diff --git a/.gitignore b/.gitignore index 5e4361fa20..80ed06450d 100644 --- a/.gitignore +++ b/.gitignore @@ -172,3 +172,6 @@ scripts/git-strip-merge # DS_Store (MacOS) .DS_Store tests_adapters/backwards_compatibility/Ref_Out + +# backwards compatibility +model_outputs \ No newline at end of file diff --git a/LICENSE b/LICENSE index b5395aa1fa..27465606a2 100644 --- a/LICENSE +++ b/LICENSE @@ -1,4 +1,4 @@ -Copyright 2018-2021 The Hugging Face Team and The AdapterHub Team. All rights reserved. +Copyright 2020-2023 The AdapterHub Team. All rights reserved. Apache License Version 2.0, January 2004 diff --git a/README.md b/README.md index 1b320ef50b..8ec212943c 100644 --- a/README.md +++ b/README.md @@ -14,42 +14,10 @@ See the License for the specific language governing permissions and limitations under the License. --> -## `adapters` Library - -This branch contains the development version of `adapters`, the next-generation library for parameter-efficient and modular transfer learning. - -> **Note**: For the stable version of `adapter-transformers`, please switch to the [master branch of the repo](https://github.com/adapter-hub/adapter-transformers). - -### Changes compared to `adapter-transformers` - -- `adapters` is a standalone package, using `transformers` as an external dependency but not patching it directly -- All adapter-related classes now are imported via `adapters` namespace, e.g.: - ```python - from adapters import BertAdapterModel - # ... - ``` -- Built-in HF model classes can be adapted for usage with adapters via a wrapper method, e.g.: - ```python - import adapters - from transformers import BertModel - - model = BertModel.from_pretrained("bert-base-uncased") - adapters.init(model) - ``` - -Features not (yet) working: - -- Loading model + adapter checkpoints using HF classes -- Using Transformers pipelines with adapters -- Using HF language modeling classes with invertible adapters - -## Documentation -To read the documentation of _Adapters_, follow the steps in [docs/README.md](docs/README.md). Currently, the documentation is **not** yet available from https://docs.adapterhub.ml/. - ---- +> **Note**: This repository holds the codebase of the _Adapters_ library, which has replaced `adapter-transformers`. For the legacy codebase, go to: https://github.com/adapter-hub/adapter-transformers-legacy.

- +

Adapters @@ -59,8 +27,8 @@ To read the documentation of _Adapters_, follow the steps in [docs/README.md](do A Unified Library for Parameter-Efficient and Modular Transfer Learning

-![Tests](https://github.com/Adapter-Hub/adapter-transformers/workflows/Tests/badge.svg?branch=adapters) -[![GitHub](https://img.shields.io/github/license/adapter-hub/adapter-transformers.svg?color=blue)](https://github.com/adapter-hub/adapter-transformers/blob/adapters/LICENSE) +![Tests](https://github.com/Adapter-Hub/adapters/workflows/Tests/badge.svg?branch=adapters) +[![GitHub](https://img.shields.io/github/license/adapter-hub/adapters.svg?color=blue)](https://github.com/adapter-hub/adapters/blob/main/LICENSE) [![PyPI](https://img.shields.io/pypi/v/adapters)](https://pypi.org/project/adapters/) `adapters` is an add-on to [HuggingFace's Transformers](https://github.com/huggingface/transformers) library, integrating adapters into state-of-the-art language models by incorporating **[AdapterHub](https://adapterhub.ml)**, a central repository for pre-trained adapter modules. @@ -77,13 +45,82 @@ pip install -U adapters ... or from source by cloning the repository: ``` -git clone https://github.com/adapter-hub/adapter-transformers.git +git clone https://github.com/adapter-hub/adapters.git git checkout adapters cd adapters pip install . ``` -## Getting Started +## Quick Tour + +#### Load pre-trained adapters: + +```python +from adapters import AutoAdapterModel +from transformers import AutoTokenizer + +model = AutoAdapterModel.from_pretrained("roberta-base") +tokenizer = AutoTokenizer.from_pretrained("roberta-base") + +model.load_adapter("AdapterHub/roberta-base-pf-imdb", source="hf", set_active=True) + +print(model(**tokenizer("This works great!", return_tensors="pt")).logits) +``` + +**[Learn More](https://docs.adapterhub.ml/loading.html)** + +#### Adapt existing model setups: + +```python +import adapters +from transformers import AutoModelForSequenceClassification + +model = AutoModelForSequenceClassification.from_pretrained("t5-base") + +adapters.init(model) + +model.add_adapter("my_lora_adapter", config="lora") +model.train_adapter("my_lora_adapter") + +# Your regular training loop... +``` + +**[Learn More](https://docs.adapterhub.ml/quickstart.html)** + +#### Flexibly configure adapters: + +```python +from adapters import ConfigUnion, PrefixTuningConfig, ParBnConfig, AutoAdapterModel + +model = AutoAdapterModel.from_pretrained("microsoft/deberta-v3-base") + +adapter_config = ConfigUnion( + PrefixTuningConfig(prefix_length=20), + ParBnConfig(reduction_factor=4), +) +model.add_adapter("my_adapter", config=adapter_config, set_active=True) +``` + +**[Learn More](https://docs.adapterhub.ml/overview.html)** + +#### Easily compose adapters in a single model: + +```python +from adapters import AdapterSetup, AutoAdapterModel +import adapters.composition as ac + +model = AutoAdapterModel.from_pretrained("roberta-base") + +qc = model.load_adapter("AdapterHub/roberta-base-pf-trec") +sent = model.load_adapter("AdapterHub/roberta-base-pf-imdb") + +with AdapterSetup(ac.Parallel(qc, sent)): + print(model(**tokenizer("What is AdapterHub?", return_tensors="pt"))) +``` + +**[Learn More](https://docs.adapterhub.ml/adapter_composition.html)** + +## Useful Resources HuggingFace's great documentation on getting started with _Transformers_ can be found [here](https://huggingface.co/transformers/index.html). `adapters` is fully compatible with _Transformers_. diff --git a/docs/_static/custom.css b/docs/_static/custom.css index 680ab48e88..e4ba5749a4 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -31,3 +31,8 @@ a { .wy-side-scroll { padding-bottom: 1em; } + +/* override table no-wrap */ +.wy-table-responsive table td, .wy-table-responsive table th { + white-space: normal; +} diff --git a/docs/classes/adapter_config.rst b/docs/classes/adapter_config.rst index d817aad7c5..91a9f506a0 100644 --- a/docs/classes/adapter_config.rst +++ b/docs/classes/adapter_config.rst @@ -6,7 +6,7 @@ Classes representing the architectures of adapter modules and fusion layers. Single (bottleneck) adapters ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. autoclass:: adapters.AdapterConfigBase +.. autoclass:: adapters.AdapterConfig :members: .. autoclass:: adapters.BnConfig diff --git a/docs/conf.py b/docs/conf.py index 83e1403c6c..5f73a52d99 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -19,9 +19,9 @@ # -- Project information ----------------------------------------------------- -project = "adapters" -copyright = "2020-2022, Adapter-Hub Team" -author = "Adapter-Hub Team" +project = "AdapterHub" +copyright = "2020-2023, AdapterHub Team" +author = "AdapterHub Team" docs_versions = [ "adapters1.1.1", diff --git a/docs/contributing/adding_adapter_methods.md b/docs/contributing/adding_adapter_methods.md index 29a7801579..9e5f53b560 100644 --- a/docs/contributing/adding_adapter_methods.md +++ b/docs/contributing/adding_adapter_methods.md @@ -18,14 +18,14 @@ Therefore, the described steps might not be applicable to each implementation. These module classes then have to be inserted into the correct locations within the Transformer model implementation. Thus, each adapter method implementation at least should provide two classes: -- a configuration class deriving from `AdapterConfigBase` that provides attributes for all configuration options of the method +- a configuration class deriving from `AdapterConfig` that provides attributes for all configuration options of the method - a module class deriving from the abstract `AdapterLayerBase` that provides the method parameters and a set of standard adapter management functions - modules supporting [adapter composition](https://docs.adapterhub.ml/adapter_composition.html) should instead derive from `ComposableAdapterLayerBase` ### Configuration All configuration classes reside in `src/adapters/configuration/adapter_config.py`. -- To add a new configuration class for a new method, create a new subclass of [`AdapterConfigBase`](adapters.AdapterConfigBase). +- To add a new configuration class for a new method, create a new subclass of [`AdapterConfig`](adapters.AdapterConfig). Make sure to set the `architecture` attribute in your class. - Finally, also make sure the config class is added to the `__init__.py` files in `src/adapters`. diff --git a/docs/index.rst b/docs/index.rst index 323913cf7b..fdddf228ec 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -6,19 +6,29 @@ AdapterHub Documentation ================================================ +.. note:: + This documentation is based on the new *Adapters* library. + + The documentation based on the legacy *adapter-transformers* library can be found at: `https://docs-legacy.adapterhub.ml `_. + *AdapterHub* is a framework simplifying the integration, training and usage of adapters and other efficient fine-tuning methods for Transformer-based language models. For a full list of currently implemented methods, see the `table in our repository `_. The framework consists of two main components: -- ``adapters``, an extension of Hugging Face's `Transformers `_ library that adds adapter components to transformer models +.. list-table:: + :widths: 50 50 + :header-rows: 1 -- `The Hub `_, a central repository collecting pre-trained adapter modules + * - `Adapters `_ + - `AdapterHub.ml `_ + * - an add-on to Hugging Face's `Transformers `_ library that adds adapters into transformer models + - a central collection of pre-trained adapter modules Currently, we support the PyTorch versions of all models as listed on the `Model Overview `_ page. .. toctree:: - :maxdepth: 2 + :maxdepth: 1 :caption: Getting Started installation @@ -79,7 +89,7 @@ Currently, we support the PyTorch versions of all models as listed on the `Model classes/models/xmod .. toctree:: - :maxdepth: 2 + :maxdepth: 1 :caption: Adapter-Related Classes classes/adapter_config diff --git a/docs/overview.md b/docs/overview.md index c9af902693..a282df20d2 100644 --- a/docs/overview.md +++ b/docs/overview.md @@ -64,7 +64,7 @@ Identifiers and configuration classes are explained in more detail in the [next ## Configuration All supported adapter methods can be added, trained, saved and shared using the same set of model class functions (see [class documentation](adapters.ModelAdaptersMixin)). -Each method is specified and configured using a specific configuration class, all of which derive from the common [`AdapterConfigBase`](adapters.AdapterConfigBase) class. +Each method is specified and configured using a specific configuration class, all of which derive from the common [`AdapterConfig`](adapters.AdapterConfig) class. E.g., adding one of the supported adapter methods to an existing model instance follows this scheme: ```python model.add_adapter("name", config=) diff --git a/docs/training.md b/docs/training.md index bc070f9b96..649ace6e28 100644 --- a/docs/training.md +++ b/docs/training.md @@ -59,7 +59,7 @@ Compared to fine-tuning the entire model, we have to make only one significant a # task adapter - only add if not existing if task_name not in model.adapters_config: # resolve the adapter config - adapter_config = AdapterConfigBase.load(adapter_args.adapter_config) + adapter_config = AdapterConfig.load(adapter_args.adapter_config) # add a new adapter model.add_adapter(task_name, config=adapter_config) # Enable adapter training diff --git a/docs/transitioning.md b/docs/transitioning.md index 0df55eec44..d2f9e5481a 100644 --- a/docs/transitioning.md +++ b/docs/transitioning.md @@ -50,6 +50,12 @@ The `adapters` library supports the configuration of adapters using [config stri For a complete list of config strings and classes see [here](https://docs.adapterhub.ml/overview.html). We strongly recommend using the new config strings, but we will continue to support the old config strings for the time being to make the transition easier. Note that with the config strings the coresponding adapter config classes have changed, e.g. `PfeifferConfig` -> `SeqBnConfig`. +Another consequence of this that the `AdapterConfig` class is now not only for the bottleneck adapters anymore, but the base class of all the configurations (previously `AdapterConfigBase`). Hence the function this class serves has changed. However, you can still load adapter configs with: +``` +adapter_config = AdapterConfig.load("lora") +``` + + ## Features that are not supported by `adapters` Compared to `adapter-transformers`, there are a few features that are no longer supported by the `adapters` library: diff --git a/examples/pytorch/dependency-parsing/run_udp.py b/examples/pytorch/dependency-parsing/run_udp.py index 1980d6ada6..8fefe1f49c 100644 --- a/examples/pytorch/dependency-parsing/run_udp.py +++ b/examples/pytorch/dependency-parsing/run_udp.py @@ -13,7 +13,7 @@ import adapters import adapters.composition as ac -from adapters import AdapterArguments, AdapterConfigBase, AutoAdapterModel, setup_adapter_training +from adapters import AdapterArguments, AdapterConfig, AutoAdapterModel, setup_adapter_training from preprocessing import preprocess_dataset from transformers import AutoConfig, AutoTokenizer, HfArgumentParser, set_seed from utils_udp import UD_HEAD_LABELS, DependencyParsingAdapterTrainer, DependencyParsingTrainer, UDTrainingArguments @@ -252,7 +252,7 @@ def main(): logger.info("Loading best model for predictions.") if adapter_args.train_adapter: - adapter_config = AdapterConfigBase.load(adapter_args.adapter_config, **adapter_config_kwargs) + adapter_config = AdapterConfig.load(adapter_args.adapter_config, **adapter_config_kwargs) model.load_adapter( os.path.join(training_args.output_dir, "best_model", task_name) if training_args.do_train @@ -262,9 +262,7 @@ def main(): **adapter_load_kwargs, ) if adapter_args.load_lang_adapter: - lang_adapter_config = AdapterConfigBase.load( - adapter_args.lang_adapter_config, **adapter_config_kwargs - ) + lang_adapter_config = AdapterConfig.load(adapter_args.lang_adapter_config, **adapter_config_kwargs) lang_adapter_name = model.load_adapter( os.path.join(training_args.output_dir, "best_model", lang_adapter_name) if training_args.do_train diff --git a/notebooks/04_Cross_Lingual_Transfer.ipynb b/notebooks/04_Cross_Lingual_Transfer.ipynb index c951107590..4cebb3e54a 100644 --- a/notebooks/04_Cross_Lingual_Transfer.ipynb +++ b/notebooks/04_Cross_Lingual_Transfer.ipynb @@ -34,20 +34,20 @@ "cell_type": "code", "execution_count": 1, "metadata": { + "ExecuteTime": { + "end_time": "2023-08-17T09:24:50.921564600Z", + "start_time": "2023-08-17T09:24:44.078877400Z" + }, "colab": { "base_uri": "https://localhost:8080/" }, "id": "qL3Sq1HQynCq", - "outputId": "aa36636a-e484-4ca8-bc6e-b54b37b098b7", - "ExecuteTime": { - "end_time": "2023-08-17T09:24:50.921564600Z", - "start_time": "2023-08-17T09:24:44.078877400Z" - } + "outputId": "aa36636a-e484-4ca8-bc6e-b54b37b098b7" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m204.3/204.3 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m61.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", @@ -84,6 +84,10 @@ "cell_type": "code", "execution_count": 2, "metadata": { + "ExecuteTime": { + "end_time": "2023-08-17T09:25:09.494858700Z", + "start_time": "2023-08-17T09:25:06.870881400Z" + }, "colab": { "base_uri": "https://localhost:8080/", "height": 258, @@ -168,120 +172,116 @@ ] }, "id": "INW7UEhC-I6b", - "outputId": "7f22b72b-0c63-4152-f6c2-01da36af2824", - "ExecuteTime": { - "end_time": "2023-08-17T09:25:09.494858700Z", - "start_time": "2023-08-17T09:25:06.870881400Z" - } + "outputId": "7f22b72b-0c63-4152-f6c2-01da36af2824" }, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": [ - "Downloading builder script: 0%| | 0.00/30.7k [00:00" - ], "text/html": [ "\n", "
