# Copyright 2023-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.
import gc
import tempfile
import unittest

import pytest
import torch
import torch.nn.functional as F
from datasets import load_dataset
from parameterized import parameterized
from torch import nn
from transformers import (
    AutoImageProcessor,
    AutoModelForCausalLM,
    AutoModelForImageClassification,
    AutoModelForSeq2SeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    LlamaForCausalLM,
    WhisperForConditionalGeneration,
)
from transformers.pytorch_utils import Conv1D

from peft import (
    AdaLoraConfig,
    AdaptionPromptConfig,
    BOFTConfig,
    HRAConfig,
    IA3Config,
    LNTuningConfig,
    LoHaConfig,
    LoKrConfig,
    LoraConfig,
    OFTConfig,
    PeftModel,
    TaskType,
    VBLoRAConfig,
    VeraConfig,
    get_peft_model,
    prepare_model_for_kbit_training,
)
from peft.import_utils import is_bnb_4bit_available, is_bnb_available
from peft.tuners.lora.config import LoraRuntimeConfig
from peft.utils import infer_device

from .testing_utils import (
    require_bitsandbytes,
    require_multi_accelerator,
    require_non_cpu,
    require_torch_gpu,
    require_torch_multi_gpu,
)


if is_bnb_available():
    import bitsandbytes as bnb

    from peft.tuners.ia3 import Linear8bitLt as IA3Linear8bitLt
    from peft.tuners.lora import Linear8bitLt as LoraLinear8bitLt
    from peft.tuners.vera import Linear8bitLt as VeraLinear8bitLt

    if is_bnb_4bit_available():
        from peft.tuners.ia3 import Linear4bit as IA3Linear4bit
        from peft.tuners.lora import Linear4bit as LoraLinear4bit
        from peft.tuners.vera import Linear4bit as VeraLinear4bit


@require_non_cpu
class PeftGPUCommonTests(unittest.TestCase):
    r"""
    A common tester to run common operations that are performed on GPU such as generation, loading in 8bit, etc.
    """

    def setUp(self):
        self.seq2seq_model_id = "google/flan-t5-base"
        self.causal_lm_model_id = "facebook/opt-350m"
        self.audio_model_id = "openai/whisper-large"
        self.device = infer_device()

    def tearDown(self):
        r"""
        Efficient mechanism to free GPU memory after each test. Based on
        https://github.com/huggingface/transformers/issues/21094
        """
        gc.collect()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        gc.collect()

    @require_bitsandbytes
    @pytest.mark.multi_gpu_tests
    @pytest.mark.single_gpu_tests
    def test_lora_bnb_8bit_quantization(self):
        r"""
        Test that tests if the 8bit quantization using LoRA works as expected
        """
        whisper_8bit = WhisperForConditionalGeneration.from_pretrained(
            self.audio_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
        )

        opt_8bit = AutoModelForCausalLM.from_pretrained(
            self.causal_lm_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
        )

        flan_8bit = AutoModelForSeq2SeqLM.from_pretrained(
            self.seq2seq_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
        )

        flan_lora_config = LoraConfig(
            r=16, lora_alpha=32, target_modules=["q", "v"], lora_dropout=0.05, bias="none", task_type="SEQ_2_SEQ_LM"
        )

        opt_lora_config = LoraConfig(
            r=16,
            lora_alpha=32,
            target_modules=["q_proj", "v_proj"],
            lora_dropout=0.05,
            bias="none",
            task_type="CAUSAL_LM",
        )

        config = LoraConfig(r=32, lora_alpha=64, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none")

        flan_8bit = get_peft_model(flan_8bit, flan_lora_config)
        assert isinstance(flan_8bit.base_model.model.encoder.block[0].layer[0].SelfAttention.q, LoraLinear8bitLt)

        opt_8bit = get_peft_model(opt_8bit, opt_lora_config)
        assert isinstance(opt_8bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, LoraLinear8bitLt)

        whisper_8bit = get_peft_model(whisper_8bit, config)
        assert isinstance(whisper_8bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, LoraLinear8bitLt)

    @require_bitsandbytes
    @pytest.mark.multi_gpu_tests
    @pytest.mark.single_gpu_tests
    def test_vera_bnb_8bit_quantization(self):
        r"""
        Test that tests if the 8bit quantization using VeRA works as expected
        """
        whisper_8bit = WhisperForConditionalGeneration.from_pretrained(
            self.audio_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
        )

        opt_8bit = AutoModelForCausalLM.from_pretrained(
            self.causal_lm_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
        )

        flan_8bit = AutoModelForSeq2SeqLM.from_pretrained(
            self.seq2seq_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
        )

        flan_vera_config = VeraConfig(
            r=16, target_modules=["q", "v"], vera_dropout=0.05, bias="none", task_type="SEQ_2_SEQ_LM"
        )

        opt_vera_config = VeraConfig(
            r=16,
            target_modules=["q_proj", "v_proj"],
            vera_dropout=0.05,
            bias="none",
            task_type="CAUSAL_LM",
        )

        config = VeraConfig(r=32, target_modules=["q_proj", "v_proj"], vera_dropout=0.05, bias="none")

        flan_8bit = get_peft_model(flan_8bit, flan_vera_config)
        assert isinstance(flan_8bit.base_model.model.encoder.block[0].layer[0].SelfAttention.q, VeraLinear8bitLt)

        opt_8bit = get_peft_model(opt_8bit, opt_vera_config)
        assert isinstance(opt_8bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, VeraLinear8bitLt)

        whisper_8bit = get_peft_model(whisper_8bit, config)
        assert isinstance(whisper_8bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, VeraLinear8bitLt)

    @require_bitsandbytes
    @pytest.mark.multi_gpu_tests
    @pytest.mark.single_gpu_tests
    def test_ia3_bnb_8bit_quantization(self):
        r"""
        Test that tests if the 8bit quantization using IA3 works as expected
        """
        whisper_8bit = WhisperForConditionalGeneration.from_pretrained(
            self.audio_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
        )

        opt_8bit = AutoModelForCausalLM.from_pretrained(
            self.causal_lm_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
        )

        flan_8bit = AutoModelForSeq2SeqLM.from_pretrained(
            self.seq2seq_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
        )

        flan_ia3_config = IA3Config(target_modules=["q", "v"], task_type="SEQ_2_SEQ_LM")

        opt_ia3_config = IA3Config(
            target_modules=["q_proj", "v_proj", "fc2"],
            feedforward_modules=["fc2"],
            task_type="CAUSAL_LM",
        )

        config = IA3Config(target_modules=["q_proj", "v_proj", "fc2"], feedforward_modules=["fc2"])

        flan_8bit = get_peft_model(flan_8bit, flan_ia3_config)
        assert isinstance(flan_8bit.base_model.model.encoder.block[0].layer[0].SelfAttention.q, IA3Linear8bitLt)

        opt_8bit = get_peft_model(opt_8bit, opt_ia3_config)
        assert isinstance(opt_8bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, IA3Linear8bitLt)

        whisper_8bit = get_peft_model(whisper_8bit, config)
        assert isinstance(whisper_8bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, IA3Linear8bitLt)

