tests/test_multitask_prompt_tuning.py

# 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 os
import tempfile

import pytest
import torch
from torch.testing import assert_close
from transformers import AutoModelForCausalLM

from peft import get_peft_model
from peft.peft_model import PeftModel
from peft.tuners.multitask_prompt_tuning import MultitaskPromptTuningConfig, MultitaskPromptTuningInit
from peft.utils import infer_device
from peft.utils.other import WEIGHTS_NAME, prepare_model_for_kbit_training
from peft.utils.save_and_load import get_peft_model_state_dict


MODELS_TO_TEST = [
    "trl-internal-testing/tiny-random-LlamaForCausalLM",
]


class TestMultiTaskPromptTuning:
    """
    Tests for the MultiTaskPromptTuning model.
    """

    @pytest.fixture
    def config(cls) -> MultitaskPromptTuningConfig:
        return MultitaskPromptTuningConfig(
            task_type="CAUSAL_LM",
            num_virtual_tokens=50,
            num_tasks=3,
            prompt_tuning_init_text=(
                "classify the following into either positive or negative, or entailment, neutral or contradiction:"
            ),
        )

    transformers_class = AutoModelForCausalLM
    torch_device = infer_device()

    @pytest.mark.parametrize("model_id", MODELS_TO_TEST)
    def test_prepare_for_training(self, model_id, config):
        model = AutoModelForCausalLM.from_pretrained(model_id)
        model = get_peft_model(model, config)
        model = model.to(self.torch_device)

        dummy_input = torch.LongTensor([[1, 1, 1]]).to(self.torch_device)
        dummy_output = model.get_input_embeddings()(dummy_input)

        assert not dummy_output.requires_grad

    @pytest.mark.parametrize("model_id", MODELS_TO_TEST)
    def test_prepare_for_int8_training(self, model_id, config):
        model = AutoModelForCausalLM.from_pretrained(model_id)
        model = prepare_model_for_kbit_training(model)
        model = model.to(self.torch_device)

        for param in model.parameters():
            assert not param.requires_grad

        model = get_peft_model(model, config)

        # For backward compatibility
        if hasattr(model, "enable_input_require_grads"):
            model.enable_input_require_grads()
        else:

            def make_inputs_require_grad(module, input, output):
                output.requires_grad_(True)

            model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)

        dummy_input = torch.LongTensor([[1, 1, 1]]).to(self.torch_device)
        dummy_output = model.get_input_embeddings()(dummy_input)

        assert dummy_output.requires_grad

    @pytest.mark.parametrize("model_id", MODELS_TO_TEST)
    def test_save_pretrained(self, model_id, config):
        seed = 420
        torch.manual_seed(seed)
        model = AutoModelForCausalLM.from_pretrained(model_id)
        model = get_peft_model(model, config)
        model = model.to(self.torch_device)

        with tempfile.TemporaryDirectory() as tmp_dirname:
            model.save_pretrained(tmp_dirname)

            torch.manual_seed(seed)
            model_from_pretrained = AutoModelForCausalLM.from_pretrained(model_id)
            model_from_pretrained = PeftModel.from_pretrained(model_from_pretrained, tmp_dirname)

            # check if the state dicts are equal
            state_dict = get_peft_model_state_dict(model)

            state_dict_from_pretrained = get_peft_model_state_dict(model_from_pretrained)

            # check if same keys
            assert state_dict.keys() == state_dict_from_pretrained.keys()

            # Check that the number of saved parameters is 4 -- 2 layers of (tokens and gate).
            assert len(state_dict) == 3

            # check if tensors equal
            for key in state_dict.keys():
                assert torch.allclose(
                    state_dict[key].to(self.torch_device), state_dict_from_pretrained[key].to(self.torch_device)
                )

            # check if `adapter_model.safetensors` is present
            assert os.path.exists(os.path.join(tmp_dirname, "adapter_model.safetensors"))

            # check if `adapter_config.json` is present
            assert os.path.exists(os.path.join(tmp_dirname, "adapter_config.json"))