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"outputId": "bb2d049b-6746-43e9-bd32-3a066536dac1", - "ExecuteTime": { - "end_time": "2023-08-17T10:54:31.730170400Z", - "start_time": "2023-08-17T10:54:28.844625500Z" - } + "outputId": "bb2d049b-6746-43e9-bd32-3a066536dac1" }, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-multilingual-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n", "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']\n" ] @@ -127,19 +127,19 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "id": "gZj6Jt-P7Ysd", "ExecuteTime": { "end_time": "2023-08-17T10:55:18.106652Z", "start_time": "2023-08-17T10:55:07.121375100Z" - } + }, + "id": "gZj6Jt-P7Ysd" }, "outputs": [], "source": [ - "from adapters import AdapterConfigBase\n", + "from adapters import AdapterConfig\n", "target_language = \"gn\" # Choose any language that a bert-base-multilingual-cased language adapter is available for\n", "source_language = \"en\" # We support \"en\", \"ja\", \"zh\", and \"ar\"\n", "\n", - "adapter_config = AdapterConfigBase.load(\n", + "adapter_config = AdapterConfig.load(\n", " None,\n", " leave_out=[11]\n", ")\n", @@ -172,11 +172,11 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "id": "o-SUUa367TBr", "ExecuteTime": { "end_time": "2023-08-17T10:56:49.120227700Z", "start_time": "2023-08-17T10:55:35.178236700Z" - } + }, + "id": "o-SUUa367TBr" }, "outputs": [], "source": [ @@ -205,11 +205,11 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "id": "bbfMD2EM8OAs", "ExecuteTime": { "end_time": "2023-08-17T10:57:18.725260400Z", "start_time": "2023-08-17T10:57:18.709640900Z" - } + }, + "id": "bbfMD2EM8OAs" }, "outputs": [], "source": [ @@ -262,6 +262,10 @@ "cell_type": "code", "execution_count": 6, "metadata": { + "ExecuteTime": { + "end_time": "2023-08-17T10:57:33.497198700Z", + "start_time": "2023-08-17T10:57:33.406508100Z" + }, "colab": { "base_uri": "https://localhost:8080/", "height": 49, @@ -280,26 +284,22 @@ ] }, "id": "xrnTO-hV8pRc", - 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This class does not define specific configuration keys, but only provides some common helper methods. @@ -21,7 +21,7 @@ class AdapterConfigBase(Mapping): architecture: Optional[str] = None def __init__(self): - raise TypeError("AdapterConfigBase is an abstract class and cannot be instantiated.") + raise TypeError("AdapterConfig is an abstract class and cannot be instantiated.") # We want to emulate a simple form of immutability while keeping the ability to add custom attributes. # Therefore, we don't allow changing attribute values if set once. @@ -57,7 +57,7 @@ def replace(self, **changes): @classmethod def from_dict(cls, config): """Creates a config class from a Python dict.""" - if isinstance(config, AdapterConfigBase): + if isinstance(config, AdapterConfig): return config # the constructor does not accept additional kwargs, so add them separately @@ -94,7 +94,7 @@ def _get_config_class(config_dict): @classmethod def load(cls, config: Union[dict, str], download_kwargs=None, **kwargs): """ - Loads a given adapter configuration specifier into a full AdapterConfigBase instance. + Loads a given adapter configuration specifier into a full AdapterConfig instance. Args: config (Union[dict, str]): The configuration to load. Can be either: @@ -119,7 +119,7 @@ def load(cls, config: Union[dict, str], download_kwargs=None, **kwargs): else: config_dict = resolve_adapter_config(config, local_map=local_map) # convert back to dict to allow attr overrides - if isinstance(config_dict, AdapterConfigBase): + if isinstance(config_dict, AdapterConfig): cls_new = config_dict.__class__ config_dict = config_dict.to_dict() else: @@ -130,7 +130,7 @@ def load(cls, config: Union[dict, str], download_kwargs=None, **kwargs): @dataclass(eq=False) -class BnConfig(AdapterConfigBase): +class BnConfig(AdapterConfig): """ Base class that models the architecture of a bottleneck adapter. @@ -361,7 +361,7 @@ class ParBnConfig(BnConfig): @dataclass(eq=False) -class PrefixTuningConfig(AdapterConfigBase): +class PrefixTuningConfig(AdapterConfig): """ The Prefix Tuning architecture proposed by Li & Liang (2021). See https://arxiv.org/pdf/2101.00190.pdf. @@ -398,7 +398,7 @@ class PrefixTuningConfig(AdapterConfigBase): @dataclass(eq=False) -class PromptTuningConfig(AdapterConfigBase): +class PromptTuningConfig(AdapterConfig): """ The Prompt Tuning architecture proposed by Lester et al. (2021). See https://arxiv.org/pdf/2104.08691.pdf @@ -425,7 +425,7 @@ class PromptTuningConfig(AdapterConfigBase): @dataclass(eq=False) -class LoRAConfig(AdapterConfigBase): +class LoRAConfig(AdapterConfig): """ The Low-Rank Adaptation (LoRA) architecture proposed by Hu et al. (2021). See https://arxiv.org/pdf/2106.09685.pdf. LoRA adapts a model by reparametrizing the weights of a layer matrix. You can merge the additional weights with the @@ -491,7 +491,7 @@ class IA3Config(LoRAConfig): use_gating: bool = False -class ConfigUnion(AdapterConfigBase): +class ConfigUnion(AdapterConfig): """ Composes multiple adaptation method configurations into one. This class can be used to define complex adaptation method setups. @@ -499,9 +499,9 @@ class ConfigUnion(AdapterConfigBase): architecture: Optional[str] = "union" - configs: List[AdapterConfigBase] + configs: List[AdapterConfig] - def __init__(self, *configs: List[AdapterConfigBase]): + def __init__(self, *configs: List[AdapterConfig]): self.validate(configs) self.configs = configs @@ -512,7 +512,7 @@ def validate(configs): setup. Args: - configs (List[AdapterConfigBase]): list of configs to check. + configs (List[AdapterConfig]): list of configs to check. Raises: TypeError: One of the configurations has a wrong type. ValueError: At least two given configurations @@ -520,8 +520,8 @@ def validate(configs): """ # perform single config checks for config in configs: - if not isinstance(config, AdapterConfigBase): - raise TypeError(f"{config} is not an instance of AdapterConfigBase") + if not isinstance(config, AdapterConfig): + raise TypeError(f"{config} is not an instance of AdapterConfig") elif isinstance(config, ConfigUnion): raise TypeError(f"{config} of type {type(config)} is not supported in a config union.") # perform pairwise check @@ -568,7 +568,7 @@ def replace(self, **changes): @classmethod def from_dict(cls, config): - if isinstance(config, AdapterConfigBase): + if isinstance(config, AdapterConfig): return config configs = [] diff --git a/src/adapters/configuration/adapter_fusion_config.py b/src/adapters/configuration/adapter_fusion_config.py index afda2964c8..552bcdbe61 100644 --- a/src/adapters/configuration/adapter_fusion_config.py +++ b/src/adapters/configuration/adapter_fusion_config.py @@ -2,11 +2,11 @@ from typing import Union from ..utils import resolve_adapter_config -from .adapter_config import AdapterConfigBase +from .adapter_config import AdapterConfig @dataclass(eq=False) -class AdapterFusionConfig(AdapterConfigBase): +class AdapterFusionConfig(AdapterConfig): """Base class that models the architecture of an adapter fusion layer.""" key: bool diff --git a/src/adapters/configuration/model_adapters_config.py b/src/adapters/configuration/model_adapters_config.py index aeb89d493f..3f4c3023da 100644 --- a/src/adapters/configuration/model_adapters_config.py +++ b/src/adapters/configuration/model_adapters_config.py @@ -6,7 +6,7 @@ from .. import __version__ from ..composition import AdapterCompositionBlock from ..utils import get_adapter_config_hash -from .adapter_config import ADAPTER_CONFIG_MAP, DEFAULT_ADAPTER_CONFIG, AdapterConfigBase, ConfigUnion +from .adapter_config import ADAPTER_CONFIG_MAP, DEFAULT_ADAPTER_CONFIG, AdapterConfig, ConfigUnion from .adapter_fusion_config import ADAPTERFUSION_CONFIG_MAP, DEFAULT_ADAPTERFUSION_CONFIG @@ -77,8 +77,8 @@ def match( config = self.get(adapter_name) if config is None: return None - elif not isinstance(config, AdapterConfigBase): - config = AdapterConfigBase.load(config) + elif not isinstance(config, AdapterConfig): + config = AdapterConfig.load(config) if isinstance(config, config_type): leave_out = config.get("leave_out", []) @@ -125,7 +125,7 @@ def add(self, adapter_name: str, config: Optional[Union[str, dict]] = None): # if it's a dict, compute it's hash and add a new entry to the config map elif isinstance(config, Mapping): config_name = get_adapter_config_hash(config) - self.config_map[config_name] = AdapterConfigBase.load(config) + self.config_map[config_name] = AdapterConfig.load(config) else: raise ValueError("Invalid adapter config: {}".format(config)) self.adapters[adapter_name] = config_name @@ -207,14 +207,14 @@ def to_dict(self): output_dict["adapters"] = copy.deepcopy(self.adapters) output_dict["config_map"] = {} for k, v in self.config_map.items(): - if isinstance(v, AdapterConfigBase): + if isinstance(v, AdapterConfig): output_dict["config_map"][k] = v.to_dict() else: output_dict["config_map"][k] = copy.deepcopy(v) output_dict["fusions"] = copy.deepcopy(self.fusions) output_dict["fusion_config_map"] = {} for k, v in self.fusion_config_map.items(): - if isinstance(v, AdapterConfigBase): + if isinstance(v, AdapterConfig): output_dict["fusion_config_map"][k] = v.to_dict() else: output_dict["fusion_config_map"][k] = copy.deepcopy(v) @@ -233,7 +233,7 @@ def build_full_config(adapter_config, model_config, save_id2label=False, **kwarg config_dict.update(kwargs) if not hasattr(model_config, "prediction_heads") and save_id2label: config_dict["label2id"] = model_config.label2id - if isinstance(adapter_config, AdapterConfigBase): + if isinstance(adapter_config, AdapterConfig): config_dict["config"] = adapter_config.to_dict() else: config_dict["config"] = adapter_config diff --git a/src/adapters/heads/base.py b/src/adapters/heads/base.py index d907aba600..d45897df10 100644 --- a/src/adapters/heads/base.py +++ b/src/adapters/heads/base.py @@ -20,6 +20,7 @@ from ..composition import