    @require_bitsandbytes
    @pytest.mark.multi_gpu_tests
    @pytest.mark.single_gpu_tests
    @parameterized.expand(["4bit", "8bit"])
    def test_lora_bnb_quantization_from_pretrained_safetensors(self, quantization):
        r"""
        Tests that the bnb quantization using LoRA works as expected with safetensors weights.
        """
        model_id = "facebook/opt-350m"
        peft_model_id = "ybelkada/test-st-lora"
        kwargs = {"device_map": "auto"}
        if quantization == "4bit":
            kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True)
        else:
            kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)

        model = AutoModelForCausalLM.from_pretrained(model_id, **kwargs)
        model = PeftModel.from_pretrained(model, peft_model_id)

        model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

        # loading a 2nd adapter works, #1239
        model.load_adapter(peft_model_id, "adapter2")
        model.set_adapter("adapter2")
        model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

        # check that both adapters are in the same layer
        assert "default" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.lora_A
        assert "adapter2" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.lora_A

    @require_bitsandbytes
    @pytest.mark.multi_gpu_tests
    @pytest.mark.single_gpu_tests
    @parameterized.expand(["4bit", "8bit"])
    def test_adalora_bnb_quantization_from_pretrained_safetensors(self, quantization):
        r"""
        Tests that the bnb quantization using AdaLora works as expected with safetensors weights.
        """
        model_id = "facebook/opt-350m"
        kwargs = {"device_map": "auto"}
        if quantization == "4bit":
            kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True)
        else:
            kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)

        model = AutoModelForCausalLM.from_pretrained(model_id, **kwargs)
        config = AdaLoraConfig(task_type=TaskType.CAUSAL_LM)
        peft_model = get_peft_model(model, config)
        peft_model = prepare_model_for_kbit_training(peft_model)
        peft_model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

        with tempfile.TemporaryDirectory() as tmp_dir:
            peft_model.save_pretrained(tmp_dir)
            model = AutoModelForCausalLM.from_pretrained(model_id, **kwargs)
            model = PeftModel.from_pretrained(model, tmp_dir)
            model = prepare_model_for_kbit_training(peft_model)
            model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

            # loading a 2nd adapter works, #1239
            model.load_adapter(tmp_dir, "adapter2")
            model.set_adapter("adapter2")
            model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

            # check that both adapters are in the same layer
            assert "default" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.lora_A
            assert "adapter2" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.lora_A

    @require_bitsandbytes
    @pytest.mark.multi_gpu_tests
    @pytest.mark.single_gpu_tests
    @parameterized.expand(["4bit", "8bit"])
    def test_vera_bnb_quantization_from_pretrained_safetensors(self, quantization):
        r"""
        Tests that the bnb quantization using VeRA works as expected with safetensors weights.
        """
        model_id = "facebook/opt-350m"
        kwargs = {"device_map": "auto"}
        if quantization == "4bit":
            kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True)
        else:
            kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)

        model = AutoModelForCausalLM.from_pretrained(model_id, **kwargs)
        config = VeraConfig(task_type=TaskType.CAUSAL_LM)
        peft_model = get_peft_model(model, config)
        peft_model = prepare_model_for_kbit_training(peft_model)
        peft_model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

        with tempfile.TemporaryDirectory() as tmp_dir:
            peft_model.save_pretrained(tmp_dir)
            model = AutoModelForCausalLM.from_pretrained(model_id, **kwargs)
            model = PeftModel.from_pretrained(model, tmp_dir)
            model = prepare_model_for_kbit_training(model)
            model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

            # loading a 2nd adapter works, #1239
            model.load_adapter(tmp_dir, "adapter2")
            model.set_adapter("adapter2")
            model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

            # check that both adapters are in the same layer
            assert "default" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.vera_A
            assert "adapter2" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.vera_A

    @require_bitsandbytes
    @pytest.mark.multi_gpu_tests
    @pytest.mark.single_gpu_tests
    @parameterized.expand(["4bit", "8bit"])
    def test_ia3_bnb_quantization_from_pretrained_safetensors(self, quantization):
        r"""
        Tests that the bnb quantization using IA³ works as expected with safetensors weights.
        """
        model_id = "facebook/opt-350m"
        kwargs = {"device_map": "auto"}
        if quantization == "4bit":
            kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True)
        else:
            kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)

        model = AutoModelForCausalLM.from_pretrained(model_id, **kwargs)
        config = IA3Config(task_type=TaskType.CAUSAL_LM)
        peft_model = get_peft_model(model, config)
        peft_model = prepare_model_for_kbit_training(peft_model)
        peft_model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

        with tempfile.TemporaryDirectory() as tmp_dir:
            peft_model.save_pretrained(tmp_dir)
            model = AutoModelForCausalLM.from_pretrained(model_id, **kwargs)
            model = PeftModel.from_pretrained(model, tmp_dir)
            model = prepare_model_for_kbit_training(model)
            model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

            # loading a 2nd adapter works, #1239
            model.load_adapter(tmp_dir, "adapter2")
            model.set_adapter("adapter2")
            model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

            # check that both adapters are in the same layer
            assert "default" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.ia3_l
            assert "adapter2" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.ia3_l

    @pytest.mark.single_gpu_tests
    def test_lora_gptq_quantization_from_pretrained_safetensors(self):
        r"""
        Tests that the autogptq quantization using LoRA works as expected with safetensors weights.
        """
        from transformers import GPTQConfig

        model_id = "marcsun13/opt-350m-gptq-4bit"
        quantization_config = GPTQConfig(bits=4, use_exllama=False)
        kwargs = {
            "pretrained_model_name_or_path": model_id,
            "torch_dtype": torch.float16,
            "device_map": "auto",
            "quantization_config": quantization_config,
        }
        model = AutoModelForCausalLM.from_pretrained(**kwargs)
        model = prepare_model_for_kbit_training(model)

        config = LoraConfig(task_type="CAUSAL_LM")
        peft_model = get_peft_model(model, config)
        peft_model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

        with tempfile.TemporaryDirectory() as tmp_dir:
            peft_model.save_pretrained(tmp_dir)
            model = AutoModelForCausalLM.from_pretrained(**kwargs)
            model = PeftModel.from_pretrained(model, tmp_dir)
            model = prepare_model_for_kbit_training(model)
            model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

            # loading a 2nd adapter works, #1239
            model.load_adapter(tmp_dir, "adapter2")
            model.set_adapter("adapter2")
            model.generate(input_ids=torch.LongTensor([[0, 2, 3, 1]]).to(0))

            # check that both adapters are in the same layer
            assert "default" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.lora_A
            assert "adapter2" in model.base_model.model.model.decoder.layers[0].self_attn.q_proj.lora_A

    @require_bitsandbytes
    @pytest.mark.multi_gpu_tests
    @pytest.mark.single_gpu_tests
    def test_lora_bnb_4bit_quantization(self):
        r"""
        Test that tests if the 4bit quantization using LoRA works as expected
        """
        whisper_4bit = WhisperForConditionalGeneration.from_pretrained(
            self.audio_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
        )

        opt_4bit = AutoModelForCausalLM.from_pretrained(
            self.causal_lm_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
        )

        flan_4bit = AutoModelForSeq2SeqLM.from_pretrained(
            self.seq2seq_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
        )

        flan_lora_config = LoraConfig(
            r=16, lora_alpha=32, target_modules=["q", "v"], lora_dropout=0.05, bias="none", task_type="SEQ_2_SEQ_LM"
        )

        opt_lora_config = LoraConfig(
            r=16,
            lora_alpha=32,
            target_modules=["q_proj", "v_proj"],
            lora_dropout=0.05,
            bias="none",
            task_type="CAUSAL_LM",
        )

        config = LoraConfig(r=32, lora_alpha=64, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none")

        flan_4bit = get_peft_model(flan_4bit, flan_lora_config)
        assert isinstance(flan_4bit.base_model.model.encoder.block[0].layer[0].SelfAttention.q, LoraLinear4bit)

        opt_4bit = get_peft_model(opt_4bit, opt_lora_config)
        assert isinstance(opt_4bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, LoraLinear4bit)

        whisper_4bit = get_peft_model(whisper_4bit, config)
        assert isinstance(whisper_4bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, LoraLinear4bit)

