     @require_bitsandbytes
     def test_rmsprop_bnb(self):
         config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)