From 49a8de86d5897bdd0f6be06e75d8c66fcea800ac Mon Sep 17 00:00:00 2001 From: mkshing Date: Thu, 21 Mar 2024 09:28:03 +0900 Subject: [PATCH] initial commit --- .github/workflows/black.yaml | 18 + .gitignore | 163 + LICENSE | 201 + README.md | 93 + assets/method.gif | Bin 0 -> 1043804 bytes configs/llm/abel-7b-002.yaml | 10 + configs/llm/arithmo2-mistral-7b.yaml | 10 + configs/llm/evollm-v1-jp-10b.yaml | 11 + configs/llm/evollm-v1-jp-7b-a.yaml | 10 + configs/llm/evollm-v1-jp-7b.yaml | 10 + configs/llm/shisa-gamma-7b-v1.yaml | 11 + configs/llm/wizardmath-7b-v1-1.yaml | 10 + configs/vlm/evovlm-v1-jp-7b.yaml | 23 + configs/vlm/heron-v1.yaml | 27 + configs/vlm/jsvlm.yaml | 16 + configs/vlm/llava-1-6-mistral-7b.yaml | 23 + evaluate.py | 70 + evomerge/__init__.py | 3 + evomerge/eval/__init__.py | 3 + evomerge/eval/ja_mgsm.py | 67 + evomerge/eval/ja_vg_vqa.py | 55 + evomerge/eval/ja_vlm_wild.py | 38 + evomerge/eval/metrics.py | 101 + evomerge/eval/utils.py | 100 + evomerge/models/__init__.py | 5 + evomerge/models/causallm.py | 60 + evomerge/models/heron_v1.py | 105 + evomerge/models/jsvlm.py | 98 + evomerge/models/llava.py | 103 + evomerge/models/prompt_templates.py | 32 + evomerge/models/utils.py | 48 + evomerge/modules/__init__.py | 0 evomerge/modules/heron/__init__.py | 0 evomerge/modules/heron/video_blip/__init__.py | 38 + evomerge/modules/heron/video_blip/bert.py | 73 + .../video_blip/configuration_video_blip.py | 439 ++ .../heron/video_blip/modeling_video_blip.py | 2602 +++++++++++ .../heron/video_blip/processing_video_blip.py | 171 + evomerge/utils.py | 61 + poetry.lock | 4091 +++++++++++++++++ pyproject.toml | 28 + 41 files changed, 9027 insertions(+) create mode 100644 .github/workflows/black.yaml create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 README.md create mode 100644 assets/method.gif create mode 100644 configs/llm/abel-7b-002.yaml create mode 100644 configs/llm/arithmo2-mistral-7b.yaml create mode 100644 configs/llm/evollm-v1-jp-10b.yaml create mode 100644 configs/llm/evollm-v1-jp-7b-a.yaml create mode 100644 configs/llm/evollm-v1-jp-7b.yaml create mode 100644 configs/llm/shisa-gamma-7b-v1.yaml create mode 100644 configs/llm/wizardmath-7b-v1-1.yaml create mode 100644 configs/vlm/evovlm-v1-jp-7b.yaml create mode 100644 configs/vlm/heron-v1.yaml create mode 100644 configs/vlm/jsvlm.yaml create mode 100644 configs/vlm/llava-1-6-mistral-7b.yaml create mode 100644 evaluate.py create mode 100644 evomerge/__init__.py create mode 100644 evomerge/eval/__init__.py create mode 100644 evomerge/eval/ja_mgsm.py create mode 100644 evomerge/eval/ja_vg_vqa.py create mode 100644 evomerge/eval/ja_vlm_wild.py create mode 100644 evomerge/eval/metrics.py create mode 100644 evomerge/eval/utils.py create mode 100644 evomerge/models/__init__.py create mode 100644 evomerge/models/causallm.py create mode 100644 evomerge/models/heron_v1.py create mode 100644 evomerge/models/jsvlm.py create mode 100644 evomerge/models/llava.py create mode 100644 evomerge/models/prompt_templates.py create mode 100644 evomerge/models/utils.py create mode 100644 evomerge/modules/__init__.py create mode 100644 evomerge/modules/heron/__init__.py create mode 100644 evomerge/modules/heron/video_blip/__init__.py create mode 100644 evomerge/modules/heron/video_blip/bert.py create mode 100644 evomerge/modules/heron/video_blip/configuration_video_blip.py create mode 100644 evomerge/modules/heron/video_blip/modeling_video_blip.py create mode 100644 evomerge/modules/heron/video_blip/processing_video_blip.py create mode 100644 evomerge/utils.py create mode 100644 poetry.lock create mode 100644 pyproject.toml diff --git a/.github/workflows/black.yaml b/.github/workflows/black.yaml new file mode 100644 index 0000000..a9834a9 --- /dev/null +++ b/.github/workflows/black.yaml @@ -0,0 +1,18 @@ +name: Run black +on: + push: + branches: [ main ] + pull_request: + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Install venv + run: | + sudo apt-get -y install python3.10-venv + - uses: psf/black@stable + with: + options: "--check --verbose -l88" + src: "." \ No newline at end of file diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..a6ad2c5 --- /dev/null +++ b/.gitignore @@ -0,0 +1,163 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ +.vscode + +lid.176.* \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..843760c --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2024 Sakana AI + + 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. diff --git a/README.md b/README.md new file mode 100644 index 0000000..b988c0f --- /dev/null +++ b/README.md @@ -0,0 +1,93 @@ +# 🐟 Evolutionary Optimization of Model Merging Recipes + +🤗 [Models](https://huggingface.co/SakanaAI) | 👀 [Demo](https://huggingface.co/spaces/SakanaAI/EvoVLM-JP) | 📚 [Paper](https://arxiv.org/abs/2403.13187) | 📝 [Blog](https://sakana.ai/evolutionary-model-merge/) | 🐦 [Twitter](https://twitter.com/SakanaAILabs) + + +
+Method +
+ + + +This repository serves as a central hub for SakanaAI's [Evolutionary Model Merge](https://arxiv.org/abs/2403.13187) series, showcasing its releases and resources. It includes models and code for reproducing the evaluation presented in our paper. Look forward to more updates and additions coming soon. + +## Models + +### Our Models + +| Model | Size | License | Source | +| :-- | --: | :-- | :-- | +| [EvoLLM-JP-v1-7B](https://huggingface.co/SakanaAI/EvoLLM-JP-v1-7B) | 7B | Microsoft Research License | [shisa-gamma-7b-v1](https://huggingface.co/augmxnt/shisa-gamma-7b-v1), [WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1), [GAIR/Abel-7B-002](https://huggingface.co/GAIR/Abel-7B-002) +| [EvoLLM-JP-v1-10B](https://huggingface.co/SakanaAI/EvoLLM-JP-v1-10B) | 10B | Microsoft Research License | EvoLLM-JP-v1-7B, [shisa-gamma-7b-v1](https://huggingface.co/augmxnt/shisa-gamma-7b-v1) | +| [EvoLLM-JP-A-v1-7B](https://huggingface.co/SakanaAI/EvoLLM-JP-A-v1-7B) | 7B | Apache 2.0 | [shisa-gamma-7b-v1](https://huggingface.co/augmxnt/shisa-gamma-7b-v1), [Arithmo2-Mistral-7B](https://huggingface.co/upaya07/Arithmo2-Mistral-7B), [GAIR/Abel-7B-002](https://huggingface.co/GAIR/Abel-7B-002) | +| [EvoVLM-JP-v1-7B](https://huggingface.co/SakanaAI/EvoVLM-JP-v1-7B) | 7B | Apache 2.0 | [LLaVA-1.6-Mistral-7B](https://huggingface.co/liuhaotian/llava-v1.6-mistral-7b), [shisa-gamma-7b-v1](https://huggingface.co/augmxnt/shisa-gamma-7b-v1) + + + + +### Comparing EvoLLM-JP w/ Source LLMs + +For details on the evaluation, please refer to Section 4.1 of the paper. + +| Model | MGSM-JA (acc ↑) | [lm-eval-harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable) (avg ↑) | +| :-- | --: | --: | +| [Shisa Gamma 7B v1](https://huggingface.co/augmxnt/shisa-gamma-7b-v1) | 9.6 | 66.1 | +| [WizardMath 7B V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1) | 18.4 | 60.1 | +| [Abel 7B 002](https://huggingface.co/GAIR/Abel-7B-002) | 30.0 | 56.5 | +| [Arithmo2 Mistral 7B](https://huggingface.co/upaya07/Arithmo2-Mistral-7B) | 24.0 | 56.4 | +| [EvoLLM-JP-A-v1-7B](https://huggingface.co/SakanaAI/EvoLLM-JP-A-v1-7B) | **52.4** | **69.0** | +| [EvoLLM-JP-v1-7B](https://huggingface.co/SakanaAI/EvoLLM-JP-v1-7B) | **52.0** | **70.5** | +| [EvoLLM-JP-v1-10B](https://huggingface.co/SakanaAI/EvoLLM-JP-v1-10B) | **55.6** | **66.2** | + + +### Comparing EvoVLM-JP w/ Existing VLMs + +For details on the evaluation, please see Section 4.2 of the paper. + + +| Model | JA-VG-VQA-500 (ROUGE-L ↑) | JA-VLM-Bench-In-the-Wild (ROUGE-L ↑) | +| :-- | --: | --: | +| [LLaVA-1.6-Mistral-7B](https://llava-vl.github.io/blog/2024-01-30-llava-next/) | 14.32 | 41.10 | +| [Japanese Stable VLM](https://huggingface.co/stabilityai/japanese-stable-vlm) | -*1 | 40.50 | +| [Heron BLIP Japanese StableLM Base 7B llava-620k](https://huggingface.co/turing-motors/heron-chat-blip-ja-stablelm-base-7b-v1-llava-620k) | 8.73*2 | 27.37*2 | +| [EvoVLM-JP-v1-7B](https://huggingface.co/SakanaAI/EvoVLM-JP-v1-7B) | **19.70** | **51.25** | + +* \*1: Japanese Stable VLM cannot be evaluated using the JA-VG-VQA-500 dataset because this model has used this dataset for training. +* \*2: We are checking with the authors to see if this current results are valid. + + + +## Reproducing the Evaluation + +### 1. Clone the Repo + +```bash +git clone https://github.com/SakanaAI/evolutionary-model-merge.git +cd evolutionary-model-merge +``` + +### 2. Download fastext Model + +We use fastext to detect language for evaluation. Please download `lid.176.ftz` from [this link](https://fasttext.cc/docs/en/language-identification.html) and place it in your current directory. If you place the file in a directory other than the current directory, specify the path to the file using the `LID176FTZ_PATH` environment variable. + + +### 3. Install Libraries + +```bash +pip install -e . +``` +We conducted our tests in the following environment: Python Version 3.10.12 and CUDA Version 12.3. +We cannot guarantee that it will work in other environments. + +### 4. Run + +To launch evaluation, run the following script with a certain config. All configs used for the paper are in `configs`. + +```bash +python evaluate.py --config_path {path-to-config} +``` + + +## Acknowledgement + +We would like to thank the developers of the source models for their contributions and for making their work available. 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Or, take one example randomely? + qa = qa_list[0] + example["question"] = qa["question"] + example["answer"] = qa["answer"] + return example + + +class JaVGVQA: + name = "JA-VG-VQA-500" + dataset_path = "SakanaAI/JA-VG-VQA-500" + dataset_split = "test" + + def __init__( + self, + verbose: bool = False, + loader_kwargs: Optional[dict] = None, + strict_japanese: bool = True, + ): + self.verbose = verbose + self.loader_kwargs = default(loader_kwargs, {}) + + dataset = load_dataset(path=self.dataset_path, split=self.dataset_split) + # extract qa + dataset = dataset.map(extract_qa) + # filter column + self.dataset = dataset.select_columns(["question", "answer", "image"]) + self.lang_detect = None + if strict_japanese: + self.lang_detect = LanguageDetector() + + def __call__(self, model) -> EvalOutput: + return evaluate( + name=self.name, + model=model, + dataset=self.dataset, + loader_kwargs=self.loader_kwargs, + lang_detect=self.lang_detect, + ) diff --git a/evomerge/eval/ja_vlm_wild.py b/evomerge/eval/ja_vlm_wild.py new file mode 100644 index 0000000..e9aea62 --- /dev/null +++ b/evomerge/eval/ja_vlm_wild.py @@ -0,0 +1,38 @@ +from typing import Optional + +from datasets import load_dataset + +from .metrics import LanguageDetector +from .utils import EvalOutput, evaluate +from ..utils import default + + +class JaVLMBenchIntheWild: + name = "JA-VLM-Bench-In-the-Wild" + dataset_path = "SakanaAI/JA-VLM-Bench-In-the-Wild" + dataset_split = "test" + + def __init__( + self, + verbose: bool = False, + loader_kwargs: Optional[dict] = None, + strict_japanese: bool = True, + ): + self.verbose = verbose + self.loader_kwargs = default(loader_kwargs, {}) + + dataset = load_dataset(path=self.dataset_path, split=self.dataset_split) + # filter column + self.dataset = dataset.select_columns(["question", "answer", "image"]) + self.lang_detect = None + if strict_japanese: + self.lang_detect = LanguageDetector() + + def __call__(self, model) -> EvalOutput: + return evaluate( + name=self.name, + model=model, + dataset=self.dataset, + loader_kwargs=self.loader_kwargs, + lang_detect=self.lang_detect, + ) diff --git a/evomerge/eval/metrics.py b/evomerge/eval/metrics.py new file mode 100644 index 0000000..83618cb --- /dev/null +++ b/evomerge/eval/metrics.py @@ -0,0 +1,101 @@ +import os +import re + +from rouge_score import rouge_scorer, scoring +from fasttext.FastText import _FastText + + +def mean(l): + return sum(l) / len(l) + + +def rouge(refs, preds): + """ + Returns `t5` style ROUGE scores. See the related implementation: + https://github.com/google-research/text-to-text-transfer-transformer/blob/3d10afd51ba97ac29eb66ae701eca274488202f7/t5/evaluation/metrics.py#L68 + + :param refs: + A `list` of reference `strs`. + :param preds: + A `list` of predicted `strs`. + """ + rouge_types = ["rouge1", "rouge2", "rougeL"] + scorer = rouge_scorer.RougeScorer(rouge_types) + + def _prepare_summary(summary): + summary = summary.replace(" . ", ".