    @require_bitsandbytes
    @pytest.mark.multi_gpu_tests
    @pytest.mark.single_gpu_tests
    def test_vera_bnb_4bit_quantization(self):
        r"""
        Test that tests if the 4bit quantization using VeRA works as expected
        """
        whisper_4bit = WhisperForConditionalGeneration.from_pretrained(
            self.audio_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
        )

        opt_4bit = AutoModelForCausalLM.from_pretrained(
            self.causal_lm_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
        )

        flan_4bit = AutoModelForSeq2SeqLM.from_pretrained(
            self.seq2seq_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
        )

        flan_vera_config = VeraConfig(
            r=16, target_modules=["q", "v"], vera_dropout=0.05, bias="none", task_type="SEQ_2_SEQ_LM"
        )

        opt_vera_config = VeraConfig(
            r=16,
            target_modules=["q_proj", "v_proj"],
            vera_dropout=0.05,
            bias="none",
            task_type="CAUSAL_LM",
        )

        config = VeraConfig(r=32, target_modules=["q_proj", "v_proj"], vera_dropout=0.05, bias="none")

        flan_4bit = get_peft_model(flan_4bit, flan_vera_config)
        assert isinstance(flan_4bit.base_model.model.encoder.block[0].layer[0].SelfAttention.q, VeraLinear4bit)

        opt_4bit = get_peft_model(opt_4bit, opt_vera_config)
        assert isinstance(opt_4bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, VeraLinear4bit)

        whisper_4bit = get_peft_model(whisper_4bit, config)
        assert isinstance(whisper_4bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, VeraLinear4bit)

    @require_bitsandbytes
    @pytest.mark.multi_gpu_tests
    @pytest.mark.single_gpu_tests
    def test_ia3_bnb_4bit_quantization(self):
        r"""
        Test that tests if the 4bit quantization using IA3 works as expected
        """
        whisper_4bit = WhisperForConditionalGeneration.from_pretrained(
            self.audio_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
        )

        opt_4bit = AutoModelForCausalLM.from_pretrained(
            self.causal_lm_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
        )

        flan_4bit = AutoModelForSeq2SeqLM.from_pretrained(
            self.seq2seq_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
        )

        flan_ia3_config = IA3Config(target_modules=["q", "v"], task_type="SEQ_2_SEQ_LM")

        opt_ia3_config = IA3Config(
            target_modules=["q_proj", "v_proj", "fc2"],
            feedforward_modules=["fc2"],
            task_type="CAUSAL_LM",
        )

        config = IA3Config(target_modules=["q_proj", "v_proj", "fc2"], feedforward_modules=["fc2"])

        flan_4bit = get_peft_model(flan_4bit, flan_ia3_config)
        assert isinstance(flan_4bit.base_model.model.encoder.block[0].layer[0].SelfAttention.q, IA3Linear4bit)

        opt_4bit = get_peft_model(opt_4bit, opt_ia3_config)
        assert isinstance(opt_4bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, IA3Linear4bit)

        whisper_4bit = get_peft_model(whisper_4bit, config)
        assert isinstance(whisper_4bit.base_model.model.model.decoder.layers[0].self_attn.v_proj, IA3Linear4bit)

    @pytest.mark.multi_gpu_tests
    @require_torch_multi_gpu
    def test_lora_causal_lm_multi_gpu_inference(self):
        r"""
        Test if LORA can be used for inference on multiple GPUs.
        """
        lora_config = LoraConfig(
            r=16,
            lora_alpha=32,
            target_modules=["q_proj", "v_proj"],
            lora_dropout=0.05,
            bias="none",
            task_type="CAUSAL_LM",
        )

        model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id, device_map="balanced")
        tokenizer = AutoTokenizer.from_pretrained(self.seq2seq_model_id)

        assert set(model.hf_device_map.values()) == set(range(torch.cuda.device_count()))

        model = get_peft_model(model, lora_config)
        assert isinstance(model, PeftModel)

        dummy_input = "This is a dummy input:"
        input_ids = tokenizer(dummy_input, return_tensors="pt").input_ids.to(self.device)

        # this should work without any problem
        _ = model.generate(input_ids=input_ids)

    @require_torch_multi_gpu
    @pytest.mark.multi_gpu_tests
    @require_bitsandbytes
    def test_lora_seq2seq_lm_multi_gpu_inference(self):
        r"""
        Test if LORA can be used for inference on multiple GPUs - 8bit version.
        """
        lora_config = LoraConfig(
            r=16, lora_alpha=32, target_modules=["q", "v"], lora_dropout=0.05, bias="none", task_type="SEQ_2_SEQ_LM"
        )

        model = AutoModelForSeq2SeqLM.from_pretrained(
            self.seq2seq_model_id, device_map="balanced", quantization_config=BitsAndBytesConfig(load_in_8bit=True)
        )
        tokenizer = AutoTokenizer.from_pretrained(self.seq2seq_model_id)

        assert set(model.hf_device_map.values()) == set(range(torch.cuda.device_count()))

        model = get_peft_model(model, lora_config)
        assert isinstance(model, PeftModel)
        assert isinstance(model.base_model.model.encoder.block[0].layer[0].SelfAttention.q, LoraLinear8bitLt)

        dummy_input = "This is a dummy input:"
        input_ids = tokenizer(dummy_input, return_tensors="pt").input_ids.to(self.device)

        # this should work without any problem
        _ = model.generate(input_ids=input_ids)

    @require_torch_multi_gpu
    @pytest.mark.multi_gpu_tests
    @require_bitsandbytes
    def test_adaption_prompt_8bit(self):
        model = LlamaForCausalLM.from_pretrained(
            "trl-internal-testing/tiny-random-LlamaForCausalLM",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
            torch_dtype=torch.float16,
            device_map="auto",
        )

        model = prepare_model_for_kbit_training(model)

        config = AdaptionPromptConfig(
            adapter_len=10,
            adapter_layers=2,
            task_type="CAUSAL_LM",
        )
        model = get_peft_model(model, config)

        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        _ = model(random_input)

    @require_torch_multi_gpu
    @pytest.mark.multi_gpu_tests
    @require_bitsandbytes
    def test_adaption_prompt_4bit(self):
        model = LlamaForCausalLM.from_pretrained(
            "trl-internal-testing/tiny-random-LlamaForCausalLM",
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
            torch_dtype=torch.float16,
            device_map="auto",
        )

        model = prepare_model_for_kbit_training(model)

        config = AdaptionPromptConfig(
            adapter_len=10,
            adapter_layers=2,
            task_type="CAUSAL_LM",
        )
        model = get_peft_model(model, config)

        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        _ = model(random_input)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_print_4bit_expected(self):
        EXPECTED_TRAINABLE_PARAMS = 294912
        EXPECTED_ALL_PARAMS = 125534208

        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
        )

        config = LoraConfig(
            r=8,
        )
        model = get_peft_model(model, config)
        trainable_params, all_params = model.get_nb_trainable_parameters()

        assert trainable_params == EXPECTED_TRAINABLE_PARAMS
        assert all_params == EXPECTED_ALL_PARAMS