AdapterCompositionBlock, BatchSplit, Parallel, parse_heads_from_composition from ..context import AdapterSetup, ForwardContext +from ..loading import PredictionHeadLoader from ..methods.modeling import Activation_Function_Class from ..model_mixin import ModelWithHeadsAdaptersMixin @@ -535,20 +536,30 @@ def _init_head_modules(self): # The following methods are required for handling LM heads - def get_output_embeddings(self): + def get_output_embeddings(self) -> Union[nn.Module, List[nn.Module]]: # Only gets the output embeddings for the currently active head - if self.active_head in self.heads: - head = self.heads[self.active_head] - return head.get_output_embeddings() - else: + embeddings = [] + for head_name in self._active_heads: + if head_name in self.heads: + head = self.heads[head_name] + output_embeddings = head.get_output_embeddings() + embeddings.append(output_embeddings) + + if len(embeddings) == 1: + return embeddings[0] + elif len(embeddings) == 0 or all([e is None for e in embeddings]): return None + else: + return embeddings - def set_output_embeddings(self, new_embeddings): + def set_output_embeddings(self, new_embeddings: Union[nn.Module, List[nn.Module]]): # Only sets the output embeddings for the currently active head - if self.active_head in self.heads: - head = self.heads[self.active_head] - if head.get_output_embeddings() is not None: - head.set_output_embeddings(new_embeddings) + if not isinstance(new_embeddings, list): + new_embeddings = [new_embeddings] * len(self._active_heads) + for head_name, emb in zip(self._active_heads, new_embeddings): + if head_name in self.heads: + head = self.heads[head_name] + head.set_output_embeddings(emb) def tie_weights(self): """ @@ -917,3 +928,48 @@ def get_labels(self, head_name=None): return None else: return list(label_dict.values()) + + # This method is called during model loading in from_pretrained() to apply the state_dict to the model. + # Override it to inject adapter head logic. + @classmethod + def _load_pretrained_model( + cls, + model, + state_dict, + loaded_keys, + *args, + **kwargs, + ): + # Filter only weights not part of base model + if state_dict is not None: + head_state_dict = { + key: value for key, value in state_dict.items() if not key.startswith(cls.base_model_prefix) + } + else: + head_state_dict = None + head_name = "default" + loader = PredictionHeadLoader(model, error_on_missing=False, convert_to_flex_head=True) + head_config, new_head_state_dict = loader.convert_static_to_flex_head(head_state_dict, load_as=head_name) + + if head_config is not None: + # add head from config + if head_name in model.heads: + logger.warning("Overwriting existing head '{}'".format(head_name)) + + model.add_prediction_head_from_config(head_name, head_config, overwrite_ok=True) + + if new_head_state_dict is not None: + for k in head_state_dict: + del state_dict[k] + loaded_keys.remove(k) + for k in new_head_state_dict: + state_dict[k] = new_head_state_dict[k] + loaded_keys.append(k) + + return super()._load_pretrained_model( + model, + state_dict, + loaded_keys, + *args, + **kwargs, + ) diff --git a/src/adapters/loading.py b/src/adapters/loading.py index ddd15c1724..8e22cfc128 100644 --- a/src/adapters/loading.py +++ b/src/adapters/loading.py @@ -7,7 +7,7 @@ import torch -from .configuration import AdapterConfigBase, build_full_config +from .configuration import AdapterConfig, build_full_config from .head_utils import STATIC_TO_FLEX_HEAD_MAP, get_head_config_and_rename_list from .utils import ( ACTIVATION_RENAME, @@ -421,7 +421,7 @@ def load( Tuple[str, str]: A tuple consisting of the local file system directory from which the weights where loaded and the name of the loaded weights. """ - requested_config = AdapterConfigBase.load(config) if config else None + requested_config = AdapterConfig.load(config) if config else None # Resolve the weights to be loaded based on the given identifier and the current adapter config model_name = self.model.model_name or model_name resolved_folder = resolve_adapter_path( @@ -767,3 +767,43 @@ def load(self, save_directory, load_as=None, loading_info=None, **kwargs): ) return save_directory, head_name + + def convert_static_to_flex_head(self, state_dict, load_as="default"): + """ + Loads a prediction head module from the given state dict, which contains a static head checkpoint. + + Args: + state_dict (dict): The static head checkpoint from which to load the head module. Can be None. + load_as (str, optional): Load the weights with this name. Defaults to None. + + Returns: + Tuple[dict, dict]: A tuple consisting of the head config and the state dict of the loaded weights. + """ + assert self.convert_to_flex_head, "load_from_state_dict() can only be used with convert_to_flex_head=True." + assert hasattr(self.model, "heads"), "load_from_state_dict() can only be used with flex heads model class." + + conversion_rename_func = None + + original_model_class = self.model.config.architectures[0] if self.model.config.architectures else None + if original_model_class in STATIC_TO_FLEX_HEAD_MAP: + head_config, conversion_rename_func = get_head_config_and_rename_list( + original_model_class, + load_as, + getattr(self.model.config, "label2id"), + ) + elif self.error_on_missing: + raise ValueError( + f"Cannot automatically convert prediction head of model class {original_model_class} to flex head." + ) + else: + return None, None + + # Load head weights + if state_dict is not None: + new_state_dict = {} + for k, v in state_dict.items(): + new_k = conversion_rename_func(k) + new_state_dict[new_k] = v + else: + new_state_dict = None + return head_config, new_state_dict diff --git a/src/adapters/model_mixin.py b/src/adapters/model_mixin.py index b41a75cedd..a4a1b8d17c 100644 --- a/src/adapters/model_mixin.py +++ b/src/adapters/model_mixin.py @@ -13,7 +13,7 @@ from transformers.modeling_outputs import ModelOutput from .composition import AdapterCompositionBlock, Fuse, Stack, parse_composition -from .configuration import ADAPTER_CONFIG_MAP, AdapterConfigBase, AdapterFusionConfig, BnConfig +from .configuration import ADAPTER_CONFIG_MAP, AdapterConfig, AdapterFusionConfig, BnConfig from .context import AdapterSetup, ForwardContext from .hub_mixin import PushAdapterToHubMixin from .loading import AdapterFusionLoader, AdapterLoader, PredictionHeadLoader, WeightsLoader @@ -542,7 +542,7 @@ def add_adapter(self, adapter_name: str, config=None, overwrite_ok: bool = False Args: adapter_name (str): The name of the adapter module to be added. - config (str or dict or AdapterConfigBase, optional): The adapter configuration, can be either: + config (str or dict or AdapterConfig, optional): The adapter configuration, can be either: - the string identifier of a pre-defined configuration dictionary - a configuration dictionary specifying the full config @@ -553,7 +553,7 @@ def add_adapter(self, adapter_name: str, config=None, overwrite_ok: bool = False Set the adapter to be the active one. By default (False), the adapter is added but not activated. """ - config = AdapterConfigBase.load(config) # ensure config is ok and up-to-date + config = AdapterConfig.load(config) # ensure config is ok and up-to-date # In case adapter already exists and we allow overwriting, explicitly delete the existing one first if overwrite_ok and adapter_name in self.adapters_config: self.delete_adapter(adapter_name) @@ -1363,6 +1363,7 @@ def train_adapter(self, adapter_setup: Union[list, AdapterCompositionBlock], tra super().train_adapter(adapter_setup, train_embeddings) else: self.base_model.train_adapter(adapter_setup, train_embeddings) + self.freeze_embeddings() def train_adapter_fusion(self, adapter_setup: Union[list, AdapterCompositionBlock], unfreeze_adapters=False): """ @@ -1373,6 +1374,7 @@ def train_adapter_fusion(self, adapter_setup: Union[list, AdapterCompositionBloc super().train_adapter_fusion(adapter_setup, unfreeze_adapters=unfreeze_adapters) else: self.base_model.train_adapter_fusion(adapter_setup, unfreeze_adapters=unfreeze_adapters) + self.freeze_embeddings() def save_head(self, save_directory: str, head_name: str = None): loader = PredictionHeadLoader(self) @@ -1543,3 +1545,15 @@ def get_adapter(self, name): return super().get_adapter(name) else: return self.base_model.get_adapter(name) + + def freeze_embeddings(self, freeze=True): + # If model has prediction head with embeddings, ensure these are frozen + if self.get_output_embeddings() is not None: + output_embeddings = self.get_output_embeddings() + if isinstance(output_embeddings, list): + for output_embedding in output_embeddings: + for p in output_embedding.parameters(): + p.requires_grad = not freeze + else: + for p in self.get_output_embeddings().parameters(): + p.requires_grad = not freeze diff --git a/src/adapters/models/__init__.py b/src/adapters/models/__init__.py index 11da5d325e..dd48552d23 100644 --- a/src/adapters/models/__init__.py +++ b/src/adapters/models/__init__.py @@ -18,7 +18,12 @@ from .gpt2.mixin_gpt2 import GPT2ModelAdapterMixin from .gptj.mixin_gptj import GPTJMLPAdaptersMixin, GPTJModelAdapterMixin from .llama.mixin_llama import LlamaModelAdapterMixin -from .t5.mixin_t5 import T5BlockAdaptersMixin, T5ModelAdaptersMixin, T5ModelAdaptersWithHeadsMixin +from .t5.mixin_t5 import ( + T5BlockAdaptersMixin, + T5ForCondiditionalGenerationWithHeadsMixin, + T5ForQuestionAnsweringWithHeadsMixin, + T5ModelAdaptersMixin, +) from .vit.mixin_vit import ViTIntermediateAdaptersMixin, ViTModelAdaptersMixin from .xmod.mixin_xmod import XmodModelAdaptersMixin @@ -57,8 +62,8 @@ "RobertaModel": BertModelAdaptersMixin, "T5Block": T5BlockAdaptersMixin, "T5Model": T5ModelAdaptersMixin, - "T5ForConditionalGeneration": T5ModelAdaptersWithHeadsMixin, - "T5ForQuestionAnswering": T5ModelAdaptersWithHeadsMixin, + "T5ForConditionalGeneration": T5ForCondiditionalGenerationWithHeadsMixin, + "T5ForQuestionAnswering": T5ForQuestionAnsweringWithHeadsMixin, "T5EncoderModel": T5ModelAdaptersMixin, "ViTIntermediate": ViTIntermediateAdaptersMixin, "ViTModel": ViTModelAdaptersMixin, diff --git a/src/adapters/models/t5/mixin_t5.py b/src/adapters/models/t5/mixin_t5.py index d1bcfbac4a..63cacfb8a7 100644 --- a/src/adapters/models/t5/mixin_t5.py +++ b/src/adapters/models/t5/mixin_t5.py @@ -1,5 +1,6 @@ -from typing import Iterable, Tuple +from typing import Iterable, Optional, Tuple +import torch import torch.nn as nn from ...methods.bottleneck import BottleneckLayer @@ -105,5 +106,28 @@ def iter_layers(self) -> Iterable[Tuple[int, nn.Module]]: yield i, layer -class T5ModelAdaptersWithHeadsMixin(ModelWithHeadsAdaptersMixin, T5ModelAdaptersMixin): - pass +# Stating "labels" and "input_ids" explicitly is required for training using Trainer class +class T5ForCondiditionalGenerationWithHeadsMixin(ModelWithHeadsAdaptersMixin, T5ModelAdaptersMixin): + def forward( + self, + *args, + input_ids: Optional[torch.LongTensor] = None, + labels: Optional[torch.LongTensor] = None, + **kwargs, + ): + return super().forward(*args, input_ids=input_ids, labels=labels, **kwargs) + + +# Stating "start_positions"/"end_positions" and "input_ids" explicitly is required for training using Trainer class +class T5ForQuestionAnsweringWithHeadsMixin(ModelWithHeadsAdaptersMixin, T5ModelAdaptersMixin): + def forward( + self, + *args, + input_ids: Optional[torch.LongTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + **kwargs, + ): + return super().forward( + *args, input_ids=input_ids, start_positions=start_positions, end_positions=end_positions, **kwargs + ) diff --git a/src/adapters/training.py b/src/adapters/training.py index b959dff101..8316011398 100644 --- a/src/adapters/training.py +++ b/src/adapters/training.py @@ -2,7 +2,7 @@ from typing import Optional from .composition import Stack -from .configuration import AdapterConfigBase +from .configuration import AdapterConfig @dataclass @@ -57,7 +57,7 @@ def setup_adapter_training( # Setup adapters if adapter_args.train_adapter: # resolve the adapter config - adapter_config = AdapterConfigBase.load(adapter_args.adapter_config, **adapter_config_kwargs) + adapter_config = AdapterConfig.load(adapter_args.adapter_config, **adapter_config_kwargs) # load a pre-trained from Hub if specified # note: this logic has changed in versions > 3.1.0: adapter is also loaded if it already exists if adapter_args.load_adapter: @@ -73,7 +73,7 @@ def setup_adapter_training( # optionally load a pre-trained language adapter if adapter_args.load_lang_adapter: # resolve the language adapter config - lang_adapter_config = AdapterConfigBase.load(adapter_args.lang_adapter_config, **adapter_config_kwargs) + lang_adapter_config = AdapterConfig.load(adapter_args.lang_adapter_config, **adapter_config_kwargs) # load the language adapter from Hub lang_adapter_name = model.load_adapter( adapter_args.load_lang_adapter, diff --git a/tests_adapters/methods/test_compacter.py b/tests_adapters/methods/test_compacter.py index 82c9acddbc..253b0fbf4f 100644 --- a/tests_adapters/methods/test_compacter.py +++ b/tests_adapters/methods/test_compacter.py @@ -30,7 +30,7 @@ def test_forward_compacter(self): def test_forward_shared_phm_compacter(self): model = self.get_model() - adapter_config = CompacterPlusPlusConfig(phm_dim=2, shared_W_phm=True, reduction_factor=8) + adapter_config = CompacterPlusPlusConfig(phm_dim=4, shared_W_phm=True, reduction_factor=4) self.run_forward_test(model, adapter_config) def test_load_compacter(self): diff --git a/tests_adapters/test_adapter_config.py b/tests_adapters/test_adapter_config.py index 302fe8f697..2bce31c7e3 100644 --- a/tests_adapters/test_adapter_config.py +++ b/tests_adapters/test_adapter_config.py @@ -4,7 +4,7 @@ from adapters import ( ADAPTER_CONFIG_MAP, - AdapterConfigBase, + AdapterConfig, ConfigUnion, DoubleSeqBnConfig, LoRAConfig, @@ -23,14 +23,12 @@ def test_config_load(self): # TODO still uses the old config names as only these are available on the Hub for config_name in ["pfeiffer", "houlsby"]: with self.subTest(config_name=config_name): - config = AdapterConfigBase.load( - config_name, download_kwargs=download_kwargs, non_linearity="leakyrelu" - ) - self.assertTrue(isinstance(config, AdapterConfigBase)) + config = AdapterConfig.load(config_name, download_kwargs=download_kwargs, non_linearity="leakyrelu") + self.assertTrue(isinstance(config, AdapterConfig)) self.assertEqual(config.non_linearity, "leakyrelu") def test_config_immutable(self): - def set_attr(config: AdapterConfigBase): + def set_attr(config: AdapterConfig): config.non_linearity = "dummy" config.r = -1 # for LoRA config.prompt_length = -1 # for PromptTuning @@ -57,8 +55,8 @@ class CustomAdapterConfig(SeqBnConfig): config = CustomAdapterConfig() config_dict = config.to_dict() self.assertEqual(config_dict["custom_attr"], "test_value") - # When calling load on an AdapterConfigBase instance, don't change the class of the config. - config = AdapterConfigBase.load(config, custom_attr="test_value_2") + # When calling load on an AdapterConfig instance, don't change the class of the config. + config = AdapterConfig.load(config, custom_attr="test_value_2") self.assertTrue(isinstance(config, CustomAdapterConfig)) self.assertEqual(config["custom_attr"], "test_value_2") @@ -80,8 +78,8 @@ def test_config_union_valid(self): config_new = ConfigUnion.from_dict(config_dict) self.assertEqual(config, config_new) - self.assertIsInstance(config_new[0], AdapterConfigBase) - self.assertIsInstance(config_new[1], AdapterConfigBase) + self.assertIsInstance(config_new[0], AdapterConfig) + self.assertIsInstance(config_new[1], AdapterConfig) def test_config_union_invalid(self): unions = [ @@ -114,7 +112,7 @@ def test_config_string_valid(self): ] for config_str, config in to_test: with self.subTest(config_str=config_str): - config_new = AdapterConfigBase.load(config_str) + config_new = AdapterConfig.load(config_str) self.assertEqual(config, config_new) def test_config_string_invalid(self): @@ -126,4 +124,4 @@ def test_config_string_invalid(self): ] for config_str, error_type in to_test: with self.subTest(config_str=config_str): - self.assertRaises(error_type, AdapterConfigBase.load, config_str) + self.assertRaises(error_type, AdapterConfig.load, config_str) diff --git a/tests_adapters/test_adapter_conversion.py b/tests_adapters/test_adapter_conversion.py index ac57daa315..df209c12ba 100644 --- a/tests_adapters/test_adapter_conversion.py +++ b/tests_adapters/test_adapter_conversion.py @@ -198,3 +198,39 @@ def test_equivalent_language_generation(self): self.assertEquals(model_gen.shape, flex_model_gen.shape) self.assertTrue(torch.equal(model_gen, flex_model_gen)) + + def test_full_model_conversion(self): + if self.config_class not in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING: + self.skipTest("No sequence classification class.") + + static_head_model = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING[self.config_class](self.config()) + adapters.init(static_head_model) + static_head_model.eval() + + with tempfile.TemporaryDirectory() as temp_dir: + static_head_model.save_pretrained(temp_dir) + + flex_head_model, loading_info = AutoAdapterModel.from_pretrained(temp_dir, output_loading_info=True) + + # Roberta-based models always have a pooler, which is not used by the tested head + keys_to_ignore = ["roberta.pooler.dense.weight", "roberta.pooler.dense.bias"] + + missing_keys = [k for k in loading_info["missing_keys"] if