diff --git a/README.md b/README.md index 0c8bebcdb9..d6b21f5de6 100644 --- a/README.md +++ b/README.md @@ -374,6 +374,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang. 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed. 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer. +1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever. 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (from Salesforce) released with the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. diff --git a/README_es.md b/README_es.md index db413d2e99..685e0bdc59 100644 --- a/README_es.md +++ b/README_es.md @@ -351,6 +351,7 @@ Número actual de puntos de control: ![](https://img.shields.io/endpoint?url=htt 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang. 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed. 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer. +1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever. 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (from Salesforce) released with the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. diff --git a/README_hd.md b/README_hd.md index 232068d513..7b42fb65eb 100644 --- a/README_hd.md +++ b/README_hd.md @@ -323,6 +323,7 @@ conda install -c huggingface transformers 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (UCSD, NVIDIA से) साथ में कागज [GroupViT: टेक्स्ट सुपरविजन से सिमेंटिक सेगमेंटेशन इमर्जेस](https://arxiv .org/abs/2202.11094) जियारुई जू, शालिनी डी मेलो, सिफ़ी लियू, वोनमिन बायन, थॉमस ब्रेउएल, जान कौट्ज़, ज़ियाओलोंग वांग द्वारा। 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (फेसबुक से) साथ में पेपर [ह्यूबर्ट: सेल्फ सुपरवाइज्ड स्पीच रिप्रेजेंटेशन लर्निंग बाय मास्क्ड प्रेडिक्शन ऑफ हिडन यूनिट्स](https ://arxiv.org/abs/2106.07447) वेई-निंग सू, बेंजामिन बोल्टे, याओ-हंग ह्यूबर्ट त्साई, कुशाल लखोटिया, रुस्लान सालाखुतदीनोव, अब्देलरहमान मोहम्मद द्वारा। 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (बर्कले से) साथ में कागज [I-BERT: Integer-only BERT Quantization](https:// arxiv.org/abs/2101.01321) सेहून किम, अमीर घोलमी, ज़ेवेई याओ, माइकल डब्ल्यू महोनी, कर्ट केटज़र द्वारा। +1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever. 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (Salesforce से) Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. द्वाराअनुसंधान पत्र [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) के साथ जारी किया गया diff --git a/README_ja.md b/README_ja.md index 9d61fede52..c8700143d7 100644 --- a/README_ja.md +++ b/README_ja.md @@ -385,6 +385,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (UCSD, NVIDIA から) Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang から公開された研究論文: [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (Facebook から) Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed から公開された研究論文: [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (Berkeley から) Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer から公開された研究論文: [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) +1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (OpenAI から) Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever から公開された研究論文: [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (Salesforce から) Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. から公開された研究論文 [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) diff --git a/README_ko.md b/README_ko.md index 0689b34740..69ddd49c8a 100644 --- a/README_ko.md +++ b/README_ko.md @@ -300,6 +300,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (UCSD, NVIDIA 에서) Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang 의 [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) 논문과 함께 발표했습니다. 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (Facebook 에서) Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed 의 [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) 논문과 함께 발표했습니다. 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (Berkeley 에서) Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer 의 [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) 논문과 함께 발표했습니다. +1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (OpenAI 에서) Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever 의 [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) 논문과 함께 발표했습니다. 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (Salesforce 에서 제공)은 Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi.의 [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500)논문과 함께 발표했습니다. diff --git a/README_zh-hans.md b/README_zh-hans.md index 105522fcf4..9a78a22cd7 100644 --- a/README_zh-hans.md +++ b/README_zh-hans.md @@ -324,6 +324,7 @@ conda install -c huggingface transformers 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (来自 UCSD, NVIDIA) 伴随论文 [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) 由 Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang 发布。 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (来自 Facebook) 伴随论文 [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) 由 Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed 发布。 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (来自 Berkeley) 伴随论文 [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) 由 Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer 发布。 +1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (来自 OpenAI) 伴随论文 [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) 由 Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever 发布。 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (来自 Salesforce) 伴随论文 [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) 由 Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi 发布。 diff --git a/README_zh-hant.md b/README_zh-hant.md index 5914956971..9416679b0b 100644 --- a/README_zh-hant.md +++ b/README_zh-hant.md @@ -336,6 +336,7 @@ conda install -c huggingface transformers 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang. 1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed. 1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer. +1. **[IDEFICS](https://huggingface.co/docs/transformers/main/model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever. 1. **[Informer](https://huggingface.co/docs/transformers/model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 1. **[InstructBLIP](https://huggingface.co/docs/transformers/model_doc/instructblip)** (from Salesforce) released with the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index adb0f475ee..70795f10f1 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -694,6 +694,8 @@ sections: - local: model_doc/decision_transformer title: Decision Transformer + - local: model_doc/idefics + title: IDEFICS - local: model_doc/trajectory_transformer title: Trajectory Transformer title: Reinforcement learning models diff --git a/docs/source/en/index.md b/docs/source/en/index.md index 62f0469aa0..85eea63380 100644 --- a/docs/source/en/index.md +++ b/docs/source/en/index.md @@ -140,6 +140,7 @@ The documentation is organized into five sections: 1. **[GroupViT](model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang. 1. **[Hubert](model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed. 1. **[I-BERT](model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer. +1. **[IDEFICS](model_doc/idefics)** (from HuggingFace) released with the paper [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://huggingface.co/papers/2306.16527) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh. 1. **[ImageGPT](model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever. 1. **[Informer](model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 1. **[InstructBLIP](model_doc/instructblip)** (from Salesforce) released with the paper [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/abs/2305.06500) by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. @@ -360,6 +361,7 @@ Flax), PyTorch, and/or TensorFlow. | GroupViT | ✅ | ✅ | ❌ | | Hubert | ✅ | ✅ | ❌ | | I-BERT | ✅ | ❌ | ❌ | +| IDEFICS | ✅ | ❌ | ❌ | | ImageGPT | ✅ | ❌ | ❌ | | Informer | ✅ | ❌ | ❌ | | InstructBLIP | ✅ | ❌ | ❌ | diff --git a/docs/source/en/model_doc/idefics.md b/docs/source/en/model_doc/idefics.md new file mode 100644 index 0000000000..e0017df0c5 --- /dev/null +++ b/docs/source/en/model_doc/idefics.md @@ -0,0 +1,63 @@ + + +# IDEFICS + +## Overview + +The IDEFICS model was proposed in [OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents +](https://huggingface.co/papers/2306.16527 +) by Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh + +The abstract from the paper is the following: + +*Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks that require reasoning over one or multiple images to generate a text. However, the datasets used to train these models have not been released, and the collection process has not been fully specified. We introduce the OBELICS dataset, an open web-scale filtered dataset of interleaved image-text documents comprising 141 million web pages extracted from Common Crawl, 353 million associated images, and 115 billion text tokens. We describe the dataset creation process, present comprehensive filtering rules, and provide an analysis of the dataset's content. To show the viability of OBELISC, we train an 80 billion parameters vision and language model on the dataset and obtain competitive performance on various multimodal benchmarks. We release the code to reproduce the dataset along with the dataset itself.