        # test with double quant
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=True,
        )

        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=bnb_config,
        )

        config = LoraConfig(
            r=8,
        )
        model = get_peft_model(model, config)
        trainable_params, all_params = model.get_nb_trainable_parameters()

        assert trainable_params == EXPECTED_TRAINABLE_PARAMS
        assert all_params == EXPECTED_ALL_PARAMS

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_modules_to_save_grad(self):
        model_id = "bigscience/bloomz-560m"

        model = AutoModelForSequenceClassification.from_pretrained(
            model_id,
            quantization_config=BitsAndBytesConfig(load_in_4bit=True),
            torch_dtype=torch.float32,
        )

        model = prepare_model_for_kbit_training(model)

        config = LoraConfig(
            r=16,
            lora_alpha=16,
            lora_dropout=0.05,
            bias="none",
            task_type="SEQ_CLS",
        )

        peft_model = get_peft_model(model, config)

        lm_head = peft_model.base_model.model.score
        original_module = lm_head.original_module
        modules_to_save = lm_head.modules_to_save.default

        inputs = torch.randn(1024).to(model.device)
        o1 = lm_head(inputs)
        o1.mean().backward()

        assert modules_to_save.weight.requires_grad is True
        assert original_module.weight.grad is None
        assert modules_to_save.weight.grad is not None

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_8bit_merge_lora(self):
        torch.manual_seed(1000)
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
        )
        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        out_base = F.softmax(model(random_input).logits, dim=-1)

        config = LoraConfig(
            r=8,
            init_lora_weights=False,
        )
        model = get_peft_model(model, config)

        with torch.inference_mode():
            out_before_merge = F.softmax(model(random_input).logits, dim=-1)

        model.merge_and_unload()
        with torch.inference_mode():
            out_after_merge = F.softmax(model(random_input).logits, dim=-1)

        atol = 1e-3
        rtol = 1
        assert not torch.allclose(out_base, out_before_merge, atol=atol, rtol=rtol)
        assert torch.allclose(out_before_merge, out_after_merge, atol=atol, rtol=rtol)
        assert isinstance(model, PeftModel)
        assert isinstance(model.base_model.model.model.decoder.layers[0].self_attn.q_proj, bnb.nn.Linear8bitLt)
        assert isinstance(model.base_model.model.model.decoder.layers[0].self_attn.v_proj, bnb.nn.Linear8bitLt)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_8bit_merge_and_disable_lora(self):
        torch.manual_seed(1000)
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
        )
        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        # compare outputs in probability space, because logits can have outliers
        # and token ids are not precise enough
        out_base = F.softmax(model(random_input).logits, dim=-1)

        config = LoraConfig(
            r=8,
            init_lora_weights=False,
        )
        model = get_peft_model(model, config)

        with torch.inference_mode():
            out_before = F.softmax(model(random_input).logits, dim=-1)

        model.merge_adapter()
        with model.disable_adapter():
            with torch.inference_mode():
                out_after = F.softmax(model(random_input).logits, dim=-1)

        atol = 1e-3
        rtol = 1
        assert not torch.allclose(out_base, out_before, atol=atol, rtol=rtol)
        assert torch.allclose(out_base, out_after, atol=atol, rtol=rtol)
        assert isinstance(model, PeftModel)
        assert isinstance(model.base_model.model.model.decoder.layers[0].self_attn.q_proj, LoraLinear8bitLt)
        assert isinstance(model.base_model.model.model.decoder.layers[0].self_attn.v_proj, LoraLinear8bitLt)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_8bit_merge_lora_with_bias(self):
        # same as test_8bit_merge_lora but with lora_bias=True
        torch.manual_seed(1000)
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
        )
        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        out_base = F.softmax(model(random_input).logits, dim=-1)

        config = LoraConfig(
            r=8,
            init_lora_weights=False,
            lora_bias=True,
        )
        model = get_peft_model(model, config)

        with torch.inference_mode():
            out_before_merge = F.softmax(model(random_input).logits, dim=-1)

        model.merge_and_unload()
        with torch.inference_mode():
            out_after_merge = F.softmax(model(random_input).logits, dim=-1)

        atol = 1e-3
        rtol = 1
        assert not torch.allclose(out_base, out_before_merge, atol=atol, rtol=rtol)
        assert torch.allclose(out_before_merge, out_after_merge, atol=atol, rtol=rtol)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_4bit_merge_lora(self):
        torch.manual_seed(3000)
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=False,
            bnb_4bit_compute_dtype=torch.float32,
        )
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=bnb_config,
            torch_dtype=torch.float32,
        )
        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        # compare outputs in probability space, because logits can have outliers
        # and token ids are not precise enough
        out_base = F.softmax(model(random_input).logits, dim=-1)

        config = LoraConfig(
            r=8,
            init_lora_weights=False,
        )
        model = get_peft_model(model, config)

        with torch.inference_mode():
            out_before_merge = F.softmax(model(random_input).logits, dim=-1)

        model.merge_and_unload()
        with torch.inference_mode():
            out_after_merge = F.softmax(model(random_input).logits, dim=-1)

        # tolerances are pretty high because some deviations are expected with quantization
        atol = 0.01
        rtol = 10
        assert not torch.allclose(out_base, out_before_merge, atol=atol, rtol=rtol)
        assert torch.allclose(out_before_merge, out_after_merge, atol=atol, rtol=rtol)
        assert isinstance(model, PeftModel)
        assert isinstance(model.base_model.model.model.decoder.layers[0].self_attn.q_proj, bnb.nn.Linear4bit)
        assert isinstance(model.base_model.model.model.decoder.layers[0].self_attn.v_proj, bnb.nn.Linear4bit)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_4bit_merge_and_disable_lora(self):
        torch.manual_seed(3000)
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=False,
            bnb_4bit_compute_dtype=torch.float32,
        )
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=bnb_config,
            torch_dtype=torch.float32,
        )
        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        # compare outputs in probability space, because logits can have outliers
        # and token ids are not precise enough
        out_base = F.softmax(model(random_input).logits, dim=-1)

        config = LoraConfig(
            r=8,
            init_lora_weights=False,
        )
        model = get_peft_model(model, config)

        with torch.inference_mode():
            out_before = F.softmax(model(random_input).logits, dim=-1)

        model.merge_adapter()
        with model.disable_adapter():
            with torch.inference_mode():
                out_after = F.softmax(model(random_input).logits, dim=-1)

        atol = 0.01
        rtol = 10
        assert not torch.allclose(out_base, out_before, atol=atol, rtol=rtol)
        assert torch.allclose(out_base, out_after, atol=atol, rtol=rtol)
        assert isinstance(model, PeftModel)
        assert isinstance(model.base_model.model.model.decoder.layers[0].self_attn.q_proj, LoraLinear4bit)
        assert isinstance(model.base_model.model.model.decoder.layers[0].self_attn.v_proj, LoraLinear4bit)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_4bit_merge_lora_with_bias(self):
        # same as test_4bit_merge_lora but with lora_bias=True
        torch.manual_seed(3000)
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=False,
            bnb_4bit_compute_dtype=torch.float32,
        )
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=bnb_config,
            torch_dtype=torch.float32,
        )
        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        # compare outputs in probability space, because logits can have outliers
        # and token ids are not precise enough
        out_base = F.softmax(model(random_input).logits, dim=-1)

        config = LoraConfig(
            r=8,
            init_lora_weights=False,
            lora_bias=True,
        )
        model = get_peft_model(model, config)

        with torch.inference_mode():
            out_before_merge = F.softmax(model(random_input).logits, dim=-1)

        model.merge_and_unload()
        with torch.inference_mode():
            out_after_merge = F.softmax(model(random_input).logits, dim=-1)