            # check if `pytorch_model.bin` is not present
            assert not os.path.exists(os.path.join(tmp_dirname, "pytorch_model.bin"))

            # check if `config.json` is not present
            assert not os.path.exists(os.path.join(tmp_dirname, "config.json"))

    @pytest.mark.parametrize("model_id", MODELS_TO_TEST)
    def test_save_pretrained_regression(self, model_id, config):
        seed = 420
        torch.manual_seed(seed)
        model = AutoModelForCausalLM.from_pretrained(model_id)
        model = get_peft_model(model, config)
        model = model.to(self.torch_device)

        with tempfile.TemporaryDirectory() as tmp_dirname:
            model.save_pretrained(tmp_dirname, safe_serialization=False)

            torch.manual_seed(seed)
            model_from_pretrained = AutoModelForCausalLM.from_pretrained(model_id)
            model_from_pretrained = PeftModel.from_pretrained(model_from_pretrained, tmp_dirname)

            # check if the state dicts are equal
            state_dict = get_peft_model_state_dict(model)

            state_dict_from_pretrained = get_peft_model_state_dict(model_from_pretrained)

            # check if same keys
            assert state_dict.keys() == state_dict_from_pretrained.keys()

            # Check that the number of saved parameters is 4 -- 2 layers of (tokens and gate).
            assert len(state_dict) == 3

            # check if tensors equal
            for key in state_dict.keys():
                assert torch.allclose(
                    state_dict[key].to(self.torch_device), state_dict_from_pretrained[key].to(self.torch_device)
                )

            # check if `adapter_model.bin` is present for regression
            assert os.path.exists(os.path.join(tmp_dirname, "adapter_model.bin"))

            # check if `adapter_config.json` is present
            assert os.path.exists(os.path.join(tmp_dirname, "adapter_config.json"))

            # check if `pytorch_model.bin` is not present
            assert not os.path.exists(os.path.join(tmp_dirname, "pytorch_model.bin"))

            # check if `config.json` is not present
            assert not os.path.exists(os.path.join(tmp_dirname, "config.json"))

    @pytest.mark.parametrize("model_id", MODELS_TO_TEST)
    def test_generate(self, model_id, config):
        model = AutoModelForCausalLM.from_pretrained(model_id)
        model = get_peft_model(model, config)
        model = model.to(self.torch_device)

        input_ids = torch.LongTensor([[1, 1, 1], [2, 1, 2]]).to(self.torch_device)
        attention_mask = torch.LongTensor([[1, 1, 1], [1, 0, 1]]).to(self.torch_device)
        task_ids = torch.LongTensor([1, 2]).to(self.torch_device)

        # check if `generate` works
        _ = model.generate(input_ids=input_ids, attention_mask=attention_mask, task_ids=task_ids)

        # check if `generate` works if positional arguments are passed
        _ = model.generate(input_ids, attention_mask=attention_mask, task_ids=task_ids)

    @pytest.mark.parametrize("model_id", MODELS_TO_TEST)
    def test_use_cache(self, model_id, config):
        """Test that MultiTaskPromptTuning works when Llama config use_cache=True."""
        torch.manual_seed(0)
        input_ids = torch.LongTensor([[1, 1, 1], [2, 1, 2]]).to(self.torch_device)
        task_ids = torch.LongTensor([1, 2]).to(self.torch_device)

        original = AutoModelForCausalLM.from_pretrained(model_id)
        mpt = get_peft_model(original, config)
        mpt = mpt.to(self.torch_device)

        expected = mpt.generate(input_ids=input_ids, max_length=8, task_ids=task_ids)

        # Set use_cache = True and generate output again.
        mpt.base_model.config.use_cache = True
        actual = mpt.generate(input_ids=input_ids, max_length=8, task_ids=task_ids)
        assert_close(expected, actual, rtol=0, atol=0)