\n") + return summary + + # Accumulate confidence intervals. + aggregator = scoring.BootstrapAggregator() + for ref, pred in zip(refs, preds): + ref = _prepare_summary(ref) + pred = _prepare_summary(pred) + aggregator.add_scores(scorer.score(ref, pred)) + result = aggregator.aggregate() + return {type: result[type].mid.fmeasure * 100 for type in rouge_types} + + +class MecabTokenizer: + def __init__(self) -> None: + from fugashi import Tagger + + self.tagger = Tagger("-Owakati") + + def normalize_answer(self, text): + """Lower case text, remove punctuation and extra whitespace, etc.""" + import emoji + import neologdn + + def white_space_fix(text): + return " ".join(text.split()) + + def remove_emoji(text): + text = "".join(["" if emoji.is_emoji(c) else c for c in text]) + emoji_pattern = re.compile( + "[" + "\U0001F600-\U0001F64F" # emoticons + "\U0001F300-\U0001F5FF" # symbols & pictographs + "\U0001F680-\U0001F6FF" # transport & map symbols + "\U0001F1E0-\U0001F1FF" # flags (iOS) + "\U00002702-\U000027B0" + "]+", + flags=re.UNICODE, + ) + return emoji_pattern.sub(r"", text) + + text = remove_emoji(text) + # see neologdn docs for details, but handles things like full/half width variation + text = neologdn.normalize(text) + text = white_space_fix(text) + return text + + def tokenize(self, text): + return self.tagger.parse(self.normalize_answer(text)).split() + + +def rouge_ja(refs, preds): + """This uses a MeCab tokenizer for Japanese text.""" + tokenizer = MecabTokenizer() + rouge_types = ["rouge1", "rouge2", "rougeL"] + # mecab-based rouge + scorer = rouge_scorer.RougeScorer( + rouge_types, + tokenizer=tokenizer, + ) + + # Accumulate confidence intervals. + aggregator = scoring.BootstrapAggregator() + for ref, pred in zip(refs, preds): + aggregator.add_scores(scorer.score(ref, pred)) + result = aggregator.aggregate() + return {type: result[type].mid.fmeasure * 100 for type in rouge_types} + + +class LanguageDetector: + def __init__(self): + model_path = os.environ.get("LID176FTZ_PATH", "lid.176.ftz") + self.model = _FastText(model_path) + + def __call__(self, text: str) -> dict: + return dict(zip(*self.model.predict(text.replace("\n", ""), k=-1))) diff --git a/evomerge/eval/utils.py b/evomerge/eval/utils.py new file mode 100644 index 0000000..0b83319 --- /dev/null +++ b/evomerge/eval/utils.py @@ -0,0 +1,100 @@ +import logging +from tqdm.auto import tqdm +from dataclasses import dataclass +from typing import Any, Dict, List, Union, Optional + +import torch +from torch.utils.data import DataLoader +from datasets import Dataset + +from .metrics import rouge_ja, LanguageDetector + + +logger = logging.getLogger(__name__) + + +@dataclass +class EvalOutput: + metrics: Dict[str, float] + results: List[Dict[str, Any]] + + +def dict_collation_fn(samples: List) -> Dict: + keys = set.intersection(*[set(sample.keys()) for sample in samples]) + batched = {key: [] for key in keys} + + for s in samples: + [batched[key].append(s[key]) for key in batched] + + result = {} + for key in batched: + result[key] = list(batched[key]) + + del samples + del batched + return result + + +def flatten_list( + results: List[Dict[str, List[Union[str, bool]]]] +) -> Dict[str, List[Union[str, bool]]]: + flatten_results = {} + for res in results: + for k in res: + if k not in flatten_results: + flatten_results[k] = res[k] + else: + flatten_results[k].extend(res[k]) + return flatten_results + + +def _evaluate( + model, example: Dict[str, List[Any]] +) -> Dict[str, List[Union[str, bool]]]: + question = example["question"] + answer = example["answer"] + image = example["image"] + + # generate responses + resps = model(text=question, image=image) + return { + "question": question, + "answer": answer, + "prediction": resps, + } + + +def compute_score(results: Dict[str, List[Any]], lang_detect=None) -> Dict[str, float]: + res_dict = rouge_ja(refs=results["answer"], preds=results["prediction"]) + # detect Japanese by fasttext and replace empty string if it's not Ja + if lang_detect: + preds = [] + for answer, pred in zip(results["answer"], results["prediction"]): + # if answer is English, pass + if lang_detect(answer).get("__label__ja", 0.0) >= 0.5: + res = lang_detect(pred) + if res.get("__label__ja", 0.0) < 0.5: + pred = "" + preds.append(pred) + res_dict_ja = rouge_ja(refs=results["answer"], preds=preds) + res_dict_ja = {f"{k}_ja": v for k, v in res_dict_ja.items()} + res_dict.update(res_dict_ja) + return res_dict + + +def evaluate( + name: str, + model: torch.nn.Module, + dataset: Dataset, + loader_kwargs: dict, + lang_detect: Optional[LanguageDetector] = None, +): + results = [] + dataloader = DataLoader(dataset, collate_fn=dict_collation_fn, **loader_kwargs) + + for example in tqdm(dataloader, desc=f"Evaluating {name}"): + res = _evaluate(model, example) + results.append(res) + results = flatten_list(results) + metrics = compute_score(results, lang_detect=lang_detect) + return EvalOutput(metrics=metrics, results=results) diff --git a/evomerge/models/__init__.py b/evomerge/models/__init__.py new file mode 100644 index 0000000..a446f77 --- /dev/null +++ b/evomerge/models/__init__.py @@ -0,0 +1,5 @@ +from .jsvlm import JSVLM +from .llava import LLaVA +from .heron_v1 import HeronV1 +from .causallm import CausalLMWithvLLM +from .prompt_templates import PROMPT_TEMPLATES diff --git a/evomerge/models/causallm.py b/evomerge/models/causallm.py new file mode 100644 index 0000000..96d0d42 --- /dev/null +++ b/evomerge/models/causallm.py @@ -0,0 +1,60 @@ +import logging +from typing import List, Union, Optional +from torch import nn +from transformers.modeling_utils import ModuleUtilsMixin +from vllm import LLM, SamplingParams + +from .prompt_templates import JA_ALPACA_COT_TEMPLATE +from .utils import set_template, build_prompt +from ..utils import default + +logger = logging.getLogger(__name__) + + +class CausalLMWithvLLM(nn.Module, ModuleUtilsMixin): + default_generation_config = { + "max_tokens": 1024, + "temperature": 0, + "top_p": 1.0, + "repetition_penalty": 1.0, + } + default_template = JA_ALPACA_COT_TEMPLATE + + def __init__( + self, + model_path: str = None, + template: Optional[str] = None, + verbose: bool = False, + model_kwargs: Optional[dict] = None, + generation_config: Optional[dict] = None, + ): + super().__init__() + self.model = None + self.tokenizer = None + self.processor = None + self.verbose = verbose + self.template = set_template(self.default_template, template) + self.model_kwargs = default(model_kwargs, {}) + self.generation_config = default( + generation_config, self.default_generation_config + ) + self.model = LLM(model=model_path, **self.model_kwargs) + self.post_init() + + def post_init(self): + stop_tokens = ["Instruction:", "Instruction", "Response:", "Response"] + self.generation_config = SamplingParams( + **self.generation_config, stop=stop_tokens + ) + + def forward(self, text: Union[str, List[str]]) -> List[str]: + text = build_prompt(text, self.template) + if self.verbose: + logger.info( + "Sample of actual inputs:\n" + "-" * 100 + f"\n{text[0]}\n" + "-" * 100 + ) + outputs = self.model.generate( + prompts=text, sampling_params=self.generation_config + ) + generated_text = [output.outputs[0].text for output in outputs] + return generated_text diff --git a/evomerge/models/heron_v1.py b/evomerge/models/heron_v1.py new file mode 100644 index 0000000..c64cd8a --- /dev/null +++ b/evomerge/models/heron_v1.py @@ -0,0 +1,105 @@ +# https://huggingface.co/turing-motors/heron-chat-blip-ja-stablelm-base-7b-v1-llava-620k +import logging +from typing import List, Union, Optional + +from PIL import Image + +import torch +from torch import nn +from transformers.modeling_utils import ModuleUtilsMixin +from transformers import LlamaTokenizer + +from ..modules.heron.video_blip import ( + VideoBlipForConditionalGeneration, + VideoBlipProcessor, +) + +from .prompt_templates import HERON_V1 +from .utils import set_template, set_model_kwargs, build_prompt +from ..utils import default + +logger = logging.getLogger(__name__) + + +class HeronV1(nn.Module, ModuleUtilsMixin): + default_template = HERON_V1 + default_generation_config = { + "do_sample": False, + "temperature": 0.0, + "max_length": 256, + "no_repeat_ngram_size": 2, + } + + def __init__( + self, + model_path: str = "turing-motors/heron-chat-blip-ja-stablelm-base-7b-v1-llava-620k", + template: Optional[str] = None, + verbose: bool = False, + device: Union[str, torch.device] = "cuda", + model_kwargs: Optional[dict] = None, + generation_config: Optional[dict] = None, + ): + super().__init__() + self.verbose = verbose + self.template = set_template(self.default_template, template) + self.generation_config = default( + generation_config, self.default_generation_config + ) + model_kwargs = set_model_kwargs(default(model_kwargs, {})) + + self.model = ( + VideoBlipForConditionalGeneration.from_pretrained( + model_path, ignore_mismatched_sizes=True, **model_kwargs + ) + .eval() + .to(device) + ) + processor = VideoBlipProcessor.from_pretrained("Salesforce/blip2-opt-2.7b") + tokenizer = LlamaTokenizer.from_pretrained( + "novelai/nerdstash-tokenizer-v1", additional_special_tokens=["▁▁"] + ) + processor.tokenizer = tokenizer + self.processor = processor + self.eos_token_id_list = [ + processor.tokenizer.pad_token_id, + processor.tokenizer.eos_token_id, + int(tokenizer.convert_tokens_to_ids("##")), + ] + + def forward( + self, + text: Union[str, List[str]], + image: Union[Image.Image, List[Image.Image]], + **generation_config, + ) -> List[str]: + + eos_token_id = generation_config.pop("eos_token_id", self.eos_token_id_list) + if len(generation_config) == 0: + generation_config = self.generation_config + if self.verbose: + logger.info( + f"Setting generation config to default\n{generation_config}" + ) + if not isinstance(image, list): + image = [image] + + text = build_prompt(text, self.template) + if self.verbose: + logger.info( + "Sample of actual inputs:\n" + "-" * 100 + f"\n{text[0]}\n" + "-" * 100 + ) + assert len(text) == len(image) + inputs = self.processor( + text=text, images=image, return_tensors="pt", padding=True + ) + # generate + with torch.inference_mode(): + output_ids = self.model.generate( + **inputs.to(self.device, dtype=self.dtype), + **generation_config, + eos_token_id=eos_token_id, + ) + generated_text = self.processor.batch_decode( + output_ids, skip_special_tokens=True + ) + return generated_text diff --git a/evomerge/models/jsvlm.py b/evomerge/models/jsvlm.py new file mode 100644 index 0000000..a2fbf79 --- /dev/null +++ b/evomerge/models/jsvlm.py @@ -0,0 +1,98 @@ +# https://huggingface.co/stabilityai/japanese-stable-vlm +import logging +from typing import List, Union, Optional + +from PIL import Image + +import torch +from torch import nn +from transformers.modeling_utils import ModuleUtilsMixin +from transformers import AutoImageProcessor, AutoModelForVision2Seq, AutoTokenizer + +from .prompt_templates import JSVLM_TEMPLATE +from .utils import set_template, set_model_kwargs, build_prompt +from ..utils import default + +logger = logging.getLogger(__name__) + + +class JSVLM(nn.Module, ModuleUtilsMixin): + default_template = JSVLM_TEMPLATE + default_generation_config = { + "do_sample": False, + "num_beams": 5, + "max_new_tokens": 128, + "min_length": 1, + "repetition_penalty": 1.5, + } + + def __init__( + self, + model_path: str = "stabilityai/japanese-stable-vlm", + template: Optional[str] = None, + verbose: bool = False, + device: Union[str, torch.device] = "cuda", + model_kwargs: Optional[dict] = None, + generation_config: Optional[dict] = None, + ): + super().__init__() + self.verbose = verbose + self.template = set_template(self.default_template, template) + self.generation_config = default( + generation_config, self.default_generation_config + ) + model_kwargs = set_model_kwargs(default(model_kwargs, {})) + + self.model = ( + AutoModelForVision2Seq.from_pretrained( + model_path, trust_remote_code=True, **model_kwargs + ) + .eval() + .to((device)) + ) + self.processor = AutoImageProcessor.from_pretrained(model_path) + self.tokenizer = AutoTokenizer.from_pretrained(model_path) + + def forward( + self, + text: Union[str, List[str]], + image: Union[Image.Image, List[Image.Image]], + **generation_config, + ) -> List[str]: + if len(generation_config) == 0: + generation_config = self.generation_config + if self.verbose: + logger.info( + f"Setting generation config to default\n{generation_config}" + ) + if not isinstance(image, list): + image = [image] + + text = build_prompt(text, self.template) + if self.verbose: + logger.info( + "Sample of actual inputs:\n" + "-" * 100 + f"\n{text[0]}\n" + "-" * 100 + ) + assert len(text) == len(image) + inputs = self.processor(images=image, return_tensors="pt") + text_encoding = self.tokenizer( + text, + add_special_tokens=False, + return_tensors="pt", + padding=True, + ) + inputs.update(text_encoding) + # generate + with torch.inference_mode(): + output_ids = self.model.generate( + **inputs.to(self.device, dtype=self.dtype), + **generation_config, + bos_token_id=self.tokenizer.bos_token_id, + eos_token_id=self.tokenizer.eos_token_id, + pad_token_id=self.tokenizer.eos_token_id, + ) + generated_text = self.tokenizer.batch_decode( + output_ids, skip_special_tokens=True + ) + generated_text = [text.strip() for text in generated_text] + return generated_text diff --git a/evomerge/models/llava.py b/evomerge/models/llava.py new file mode 100644 index 0000000..89b1458 --- /dev/null +++ b/evomerge/models/llava.py @@ -0,0 +1,103 @@ +import logging +from typing import List, Union, Optional + +from PIL import Image + +import torch +from torch import nn +from transformers.modeling_utils import ModuleUtilsMixin +from transformers import AutoProcessor, LlavaForConditionalGeneration + +from .prompt_templates import LLAVA_MISTRAL_TEMPLATE +from .utils import set_template, set_model_kwargs, get_output_ids +from ..utils import default + +logger = logging.getLogger(__name__) + + +def build_prompt(text: Union[str, List[str]], template: str) -> List[str]: + if isinstance(text, str): + text = [text] + return [template.format(input=f"\n{t}") for t in text] + + +class LLaVA(nn.Module, ModuleUtilsMixin): + default_template = LLAVA_MISTRAL_TEMPLATE + # taken from https://github.com/haotian-liu/LLaVA/blob/main/predict.py#L87 + default_generation_config = { + "do_sample": True, + "max_new_tokens": 1024, + "temperature": 0.2, + "top_p": 1.0, + "use_cache": True, + } + + def __init__( + self, + model_path: str = "llava-hf/llava-v1.6-mistral-7b-hf", + template: Optional[str] = None, + verbose: bool = False, + device: Union[str, torch.device] = "cuda", + model_kwargs: Optional[dict] = None, + generation_config: Optional[dict] = None, + ): + super().