        # tolerances are pretty high because some deviations are expected with quantization
        atol = 0.01
        rtol = 10
        assert not torch.allclose(out_base, out_before_merge, atol=atol, rtol=rtol)
        assert torch.allclose(out_before_merge, out_after_merge, atol=atol, rtol=rtol)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_4bit_lora_mixed_adapter_batches_lora(self):
        # check that we can pass mixed adapter names to the model
        torch.manual_seed(3000)
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=False,
            bnb_4bit_compute_dtype=torch.float32,
        )
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=bnb_config,
            torch_dtype=torch.float32,
        ).eval()
        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
        # input with 9 samples
        inputs = tokenizer(
            [
                "Hello, my dog is cute",
                "Hello, my cat is awesome",
                "Hello, my fish is great",
                "Salut, mon chien est mignon",
                "Salut, mon chat est génial",
                "Salut, mon poisson est super",
                "Hallo, mein Hund ist süß",
                "Hallo, meine Katze ist toll",
                "Hallo, mein Fisch ist großartig",
            ],
            return_tensors="pt",
            padding=True,
        ).to(model.device)
        with torch.inference_mode():
            out_base = model(**inputs).logits

        config0 = LoraConfig(
            r=8,
            init_lora_weights=False,
        )
        model = get_peft_model(model, config0).eval()
        with torch.inference_mode():
            out_adapter0 = model(**inputs).logits

        config1 = LoraConfig(
            r=16,
            init_lora_weights=False,
        )
        model.add_adapter("adapter1", config1)
        model.set_adapter("adapter1")
        with torch.inference_mode():
            out_adapter1 = model(**inputs).logits

        atol, rtol = 3e-5, 1e-5
        # sanity check, outputs have the right shape and are not the same
        assert len(out_base) >= 3
        assert len(out_base) == len(out_adapter0) == len(out_adapter1)
        assert not torch.allclose(out_base, out_adapter0, atol=atol, rtol=rtol)
        assert not torch.allclose(out_base, out_adapter1, atol=atol, rtol=rtol)
        assert not torch.allclose(out_adapter0, out_adapter1, atol=atol, rtol=rtol)

        # mixed adapter batch
        adapters = ["__base__", "default", "adapter1"]
        adapter_names = [adapters[i % 3] for i in (range(9))]
        with torch.inference_mode():
            out_mixed = model(**inputs, adapter_names=adapter_names).logits

        assert torch.allclose(out_base[::3], out_mixed[::3], atol=atol, rtol=rtol)
        assert torch.allclose(out_adapter0[1::3], out_mixed[1::3], atol=atol, rtol=rtol)
        assert torch.allclose(out_adapter1[2::3], out_mixed[2::3], atol=atol, rtol=rtol)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_8bit_lora_mixed_adapter_batches_lora(self):
        # check that we can pass mixed adapter names to the model
        # note that with 8bit, we have quite a bit of imprecision, therefore we use softmax and higher tolerances
        torch.manual_seed(3000)
        bnb_config = BitsAndBytesConfig(load_in_8bit=True)
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=bnb_config,
            torch_dtype=torch.float32,
        ).eval()
        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
        # input with 9 samples
        inputs = tokenizer(
            [
                "Hello, my dog is cute",
                "Hello, my cat is awesome",
                "Hello, my fish is great",
                "Salut, mon chien est mignon",
                "Salut, mon chat est génial",
                "Salut, mon poisson est super",
                "Hallo, mein Hund ist süß",
                "Hallo, meine Katze ist toll",
                "Hallo, mein Fisch ist großartig",
            ],
            return_tensors="pt",
            padding=True,
        ).to(model.device)
        with torch.inference_mode():
            out_base = F.softmax(model(**inputs).logits, dim=-1)

        config0 = LoraConfig(
            r=8,
            init_lora_weights=False,
        )
        model = get_peft_model(model, config0).eval()
        with torch.inference_mode():
            out_adapter0 = F.softmax(model(**inputs).logits, dim=-1)

        config1 = LoraConfig(
            r=16,
            init_lora_weights=False,
        )
        model.add_adapter("adapter1", config1)
        model.set_adapter("adapter1")
        with torch.inference_mode():
            out_adapter1 = F.softmax(model(**inputs).logits, dim=-1)

        atol = 0.01
        rtol = 0.5
        # sanity check, outputs have the right shape and are not the same
        assert len(out_base) >= 3
        assert len(out_base) == len(out_adapter0) == len(out_adapter1)
        assert not torch.allclose(out_base, out_adapter0, atol=atol, rtol=rtol)
        assert not torch.allclose(out_base, out_adapter1, atol=atol, rtol=rtol)
        assert not torch.allclose(out_adapter0, out_adapter1, atol=atol, rtol=rtol)

        # mixed adapter batch
        adapters = ["__base__", "default", "adapter1"]
        adapter_names = [adapters[i % 3] for i in (range(9))]
        with torch.inference_mode():
            out_mixed = F.softmax(model(**inputs, adapter_names=adapter_names).logits, dim=-1)

        assert torch.allclose(out_base[::3], out_mixed[::3], atol=atol, rtol=rtol)
        assert torch.allclose(out_adapter0[1::3], out_mixed[1::3], atol=atol, rtol=rtol)
        assert torch.allclose(out_adapter1[2::3], out_mixed[2::3], atol=atol, rtol=rtol)

    @require_non_cpu
    @pytest.mark.single_gpu_tests
    def test_serialization_shared_tensors(self):
        model_checkpoint = "roberta-base"
        peft_config = LoraConfig(
            task_type=TaskType.TOKEN_CLS, inference_mode=False, r=16, lora_alpha=16, lora_dropout=0.1, bias="all"
        )
        model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, num_labels=11).to(self.device)
        model = get_peft_model(model, peft_config)

        with tempfile.TemporaryDirectory() as tmp_dir:
            model.save_pretrained(tmp_dir, safe_serialization=True)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_4bit_dora_inference(self):
        # check for same result with and without DoRA when initializing with init_lora_weights=False
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=False,
            bnb_4bit_compute_dtype=torch.float32,
        )
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=bnb_config,
            torch_dtype=torch.float32,
        )

        torch.manual_seed(0)
        config_lora = LoraConfig(r=8, init_lora_weights=False, use_dora=False)
        model = get_peft_model(model, config_lora).eval()

        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        logits_lora = model(random_input).logits

        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=bnb_config,
            torch_dtype=torch.float32,
        )
        torch.manual_seed(0)
        config_dora = LoraConfig(r=8, init_lora_weights=False, use_dora=True)
        model = get_peft_model(model, config_dora)

        logits_dora = model(random_input).logits

        assert torch.allclose(logits_lora, logits_dora)
        # sanity check
        assert isinstance(model.base_model.model.model.decoder.layers[0].self_attn.q_proj, LoraLinear4bit)
        assert isinstance(model.base_model.model.model.decoder.layers[0].self_attn.v_proj, LoraLinear4bit)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_8bit_dora_inference(self):
        # check for same result with and without DoRA when initializing with init_lora_weights=False
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
            torch_dtype=torch.float32,
        ).eval()

        torch.manual_seed(0)
        config_lora = LoraConfig(r=8, init_lora_weights=False, use_dora=False)
        model = get_peft_model(model, config_lora).eval()

        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        logits_lora = model(random_input).logits