k not in keys_to_ignore] + + self.assertEqual(0, len(missing_keys), "Missing keys: {}".format(", ".join(missing_keys))) + self.assertEqual( + 0, + len(loading_info["unexpected_keys"]), + "Unexpected keys: {}".format(", ".join(loading_info["unexpected_keys"])), + ) + + # static head is re-loaded as "default" + self.assertIn("default", flex_head_model.heads) + + # check equal output + in_data = self.get_input_samples(config=flex_head_model.config) + static_head_model.to(torch_device) + flex_head_model.to(torch_device) + output1 = static_head_model(**in_data) + output2 = flex_head_model(**in_data, head="default") + self.assertTrue(torch.allclose(output1.logits, output2.logits)) diff --git a/tests_adapters/test_adapter_fusion_common.py b/tests_adapters/test_adapter_fusion_common.py index 474752eb6e..4ee25fa06a 100644 --- a/tests_adapters/test_adapter_fusion_common.py +++ b/tests_adapters/test_adapter_fusion_common.py @@ -5,7 +5,7 @@ import torch -from adapters import ADAPTER_MODEL_MAPPING, ADAPTERFUSION_CONFIG_MAP, AdapterConfigBase, AutoAdapterModel, SeqBnConfig +from adapters import ADAPTER_MODEL_MAPPING, ADAPTERFUSION_CONFIG_MAP, AdapterConfig, AutoAdapterModel, SeqBnConfig from adapters.composition import Fuse from adapters.utils import ADAPTERFUSION_WEIGHTS_NAME from adapters.wrappers import load_model @@ -16,7 +16,7 @@ class AdapterFusionModelTestMixin: def test_add_adapter_fusion(self): config_name = "seq_bn" - adapter_config = AdapterConfigBase.load(config_name) + adapter_config = AdapterConfig.load(config_name) for adater_fusion_config_name, adapter_fusion_config in ADAPTERFUSION_CONFIG_MAP.items(): model = self.get_model() diff --git a/tests_adapters/test_adapter_heads.py b/tests_adapters/test_adapter_heads.py index 078cd6e095..b69aec2d98 100644 --- a/tests_adapters/test_adapter_heads.py +++ b/tests_adapters/test_adapter_heads.py @@ -192,6 +192,25 @@ def test_masked_lm_head(self): label_dict=label_dict, ) + def test_lm_head_freeze_output_embeddings(self): + if self.config_class not in ADAPTER_MODEL_MAPPING or ( + not hasattr(ADAPTER_MODEL_MAPPING[self.config_class], "add_seq2seq_lm_head") + and not hasattr(ADAPTER_MODEL_MAPPING[self.config_class], "add_causal_lm_head") + ): + self.skipTest("No seq2seq or causal language model head") + + model1 = AutoAdapterModel.from_config(self.config()) + model1.add_adapter("adapter1") + if hasattr(model1, "add_seq2seq_lm_head"): + model1.add_seq2seq_lm_head("adapter1") + else: + model1.add_causal_lm_head("adapter1") + + model1.train_adapter("adapter1") + + for n, p in model1.get_output_embeddings().named_parameters(): + self.assertFalse(p.requires_grad, f"Parameter {n} should not be trainable.") + def test_dependency_parsing_head(self): if not hasattr(ADAPTER_MODEL_MAPPING[self.config_class], "add_dependency_parsing_head"): self.skipTest("No dependency parsing head") diff --git a/tests_adapters/test_adapter_hub.py b/tests_adapters/test_adapter_hub.py index 24ac67c8b8..7ebf9acb6c 100644 --- a/tests_adapters/test_adapter_hub.py +++ b/tests_adapters/test_adapter_hub.py @@ -4,7 +4,7 @@ import numpy as np import adapters -from adapters import ADAPTER_CONFIG_MAP, AdapterConfigBase, BertAdapterModel, get_adapter_config_hash +from adapters import ADAPTER_CONFIG_MAP, AdapterConfig, BertAdapterModel, get_adapter_config_hash from adapters.trainer import AdapterTrainer as Trainer from adapters.utils import find_in_index from transformers import ( # get_adapter_config_hash, @@ -66,7 +66,7 @@ def test_load_task_adapter_from_hub(self): self.assertNotIn(adapter_name, model.base_model.invertible_adapters) # check if config is valid - expected_hash = get_adapter_config_hash(AdapterConfigBase.load(config)) + expected_hash = get_adapter_config_hash(AdapterConfig.load(config)) real_hash = get_adapter_config_hash(model.adapters_config.get(adapter_name)) self.assertEqual(expected_hash, real_hash) @@ -116,7 +116,7 @@ def test_load_lang_adapter_from_hub(self): with self.subTest(config=config): model = AutoModel.from_pretrained("bert-base-multilingual-cased") adapters.init(model) - config = AdapterConfigBase.load(config, non_linearity="gelu", reduction_factor=2) + config = AdapterConfig.load(config, non_linearity="gelu", reduction_factor=2) loading_info = {} adapter_name = model.load_adapter( @@ -155,7 +155,7 @@ def test_load_adapter_with_head_from_hub(self): self.assertIn(adapter_name, model.adapters_config.adapters) # check if config is valid - expected_hash = get_adapter_config_hash(AdapterConfigBase.load("houlsby")) + expected_hash = get_adapter_config_hash(AdapterConfig.load("houlsby")) real_hash = get_adapter_config_hash(model.adapters_config.get(adapter_name)) self.assertEqual(expected_hash, real_hash) diff --git a/utils/back_comp/README.md b/utils/back_comp/README.md new file mode 100644 index 0000000000..14896c613a --- /dev/null +++ b/utils/back_comp/README.md @@ -0,0 +1,26 @@ +# Backwards Compatibility Tests + +## Motivation + +This directory contains a set of tests that can be run to ensure that newly introduced changes or refactorings do not break existing functionalities. These tests verify model output consistency between two branches; here, we use the names `dev` and `main` for demonstration purposes, but these tests can be performed between any two branches where the `back_comp` directory with tests is available. +For this, the test script performs a forward pass for each supported model and compares the outputs between `dev` and `main` to identify any differences. + +## Requirements + +To execute these tests, you must meet the following requirements: + +- Ability to run bash scripts (in-built on Linux/macOS; for Windows, consider using third-party software like [GNU Bash](https://www.gnu.org/software/bash/)). +- Git as the version control system to switch between branches. +- The ability to check out the desired branch. If the branch is from another fork, you may need to add the repository as a remote. Refer to [GitHub's instructions](https://docs.github.com/en/get-started/getting-started-with-git/managing-remote-repositories) for details. +- A Python virtual environment to modify the installed package version of `adapters`. + +## Procedure + +To perform the compatibility tests, follow these steps: + +1. Determine a directory where you want to save the model output generated by the tests. Save this directory path to the variable `SaveDir` in the shell script `compare.sh`. (Careful: select a directory OUTSIDE of the repository; otherwise, the saved model output is no longer available when changing the branch.) +2. Select the branch you want to compare with `main` and save its name to the variable `Branch`. +3. Make sure you are checked out in `main` before starting the test script. +4. In your command line, navigate to the `back_comp` directory and execute the script by running `sh compare.sh`. + +The results will be displayed in the command line for visualization. \ No newline at end of file diff --git a/utils/back_comp/Utils.py b/utils/back_comp/Utils.py new file mode 100644 index 0000000000..8ed482130c --- /dev/null +++ b/utils/back_comp/Utils.py @@ -0,0 +1,498 @@ +import os +import random +from typing import Any, Union + +import numpy as np +import torch +from PIL import Image +from torch import squeeze + +import jsonlines +import requests +import transformers +from adapters import AutoAdapterModel, init +from transformers import ( + AlbertConfig, + BartConfig, + BatchEncoding, + BeitConfig, + BeitImageProcessor, + BertConfig, + CLIPProcessor, + CLIPVisionConfig, + CLIPVisionModelWithProjection, + DebertaConfig, + DebertaV2Config, + DistilBertConfig, + EncoderDecoderConfig, + EncoderDecoderModel, + GPT2Config, + GPTJConfig, + MBartConfig, + RobertaConfig, + T5Config, + ViTConfig, + ViTImageProcessor, + XLMRobertaConfig, +) + + +def create_output(model: Any, model_name: str): + """Given a model run a forward pass with some dummy data. + Args: + model: The model for which the forward pass is run. + model_name: The name of the model. + Returns: + The model output.""" + + dummy_data = generate_dummy_data(model_name) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # use GPU if available + model.to(device) + dummy_data.to(device) + with torch.no_grad(): + model_output = model(**dummy_data) + + return model_output + + +def load_model(model_name: str, model_save_dir: str): + """Loads a pre-trained model from a specified path based on the specified model_name. + Args: + model_name (str): The name of the model to be loaded. + model_save_dir (str): The directory path where the pre-trained model is saved. + Returns: + Any: The loaded pre-trained model.""" + if model_name == "clip": + model = CLIPVisionModelWithProjection.from_pretrained(model_save_dir) + init(model) + elif model_name == "encoder_decoder": + model = EncoderDecoderModel.from_pretrained(model_save_dir) + init(model) + else: + model = AutoAdapterModel.from_pretrained(model_save_dir) + model.eval() + init(model) + return model + + +def get_old_adapter_config_strings(): + """Returns a list of strings representing old adapter configuration strings.""" + return [ + "pfeiffer", + "houlsby", + "pfeiffer+inv", + "houlsby+inv", + "parallel", + "scaled_parallel", + "compacter", + "compacter++", + "prefix_tuning", + "prefix_tuning_flat", + "lora", + "ia3", + "mam", + "unipelt", + ] + + +def get_new_adapter_config_strings(): + """Returns a list of strings representing new adapter configurations.""" + return [ + "seq_bn", + "double_seq_bn", + "seq_bn_inv", + "double_seq_bn_inv", + "par_bn", + "scaled_par_bn", + "compacter", + "compacter++", + "prefix_tuning", + "prefix_tuning_flat", + "lora", + "ia3", + "mam", + "unipelt", + ] + + +def get_model_names(): + """Returns a list of strings representing the testable model names.""" + return [ + "bart", + "albert", + "beit", + "bert", + "clip", + "deberta", + "debertaV2", + "distilbert", + "encoder_decoder", + "gpt2", + "gptj", + "mbart", + "roberta", + "t5", + "vit", + "xlm-r", + ] + + +def create_model(model_name: str, model_class: Any) -> Any: + """Creates and returns an instance of a specified test model. + Args: + model_name (str): Specifies which model to instantiate. + Raises: + NotImplementedError: If the specified model type is not implemented.""" + + if model_name == "bart": + bart_config = BartConfig( + d_model=16, + encoder_layers=2, + decoder_layers=2, + encoder_attention_heads=4, + decoder_attention_heads=4, + encoder_ffn_dim=4, + decoder_ffn_dim=4, + ) + model = model_class.from_config(bart_config) + + elif model_name == "albert": + albert_config = AlbertConfig( + embedding_size=16, + hidden_size=64, + num_hidden_layers=5, + num_attention_heads=4, + intermediate_size=37, + num_hidden_groups=2, + ) + model = model_class.from_config(albert_config) + + elif model_name == "beit": + beit_config = BeitConfig( + image_size=224, + hidden_size=32, + num_hidden_layers=4, + num_attention_heads=4, + intermediate_size=37, + ) + model = model_class.from_config(beit_config) + + elif model_name == "bert": + bert_config = BertConfig( + hidden_size=32, + num_hidden_layers=4, + num_attention_heads=4, + intermediate_size=37, + ) + model = model_class.from_config(bert_config) + + elif model_name == "clip": + clip_config = CLIPVisionConfig( + image_size=30, + hidden_size=32, + num_hidden_layers=4, + num_attention_heads=4, + intermediate_size=37, + ) + model = CLIPVisionModelWithProjection(clip_config) + + elif model_name == "deberta": + deberta_config = DebertaConfig( + hidden_size=32, + num_hidden_layers=5, + num_attention_heads=4, + intermediate_size=37, + hidden_act="gelu", + relative_attention=True, + pos_att_type="p2c|c2p", + ) + model = model_class.from_config(deberta_config) + + elif model_name == "debertaV2": + debertaV2_config = DebertaV2Config( + hidden_size=32, + num_hidden_layers=5, + num_attention_heads=4, + intermediate_size=37, + hidden_act="gelu", + relative_attention=True, + pos_att_type="p2c|c2p", + ) + model = model_class.from_config(debertaV2_config) + + elif model_name == "distilbert": + distilbert_config = DistilBertConfig( + dim=32, + n_layers=4, + n_heads=4, + hidden_dim=37, + ) + model = model_class.from_config(distilbert_config) + + elif model_name == "encoder_decoder": + enc_dec_config = EncoderDecoderConfig.from_encoder_decoder_configs( + BertConfig( + hidden_size=32, + num_hidden_layers=4, + num_attention_heads=4, + intermediate_size=37, + ), + BertConfig( + hidden_size=32, + num_hidden_layers=4, + num_attention_heads=4, + intermediate_size=37, + is_decoder=True, + add_cross_attention=True, + ), + ) + model = EncoderDecoderModel(enc_dec_config) + + elif model_name == "gpt2": + gpt2_config = GPT2Config( + n_embd=32, + n_layer=4, + n_head=4, + # set pad token to eos token + pad_token_id=50256, + ) + model = model_class.from_config(gpt2_config) + + elif model_name == "gptj": + gptj_config = GPTJConfig( + n_embd=32, + n_layer=4, + n_head=4, + rotary_dim=4, + # set pad token to eos token + pad_token_id=50256, + resid_pdrop=0.1, + ) + model = model_class.from_config(gptj_config) + + elif model_name == "mbart": + mbart_config = MBartConfig( + d_model=16, + encoder_layers=2, + decoder_layers=2, + encoder_attention_heads=4, + decoder_attention_heads=4, + encoder_ffn_dim=4, + decoder_ffn_dim=4, + vocab_size=250027, + ) + model = model_class.from_config(mbart_config) + + elif model_name == "roberta": + roberta_config = RobertaConfig( + hidden_size=32, + num_hidden_layers=4, + num_attention_heads=4, + intermediate_size=37, + vocab_size=50265, + ) + model = model_class.from_config(roberta_config) + + elif model_name == "t5": + t5_config = T5Config( + d_model=16, + num_layers=2, + num_decoder_layers=2, + num_heads=4, + d_ff=4, + d_kv=16 // 4, + tie_word_embeddings=False, + decoder_start_token_id=0, + ) + model = model_class.from_config(t5_config) + + elif model_name == "vit": + vit_config = ViTConfig( + image_size=224, + hidden_size=32, + num_hidden_layers=4, + num_attention_heads=4, + intermediate_size=37, + ) + model = model_class.from_config(vit_config) + + elif model_name == "xlm-r": + xlm_config = XLMRobertaConfig( + hidden_size=32, + num_hidden_layers=4, + num_attention_heads=4, + intermediate_size=37, + vocab_size=250002, + ) + model = model_class.from_config(xlm_config) + + else: + raise NotImplementedError("The specified model type is not implemented.") + + return model + + +def generate_dummy_data(model: str = ""): + """Generates dummy data for text and vision transformers. + Args: + model (str, optional): The name of the transformer model. Defaults to an empty string.""" + # For the vision models load an image and process it + if model == "beit" or model == "clip" or model == "vit": + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + image = Image.open(requests.get(url, stream=True).raw) + if model == "beit": + processor = BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224-pt22k") + if model == "clip": + processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + if model == "vit": + processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k") + return processor(images=image, return_tensors="pt") + + # For text just create and process a dummy string. + else: + input_ids = [i for i in range(20)] + attention_mask = [1 for i in range(len(input_ids))] + input_ids_tensor = torch.tensor([input_ids]) + attention_mask_tensor = torch.tensor([attention_mask]) + if model == "t5" or model == "encoder_decoder": + return BatchEncoding( + { + "input_ids": input_ids_tensor, + "decoder_input_ids": input_ids_tensor, + "attention_mask": attention_mask_tensor, + } + ) + return BatchEncoding({"input_ids": input_ids_tensor, "attention_mask": attention_mask_tensor}) + + +def fix_seeds(seed: int = 42): + """Sets seeds manually. + Args: + seed (int, optional): The seed value to use for random number generation.""" + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def decode_tuple(tuple_to_decode: tuple): + """Reconstructs a potentially nested tuple of type `torch.Tensor` as a nested list. + Args: + tuple_to_decode (tuple): The tuple to decode. + Returns: + list: A nested list containing the same values as the input tuple. + Raises: + TypeError: If the tuple holds values of different type than `tuple` or `torch.Tensor`.""" + inner_model_output = [] + if isinstance(tuple_to_decode, torch.Tensor): + return tuple_to_decode.cpu().numpy().astype(np.float32).tolist() + elif isinstance(tuple_to_decode, tuple): + for value in tuple_to_decode: + inner_model_output.append(decode_tuple(value)) + return inner_model_output + else: + raise TypeError( + "ERROR occured during decoding of output tensors! The tuple holds values of different type " + "than `tuple` or `torch.Tensor`." + ) + + +def convert_tensors_to_list(model_output: transformers.utils.ModelOutput): + """Converts the model output, which consists of a Tuple of Tensors to a Tuple of lists, while preserving the + original dimensions. The converted output is returned. + Args: + model_output (transformers.utils.ModelOutput): The model's output of the forward pass. + Returns: + The converted model output as a tuple of lists and the last hidden state tensor also seperately. + Raises: + TypeError: If the model output is not a tuple of tensors.""" + # Model ouputs can't be unpacked directly, convert to tuple first + model_output_tensors = model_output.to_tuple() + model_output_numpy = [] + + # Recursively search each tuple entry + for output_value in model_output_tensors: + if isinstance(output_value, torch.Tensor): + model_output_numpy.append(squeeze(output_value.cpu()).numpy().astype(np.float32).tolist()) + + elif isinstance(output_value, tuple): + model_output_numpy.append(decode_tuple(output_value)) + + return model_output_numpy, model_output_tensors[0].cpu() + + +def save_to_jsonl(model_output: list, adapter_config: str, file_path: str): + """Save model output to a JSON Lines (.jsonl) file as a dictionary. Each line represents one model, where the key is the model name (specified by adapter_config) + and the value is the model output stored as a list of lists. If an output for a model already exists, it is overwritten. + Args: + model_output (list): The model's output as a list. + adapter_config (str): The model name, serving as the key for the dictionary. + file_path (str): The path of the file to save the new entry.""" + # Check if the file exists + if os.path.exists(file_path): + # Load content from .jsonl file + with jsonlines.open(file_path, mode="r") as f: + data = [line for line in f] + # Create empty list if file doesn't exist + else: + data = [] + + # Update result with new one if unique_id already exists in the file + for i, line in enumerate(data): + if adapter_config in line: + data[i] = {adapter_config: model_output} + break + # Add new result to the list if unique_id doesn't exist in the file + else: + data.append({adapter_config: model_output}) + with jsonlines.open(file_path, mode="w") as writer: + writer.write_all(data) + + +def compare_lists_close(a: list, b: list, rtol=1e-05, atol=1e-08): + """Reimplementation of `allclose()` for lists.""" + # Check if list a and b are numbers + if isinstance(a, (int, float)) and isinstance(b, (int, float)): + bad = abs(a - b) <= atol + rtol * abs(b) + if not bad: + print(f"Old package = {a}, New package = {b}") + print(f"Value diff: {abs(a - b)}") + return bad + + # Check if a and b are lists + if isinstance(a, list) and isinstance(b, list): + # Check if lenghts of the lists are equal + if len(a) != len(b): + return False + + for i in range(len(a)): + if not compare_lists_close(a[i], b[i], rtol=rtol, atol=atol): + return False + + return True + # If the inputs are not compatible types + return False + + +def restore_from_jsonl(config: str, file_path: str) -> Union[int, list]: + """Restores the model output from a JSON Lines (.jsonl) file as a list of lists for the specified model. + Args: + config (str): Name of the adapter config to restore the output for. + file_path (str): Path to the .jsonl file containing the model outputs. + Returns: + A list of lists representing the model output for the specified model. + Returns -1 if there is no output for the specified model in the file.""" + # Check if the file exists + if os.path.exists(file_path): + # Load content from .jsonl file + with jsonlines.open(file_path, mode="r") as f: + data = [line for line in f] + else: + raise FileExistsError(f"There exists no file at the specified path. \npath:{file_path}") + # Get result of specified model + for i, line in enumerate(data): + if config in line: + return data[i][config] + else: + print(f"File does not contain an output for the model {config}.") + return -1 diff --git a/utils/back_comp/compare.sh b/utils/back_comp/compare.sh new file mode 100644 index 0000000000..b4236192bd --- /dev/null +++ b/utils/back_comp/compare.sh @@ -0,0 +1,31 @@ +#!/bin/bash + +# This script performs backward compatibility tests by comparing adapter versions of different branches. +# The goal is to check if the model output produced under the same conditions is identical between branches. +# To do this, we need to determine a directory path to save the reference output produced by the current branch. +# It's important that this directory is outside the adapters repository to remain accessible when switching branches. + +# Select a directory to save the reference outputs (must be outside the repository!) +SaveDir="" + +# Now, determine the branch you want to compare against. +Branch= + +# After setting these variables, you can execute this script from the back_comp directory using the command: `sh compare.sh` + + +cd .. +pip install -e ".[dev]" # # Ensure that the adapters version of the current branch is installed +cd back_comp + +echo "Creating reference outputs..." +python create_outputs.py --path="$SaveDir" +cd .. + + +git checkout $Branch # Switch branch +pip install -e ".[dev]" # Install the other adapter version + +cd back_comp +echo "Comparing to reference outputs..." +python compare_outputs.py --path="$SaveDir" \ No newline at end of file diff --git a/utils/back_comp/compare_outputs.py b/utils/back_comp/compare_outputs.py new file mode 100644 index 0000000000..0775bf1bd0 --- /dev/null +++ b/utils/back_comp/compare_outputs.py @@ -0,0 +1,43 @@ +import argparse +import os + +from Utils import ( + compare_lists_close, + convert_tensors_to_list, + create_output, + fix_seeds, + get_model_names, + get_new_adapter_config_strings, + load_model, + restore_from_jsonl, +) + + +parser = argparse.ArgumentParser() +parser.add_argument("--path", type=str) +args = parser.parse_args() + + +# Create the root path +base_dir = os.path.join(args.path, "model_outputs") +fix_seeds() + +for model_name in get_model_names(): + # Load the reference model + print(f"Model = {model_name}") + model_dir = os.path.join(base_dir, model_name) + model = load_model(model_name, os.path.join(model_dir, "model_weights")) + + for adapter_config in get_new_adapter_config_strings(): + # Create a new model output + adapter_name = model.load_adapter(os.path.join(model_dir, "weights_" + adapter_config)) + model.set_active_adapters(adapter_name) + model_output = create_output(model, model_name) + + # Compare the model output to the reference output + model_output_n, last_hidden_state = convert_tensors_to_list(model_output) + ref_output = restore_from_jsonl(config=adapter_config, file_path=os.path.join(model_dir, "output.jsonl")) + is_equal = compare_lists_close(ref_output, model_output_n, rtol=1e-05, atol=1e-08) + print(f"Adapter: {adapter_config} -> {is_equal}") + + model.delete_adapter(adapter_name) diff --git a/utils/back_comp/create_outputs.py b/utils/back_comp/create_outputs.py new file mode 100644 index 0000000000..8476b5746a --- /dev/null +++ b/utils/back_comp/create_outputs.py @@ -0,0 +1,64 @@ +import argparse +import os + +from adapters import AutoAdapterModel, CompacterConfig, CompacterPlusPlusConfig +from Utils import ( + convert_tensors_to_list, + create_model, + create_output, + fix_seeds, + get_model_names, + get_new_adapter_config_strings, + load_model, + save_to_jsonl, +) + + +parser = argparse.ArgumentParser() +parser.add_argument("--path", type=str) +args = parser.parse_args() + + +# Create the root path +base_dir = os.path.join(args.path, "model_outputs") +fix_seeds() + +for model_name in get_model_names(): + print(f"Model = {model_name}") + # Create the dir to contain model- and adapter-weights and model outputs + model_dir = os.path.join(base_dir, model_name) + os.makedirs(model_dir, exist_ok=True) + + model = create_model(model_name=model_name, model_class=AutoAdapterModel) + # Save the model weights to reuse later + model_save_dir = os.path.join(model_dir, "model_weights") + os.makedirs(model_save_dir, exist_ok=True) + model.save_pretrained(model_save_dir, from_pt=True) # save the base model + + for config in get_new_adapter_config_strings(): + # Load the reference model + model = load_model(model_name, os.path.join(model_dir, "model_weights")) + + # Add the adapter which is tested + # For the compacter style adapters the phm_dim and reduction factor are set manually to ensure that the bottleneck dimension is divisible by phm_dim + if config == "compacter++": + adapter_config = CompacterPlusPlusConfig(phm_dim=2, reduction_factor=8) + elif config == "compacter": + adapter_config = CompacterConfig(phm_dim=2, reduction_factor=8) + else: + adapter_config = config + adapter_name = "weights_" + config + model.add_adapter(adapter_name, config=adapter_config) + model.set_active_adapters(adapter_name) + + model_output = create_output(model, model_name) + + # Process and save the output + model_output_n, last_hidden_state = convert_tensors_to_list(model_output) + save_to_jsonl(model_output_n, config, os.path.join(model_dir, "output.jsonl")) + + # Save the adapter weights + adapter_save_dir = os.path.join(model_dir, adapter_name) + os.makedirs(adapter_save_dir, exist_ok=True) + model.save_adapter(save_directory=adapter_save_dir, adapter_name=adapter_name) + model.delete_adapter(adapter_name)