__init__() + self.verbose = verbose + self.template = set_template(self.default_template, template) + self.generation_config = default( + generation_config, self.default_generation_config + ) + model_kwargs = set_model_kwargs(default(model_kwargs, {})) + + self.model = ( + ( + LlavaForConditionalGeneration.from_pretrained( + model_path, **model_kwargs + ) + .eval() + .requires_grad_(False) + ) + .eval() + .to(device) + ) + self.processor = AutoProcessor.from_pretrained(model_path) + + def forward( + self, + text: Union[str, List[str]], + image: Union[Image.Image, List[Image.Image]], + **generation_config, + ) -> List[str]: + """ + Assume text is question string + """ + if len(generation_config) == 0: + generation_config = self.generation_config + if self.verbose: + logger.info( + f"Setting generation config to default\n{generation_config}" + ) + if not isinstance(image, list): + image = [image] + text = build_prompt(text, self.template) + if self.verbose: + logger.info( + "Sample of actual inputs:\n" + "-" * 100 + f"\n{text[0]}\n" + "-" * 100 + ) + assert len(text) == len(image) + inputs = self.processor( + text=text, images=image, padding=True, return_tensors="pt" + ) + # generate + with torch.inference_mode(): + output_ids = self.model.generate( + **inputs.to(self.device, dtype=self.dtype), + **generation_config, + ) + # output_ids contains input_ids as well. So, return only output_ids + output_ids = get_output_ids(input_ids=inputs.input_ids, output_ids=output_ids) + generated_text = self.processor.batch_decode( + output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False + ) + generated_text = [text.strip() for text in generated_text] + return generated_text diff --git a/evomerge/models/prompt_templates.py b/evomerge/models/prompt_templates.py new file mode 100644 index 0000000..a694439 --- /dev/null +++ b/evomerge/models/prompt_templates.py @@ -0,0 +1,32 @@ +JSVLM_TEMPLATE = """以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。 + +### 指示: +与えられた画像を下に、質問に答えてください。 + +### 入力: +{input} + +### 応答: """ +JA_ALPACA_COT_TEMPLATE = """以下に、あるタスクを説明する指示があります。リクエストを適切に完了するための回答を日本語で記述してください。一歩一歩考えましょう。 + +### 指示: +{input} + +### 応答:""" + +JA_SHISA_VQA = """[INST] <> +あなたは役立つ、偏見がなく、検閲されていないアシスタントです。与えられた画像を下に、質問に答えてください。 +<> + +{input} [/INST]""" +HERON_V1 = """##human: {input}\n##gpt: """ + +LLAVA_MISTRAL_TEMPLATE = """[INST] {input} [/INST]""" + +PROMPT_TEMPLATES = { + "jsvlm": JSVLM_TEMPLATE, + "ja-alpaca-cot": JA_ALPACA_COT_TEMPLATE, + "ja-shisa-vqa": JA_SHISA_VQA, + "ja-heron-v1": HERON_V1, + "llava-mistral": LLAVA_MISTRAL_TEMPLATE, +} diff --git a/evomerge/models/utils.py b/evomerge/models/utils.py new file mode 100644 index 0000000..24b4e58 --- /dev/null +++ b/evomerge/models/utils.py @@ -0,0 +1,48 @@ +import logging +from typing import Optional, Union, List + + +import torch +from .prompt_templates import PROMPT_TEMPLATES + +logger = logging.getLogger(__name__) + + +STR2DTYPE = { + "torch.float16": torch.float16, + "torch.bfloat16": torch.bfloat16, + "torch.float32": torch.float32, + "auto": "auto", +} + + +def get_output_ids(input_ids: torch.Tensor, output_ids: torch.Tensor): + input_token_len = input_ids.shape[1] + n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() + if n_diff_input_output > 0: + logger.warn( + f"[Warning] {n_diff_input_output} output_ids are not the same as the input_ids" + ) + return output_ids[:, input_token_len:] + + +def set_template(default_template: str, template: Optional[str] = None): + if template is None: + template = default_template + if template in PROMPT_TEMPLATES: + template = PROMPT_TEMPLATES[template] + logger.info(f"prompt template:\n{template}") + return template + + +def set_model_kwargs(model_kwargs: dict) -> dict: + torch_dtype = model_kwargs.pop("torch_dtype", None) + if torch_dtype is not None and isinstance(torch_dtype, str): + model_kwargs["torch_dtype"] = STR2DTYPE[torch_dtype] + return model_kwargs + + +def build_prompt(text: Union[str, List[str]], template: str) -> List[str]: + if isinstance(text, str): + text = [text] + return [template.format(input=t) for t in text] diff --git a/evomerge/modules/__init__.py b/evomerge/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/evomerge/modules/heron/__init__.py b/evomerge/modules/heron/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/evomerge/modules/heron/video_blip/__init__.py b/evomerge/modules/heron/video_blip/__init__.py new file mode 100644 index 0000000..09e811b --- /dev/null +++ b/evomerge/modules/heron/video_blip/__init__.py @@ -0,0 +1,38 @@ +# all files are token from https://github.com/turingmotors/heron/tree/main/heron/models/video_blip + +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# 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. +from typing import TYPE_CHECKING + +from transformers.utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_torch_available, +) + +from .configuration_video_blip import ( + BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP, + VideoBlipConfig, + VideoBlipQFormerConfig, + VideoBlipVisionConfig, +) +from .modeling_video_blip import ( + BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST, + VideoBlipForConditionalGeneration, + VideoBlipModel, + VideoBlipPreTrainedModel, + VideoBlipQFormerModel, + VideoBlipVisionModel, +) +from .processing_video_blip import VideoBlipProcessor diff --git a/evomerge/modules/heron/video_blip/bert.py b/evomerge/modules/heron/video_blip/bert.py new file mode 100644 index 0000000..79c3976 --- /dev/null +++ b/evomerge/modules/heron/video_blip/bert.py @@ -0,0 +1,73 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# 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. + +""" +NOTICE: This code is subject to the terms of the Apache License 2.0. + +The code is modified from the original one. + +Additional contributions by Turing Inc. team +""" + +import torch +from torch import Tensor, device, dtype, nn +from transformers.activations import ACT2FN + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = BertLMPredictionHead(config) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores diff --git a/evomerge/modules/heron/video_blip/configuration_video_blip.py b/evomerge/modules/heron/video_blip/configuration_video_blip.py new file mode 100644 index 0000000..8ef9174 --- /dev/null +++ b/evomerge/modules/heron/video_blip/configuration_video_blip.py @@ -0,0 +1,439 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# 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. + +""" +NOTICE: This code is subject to the terms of the Apache License 2.0. + +The code is modified from the original one. +original code: https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip_2/configuration_blip_2.py + +Additional contributions by Turing Inc. team +""" + + +import copy +import os +from typing import Union + +from transformers import AutoConfig +from transformers.configuration_utils import PretrainedConfig +from transformers.models.auto import CONFIG_MAPPING +from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES +from transformers.utils import logging + +logger = logging.get_logger(__name__) + +BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json", +} + + +class VideoBlipVisionConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Blip2VisionModel`]. It is used to instantiate a + BLIP-2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a + configuration defaults will yield a similar configuration to that of the BLIP-2 + [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + hidden_size (`int`, *optional*, defaults to 1408): + Dimensionality of the encoder layers and the pooler layer. + intermediate_size (`int`, *optional*, defaults to 6144): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + num_hidden_layers (`int`, *optional*, defaults to 39): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer encoder. + image_size (`int`, *optional*, defaults to 224): + The size (resolution) of each image. + patch_size (`int`, *optional*, defaults to 14): + The size (resolution) of each patch. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults + to 1e-5): The epsilon used by the layer normalization layers. + dropout (`float`, *optional*, defaults to 0.0): + The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + initializer_factor (`float``, *optional*, defaults to 1): + A factor for initializing all weight matrices (should be kept to 1, used internally for initialization + testing). + qkv_bias (`bool`, *optional*, defaults to `True`): + Whether to add a bias to the queries and values in the self-attention layers. + + Example: + + ```python + >>> from transformers import Blip2VisionConfig, Blip2VisionModel + + >>> # Initializing a Blip2VisionConfig with Salesforce/blip2-opt-2.7b style configuration + >>> configuration = Blip2VisionConfig() + + >>> # Initializing a Blip2VisionModel (with random weights) from the Salesforce/blip2-opt-2.7b style configuration + >>> model = Blip2VisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "blip_2_vision_model" + + def __init__( + self, + hidden_size=1408, + intermediate_size=6144, + projection_dim=512, + num_hidden_layers=39, + num_attention_heads=16, + num_channels=3, + image_size=224, + patch_size=14, + hidden_act="gelu", + layer_norm_eps=0.00001, + dropout=0.0, + attention_dropout=0.0, + initializer_range=1e-10, + initializer_factor=1.0, + qkv_bias=True, + **kwargs, + ): + super().__init__(**kwargs) + + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.projection_dim = projection_dim + self.dropout = dropout + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.num_channels = num_channels + self.patch_size = patch_size + self.image_size = image_size + self.initializer_range = initializer_range + self.initializer_factor = initializer_factor + self.attention_dropout = attention_dropout + self.layer_norm_eps = layer_norm_eps + self.hidden_act = hidden_act + self.qkv_bias = qkv_bias + + @classmethod + def from_pretrained( + cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs + ) -> "PretrainedConfig": + config_dict, kwargs = cls.get_config_dict( + pretrained_model_name_or_path, **kwargs + ) + + # get the vision config dict if we are loading from Blip2Config + if config_dict.get("model_type") == "blip-2": + config_dict = config_dict["vision_config"] + + if ( + "model_type" in config_dict + and hasattr(cls, "model_type") + and config_dict["model_type"] != cls.model_type + ): + logger.warning( + f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " + f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." + ) + + return cls.from_dict(config_dict, **kwargs) + + +class VideoBlipQFormerConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Blip2QFormerModel`]. It is used to instantiate a + BLIP-2 Querying Transformer (Q-Former) model according to the specified arguments, defining the model architecture. + Instantiating a configuration with the defaults will yield a similar configuration to that of the BLIP-2 + [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) architecture. Configuration objects + inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from + [`PretrainedConfig`] for more information. + + Note that [`Blip2QFormerModel`] is very similar to [`BertLMHeadModel`] with interleaved cross-attention. + + Args: + vocab_size (`int`, *optional*, defaults to 30522): + Vocabulary size of the Q-Former model. Defines the number of different tokens that can be represented by + the `inputs_ids` passed when calling the model. + hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the encoder layers and the pooler layer. + num_hidden_layers (`int`, *optional*, defaults to 12): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 12): + Number of attention heads for each attention layer in the Transformer encoder. + intermediate_size (`int`, *optional*, defaults to 3072): + Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. + hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + hidden_dropout_prob (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): + The dropout ratio for the attention probabilities. + max_position_embeddings (`int`, *optional*, defaults to 512): + The maximum sequence length that this model might ever be used with. Typically set this to something large + just in case (e.g., 512 or 1024 or 2048). + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-12): + The epsilon used by the layer normalization layers. + position_embedding_type (`str`, *optional*, defaults to `"absolute"`): + Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For + positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to + [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). + For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models + with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). + classifier_dropout (`float`, *optional*): + The dropout ratio for the classification head. + cross_attention_frequency (`int`, *optional*, defaults to 2): + The frequency of adding cross-attention to the Transformer layers. + encoder_hidden_size (`int`, *optional*, defaults to 1408): + The hidden size of the hidden states for cross-attention. + + Examples: + + ```python + >>> from transformers import Blip2QFormerConfig, Blip2QFormerModel + + >>> # Initializing a BLIP-2 Salesforce/blip2-opt-2.7b style configuration + >>> configuration = Blip2QFormerConfig() + + >>> # Initializing a model (with random weights) from the Salesforce/blip2-opt-2.7b style configuration + >>> model = Blip2QFormerModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "blip_2_qformer" + + def __init__( + self, + vocab_size=30522, + hidden_size=768, + num_hidden_layers=12, + num_attention_heads=12, + intermediate_size=3072, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=512, + initializer_range=0.02, + layer_norm_eps=1e-12, + pad_token_id=0, + position_embedding_type="absolute", + classifier_dropout=None, + cross_attention_frequency=2, + encoder_hidden_size=1408, + **kwargs, + ): + super().