        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
            torch_dtype=torch.float32,
        )
        torch.manual_seed(0)
        config_dora = LoraConfig(r=8, init_lora_weights=False, use_dora=True)
        model = get_peft_model(model, config_dora)

        logits_dora = model(random_input).logits

        assert torch.allclose(logits_lora, logits_dora)
        # sanity check
        assert isinstance(model.base_model.model.model.decoder.layers[0].self_attn.q_proj, LoraLinear8bitLt)
        assert isinstance(model.base_model.model.model.decoder.layers[0].self_attn.v_proj, LoraLinear8bitLt)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_4bit_dora_merging(self):
        # Check results for merging, unmerging, unloading
        torch.manual_seed(0)
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=False,
            bnb_4bit_compute_dtype=torch.float32,
        )
        model = AutoModelForCausalLM.from_pretrained(
            "trl-internal-testing/tiny-random-LlamaForCausalLM",
            quantization_config=bnb_config,
            torch_dtype=torch.float32,
        ).eval()
        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        # compare outputs in probability space, because logits can have outliers
        # and token ids are not precise enough
        out_base = F.softmax(model(random_input).logits, dim=-1)

        config = LoraConfig(
            r=8,
            init_lora_weights=False,
            use_dora=True,
        )
        model = get_peft_model(model, config).eval()

        # Note: By default, DoRA is a no-op before training, even if we set init_lora_weights=False. In order to
        # measure any differences, we need to change the magnitude vector.
        for name, module in model.named_modules():
            if isinstance(module, LoraLinear4bit):
                module.lora_magnitude_vector["default"].weight = torch.nn.Parameter(
                    10 * torch.rand_like(module.lora_magnitude_vector["default"].weight)
                )

        with torch.inference_mode():
            out_dora = F.softmax(model(random_input).logits, dim=-1)

            model.merge_adapter()
            out_merged = F.softmax(model(random_input).logits, dim=-1)

            model.unmerge_adapter()
            out_unmerged = F.softmax(model(random_input).logits, dim=-1)

            model = model.merge_and_unload()
            out_unloaded = F.softmax(model(random_input).logits, dim=-1)

        atol = 1e-5
        rtol = 1e-3
        # sanity check that using DoRA changes the results
        assert not torch.allclose(out_base, out_dora, atol=atol, rtol=rtol)
        assert torch.allclose(out_dora, out_merged, atol=atol, rtol=rtol)
        assert torch.allclose(out_dora, out_unmerged, atol=atol, rtol=rtol)
        assert torch.allclose(out_dora, out_unloaded, atol=atol, rtol=rtol)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    @require_bitsandbytes
    def test_8bit_dora_merging(self):
        # Check results for merging, unmerging, unloading
        torch.manual_seed(0)
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            quantization_config=BitsAndBytesConfig(load_in_8bit=True),
            torch_dtype=torch.float32,
        ).eval()

        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        # compare outputs in probability space, because logits can have outliers
        # and token ids are not precise enough
        out_base = F.softmax(model(random_input).logits, dim=-1)

        config = LoraConfig(
            r=8,
            init_lora_weights=False,
            use_dora=True,
        )
        model = get_peft_model(model, config).eval()

        # Note: By default, DoRA is a no-op before training, even if we set init_lora_weights=False. In order to
        # measure any differences, we need to change the magnitude vector.
        for name, module in model.named_modules():
            if isinstance(module, LoraLinear8bitLt):
                module.lora_magnitude_vector["default"].weight = torch.nn.Parameter(
                    10 * torch.rand_like(module.lora_magnitude_vector["default"].weight)
                )

        with torch.inference_mode():
            out_dora = F.softmax(model(random_input).logits, dim=-1)

            model.merge_adapter()
            out_merged = F.softmax(model(random_input).logits, dim=-1)

            model.unmerge_adapter()
            out_unmerged = F.softmax(model(random_input).logits, dim=-1)

            model = model.merge_and_unload()
            out_unloaded = F.softmax(model(random_input).logits, dim=-1)

        atol = 1e-3
        rtol = 1
        # sanity check that using DoRA changes the results
        assert not torch.allclose(out_base, out_dora, atol=atol, rtol=rtol)
        assert torch.allclose(out_dora, out_merged, atol=atol, rtol=rtol)
        assert torch.allclose(out_dora, out_unmerged, atol=atol, rtol=rtol)
        assert torch.allclose(out_dora, out_unloaded, atol=atol, rtol=rtol)

    @pytest.mark.single_gpu_tests
    def test_dora_ephemeral_gpu_offload(self):
        torch.manual_seed(0)

        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            torch_dtype=torch.float32,
        ).eval()

        config = LoraConfig(
            r=128,
            init_lora_weights=False,
            use_dora=True,
            runtime_config=LoraRuntimeConfig(
                ephemeral_gpu_offload=True
            ),  # we enable this, but only to verify that it's gone later
        )
        peft_model = get_peft_model(model, config).eval()
        # Check that ephemeral GPU offloading is present
        assert peft_model.peft_config["default"].runtime_config.ephemeral_gpu_offload

        # Save to disk
        with tempfile.TemporaryDirectory() as tmp_dir:
            peft_model.save_pretrained(tmp_dir)

            # Load from disk 100% on CPU without ephemeral GPU offloading
            peft_model_cpu = PeftModel.from_pretrained(
                model,
                tmp_dir,
                device_map={"": "cpu"},
            ).eval()

            # Check that ephemeral GPU offloading is absent
            assert not peft_model_cpu.peft_config["default"].runtime_config.ephemeral_gpu_offload

            # Load again, with ephemeral GPU offloading enabled
            peft_model_ego = PeftModel.from_pretrained(
                model,
                tmp_dir,
                device_map={"": "cpu"},
                ephemeral_gpu_offload=True,
            ).eval()

        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        with torch.inference_mode():
            out_peft_model_cpu = F.softmax(peft_model_cpu(random_input).logits, dim=-1)
            out_peft_model_ego = F.softmax(peft_model_ego(random_input).logits, dim=-1)

        # The results should be the same
        assert torch.allclose(out_peft_model_cpu, out_peft_model_ego)

    @require_multi_accelerator
    @pytest.mark.multi_gpu_tests
    def test_dora_ephemeral_gpu_offload_multigpu(self):
        torch.manual_seed(0)

        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            torch_dtype=torch.float32,
        ).eval()

        config = LoraConfig(
            r=16,  # too small and the time difference is too small
            init_lora_weights=False,
            use_dora=True,
            runtime_config=LoraRuntimeConfig(ephemeral_gpu_offload=True),
        )
        peft_model = get_peft_model(model, config).eval()

        layer = peft_model.base_model.model.model.decoder.layers[0].self_attn.v_proj
        lora_A, lora_B = layer.lora_A, layer.lora_B

        possible_combinations = ["cpu", self.device, f"{self.device}:0", f"{self.device}:1"]
        for device_A in possible_combinations:
            la = lora_A.to(device_A)
            for device_B in possible_combinations:
                lb = lora_B.to(device_B)
                layer.lora_A, layer.lora_B = la, lb
                layer.dora_init(layer.active_adapter[0])  # should not raise an error

    def test_apply_GS_hra_inference(self):
        # check for different result with and without apply_GS
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            torch_dtype=torch.float32,
        ).eval()

        torch.manual_seed(0)
        config_hra = HRAConfig(r=8, init_weights=True, apply_GS=False)
        model = get_peft_model(model, config_hra).eval()

        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)
        logits_hra = model(random_input).logits