    @pytest.mark.parametrize("model_id", MODELS_TO_TEST)
    def test_bf16_inference(self, model_id, config):
        """Test that MultiTaskPromptTuning works when Llama using a half-precision model."""
        input_ids = torch.LongTensor([[1, 1, 1], [2, 1, 2]]).to(self.torch_device)
        task_ids = torch.tensor([1, 2]).to(self.torch_device)

        original = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
        mpt = get_peft_model(original, config)
        mpt = mpt.to(self.torch_device)
        _ = mpt.generate(input_ids=input_ids, task_ids=task_ids)

    @pytest.mark.parametrize("model_id", MODELS_TO_TEST)
    def test_generate_text_with_random_init(self, model_id, config) -> None:
        torch.manual_seed(0)
        model = AutoModelForCausalLM.from_pretrained(model_id)
        config.prompt_tuning_init = MultitaskPromptTuningInit.RANDOM
        model = get_peft_model(model, config)
        model = model.to(self.torch_device)

        input_ids = torch.LongTensor([[1, 1, 1], [2, 1, 2]]).to(self.torch_device)
        attention_mask = torch.LongTensor([[1, 1, 1], [1, 0, 1]]).to(self.torch_device)
        task_ids = torch.LongTensor([0]).to(self.torch_device)

        # check if `generate` works
        _ = model.generate(input_ids=input_ids, attention_mask=attention_mask, task_ids=task_ids)

        with pytest.raises(ValueError):
            # check if `generate` raises an error if task_ids are not passed
            _ = model.generate(input_ids, attention_mask=attention_mask)

    @pytest.mark.parametrize(
        "prompt_tuning_init",
        [
            MultitaskPromptTuningInit.AVERAGE_SOURCE_TASKS,
            MultitaskPromptTuningInit.EXACT_SOURCE_TASK,
            MultitaskPromptTuningInit.ONLY_SOURCE_SHARED,
        ],
    )
    @pytest.mark.parametrize("model_id", MODELS_TO_TEST)
    def test_generate_text_with_other_init(self, prompt_tuning_init, model_id, config) -> None:
        # This test is flaky, hence fixing the seed. The reason is somehow related to:
        # https://github.com/huggingface/transformers/blob/e786844425b6b1112c76513d66217ce2fe6aea41/src/transformers/generation/utils.py#L2691
        # When an EOS token is generated, the loop is exited and the pytest.raises at the bottom is not triggered
        # because `forward` of the PEFT model, which should raise the error, is never called.
        torch.manual_seed(42)  # seed 43 fails with transformers v4.42.3 and torch v2.3.1

        with tempfile.TemporaryDirectory() as tmp_dirname:
            model = AutoModelForCausalLM.from_pretrained(model_id)
            model = get_peft_model(model, config)
            model.save_pretrained(tmp_dirname, safe_serialization=False)  # bc torch.load is used

            config = MultitaskPromptTuningConfig(
                task_type="CAUSAL_LM",
                num_virtual_tokens=50,
                num_tasks=1,
                prompt_tuning_init_text=(
                    "classify the following into either positive or negative, or entailment, neutral or contradiction:"
                ),
                prompt_tuning_init=prompt_tuning_init,
                prompt_tuning_init_state_dict_path=os.path.join(tmp_dirname, WEIGHTS_NAME),
            )
            model = AutoModelForCausalLM.from_pretrained(model_id)
            model = get_peft_model(model, config)
            model = model.to(self.torch_device)

            input_ids = torch.LongTensor([[1, 1, 1], [2, 1, 2]]).to(self.torch_device)
            attention_mask = torch.LongTensor([[1, 1, 1], [1, 0, 1]]).to(self.torch_device)
            task_ids = torch.LongTensor([0]).to(self.torch_device)

            # check if `generate` works
            _ = model.generate(input_ids=input_ids, attention_mask=attention_mask, task_ids=task_ids)

            with pytest.raises(ValueError, match="task_ids cannot be None"):
                # check if `generate` raises an error if task_ids are not passed
                _ = model.generate(input_ids, attention_mask=attention_mask)