__init__(pad_token_id=pad_token_id, **kwargs) + + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.hidden_act = hidden_act + self.intermediate_size = intermediate_size + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + self.position_embedding_type = position_embedding_type + self.classifier_dropout = classifier_dropout + self.cross_attention_frequency = cross_attention_frequency + self.encoder_hidden_size = encoder_hidden_size + + @classmethod + def from_pretrained( + cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs + ) -> "PretrainedConfig": + config_dict, kwargs = cls.get_config_dict( + pretrained_model_name_or_path, **kwargs + ) + + # get the qformer config dict if we are loading from Blip2Config + if config_dict.get("model_type") == "blip-2": + config_dict = config_dict["qformer_config"] + + if ( + "model_type" in config_dict + and hasattr(cls, "model_type") + and config_dict["model_type"] != cls.model_type + ): + logger.warning( + f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " + f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." + ) + + return cls.from_dict(config_dict, **kwargs) + + +class VideoBlipConfig(PretrainedConfig): + r""" + [`Blip2Config`] is the configuration class to store the configuration of a [`Blip2ForConditionalGeneration`]. It is + used to instantiate a BLIP-2 model according to the specified arguments, defining the vision model, Q-Former model + and language model configs. Instantiating a configuration with the defaults will yield a similar configuration to + that of the BLIP-2 [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + vision_config (`dict`, *optional*): + Dictionary of configuration options used to initialize [`Blip2VisionConfig`]. + qformer_config (`dict`, *optional*): + Dictionary of configuration options used to initialize [`Blip2QFormerConfig`]. + text_config (`dict`, *optional*): + Dictionary of configuration options used to initialize any [`PretrainedConfig`]. + num_query_tokens (`int`, *optional*, defaults to 32): + The number of query tokens passed through the Transformer. + + kwargs (*optional*): + Dictionary of keyword arguments. + + Example: + + ```python + >>> from transformers import ( + ... Blip2VisionConfig, + ... Blip2QFormerConfig, + ... OPTConfig, + ... Blip2Config, + ... Blip2ForConditionalGeneration, + ... ) + + >>> # Initializing a Blip2Config with Salesforce/blip2-opt-2.7b style configuration + >>> configuration = Blip2Config() + + >>> # Initializing a Blip2ForConditionalGeneration (with random weights) from the Salesforce/blip2-opt-2.7b style configuration + >>> model = Blip2ForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + + >>> # We can also initialize a Blip2Config from a Blip2VisionConfig, Blip2QFormerConfig and any PretrainedConfig + + >>> # Initializing BLIP-2 vision, BLIP-2 Q-Former and language model configurations + >>> vision_config = Blip2VisionConfig() + >>> qformer_config = Blip2QFormerConfig() + >>> text_config = OPTConfig() + + >>> config = Blip2Config.from_text_vision_configs(vision_config, qformer_config, text_config) + ```""" + + model_type = "video_blip" + is_composition = True + + def __init__( + self, + vision_config=None, + qformer_config=None, + text_config=None, + num_query_tokens=32, + llm_hidden_size=1000, + num_frames=8, + **kwargs, + ): + super().__init__(**kwargs) + + if vision_config is None: + vision_config = {} + logger.info( + "vision_config is None. initializing the Blip2VisionConfig with default values." + ) + + if qformer_config is None: + qformer_config = {} + logger.info( + "qformer_config is None. Initializing the Blip2QFormerConfig with default values." + ) + + if text_config is None: + text_config = {} + logger.info( + "text_config is None. Initializing the text config with default values (`OPTConfig`)." + ) + + self.vision_config = VideoBlipVisionConfig(**vision_config) + self.qformer_config = VideoBlipQFormerConfig(**qformer_config) + text_model_type = ( + text_config["model_type"] if "model_type" in text_config else "opt" + ) + if text_model_type == "": + text_model_type = "gpt_neox" + + # self.text_config = CONFIG_MAPPING[text_model_type](**text_config) + model_path = text_config["_name_or_path"] + print("model_path", model_path) + self.text_config = AutoConfig.from_pretrained( + model_path, trust_remote_code=True + ) + + self.tie_word_embeddings = self.text_config.tie_word_embeddings + self.is_encoder_decoder = self.text_config.is_encoder_decoder + + self.num_query_tokens = num_query_tokens + self.qformer_config.encoder_hidden_size = self.vision_config.hidden_size + self.use_decoder_only_language_model = True + self.initializer_factor = 1.0 + self.initializer_range = 0.02 + + # custom + self.num_frames = num_frames + self.llm_hidden_size = llm_hidden_size + + @classmethod + def from_vision_qformer_text_configs( + cls, + vision_config: VideoBlipVisionConfig, + qformer_config: VideoBlipQFormerConfig, + text_config: PretrainedConfig, + **kwargs, + ): + r""" + Instantiate a [`Blip2Config`] (or a derived class) from a BLIP-2 vision model, Q-Former and language model + configurations. + + Returns: + [`Blip2Config`]: An instance of a configuration object + """ + + return cls( + vision_config=vision_config.to_dict(), + qformer_config=qformer_config.to_dict(), + text_config=text_config.to_dict(), + **kwargs, + ) + + def to_dict(self): + """ + Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. + + Returns: + `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, + """ + output = copy.deepcopy(self.__dict__) + output["vision_config"] = self.vision_config.to_dict() + output["qformer_config"] = self.qformer_config.to_dict() + output["text_config"] = self.text_config.to_dict() + output["model_type"] = self.__class__.model_type + return output diff --git a/evomerge/modules/heron/video_blip/modeling_video_blip.py b/evomerge/modules/heron/video_blip/modeling_video_blip.py new file mode 100644 index 0000000..816e66d --- /dev/null +++ b/evomerge/modules/heron/video_blip/modeling_video_blip.py @@ -0,0 +1,2602 @@ +# coding=utf-8 +# Copyright 2023 The Salesforce Authors and The HuggingFace Team. All rights reserved. +# +# 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. + +""" +NOTICE: This code is subject to the terms of the Apache License 2.0. + +The code is modified from the original one. +original code: https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip_2/modeling_blip_2.py + +Additional contributions by Turing Inc. team +""" + +import math +from dataclasses import dataclass +from typing import Any, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss +from torch.nn import functional as F +from transformers.activations import ACT2FN +from transformers.modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPooling, + BaseModelOutputWithPoolingAndCrossAttentions, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.models.auto import AutoModelForCausalLM, AutoModelForSeq2SeqLM +from transformers.pytorch_utils import ( + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) + +from .bert import BertOnlyMLMHead +from .configuration_video_blip import ( + VideoBlipConfig, + VideoBlipQFormerConfig, + VideoBlipVisionConfig, +) + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "Salesforce/blip2-opt-2.7b" + +BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "Salesforce/blip2-opt-2.7b", + # See all BLIP-2 models at https://huggingface.co/models?filter=blip +] + + +@dataclass +class VideoBlipForConditionalGenerationModelOutput(ModelOutput): + """ + Class defining the outputs of [`Blip2ForConditionalGeneration`]. + + Args: + loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): + Language modeling loss from the language model. + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head of the language model. + vision_outputs (`BaseModelOutputWithPooling`): + Outputs of the vision encoder. + qformer_outputs (`BaseModelOutputWithPoolingAndCrossAttentions`): + Outputs of the Q-Former (Querying Transformer). + language_model_outputs (`CausalLMOutputWithPast` or `Seq2SeqLMOutput`): + Outputs of the language model. + """ + + loss: Optional[Tuple[torch.FloatTensor]] = None + logits: Optional[Tuple[torch.FloatTensor]] = None + vision_outputs: Optional[torch.FloatTensor] = None + qformer_outputs: Optional[Tuple[torch.FloatTensor]] = None + language_model_outputs: Optional[Tuple[torch.FloatTensor]] = None + + def to_tuple(self) -> Tuple[Any]: + return tuple( + ( + self[k] + if k + not in ["vision_outputs", "qformer_outputs", "language_model_outputs"] + else getattr(self, k).to_tuple() + ) + for k in self.keys() + ) + + +# Copied from transformers.models.blip.modeling_blip.BlipVisionEmbeddings with Blip->Blip2 +class VideoBlipVisionEmbeddings(nn.Module): + def __init__(self, config: VideoBlipVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + self.class_embedding = nn.Parameter( + torch.randn(1, 1, self.embed_dim), + ) + + self.patch_embedding = nn.Conv2d( + in_channels=3, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + ) + + self.num_patches = (self.image_size // self.patch_size) ** 2 + self.num_positions = self.num_patches + 1 + + self.position_embedding = nn.Parameter( + torch.randn(1, self.num_positions, self.embed_dim) + ) + + def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: + batch_size = pixel_values.shape[0] + target_dtype = self.patch_embedding.weight.dtype + patch_embeds = self.patch_embedding( + pixel_values + ) # shape = [*, width, grid, grid] + patch_embeds = patch_embeds.flatten(2).transpose(1, 2) + + class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype) + embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + embeddings = embeddings + self.position_embedding[ + :, : embeddings.size(1), : + ].to(target_dtype) + return embeddings + + +class VideoBlipAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.dropout = nn.Dropout(config.attention_dropout) + + # small tweak here compared to CLIP, no bias here + self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=False) + + if config.qkv_bias: + q_bias = nn.Parameter(torch.zeros(self.embed_dim)) + v_bias = nn.Parameter(torch.zeros(self.embed_dim)) + else: + q_bias = None + v_bias = None + + if q_bias is not None: + qkv_bias = torch.cat( + (q_bias, torch.zeros_like(v_bias, requires_grad=False), v_bias) + ) + self.qkv.bias = nn.Parameter(qkv_bias) + + self.projection = nn.Linear(self.embed_dim, self.embed_dim) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return ( + tensor.view(bsz, seq_len, self.num_heads, self.head_dim) + .transpose(1, 2) + .contiguous() + ) + + def forward( + self, + hidden_states: torch.Tensor, + head_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + bsz, tgt_len, embed_dim = hidden_states.size() + + mixed_qkv = self.qkv(hidden_states) + + mixed_qkv = mixed_qkv.reshape( + bsz, tgt_len, 3, self.num_heads, embed_dim // self.num_heads + ).permute(2, 0, 3, 1, 4) + query_states, key_states, value_states = ( + mixed_qkv[0], + mixed_qkv[1], + mixed_qkv[2], + ) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2)) + + attention_scores = attention_scores * self.scale + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_states).permute(0, 2, 1, 3) + + new_context_layer_shape = context_layer.size()[:-2] + (self.embed_dim,) + context_layer = context_layer.reshape(new_context_layer_shape) + + output = self.projection(context_layer) + + outputs = (output, attention_probs) if output_attentions else (output, None) + + return outputs + + +# Copied from transformers.models.blip.modeling_blip.BlipMLP +class VideoBlipMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +# Copied from transformers.models.blip.modeling_blip.BlipEncoderLayer with Blip->Blip2 +class VideoBlipEncoderLayer(nn.Module): + def __init__(self, config: VideoBlipConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.self_attn = VideoBlipAttention(config) + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = VideoBlipMLP(config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + `(config.encoder_attention_heads,)`. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, attn_weights = self.self_attn( + hidden_states=hidden_states, + head_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states = hidden_states + residual + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + + hidden_states = hidden_states + residual + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class VideoBlipPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = VideoBlipConfig + base_model_prefix = "blip" + supports_gradient_checkpointing = True + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"language_model.encoder.embed_tokens.weight", + r"language_model.decoder.embed_tokens.weight", + r"language_model.lm_head.weight", + ] + + _no_split_modules = ["Blip2Attention", "T5Block", "OPTDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _keep_in_fp32_modules = ["wo"] + + def _init_weights(self, module): + """Initialize the weights""" + factor = self.config.initializer_range + if ( + isinstance(module, nn.Conv2d) + or isinstance(module, nn.Embedding) + or isinstance(module, nn.Linear) + ): + module.weight.data.normal_(mean=0.0, std=factor) + if hasattr(module, "bias") and module.bias is not None: + module.bias.data.zero_() + + if isinstance(module, VideoBlipVisionEmbeddings): + if hasattr(self.config, "vision_config"): + factor = self.config.vision_config.initializer_range + nn.init.trunc_normal_(module.position_embedding, mean=0.0, std=factor) + nn.init.trunc_normal_(module.class_embedding, mean=0.0, std=factor) + + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + if module.weight is not None: + module.weight.data.fill_(1.0) + elif isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, VideoBlipEncoder): + module.gradient_checkpointing = value + + +BLIP_2_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`Blip2Config`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +BLIP_2_VISION_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`Blip2Processor`]. See [`Blip2Processor.__call__`] for + details. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +BLIP_2_TEXT_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + [What are attention masks?](../glossary#attention-mask) + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` + is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). + + To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5 + Training](./t5#training). + decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + +BLIP_2_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`Blip2Processor`]. See [`Blip2Processor.__call__`] for + details. + + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be + provided to serve as text prompt, which the language model can continue. + + Indices can be obtained using [`Blip2Processor`]. See [`Blip2Processor.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary of the language model. Only relevant in case an + encoder-decoder language model (like T5) is used. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) + + decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + + Only relevant in case an encoder-decoder language model (like T5) is used. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +# Copied from transformers.models.blip.modeling_blip.BlipEncoder with Blip->Blip2 +class VideoBlipEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`Blip2EncoderLayer`]. + + Args: + config (`Blip2Config`): + The corresponding vision configuration for the `Blip2Encoder`. + """ + + def __init__(self, config: VideoBlipConfig): + super().__init__() + self.config = config + self.layers = nn.ModuleList( + [VideoBlipEncoderLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + inputs_embeds, + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutput]: + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Embedded representation of the inputs. Should be float, not int tokens. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + hidden_states = inputs_embeds + for idx, encoder_layer in enumerate(self.layers): + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + if self.gradient_checkpointing and self.training: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(encoder_layer), + hidden_states, + attention_mask, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [hidden_states, encoder_states, all_attentions] + if v is not None + ) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=encoder_states, + attentions=all_attentions, + ) + + +# Copied from transformers.models.blip.modeling_blip.BlipVisionModel with Blip->Blip2, BLIP->BLIP_2 +class VideoBlipVisionModel(VideoBlipPreTrainedModel): + main_input_name = "pixel_values" + config_class = VideoBlipVisionConfig + + def __init__(self, config: VideoBlipVisionConfig): + super().__init__(config) + self.config = config + embed_dim = config.hidden_size + + self.embeddings = VideoBlipVisionEmbeddings(config) + self.encoder = VideoBlipEncoder(config) + self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + + self.post_init() + + @add_start_docstrings_to_model_forward(BLIP_2_VISION_INPUTS_DOCSTRING) + @replace_return_docstrings( + output_type=BaseModelOutputWithPooling, config_class=VideoBlipVisionConfig + ) + def forward( + self, + pixel_values: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, BaseModelOutputWithPooling]: + r""" + Returns: + + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + hidden_states = self.embeddings(pixel_values) + + encoder_outputs = self.encoder( + inputs_embeds=hidden_states, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + last_hidden_state = encoder_outputs[0] + last_hidden_state = self.post_layernorm(last_hidden_state) + + pooled_output = last_hidden_state[:, 0, :] + pooled_output = self.post_layernorm(pooled_output) + + if not return_dict: + return (last_hidden_state, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + def get_input_embeddings(self): + return self.embeddings + + +class VideoBlipQFormerMultiHeadAttention(nn.Module): + def __init__(self, config, is_cross_attention=False): + super().__init__() + self.config = config + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention heads (%d)" + % (config.hidden_size, config.num_attention_heads) + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if is_cross_attention: + self.key = nn.Linear(config.encoder_hidden_size, self.all_head_size) + self.value = nn.Linear(config.encoder_hidden_size, self.all_head_size) + else: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + self.save_attention = False + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + mixed_query_layer = self.query(hidden_states) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + if is_cross_attention and self.save_attention: + self.save_attention_map(attention_probs) + attention_probs.register_hook(self.save_attn_gradients) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs_dropped = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs_dropped = attention_probs_dropped * head_mask + + context_layer = torch.matmul(attention_probs_dropped, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->Blip2QFormer +class VideoBlipQFormerSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class VideoBlipQFormerAttention(nn.Module): + def __init__(self, config, is_cross_attention=False): + super().__init__() + self.attention = VideoBlipQFormerMultiHeadAttention(config, is_cross_attention) + self.output = VideoBlipQFormerSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.attention.num_attention_heads, + self.attention.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.attention.query = prune_linear_layer(self.attention.query, index) + self.attention.key = prune_linear_layer(self.attention.key, index) + self.attention.value = prune_linear_layer(self.attention.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.attention.num_attention_heads = self.attention.num_attention_heads - len( + heads + ) + self.attention.all_head_size = ( + self.attention.attention_head_size * self.attention.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + ) -> Tuple[torch.Tensor]: + self_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Blip2QFormer +class VideoBlipQFormerIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # print("hidden_states", hidden_states.shape, "dense", self.dense.weight.shape) + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Blip2QFormer +class VideoBlipQFormerOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class VideoBlipQFormerLayer(nn.Module): + def __init__(self, config, layer_idx): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = VideoBlipQFormerAttention(config) + + self.layer_idx = layer_idx + + if layer_idx % config.cross_attention_frequency == 0: + self.crossattention = VideoBlipQFormerAttention( + config, is_cross_attention=True + ) + self.has_cross_attention = True + else: + self.has_cross_attention = False + + self.intermediate_query = VideoBlipQFormerIntermediate(config) + self.output_query = VideoBlipQFormerOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + query_length=0, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:-1] + + present_key_value = self_attention_outputs[-1] + + if query_length > 0: + query_attention_output = attention_output[:, :query_length, :] + + if self.has_cross_attention and encoder_hidden_states is not None: + if encoder_hidden_states is None: + raise ValueError( + "encoder_hidden_states must be given for cross-attention layers" + ) + cross_attention_outputs = self.crossattention( + query_attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, + ) + query_attention_output = cross_attention_outputs[0] + # add cross attentions if we output attention weights + outputs = outputs + cross_attention_outputs[1:-1] + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk_query, + self.chunk_size_feed_forward, + self.seq_len_dim, + query_attention_output, + ) + + if attention_output.shape[1] > query_length: + layer_output_text = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output[:, query_length:, :], + ) + layer_output = torch.cat([layer_output, layer_output_text], dim=1) + else: + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + def feed_forward_chunk_query(self, attention_output): + intermediate_output = self.intermediate_query(attention_output) + layer_output = self.output_query(intermediate_output, attention_output) + return layer_output + + +class VideoBlipQFormerEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [ + VideoBlipQFormerLayer(config, layer_idx) + for layer_idx in range(config.num_hidden_layers) + ] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + query_length=0, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions else None + + next_decoder_cache = () if use_cache else None + + for i in range(self.config.num_hidden_layers): + layer_module = self.layer[i] + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if getattr(self.config, "gradient_checkpointing", False) and self.training: + if use_cache: + logger.warn( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module( + *inputs, past_key_value, output_attentions, query_length + ) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + query_length, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if layer_module.has_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +class VideoBlipQFormerEmbeddings(nn.Module): + """Construct the embeddings from word and position embeddings.""" + + def __init__(self, config): + super().__init__() + vocab_size = 50272 + vocab_size = config.vocab_size + # print("config.vocab_size", config.vocab_size) + self.word_embeddings = nn.Embedding( + vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", + torch.arange(config.max_position_embeddings).expand((1, -1)), + persistent=False, + ) + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + + self.config = config + + def forward( + self, + input_ids=None, + position_ids=None, + query_embeds=None, + past_key_values_length=0, + ): + if input_ids is not None: + seq_length = input_ids.size()[1] + else: + seq_length = 0 + + if position_ids is None: + position_ids = self.position_ids[ + :, past_key_values_length : seq_length + past_key_values_length + ].clone() + + if input_ids is not None: + embeddings = self.word_embeddings(input_ids) + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings( + position_ids.to(embeddings.device) + ) + embeddings = embeddings + position_embeddings + + if query_embeds is not None: + embeddings = torch.cat((query_embeds, embeddings), dim=1) + else: + embeddings = query_embeds + + embeddings = embeddings.to(self.layernorm.weight.dtype) + embeddings = self.layernorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class VideoBlipQFormerModel(VideoBlipPreTrainedModel): + """ + Querying Transformer (Q-Former), used in BLIP-2. + """ + + def __init__(self, config: VideoBlipQFormerConfig): + super().