        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            torch_dtype=torch.float32,
        )
        torch.manual_seed(0)
        config_hra_GS = HRAConfig(r=8, init_weights=True, apply_GS=True)
        model = get_peft_model(model, config_hra_GS)

        logits_hra_GS = model(random_input).logits

        assert not torch.allclose(logits_hra, logits_hra_GS)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    def test_apply_GS_hra_conv2d_inference(self):
        # check for different result with and without apply_GS
        model_id = "microsoft/resnet-18"
        image_processor = AutoImageProcessor.from_pretrained(model_id)
        dataset = load_dataset("huggingface/cats-image", trust_remote_code=True)
        image = dataset["test"]["image"][0]
        data = image_processor(image, return_tensors="pt")

        model = AutoModelForImageClassification.from_pretrained(model_id).eval()
        torch.manual_seed(0)
        config_hra = HRAConfig(r=8, init_weights=True, target_modules=["convolution"], apply_GS=False)
        model = get_peft_model(model, config_hra).eval()

        logits_hra = model(**data).logits

        model = AutoModelForImageClassification.from_pretrained(model_id).eval()
        torch.manual_seed(0)
        config_hra_GS = HRAConfig(r=8, init_weights=True, target_modules=["convolution"], apply_GS=True)
        model = get_peft_model(model, config_hra_GS)

        logits_hra_GS = model(**data).logits

        assert not torch.allclose(logits_hra, logits_hra_GS)

    @require_torch_gpu
    @pytest.mark.single_gpu_tests
    def test_r_odd_hra_inference(self):
        # check that an untrained HRA adapter can't be initialized as an identity tranformation
        # when r is an odd number
        model = AutoModelForCausalLM.from_pretrained(
            "facebook/opt-125m",
            torch_dtype=torch.float32,
        ).eval()

        random_input = torch.LongTensor([[1, 0, 1, 0, 1, 0]]).to(model.device)

        torch.manual_seed(0)
        logits = model(random_input).logits

        config_hra = HRAConfig(r=7, init_weights=True, apply_GS=False)
        model = get_peft_model(model, config_hra).eval()
        logits_hra = model(random_input).logits

        assert not torch.allclose(logits, logits_hra)


@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires a CUDA GPU")
@pytest.mark.single_gpu_tests
class TestSameAdapterDifferentDevices:
    # 1639
    # The original issue comes down to the following problem: If the user has a base layer on CUDA, moves the adapter to
    # CPU, then adds another adapter (which will automatically be moved to CUDA), then the first adapter will also be
    # moved to CUDA.
    @pytest.fixture
    def mlp(self):
        class MLP(nn.Module):
            def __init__(self, bias=True):
                super().__init__()
                self.lin0 = nn.Linear(8, 32, bias=bias)
                self.lin1 = nn.Linear(32, 2, bias=bias)

        return MLP()

    @pytest.fixture
    def emb_conv1d(self):
        class ModelEmbConv1D(nn.Module):
            def __init__(self, emb_size=100):
                super().__init__()
                self.emb = nn.Embedding(emb_size, 5)
                self.conv1d = Conv1D(1, 5)

        return ModelEmbConv1D()

    @pytest.fixture
    def conv2d(self):
        class ModelConv2D(nn.Module):
            def __init__(self):
                super().__init__()
                self.conv2d = nn.Conv2d(5, 10, 3)

        return ModelConv2D()

    def test_lora_one_target_add_new_adapter_does_not_change_device(self, mlp):
        config = LoraConfig(target_modules=["lin0"])
        model = get_peft_model(mlp, config)
        model = model.cuda()
        model.lin0.lora_A.cpu()
        model.lin0.lora_B.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin0.lora_A.default.weight.device.type == "cpu"
        assert model.lin0.lora_B.default.weight.device.type == "cpu"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin0.lora_A.default.weight.device.type == "cpu"
        assert model.lin0.lora_B.default.weight.device.type == "cpu"
        # the rest should be on GPU
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.lora_A.other.weight.device.type == "cuda"
        assert model.lin0.lora_B.other.weight.device.type == "cuda"

    def test_lora_multiple_targets_add_new_adapater_does_not_change_device(self, mlp):
        # same as the previous test, but targeting multiple layers
        config = LoraConfig(target_modules=["lin0", "lin1"])
        model = get_peft_model(mlp, config)
        model = model.cuda()
        # move lin1 to CPU but leave lin0 on GPU
        model.lin1.lora_A.cpu()
        model.lin1.lora_B.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin1.lora_A.default.weight.device.type == "cpu"
        assert model.lin1.lora_B.default.weight.device.type == "cpu"
        assert model.lin1.base_layer.weight.device.type == "cuda"
        assert model.lin0.lora_A.default.weight.device.type == "cuda"
        assert model.lin0.lora_B.default.weight.device.type == "cuda"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin1.lora_A.default.weight.device.type == "cpu"
        assert model.lin1.lora_B.default.weight.device.type == "cpu"
        assert model.lin1.base_layer.weight.device.type == "cuda"
        # the rest should be on GPU
        assert model.lin0.lora_A.default.weight.device.type == "cuda"
        assert model.lin0.lora_B.default.weight.device.type == "cuda"
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.lora_A.other.weight.device.type == "cuda"
        assert model.lin0.lora_B.other.weight.device.type == "cuda"
        assert model.lin1.lora_A.other.weight.device.type == "cuda"
        assert model.lin1.lora_B.other.weight.device.type == "cuda"

    def test_lora_embedding_target_add_new_adapter_does_not_change_device(self, emb_conv1d):
        # same as first test, but targeting the embedding layer
        config = LoraConfig(target_modules=["emb"])
        model = get_peft_model(emb_conv1d, config)
        model = model.cuda()
        model.emb.lora_embedding_A.cpu()
        model.emb.lora_embedding_B.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.emb.lora_embedding_A.default.device.type == "cpu"
        assert model.emb.lora_embedding_B.default.device.type == "cpu"
        assert model.emb.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.emb.lora_embedding_A.default.device.type == "cpu"
        assert model.emb.lora_embedding_B.default.device.type == "cpu"
        # the rest should be on GPU
        assert model.emb.weight.device.type == "cuda"
        assert model.emb.lora_embedding_A.other.device.type == "cuda"
        assert model.emb.lora_embedding_B.other.device.type == "cuda"

    def test_lora_conv1d_target_add_new_adapter_does_not_change_device(self, emb_conv1d):
        # same as first test, but targeting the Conv1D layer
        config = LoraConfig(target_modules=["conv1d"])
        model = get_peft_model(emb_conv1d, config)
        model = model.cuda()
        model.conv1d.lora_A.cpu()
        model.conv1d.lora_B.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.conv1d.lora_A.default.weight.device.type == "cpu"
        assert model.conv1d.lora_B.default.weight.device.type == "cpu"
        assert model.conv1d.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.conv1d.lora_A.default.weight.device.type == "cpu"
        assert model.conv1d.lora_B.default.weight.device.type == "cpu"
        # the rest should be on GPU
        assert model.conv1d.weight.device.type == "cuda"
        assert model.conv1d.lora_A.other.weight.device.type == "cuda"
        assert model.conv1d.lora_B.other.weight.device.type == "cuda"

    def test_lora_dora_add_new_adapter_does_not_change_device(self, mlp):
        # same as first test, but also using DoRA
        config = LoraConfig(target_modules=["lin0"], use_dora=True)
        model = get_peft_model(mlp, config)
        model = model.cuda()
        model.lin0.lora_A.cpu()
        model.lin0.lora_B.cpu()
        model.lin0.lora_magnitude_vector.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin0.lora_A.default.weight.device.type == "cpu"
        assert model.lin0.lora_B.default.weight.device.type == "cpu"
        assert model.lin0.lora_magnitude_vector.default.weight.device.type == "cpu"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin0.lora_A.default.weight.device.type == "cpu"
        assert model.lin0.lora_B.default.weight.device.type == "cpu"
        assert model.lin0.lora_magnitude_vector.default.weight.device.type == "cpu"
        # the rest should be on GPU
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.lora_A.other.weight.device.type == "cuda"
        assert model.lin0.lora_B.other.weight.device.type == "cuda"
        assert model.lin0.lora_magnitude_vector.other.weight.device.type == "cuda"