__init__(config) + self.config = config + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + self.encoder = VideoBlipQFormerEncoder(config) + self.embeddings = VideoBlipQFormerEmbeddings(config) + + self.cls = BertOnlyMLMHead(config) + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + def get_extended_attention_mask( + self, + attention_mask: torch.Tensor, + input_shape: Tuple[int], + device: torch.device, + has_query: bool = False, + ) -> torch.Tensor: + """ + Makes broadcastable attention and causal masks so that future and masked tokens are ignored. + + Arguments: + attention_mask (`torch.Tensor`): + Mask with ones indicating tokens to attend to, zeros for tokens to ignore. + input_shape (`Tuple[int]`): + The shape of the input to the model. + device (`torch.device`): + The device of the input to the model. + + Returns: + `torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`. + """ + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if attention_mask.dim() == 3: + extended_attention_mask = attention_mask[:, None, :, :] + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - the model is an encoder, so make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + extended_attention_mask = attention_mask[:, None, None, :] + else: + raise ValueError( + "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( + input_shape, attention_mask.shape + ) + ) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to( + dtype=self.dtype + ) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + return extended_attention_mask + + def forward( + self, + query_embeds=None, + input_ids=None, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of: + shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and + value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are + used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key + value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape + `(batch_size, sequence_length)`. + use_cache (`bool`, `optional`): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] - self.config.query_length + if past_key_values is not None + else 0 + ) + + if input_ids is not None: + # assert query_embeds is None, "query_embeds is not None" + embedding_output = self.embeddings( + input_ids, + query_embeds=query_embeds, + past_key_values_length=past_key_values_length, + ) + # print("embedding_output", embedding_output.shape) + query_length = embedding_output.shape[1] + else: + assert input_ids is None, "input_ids is not None" + query_length = query_embeds.shape[1] if query_embeds is not None else 0 + embedding_output = self.layernorm(query_embeds) + embedding_output = self.dropout(embedding_output) + + input_shape = embedding_output.size()[:-1] + batch_size, seq_length = input_shape + device = embedding_output.device + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + if type(encoder_hidden_states) == list: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[ + 0 + ].size() + else: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + + if type(encoder_attention_mask) == list: + encoder_extended_attention_mask = [ + self.invert_attention_mask(mask) for mask in encoder_attention_mask + ] + elif encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + query_length=query_length, + ) + sequence_output = encoder_outputs[0] + pooled_output = sequence_output[:, 0, :] + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +@add_start_docstrings( + """ + BLIP-2 Model for generating text and image features. The model consists of a vision encoder, Querying Transformer + (Q-Former) and a language model. + """, + BLIP_2_START_DOCSTRING, +) +class VideoBlipModel(VideoBlipPreTrainedModel): + config_class = VideoBlipConfig + main_input_name = "pixel_values" + + def __init__(self, config: VideoBlipConfig): + super().__init__(config) + + self.vision_model = VideoBlipVisionModel(config.vision_config) + + self.query_tokens = nn.Parameter( + torch.zeros(1, config.num_query_tokens, config.qformer_config.hidden_size) + ) + self.qformer = VideoBlipQFormerModel(config.qformer_config) + + self.language_projection = nn.Linear( + config.qformer_config.hidden_size, config.llm_hidden_size + ) + # if config.use_decoder_only_language_model: + language_model = AutoModelForCausalLM.from_config( + config.text_config, trust_remote_code=True + ) + # else: + # language_model = AutoModelForSeq2SeqLM.from_config(config.text_config) + self.language_model = language_model + + self.pretrain = False + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.language_model.set_input_embeddings(value) + + def set_output_embeddings(self, new_embeddings): + self.language_model.set_output_embeddings(new_embeddings) + + def get_output_embeddings(self) -> nn.Module: + return self.language_model.get_output_embeddings() + + def get_encoder(self): + return self.language_model.get_encoder() + + def get_decoder(self): + return self.language_model.get_decoder() + + def _tie_weights(self): + if not self.config.use_decoder_only_language_model: + self.language_model.encoder.embed_tokens = self.language_model.shared + self.language_model.decoder.embed_tokens = self.language_model.shared + + @add_start_docstrings_to_model_forward(BLIP_2_TEXT_INPUTS_DOCSTRING) + def get_text_features( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + decoder_input_ids: Optional[torch.Tensor] = None, + decoder_attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + r""" + Returns: + text_outputs (`CausalLMOutputWithPast`, or `tuple(torch.FloatTensor)` if `return_dict=False`): + The language model outputs. If `return_dict=True`, the output is a [`CausalLMOutputWithPast`] that + contains the language model logits, the past key values and the hidden states if + `output_hidden_states=True`. + Examples: + ```python + >>> import torch + >>> from transformers import AutoTokenizer, Blip2Model + + >>> device = "cuda" if torch.cuda.is_available() else "cpu" + + >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16) + + >>> model.to(device) # doctest: +IGNORE_RESULT + + >>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/blip2-opt-2.7b") + >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt").to(device) + >>> text_features = model.get_text_features(**inputs) + ```""" + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.use_decoder_only_language_model: + text_outputs = self.language_model( + input_ids=input_ids, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + else: + inputs_embeds = self.language_model.get_input_embeddings()(input_ids) + + text_outputs = self.language_model( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + labels=labels, + ) + + return text_outputs + + @add_start_docstrings_to_model_forward(BLIP_2_VISION_INPUTS_DOCSTRING) + def get_image_features( + self, + pixel_values: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + r""" + Returns: + vision_outputs (`BaseModelOutputWithPooling` or tuple of `torch.FloatTensor`): + The vision model outputs. If `return_dict=True`, the output is a [`BaseModelOutputWithPooling`] that + contains the image features, the pooled image features and the hidden states if + `output_hidden_states=True`. + Examples: + ```python + >>> import torch + >>> from PIL import Image + >>> import requests + >>> from transformers import AutoProcessor, Blip2Model + + >>> device = "cuda" if torch.cuda.is_available() else "cpu" + + >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16) + + >>> model.to(device) # doctest: +IGNORE_RESULT + + >>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b") + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + >>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16) + >>> image_outputs = model.get_image_features(**inputs) + ```""" + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + vision_outputs = self.vision_model( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + return vision_outputs + + @add_start_docstrings_to_model_forward(BLIP_2_INPUTS_DOCSTRING) + def get_qformer_features( + self, + pixel_values: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ): + r""" + Returns: + vision_outputs (`BaseModelOutputWithPooling` or tuple of `torch.FloatTensor`): + The vision model outputs. If `return_dict=True`, the output is a [`BaseModelOutputWithPooling`] that + contains the image features, the pooled image features and the hidden states if + `output_hidden_states=True`. + Examples: + ```python + >>> import torch + >>> from PIL import Image + >>> import requests + >>> from transformers import Blip2Processor, Blip2Model + + >>> device = "cuda" if torch.cuda.is_available() else "cpu" + + >>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") + >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16) + >>> model.to(device) # doctest: +IGNORE_RESULT + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + >>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16) + >>> qformer_outputs = model.get_qformer_features(**inputs) + ```""" + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + vision_outputs = self.vision_model( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + image_embeds = vision_outputs[0] + + # step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention + image_attention_mask = torch.ones( + image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device + ) + + query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) + query_outputs = self.qformer( + query_embeds=query_tokens, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + return query_outputs + + @add_start_docstrings_to_model_forward(BLIP_2_INPUTS_DOCSTRING) + @replace_return_docstrings( + output_type=VideoBlipForConditionalGenerationModelOutput, + config_class=VideoBlipVisionConfig, + ) + def forward( + self, + pixel_values: torch.FloatTensor, + input_ids: torch.FloatTensor, + attention_mask: Optional[torch.LongTensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + labels: Optional[torch.LongTensor] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, VideoBlipForConditionalGenerationModelOutput]: + r""" + Returns: + + Examples: + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import Blip2Processor, Blip2Model + >>> import torch + + >>> device = "cuda" if torch.cuda.is_available() else "cpu" + + >>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") + >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16) + >>> model.to(device) # doctest: +IGNORE_RESULT + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> prompt = "Question: how many cats are there? Answer:" + >>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device, torch.float16) + + >>> outputs = model(**inputs) + ```""" + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + # step 1: forward the images through the vision encoder, + # to get image embeddings of shape (batch_size, seq_len, hidden_size) + vision_outputs = self.vision_model( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + image_embeds = vision_outputs[0] + + # step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention + image_attention_mask = torch.ones( + image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device + ) + + query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) + query_outputs = self.qformer( + query_embeds=query_tokens, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + query_output = query_outputs[0] + + # step 3: use the language model, conditioned on the query outputs and the prompt + language_model_inputs = self.language_projection(query_output) + language_model_attention_mask = torch.ones( + language_model_inputs.size()[:-1], + dtype=torch.long, + device=language_model_inputs.device, + ) + inputs_embeds = self.language_model.get_input_embeddings()(input_ids) + inputs_embeds = torch.cat([language_model_inputs, inputs_embeds], dim=1) + + if attention_mask is None: + attention_mask = torch.ones_like(input_ids) + expected_device = language_model_attention_mask.device + attention_mask = torch.cat( + [language_model_attention_mask, attention_mask.to(expected_device)], dim=1 + ) + + if self.config.use_decoder_only_language_model: + outputs = self.language_model( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + logits = outputs.logits if return_dict else outputs[0] + loss = None + # we compute the loss here since we need to take into account the sequence length of the query embeds + if labels is not None: + labels = labels.to(logits.device) + logits = logits[:, -labels.size(1) :, :] + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous().to(logits.device) + + # Flatten the tokens + loss_fct = CrossEntropyLoss(reduction="mean") + + loss = loss_fct( + shift_logits.view(-1, self.config.text_config.vocab_size), + shift_labels.view(-1), + ) + else: + outputs = self.language_model( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + labels=labels, + ) + loss = outputs.loss if return_dict else outputs[0] + logits = outputs.logits if return_dict else outputs[1] + + if not return_dict: + output = (logits, vision_outputs, query_outputs, outputs) + return ((loss,) + output) if loss is not None else output + + return VideoBlipForConditionalGenerationModelOutput( + loss=loss, + logits=logits, + vision_outputs=vision_outputs, + qformer_outputs=query_outputs, + language_model_outputs=outputs, + ) + + +import torch.distributed as dist + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +@torch.no_grad() +def concat_all_gather(tensor): + """ + Performs all_gather operation on the provided tensors. + *** Warning ***: torch.distributed.all_gather has no gradient. + """ + # if use distributed training + if not is_dist_avail_and_initialized(): + return tensor + + tensors_gather = [ + torch.ones_like(tensor) for _ in range(torch.distributed.get_world_size()) + ] + torch.distributed.all_gather(tensors_gather, tensor, async_op=False) + + output = torch.cat(tensors_gather, dim=0) + return output + + +class GatherLayer(torch.autograd.Function): + """ + Gather tensors from all workers with support for backward propagation: + This implementation does not cut the gradients as torch.distributed.all_gather does. + """ + + @staticmethod + def forward(ctx, x): + output = [ + torch.zeros_like(x) for _ in range(torch.distributed.get_world_size()) + ] + torch.distributed.all_gather(output, x) + return tuple(output) + + @staticmethod + def backward(ctx, *grads): + all_gradients = torch.stack(grads) + torch.distributed.all_reduce(all_gradients) + return all_gradients[torch.distributed.get_rank()] + + +def all_gather_with_grad(tensors): + """ + Performs all_gather operation on the provided tensors. + Graph remains connected for backward grad computation. + """ + # Queue the gathered tensors + world_size = torch.distributed.get_world_size() + # There is no need for reduction in the single-proc case + if world_size == 1: + return tensors + + # tensor_all = GatherLayer.apply(tensors) + tensor_all = GatherLayer.apply(tensors) + + return torch.cat(tensor_all, dim=0) + + +@dataclass +class BlipOutput(ModelOutput): + loss: Optional[torch.FloatTensor] = None + + loss_itc: Optional[torch.FloatTensor] = None + + loss_itm: Optional[torch.FloatTensor] = None + + loss_lm: Optional[torch.FloatTensor] = None + + +@add_start_docstrings( + """ + BLIP-2 Model for generating text given an image and an optional text prompt. The model consists of a vision + encoder, Querying Transformer (Q-Former) and a language model. + + One can optionally pass `input_ids` to the model, which serve as a text prompt, to make the language model continue + the prompt. Otherwise, the language model starts generating text from the [BOS] (beginning-of-sequence) token. + """, + BLIP_2_START_DOCSTRING, +) +class VideoBlipForConditionalGeneration(VideoBlipPreTrainedModel): + config_class = VideoBlipConfig + main_input_name = "pixel_values" + + def __init__(self, config: VideoBlipConfig): + super().