    def test_adalora_add_new_adapter_does_not_change_device(self, mlp):
        # same as first test, but using AdaLORA
        # AdaLora does not like multiple trainable adapters, hence inference_mode=True
        config = AdaLoraConfig(target_modules=["lin0"], inference_mode=True)
        model = get_peft_model(mlp, config)
        model = model.cuda()
        model.lin0.lora_A.cpu()
        model.lin0.lora_E.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin0.lora_A.default.device.type == "cpu"
        assert model.lin0.lora_E.default.device.type == "cpu"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin0.lora_A.default.device.type == "cpu"
        assert model.lin0.lora_E.default.device.type == "cpu"
        # the rest should be on GPU
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.lora_A.other.device.type == "cuda"
        assert model.lin0.lora_E.other.device.type == "cuda"

    def test_boft_add_new_adapter_does_not_change_device(self, mlp):
        # same as first test, but using BoFT
        config = BOFTConfig(target_modules=["lin0"])
        model = get_peft_model(mlp, config)
        model = model.cuda()
        model.lin0.boft_R.cpu()
        model.lin0.boft_s.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin0.boft_R.default.device.type == "cpu"
        assert model.lin0.boft_s.default.device.type == "cpu"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin0.boft_R.default.device.type == "cpu"
        assert model.lin0.boft_s.default.device.type == "cpu"
        # the rest should be on GPU
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.boft_R.other.device.type == "cuda"
        assert model.lin0.boft_s.other.device.type == "cuda"

    def test_ia3_add_new_adapter_does_not_change_device(self, mlp):
        # same as first test, but using IA3
        config = IA3Config(target_modules=["lin0"], feedforward_modules=["lin0"])
        model = get_peft_model(mlp, config)
        model = model.cuda()
        model.lin0.ia3_l.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin0.ia3_l.default.device.type == "cpu"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin0.ia3_l.default.device.type == "cpu"
        # the rest should be on GPU
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.ia3_l.other.device.type == "cuda"

    @pytest.mark.xfail(reason="LN Tuning handling of multiple adapters may not be correct", strict=True)
    def test_ln_tuning_add_new_adapter_does_not_change_device(self, mlp):
        # same as first test, but using LN tuning
        config = LNTuningConfig(target_modules=["lin0"])
        model = get_peft_model(mlp, config)
        model = model.cuda()
        model.lin0.ln_tuning_layers.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin0.ln_tuning_layers.default.weight.device.type == "cpu"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin0.ln_tuning_layers.default.weight.device.type == "cpu"
        # the rest should be on GPU
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.ln_tuning_layers.other.weight.device.type == "cuda"

    def test_loha_add_new_adapter_does_not_change_device(self, mlp):
        # same as first test, but using LoHa
        config = LoHaConfig(target_modules=["lin0"])
        model = get_peft_model(mlp, config)
        model = model.cuda()
        model.lin0.hada_w1_a.cpu()
        model.lin0.hada_w2_b.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin0.hada_w1_a.default.device.type == "cpu"
        assert model.lin0.hada_w2_b.default.device.type == "cpu"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin0.hada_w1_a.default.device.type == "cpu"
        assert model.lin0.hada_w2_b.default.device.type == "cpu"
        # the rest should be on GPU
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.hada_w1_a.other.device.type == "cuda"
        assert model.lin0.hada_w2_b.other.device.type == "cuda"

    def test_lokr_add_new_adapter_does_not_change_device(self, mlp):
        # same as first test, but using LoKr
        config = LoKrConfig(target_modules=["lin0"])
        model = get_peft_model(mlp, config)
        model = model.cuda()
        model.lin0.lokr_w1.cpu()
        model.lin0.lokr_w2.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin0.lokr_w1.default.device.type == "cpu"
        assert model.lin0.lokr_w2.default.device.type == "cpu"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin0.lokr_w1.default.device.type == "cpu"
        assert model.lin0.lokr_w2.default.device.type == "cpu"
        # the rest should be on GPU
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.lokr_w1.other.device.type == "cuda"
        assert model.lin0.lokr_w2.other.device.type == "cuda"

    def test_oft_add_new_adapter_does_not_change_device(self, mlp):
        # same as first test, but using OFT
        config = OFTConfig(target_modules=["lin0"])
        model = get_peft_model(mlp, config)
        model = model.cuda()
        model.lin0.oft_r.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin0.oft_r.default.device.type == "cpu"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin0.oft_r.default.device.type == "cpu"
        # the rest should be on GPU
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.oft_r.other.device.type == "cuda"

    def test_vera_add_new_adapter_does_not_change_device(self, mlp):
        # same as first test, but using VERA
        config = VeraConfig(target_modules=["lin0"])
        model = get_peft_model(mlp, config)
        model = model.cuda()
        model.lin0.vera_A.cpu()
        model.lin0.vera_lambda_d.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin0.vera_A.default.device.type == "cpu"
        assert model.lin0.vera_lambda_d.default.device.type == "cpu"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin0.vera_A.default.device.type == "cpu"
        assert model.lin0.vera_lambda_d.default.device.type == "cpu"
        # the rest should be on GPU
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.vera_A.other.device.type == "cuda"
        assert model.lin0.vera_lambda_d.other.device.type == "cuda"

    def test_vblora_add_new_adapter_does_not_change_device(self, mlp):
        # same as first test, but using VBLoRA
        config = VBLoRAConfig(target_modules=["lin0"], vector_length=2)
        model = get_peft_model(mlp, config)
        model = model.cuda()
        model.lin0.vblora_logits_A.cpu()
        model.lin0.vblora_logits_B.cpu()
        model.lin0.vblora_vector_bank.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin0.vblora_logits_A.default.device.type == "cpu"
        assert model.lin0.vblora_logits_B.default.device.type == "cpu"
        assert model.lin0.vblora_vector_bank.default.device.type == "cpu"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin0.vblora_logits_A.default.device.type == "cpu"
        assert model.lin0.vblora_logits_B.default.device.type == "cpu"
        assert model.lin0.vblora_vector_bank.default.device.type == "cpu"
        # the rest should be on GPU
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.vblora_logits_A.other.device.type == "cuda"
        assert model.lin0.vblora_logits_B.other.device.type == "cuda"
        assert model.lin0.vblora_vector_bank.other.device.type == "cuda"

    def test_hra_add_new_adapter_does_not_change_device(self, mlp):
        # same as first test, but using HRA
        config = HRAConfig(target_modules=["lin0"])
        model = get_peft_model(mlp, config)
        model = model.cuda()
        model.lin0.hra_u.cpu()

        # check that the adapter is indeed on CPU and the base model on GPU
        assert model.lin0.hra_u.default.device.type == "cpu"
        assert model.lin0.base_layer.weight.device.type == "cuda"

        model.add_adapter("other", config)
        # check that after adding a new adapter, the old adapter is still on CPU
        assert model.lin0.hra_u.default.device.type == "cpu"
        # the rest should be on GPU
        assert model.lin0.base_layer.weight.device.type == "cuda"
        assert model.lin0.hra_u.other.device.type == "cuda"