__init__(config) + + # 画像フレーム数 + self.num_frames = config.num_frames + + self.vision_model = VideoBlipVisionModel(config.vision_config) + + self.query_tokens = nn.Parameter( + torch.zeros(1, config.num_query_tokens, config.qformer_config.hidden_size) + ) + self.qformer = VideoBlipQFormerModel(config.qformer_config) + + # print("config.text_config.hidden_size", config.text_config.hidden_size) + self.language_projection = nn.Linear( + config.qformer_config.hidden_size, config.llm_hidden_size + ) + self.text_projection = nn.Linear( + config.qformer_config.hidden_size, config.text_config.hidden_size + ) + # self.language_projection_replace = nn.Linear(config.qformer_config.hidden_size, config.llm_hidden_size) + + # print("config.use_decoder_only_language_model", config.use_decoder_only_language_model) + if config.use_decoder_only_language_model: + language_model = AutoModelForCausalLM.from_config( + config.text_config, trust_remote_code=True, torch_dtype=torch.float16 + ) + else: + language_model = AutoModelForSeq2SeqLM.from_config(config.text_config) + self.language_model = language_model + + self.img_temperal_embedding = nn.ParameterList( + nn.Parameter(torch.zeros(1, 1, config.vision_config.hidden_size)) + for _ in range(self.num_frames) + ) + + self.itm_head = nn.Linear(config.qformer_config.hidden_size, 2) + + self.temp = nn.Parameter(0.07 * torch.ones([])) + + self.bos_token_id = config.bos_token_id + self.pad_token_id = config.pad_token_id + + # Initialize weights and apply final processing + self.post_init() + + @classmethod + def create( + cls, + language_model_name: str, + num_frames: int = 1, + torch_dtype: torch.dtype = torch.float16, + ): + """ + Args: + language_model : The name of language model used as a decoder. + num_frames : Number of images input to the vision encoder. Default is 1. + torch_dtype : Data type of the model. Default is torch.float16. + """ + BASE_BLIP_MODEL = "Salesforce/blip2-opt-2.7b" + if language_model_name == "stabilityai/japanese-stablelm-base-alpha-7b": + language_model = AutoModelForCausalLM.from_pretrained( + language_model_name, torch_dtype=torch_dtype, trust_remote_code=True + ) + video_blip_config = VideoBlipConfig.from_pretrained(BASE_BLIP_MODEL) + video_blip_config.llm_hidden_size = language_model.config.hidden_size + video_blip_config.num_frames = num_frames + model = cls.from_pretrained( + BASE_BLIP_MODEL, + config=video_blip_config, + torch_dtype=torch_dtype, + ignore_mismatched_sizes=True, + ) + model.language_model = language_model + model.config.text_config = language_model.config + return model + # model.language_model = get_peft_model(language_model, peft_config) + # model.config.text_config = language_model.config + + elif "llama" in language_model_name or "ELYZA" in language_model_name: + language_model = AutoModelForCausalLM.from_pretrained( + language_model_name, torch_dtype=torch_dtype, trust_remote_code=True + ) + print("language_model", language_model) + video_blip_config = VideoBlipConfig.from_pretrained(BASE_BLIP_MODEL) + video_blip_config.llm_hidden_size = language_model.config.hidden_size + video_blip_config.num_frames = num_frames + model = VideoBlipForConditionalGeneration.from_pretrained( + BASE_BLIP_MODEL, + config=video_blip_config, + torch_dtype=torch_dtype, + ignore_mismatched_sizes=True, + ) + model.language_model = language_model + model.config.text_config = language_model.config + return model + + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.language_model.set_input_embeddings(value) + + def set_output_embeddings(self, new_embeddings): + self.language_model.set_output_embeddings(new_embeddings) + + def get_output_embeddings(self) -> nn.Module: + return self.language_model.get_output_embeddings() + + def get_encoder(self): + return self.language_model.get_encoder() + + def get_decoder(self): + return self.language_model.get_decoder() + + def _tie_weights(self): + if not self.config.use_decoder_only_language_model: + self.language_model.encoder.embed_tokens = self.language_model.shared + self.language_model.decoder.embed_tokens = self.language_model.shared + + def _preprocess_accelerate(self): + r""" + Some pre-processing hacks to make the model `accelerate` compatible. Check + https://github.com/huggingface/transformers/pull/21707 for more details. + """ + hf_device_map = self.hf_device_map + + if ( + len(hf_device_map) > 1 + and "language_model" not in hf_device_map + and torch.cuda.device_count() > 1 + ): + # warn users about unexpected behavior when using multi-GPU + BLIP-2 + `accelerate`. + logger.warning( + "The `language_model` is not in the `hf_device_map` dictionary and you are running your script" + " in a multi-GPU environment. this may lead to unexpected behavior when using `accelerate`." + " Please pass a `device_map` that contains `language_model` to remove this warning." + " Please refer to https://github.com/huggingface/blog/blob/main/accelerate-large-models.md for" + " more details on creating a `device_map` for large models.", + ) + + if hasattr(self.language_model, "_hf_hook"): + self.language_model._hf_hook.io_same_device = ( + True # For `generate` compatibility + ) + + @add_start_docstrings_to_model_forward(BLIP_2_INPUTS_DOCSTRING) + @replace_return_docstrings( + output_type=VideoBlipForConditionalGenerationModelOutput, + config_class=VideoBlipVisionConfig, + ) + def forward( + self, + pixel_values: torch.FloatTensor, + input_ids: torch.FloatTensor, + attention_mask: Optional[torch.LongTensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + labels: Optional[torch.LongTensor] = None, + return_dict: Optional[bool] = None, + **kwargs, + ) -> Union[Tuple, VideoBlipForConditionalGenerationModelOutput]: + r""" + Returns: + + Examples: + + Image captioning (without providing a text prompt): + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import Blip2Processor, Blip2ForConditionalGeneration + >>> import torch + + >>> device = "cuda" if torch.cuda.is_available() else "cpu" + + >>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") + >>> model = Blip2ForConditionalGeneration.from_pretrained( + ... "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16 + ... ) + >>> model.to(device) # doctest: +IGNORE_RESULT + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16) + + >>> generated_ids = model.generate(**inputs) + >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() + >>> print(generated_text) + two cats laying on a couch + ``` + + Visual question answering (prompt = question): + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import Blip2Processor, Blip2ForConditionalGeneration + >>> import torch + + >>> device = "cuda" if torch.cuda.is_available() else "cpu" + + >>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") + >>> model = Blip2ForConditionalGeneration.from_pretrained( + ... "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16 + ... ) + >>> model.to(device) # doctest: +IGNORE_RESULT + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> prompt = "Question: how many cats are there? Answer:" + >>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device, torch.float16) + + >>> generated_ids = model.generate(**inputs) + >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() + >>> print(generated_text) + two + ```""" + + # pretrain code + # if True: + # return self.forward_pretrain(pixel_values, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, output_attentions, output_hidden_states, labels, return_dict) + + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + # step 1: forward the images through the vision encoder, + # to get image embeddings of shape (batch_size, seq_len, hidden_size) + + if pixel_values.ndim == 5: + visual_features = [] + num_frames = pixel_values.shape[1] + for frame_idx in range(num_frames): + vision_outputs = self.vision_model( + pixel_values=pixel_values[:, frame_idx, :, :], + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + visual_features_frame = vision_outputs[0] + + # visual_features_frame = self.image_encoder(pixel_values[:, frame_idx, :, :]).last_hidden_state + visual_features_frame += self.img_temperal_embedding[frame_idx] + visual_features.append(visual_features_frame) + + # finally, concatenate all features along sequence dimension + image_embeds = torch.cat(visual_features, dim=1) + elif pixel_values.ndim == 4: + vision_outputs = self.vision_model( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + image_embeds = vision_outputs[0] + + # step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention + image_attention_mask = torch.ones( + image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device + ) + + query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) + query_outputs = self.qformer( + query_embeds=query_tokens, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + query_output = query_outputs[0] + + # step 3: use the language model, conditioned on the query outputs and the prompt + language_model_inputs = self.language_projection(query_output) + language_model_attention_mask = torch.ones( + language_model_inputs.size()[:-1], + dtype=torch.long, + device=language_model_inputs.device, + ) + inputs_embeds = self.language_model.get_input_embeddings()(input_ids) + inputs_embeds = torch.cat( + [language_model_inputs, inputs_embeds.to(language_model_inputs.device)], + dim=1, + ) + + if attention_mask is None: + attention_mask = torch.ones_like(input_ids) + expected_device = language_model_attention_mask.device + attention_mask = torch.cat( + [language_model_attention_mask, attention_mask.to(expected_device)], dim=1 + ) + + if self.config.use_decoder_only_language_model: + outputs = self.language_model( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + logits = outputs.logits if return_dict else outputs[0] + loss = None + # we compute the loss here since we need to take into account the sequence length of the query embeds + if labels is not None: + labels = labels.to(logits.device) + logits = logits[:, -labels.size(1) :, :] + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous().to(logits.device) + + # Flatten the tokens + loss_fct = CrossEntropyLoss(reduction="mean") + + loss = loss_fct( + shift_logits.view(-1, self.config.text_config.vocab_size), + shift_labels.view(-1), + ) + else: + outputs = self.language_model( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + labels=labels, + ) + loss = outputs.loss if return_dict else outputs[0] + logits = outputs.logits if return_dict else outputs[1] + + if not return_dict: + output = (logits, vision_outputs, query_outputs, outputs) + return ((loss,) + output) if loss is not None else output + + return VideoBlipForConditionalGenerationModelOutput( + loss=loss, + logits=logits, + vision_outputs=vision_outputs, + qformer_outputs=query_outputs, + language_model_outputs=outputs, + ) + + def forward_pretrain( + self, + pixel_values: torch.FloatTensor, + input_ids: torch.FloatTensor, + attention_mask: Optional[torch.LongTensor] = None, + decoder_input_ids: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + labels: Optional[torch.LongTensor] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, VideoBlipForConditionalGenerationModelOutput]: + """pretrain loss""" + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + # step 1: forward the images through the vision encoder, + # to get image embeddings of shape (batch_size, seq_len, hidden_size) + + visual_features = [] + num_frames = pixel_values.shape[1] + for frame_idx in range(num_frames): + vision_outputs = self.vision_model( + pixel_values=pixel_values[:, frame_idx, :, :], + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + visual_features_frame = vision_outputs[0] + + # visual_features_frame = self.image_encoder(pixel_values[:, frame_idx, :, :]).last_hidden_state + visual_features_frame += self.img_temperal_embedding[frame_idx] + visual_features.append(visual_features_frame) + + # finally, concatenate all features along sequence dimension + image_embeds = torch.cat(visual_features, dim=1) + + # step 2: forward the query tokens through the QFormer, using the image embeddings for cross-attention + image_attention_mask = torch.ones( + image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device + ) + + query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) + query_outputs = self.qformer( + query_embeds=query_tokens, + use_cache=True, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + query_output = query_outputs[0] + + image_feats = F.normalize(self.language_projection(query_output), dim=-1) + + text_outputs = self.qformer( + input_ids=input_ids, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + text_outputs = text_outputs[0] + text_feature = F.normalize(self.text_projection(text_outputs)[:, 0, :], dim=-1) + + image_feats_all = concat_all_gather( + image_feats + ) # [batch_size*num_gpu, num_query_tokens, embed_dim] + text_feat_all = concat_all_gather( + text_feature + ) # [batch_size*num_gpu, embed_dim] + + sim_q2t = torch.matmul( + image_feats.unsqueeze(1), text_feat_all.unsqueeze(-1) + ).squeeze() + # [batch_size, batch_size, num_query_tokens] + + # image-text similarity: aggregate across all query tokens + sim_i2t, _ = sim_q2t.max(-1) + sim_i2t = sim_i2t / self.temp + + # text-query similarity: [batch_size, batch_size*num_gpu, num_query_tokens] + sim_t2q = torch.matmul( + text_feature.unsqueeze(1).unsqueeze(1), image_feats_all.permute(0, 2, 1) + ).squeeze() + + # text-image similarity: aggregate across all query tokens + sim_t2i, _ = sim_t2q.max(-1) + sim_t2i = sim_t2i / self.temp # [batch_size, batch_size*num_gpu] + + rank = dist.get_rank() + bs = pixel_values.size(0) + targets = torch.linspace(rank * bs, rank * bs + bs - 1, bs, dtype=int).to( + pixel_values.device + ) + + loss_itc = ( + F.cross_entropy(sim_i2t, targets, label_smoothing=0.1) + + F.cross_entropy(sim_t2i, targets, label_smoothing=0.1) + ) / 2 + + ###============== Image-text Matching ===================### + + text_input_ids_world = concat_all_gather(input_ids) + text_attention_mask_world = concat_all_gather(attention_mask) + image_embeds_world = all_gather_with_grad(image_embeds) + + with torch.no_grad(): + sim_t2i[:, rank * bs : rank * bs + bs].fill_diagonal_(-10000) + sim_i2t[:, rank * bs : rank * bs + bs].fill_diagonal_(-10000) + + weights_t2i = F.softmax(sim_t2i, dim=1) + weights_i2t = F.softmax(sim_i2t, dim=1) + + # select a negative image for each text + image_embeds_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_t2i[b], 1).item() + image_embeds_neg.append(image_embeds_world[neg_idx]) + image_embeds_neg = torch.stack(image_embeds_neg, dim=0) + + # select a negative text for each image + text_ids_neg = [] + text_atts_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_i2t[b], 1).item() + text_ids_neg.append(text_input_ids_world[neg_idx]) + text_atts_neg.append(text_attention_mask_world[neg_idx]) + + text_ids_neg = torch.stack(text_ids_neg, dim=0) + text_atts_neg = torch.stack(text_atts_neg, dim=0) + + text_ids_all = torch.cat( + [input_ids, input_ids, text_ids_neg], dim=0 + ) # pos, pos, neg + text_atts_all = torch.cat( + [attention_mask, attention_mask, text_atts_neg], + dim=0, + ) + + query_tokens_itm = self.query_tokens.expand(text_ids_all.shape[0], -1, -1) + query_atts_itm = torch.ones(query_tokens_itm.size()[:-1], dtype=torch.long).to( + pixel_values.device + ) + attention_mask_all = torch.cat([query_atts_itm, text_atts_all], dim=1) + + image_embeds_all = torch.cat( + [image_embeds, image_embeds_neg, image_embeds], dim=0 + ) # pos, neg, pos + image_atts_all = torch.ones(image_embeds_all.size()[:-1], dtype=torch.long).to( + pixel_values.device + ) + + # point 1 + output_itm = self.qformer( + input_ids=text_ids_all, + query_embeds=query_tokens_itm, + attention_mask=attention_mask_all, + encoder_hidden_states=image_embeds_all, + encoder_attention_mask=image_atts_all, + return_dict=True, + ) + + vl_embeddings = output_itm.last_hidden_state[:, : query_tokens_itm.size(1), :] + vl_output = self.itm_head(vl_embeddings) + logits = vl_output.mean(dim=1) + + itm_labels = torch.cat( + [torch.ones(bs, dtype=torch.long), torch.zeros(2 * bs, dtype=torch.long)], + dim=0, + ).to(pixel_values.device) + loss_itm = F.cross_entropy(logits, itm_labels) + + # checkpoint 2 + + ##================= Image Captioning ========================## + decoder_input_ids = input_ids.clone() + decoder_input_ids[:, 0] = self.bos_token_id + labels = decoder_input_ids.masked_fill( + decoder_input_ids == self.pad_token_id, -100 + ) + + query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to( + pixel_values.device + ) + attention_mask = torch.cat([query_atts, attention_mask], dim=1) + + lm_output = self.qformer( + input_ids=decoder_input_ids, + attention_mask=attention_mask, + past_key_values=query_outputs.past_key_values, + return_dict=True, + ) + + # LMHead + sequence_output = lm_output[0] + prediction_scores = self.qformer.cls(sequence_output) + + reduction = "mean" + + labels = input_ids + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() + labels = labels[:, 1:].contiguous() + loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) + lm_loss = loss_fct( + shifted_prediction_scores.view(-1, self.config.qformer_config.vocab_size), + labels.view(-1), + ) + + return BlipOutput( + loss=loss_itc + loss_itm + lm_loss, + loss_itc=loss_itc, + loss_itm=loss_itm, + loss_lm=lm_loss, + ) + + @torch.no_grad() + def generate( + self, + pixel_values: torch.FloatTensor, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.LongTensor] = None, + **generate_kwargs, + ) -> torch.LongTensor: + """ + Overrides `generate` function to be able to use the model as a conditional generator. + + Args: + pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)): + Input images to be processed. + input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*): + The sequence used as a prompt for the generation. + attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*): + Mask to avoid performing attention on padding token indices + + Returns: + captions (list): A list of strings of length batch_size * num_captions. + """ + if hasattr(self, "hf_device_map"): + # preprocess for `accelerate` + self._preprocess_accelerate() + + batch_size = pixel_values.shape[0] + + if pixel_values.ndim == 5: + # when the pixel_values is a video, sequence of images + visual_features = [] + num_frames = pixel_values.shape[1] + for frame_idx in range(num_frames): + vision_outputs = self.vision_model( + pixel_values=pixel_values[:, frame_idx, :, :] + ) + visual_features_frame = vision_outputs[0] + + visual_features_frame += self.img_temperal_embedding[frame_idx] + visual_features.append(visual_features_frame) + + # finally, concatenate all features along sequence dimension + image_embeds = torch.cat(visual_features, dim=1) + + elif pixel_values.ndim == 4: + # when the pixel_values is a image, not video + vision_outputs = self.vision_model( + pixel_values=pixel_values, + ) + image_embeds = vision_outputs[0] + + image_attention_mask = torch.ones( + image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device + ) + + query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) + query_outputs = self.qformer( + query_embeds=query_tokens, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_attention_mask, + return_dict=True, + ) + query_output = query_outputs.last_hidden_state + + language_model_inputs = self.language_projection(query_output) + language_attention_mask = torch.ones( + language_model_inputs.size()[:-1], + dtype=torch.long, + device=language_model_inputs.device, + ) + if input_ids is None: + input_ids = ( + torch.LongTensor([[self.config.text_config.bos_token_id]]) + .repeat(batch_size, 1) + .to(image_embeds.device) + ) + if attention_mask is None: + attention_mask = torch.ones_like(input_ids) + attention_mask = torch.cat( + [ + language_attention_mask, + attention_mask.to(language_attention_mask.device), + ], + dim=1, + ) + + # concatenate query embeddings with prompt embeddings + inputs_embeds = self.get_input_embeddings()(input_ids) + inputs_embeds = torch.cat( + [language_model_inputs, inputs_embeds.to(language_model_inputs.device)], + dim=1, + ) + + outputs = self.language_model.generate( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + **generate_kwargs, + ) + + return outputs diff --git a/evomerge/modules/heron/video_blip/processing_video_blip.py b/evomerge/modules/heron/video_blip/processing_video_blip.py new file mode 100644 index 0000000..3ee13b1 --- /dev/null +++ b/evomerge/modules/heron/video_blip/processing_video_blip.py @@ -0,0 +1,171 @@ +# coding=utf-8 +# Copyright 2023 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. + +""" +NOTICE: This code is subject to the terms of the Apache License 2.0. + +The code is modified from the original one. +original code: https://github.com/huggingface/transformers/blob/main/src/transformers/models/blip_2/processing_blip_2.py + +Additional contributions by Turing Inc. team +""" + +from typing import List, Optional, Union + +from transformers.image_utils import ImageInput +from transformers.processing_utils import ProcessorMixin +from transformers.tokenization_utils_base import ( + BatchEncoding, + PaddingStrategy, + PreTokenizedInput, + TextInput, + TruncationStrategy, +) +from transformers.utils import TensorType + + +class VideoBlipProcessor(ProcessorMixin): + r""" + Constructs a BLIP-2 processor which wraps a BLIP image processor and an OPT/T5 tokenizer into a single processor. + + [`BlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`AutoTokenizer`]. See the docstring + of [`~BlipProcessor.__call__`] and [`~BlipProcessor.decode`] for more information. + + Args: + image_processor (`BlipImageProcessor`): + An instance of [`BlipImageProcessor`]. The image processor is a required input. + tokenizer (`AutoTokenizer`): + An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input. + """ + + attributes = ["image_processor", "tokenizer"] + image_processor_class = "BlipImageProcessor" + tokenizer_class = "AutoTokenizer" + + # Copied from transformers.models.blip.processing_blip.BlipProcessor.__init__ + def __init__(self, image_processor, tokenizer): + tokenizer.return_token_type_ids = False + super().__init__(image_processor, tokenizer) + self.current_processor = self.image_processor + + # Copied from transformers.models.blip.processing_blip.BlipProcessor.__call__ + def __call__( + self, + images=None, + text: Union[ + TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput] + ] = None, + add_special_tokens: bool = True, + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length: Optional[int] = None, + stride: int = 0, + pad_to_multiple_of: Optional[int] = None, + return_attention_mask: Optional[bool] = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_token_type_ids: bool = False, + return_length: bool = False, + verbose: bool = True, + return_tensors: Optional[Union[str, TensorType]] = None, + **kwargs, + ) -> BatchEncoding: + """ + This method uses [`BlipImageProcessor.__call__`] method to prepare image(s) for the model, and + [`BertTokenizerFast.__call__`] to prepare text for the model. + + Please refer to the docstring of the above two methods for more information. + """ + if images is None and text is None: + raise ValueError("You have to specify either images or text.") + + # Get only text + if images is None: + self.current_processor = self.tokenizer + text_encoding = self.tokenizer( + text=text, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_token_type_ids=return_token_type_ids, + return_length=return_length, + verbose=verbose, + return_tensors=return_tensors, + **kwargs, + ) + return text_encoding + + # add pixel_values + encoding_image_processor = self.image_processor( + images, return_tensors=return_tensors + ) + + if text is not None: + text_encoding = self.tokenizer( + text=text, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_token_type_ids=return_token_type_ids, + return_length=return_length, + verbose=verbose, + return_tensors=return_tensors, + **kwargs, + ) + else: + text_encoding = None + + if text_encoding is not None: + encoding_image_processor.update(text_encoding) + + return encoding_image_processor + + # Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + # Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer + to the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + @property + # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) diff --git a/evomerge/utils.py b/evomerge/utils.py new file mode 100644 index 0000000..68f9275 --- /dev/null +++ b/evomerge/utils.py @@ -0,0 +1,61 @@ +import importlib +import json +import os +import yaml +import random +import numpy as np +import torch + + +def default(val, d): + if val is not None: + return val + return d + + +def load_config(config_path: str) -> dict: + ext = os.path.splitext(config_path)[-1] + if ext in [".yaml", ".yml"]: + with open(config_path, "r", encoding="utf-8") as fp: + config = yaml.safe_load(fp) + elif ext == ".json": + with open(config_path) as fp: + config = json.load(fp) + else: + raise RuntimeError + return config + + +# copied from https://github.com/Stability-AI/generative-models/blob/main/sgm/util.py#L168 +def instantiate_from_config(config): + if not "target" in config: + if config == "__is_first_stage__": + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +# copied from https://github.com/Stability-AI/generative-models/blob/main/sgm/util.py#L168 +def get_obj_from_str(string, reload=False, invalidate_cache=True): + module, cls = string.rsplit(".", 1) + if invalidate_cache: + importlib.invalidate_caches() + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +def set_seed(seed: int): + """ + Helper function 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