* + +This model was contributed by [HuggingFaceM4](https://huggingface.co/HuggingFaceM4). The original code can be found [here](). (TODO: don't have a public link yet). + + + + +Idefics modeling code in Transformers is for finetuning and inferencing the pre-trained Idefics models. + +To train a new Idefics model from scratch use the m4 codebase (a link will be provided once it's made public) + + + + +## IdeficsConfig + +[[autodoc]] IdeficsConfig + +## IdeficsModel + +[[autodoc]] IdeficsModel + - forward + +## IdeficsForVisionText2Text + +[[autodoc]] IdeficsForVisionText2Text + - forward + +## IdeficsImageProcessor + +[[autodoc]] IdeficsImageProcessor + - preprocess + +## IdeficsProcessor + +[[autodoc]] IdeficsProcessor + - __call__ diff --git a/docs/source/en/tasks/language_modeling.md b/docs/source/en/tasks/language_modeling.md index 695788fc20..36af74245a 100644 --- a/docs/source/en/tasks/language_modeling.md +++ b/docs/source/en/tasks/language_modeling.md @@ -154,7 +154,7 @@ This dataset contains the token sequences, but some of these are longer than the You can now use a second preprocessing function to - concatenate all the sequences -- split the concatenated sequences into shorter chunks defined by `block_size`, which should be both shorter than the maximum input length and short enough for your GPU RAM. +- split the concatenated sequences into shorter chunks defined by `block_size`, which should be both shorter than the maximum input length and short enough for your GPU RAM. ```py >>> block_size = 128 diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py index 9fc2d41bc1..cb7f5d2fb5 100644 --- a/src/transformers/__init__.py +++ b/src/transformers/__init__.py @@ -362,6 +362,10 @@ _import_structure = { "models.herbert": ["HerbertTokenizer"], "models.hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"], "models.ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig"], + "models.idefics": [ + "IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP", + "IdeficsConfig", + ], "models.imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig"], "models.informer": ["INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "InformerConfig"], "models.instructblip": [ @@ -939,6 +943,7 @@ else: _import_structure["models.efficientnet"].append("EfficientNetImageProcessor") _import_structure["models.flava"].extend(["FlavaFeatureExtractor", "FlavaImageProcessor", "FlavaProcessor"]) _import_structure["models.glpn"].extend(["GLPNFeatureExtractor", "GLPNImageProcessor"]) + _import_structure["models.idefics"].extend(["IdeficsImageProcessor"]) _import_structure["models.imagegpt"].extend(["ImageGPTFeatureExtractor", "ImageGPTImageProcessor"]) _import_structure["models.layoutlmv2"].extend(["LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor"]) _import_structure["models.layoutlmv3"].extend(["LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor"]) @@ -1929,6 +1934,15 @@ else: "IBertPreTrainedModel", ] ) + _import_structure["models.idefics"].extend( + [ + "IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST", + "IdeficsForVisionText2Text", + "IdeficsModel", + "IdeficsPreTrainedModel", + "IdeficsProcessor", + ] + ) _import_structure["models.imagegpt"].extend( [ "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -4361,6 +4375,10 @@ if TYPE_CHECKING: from .models.herbert import HerbertTokenizer from .models.hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig from .models.ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig + from .models.idefics import ( + IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP, + IdeficsConfig, + ) from .models.imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig from .models.informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig from .models.instructblip import ( @@ -4873,6 +4891,7 @@ if TYPE_CHECKING: from .models.efficientnet import EfficientNetImageProcessor from .models.flava import FlavaFeatureExtractor, FlavaImageProcessor, FlavaProcessor from .models.glpn import GLPNFeatureExtractor, GLPNImageProcessor + from .models.idefics import IdeficsImageProcessor from .models.imagegpt import ImageGPTFeatureExtractor, ImageGPTImageProcessor from .models.layoutlmv2 import LayoutLMv2FeatureExtractor, LayoutLMv2ImageProcessor from .models.layoutlmv3 import LayoutLMv3FeatureExtractor, LayoutLMv3ImageProcessor @@ -5700,6 +5719,13 @@ if TYPE_CHECKING: IBertModel, IBertPreTrainedModel, ) + from .models.idefics import ( + IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST, + IdeficsForVisionText2Text, + IdeficsModel, + IdeficsPreTrainedModel, + IdeficsProcessor, + ) from .models.imagegpt import ( IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST, ImageGPTForCausalImageModeling, diff --git a/src/transformers/image_processing_utils.py b/src/transformers/image_processing_utils.py index 5f935566cd..22253fecfe 100644 --- a/src/transformers/image_processing_utils.py +++ b/src/transformers/image_processing_utils.py @@ -17,9 +17,11 @@ import copy import json import os import warnings -from typing import Any, Dict, Iterable, Optional, Tuple, Union +from io import BytesIO +from typing import Any, Dict, Iterable, List, Optional, Tuple, Union import numpy as np +import requests from .dynamic_module_utils import custom_object_save from .feature_extraction_utils import BatchFeature as BaseBatchFeature @@ -34,10 +36,14 @@ from .utils import ( download_url, is_offline_mode, is_remote_url, + is_vision_available, logging, ) +if is_vision_available(): + from PIL import Image + logger = logging.get_logger(__name__) @@ -508,6 +514,28 @@ class ImageProcessingMixin(PushToHubMixin): cls._auto_class = auto_class + def fetch_images(self, image_url_or_urls: Union[str, List[str]]): + """ + Convert a single or a list of urls into the corresponding `PIL.Image` objects. + + If a single url is passed, the return value will be a single object. If a list is passed a list of objects is + returned. + """ + headers = { + "User-Agent": ( + "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0" + " Safari/537.36" + ) + } + if isinstance(image_url_or_urls, list): + return [self.fetch_images(x) for x in image_url_or_urls] + elif isinstance(image_url_or_urls, str): + response = requests.get(image_url_or_urls, stream=True, headers=headers) + response.raise_for_status() + return Image.open(BytesIO(response.content)) + else: + raise ValueError(f"only a single or a list of entries is supported but got type={type(image_url_or_urls)}") + class BaseImageProcessor(ImageProcessingMixin): def __init__(self, **kwargs): diff --git a/src/transformers/models/__init__.py b/src/transformers/models/__init__.py index 7af9ff766a..c0aa474f3a 100644 --- a/src/transformers/models/__init__.py +++ b/src/transformers/models/__init__.py @@ -101,6 +101,7 @@ from . import ( herbert, hubert, ibert, + idefics, imagegpt, informer, instructblip, diff --git a/src/transformers/models/auto/configuration_auto.py b/src/transformers/models/auto/configuration_auto.py index 7230c3f1fa..0bc71e294d 100755 --- a/src/transformers/models/auto/configuration_auto.py +++ b/src/transformers/models/auto/configuration_auto.py @@ -109,6 +109,7 @@ CONFIG_MAPPING_NAMES = OrderedDict( ("groupvit", "GroupViTConfig"), ("hubert", "HubertConfig"), ("ibert", "IBertConfig"), + ("idefics", "IdeficsConfig"), ("imagegpt", "ImageGPTConfig"), ("informer", "InformerConfig"), ("instructblip", "InstructBlipConfig"), @@ -311,6 +312,7 @@ CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict( ("groupvit", "GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP"), ("hubert", "HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"), ("ibert", "IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"), + ("idefics", "IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP"), ("imagegpt", "IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP"), ("informer", "INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"), ("instructblip", "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP"), @@ -514,6 +516,7 @@ MODEL_NAMES_MAPPING = OrderedDict( ("herbert", "HerBERT"), ("hubert", "Hubert"), ("ibert", "I-BERT"), + ("idefics", "IDEFICS"), ("imagegpt", "ImageGPT"), ("informer", "Informer"), ("instructblip", "InstructBLIP"), diff --git a/src/transformers/models/auto/image_processing_auto.py b/src/transformers/models/auto/image_processing_auto.py index 075fe0c96d..37ccc829de 100644 --- a/src/transformers/models/auto/image_processing_auto.py +++ b/src/transformers/models/auto/image_processing_auto.py @@ -67,6 +67,7 @@ IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict( ("git", "CLIPImageProcessor"), ("glpn", "GLPNImageProcessor"), ("groupvit", "CLIPImageProcessor"), + ("idefics", "IdeficsImageProcessor"), ("imagegpt", "ImageGPTImageProcessor"), ("instructblip", "BlipImageProcessor"), ("layoutlmv2", "LayoutLMv2ImageProcessor"), diff --git a/src/transformers/models/auto/modeling_auto.py b/src/transformers/models/auto/modeling_auto.py index a0c22f5876..f42f5b12dc 100755 --- a/src/transformers/models/auto/modeling_auto.py +++ b/src/transformers/models/auto/modeling_auto.py @@ -106,6 +106,7 @@ MODEL_MAPPING_NAMES = OrderedDict( ("groupvit", "GroupViTModel"), ("hubert", "HubertModel"), ("ibert", "IBertModel"), + ("idefics", "IdeficsModel"), ("imagegpt", "ImageGPTModel"), ("informer", "InformerModel"), ("jukebox", "JukeboxModel"), @@ -246,6 +247,7 @@ MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict( ("gpt_bigcode", "GPTBigCodeForCausalLM"), ("gptsan-japanese", "GPTSanJapaneseForConditionalGeneration"), ("ibert", "IBertForMaskedLM"), + ("idefics", "IdeficsForVisionText2Text"), ("layoutlm", "LayoutLMForMaskedLM"), ("longformer", "LongformerForMaskedLM"), ("luke", "LukeForMaskedLM"), diff --git a/src/transformers/models/auto/processing_auto.py b/src/transformers/models/auto/processing_auto.py index dac242061d..dcd5d9ddf6 100644 --- a/src/transformers/models/auto/processing_auto.py +++ b/src/transformers/models/auto/processing_auto.py @@ -57,6 +57,7 @@ PROCESSOR_MAPPING_NAMES = OrderedDict( ("git", "GitProcessor"), ("groupvit", "CLIPProcessor"), ("hubert", "Wav2Vec2Processor"), + ("idefics", "IdeficsProcessor"), ("instructblip", "InstructBlipProcessor"), ("layoutlmv2", "LayoutLMv2Processor"), ("layoutlmv3", "LayoutLMv3Processor"), diff --git a/src/transformers/models/auto/tokenization_auto.py b/src/transformers/models/auto/tokenization_auto.py index 5d5f194975..f2e4242ef5 100644 --- a/src/transformers/models/auto/tokenization_auto.py +++ b/src/transformers/models/auto/tokenization_auto.py @@ -169,6 +169,7 @@ else: ("herbert", ("HerbertTokenizer", "HerbertTokenizerFast" if is_tokenizers_available() else None)), ("hubert", ("Wav2Vec2CTCTokenizer", None)), ("ibert", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), + ("idefics", (None, "LlamaTokenizerFast" if is_tokenizers_available() else None)), ("instructblip", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("jukebox", ("JukeboxTokenizer", None)), ("layoutlm", ("LayoutLMTokenizer", "LayoutLMTokenizerFast" if is_tokenizers_available() else None)), diff --git a/src/transformers/models/idefics/__init__.py b/src/transformers/models/idefics/__init__.py new file mode 100644 index 0000000000..0263e67f21 --- /dev/null +++ b/src/transformers/models/idefics/__init__.py @@ -0,0 +1,75 @@ +# Copyright 2022 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 ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available + + +_import_structure = {"configuration_idefics": ["IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP", "IdeficsConfig"]} + +try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["image_processing_idefics"] = ["IdeficsImageProcessor"] + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_idefics"] = [ + "IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST", + "IdeficsForVisionText2Text", + "IdeficsGatedCrossAttentionLayer", + "IdeficsModel", + "IdeficsPreTrainedModel", + ] + _import_structure["processing_idefics"] = ["IdeficsProcessor"] + + +if TYPE_CHECKING: + from .configuration_idefics import IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP, IdeficsConfig + + try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .image_processing_idefics import IdeficsImageProcessor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_idefics import ( + IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST, + IdeficsForVisionText2Text, + IdeficsGatedCrossAttentionLayer, + IdeficsModel, + IdeficsPreTrainedModel, + ) + from .processing_idefics import IdeficsProcessor + + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure) diff --git a/src/transformers/models/idefics/configuration_idefics.py b/src/transformers/models/idefics/configuration_idefics.py new file mode 100644 index 0000000000..0d3fa7a589 --- /dev/null +++ b/src/transformers/models/idefics/configuration_idefics.py @@ -0,0 +1,326 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# 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. +""" Idefics model configuration""" + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + +IDEFICS_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "HuggingFaceM4/idefics-9b": "https://huggingface.co/HuggingFaceM4/idefics-9b/blob/main/config.json", + "HuggingFaceM4/idefics-80b": "https://huggingface.co/HuggingFaceM4/idefics-80b/blob/main/config.json", +} + + +class IdeficsVisionConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an + Idefics 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 Idefics-9B. + + e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b) + + 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 768): + Dimensionality of the encoder layers and the pooler layer. (elsewhere referred to as `hidden_size`) + image_size (`int`, *optional*, defaults to 224): + The size (resolution) of each image. + intermediate_size (`int`, *optional*, defaults to 5120): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + patch_size (`int`, *optional*, defaults to 14): + The size (resolution) of each patch. + num_hidden_layers (`int`, *optional*, defaults to 32): + 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_num_channels (`int`, *optional*, defaults to `3`): + Number of image channels. + hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. + layer_norm_eps (`float`, *optional*, defaults to 1e-5): + The epsilon used by the layer normalization layers. + 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 1.0): + A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization + testing). + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + """ + model_type = "idefics" + attribute_map = { + "hidden_size": "embed_dim", + } + + def __init__( + self, + embed_dim=768, + image_size=224, + intermediate_size=5120, + patch_size=14, + num_hidden_layers=32, + num_attention_heads=16, + num_channels=3, + hidden_act="quick_gelu", + layer_norm_eps=1e-5, + attention_dropout=0.0, + initializer_range=0.02, + initializer_factor=1.0, + **kwargs, + ): + self.embed_dim = embed_dim + self.image_size = image_size + self.intermediate_size = intermediate_size + self.patch_size = patch_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.num_channels = num_channels + self.layer_norm_eps = layer_norm_eps + self.attention_dropout = attention_dropout + self.initializer_range = initializer_range + self.initializer_factor = initializer_factor + self.hidden_act = hidden_act + + super().__init__(**kwargs) + + +class IdeficsPerceiverConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an + Idefics 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 Idefics-9B. + + e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b) + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + use_resampler (`bool`, *optional*, defaults to `False`): + Whether or not to use the resampler + resampler_n_latents (`int`, *optional*, defaults to ): + Number of latent embeddings to resample ("compress") the input sequence to (usually < 128). + resampler_depth (`int`, *optional*, defaults to 6): + Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3). + resampler_n_heads (`int`, *optional*, defaults to 16): + Number of heads in each Transformer block (for multi-headed self-attention). + resampler_head_dim (`int`, *optional*, defaults to 96): + Dimensionality of each head projection in the Transformer block. + qk_layer_norms_perceiver (`bool`, *optional*, defaults to `False`): + Whether or not to use qk layer norms in perceiver + """ + model_type = "idefics" + + def __init__( + self, + use_resampler=False, + resampler_n_latents=64, + resampler_depth=6, + resampler_n_heads=16, + resampler_head_dim=96, + qk_layer_norms_perceiver=False, + **kwargs, + ): + self.use_resampler = use_resampler + self.resampler_n_latents = resampler_n_latents + self.resampler_depth = resampler_depth + self.resampler_n_heads = resampler_n_heads + self.resampler_head_dim = resampler_head_dim + self.qk_layer_norms_perceiver = qk_layer_norms_perceiver + + super().__init__(**kwargs) + + +class IdeficsConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an + Idefics 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 Idefics-9B. + + e.g. [HuggingFaceM4/idefics-9b](https://huggingface.co/HuggingFaceM4/idefics-9b) + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + additional_vocab_size (`int`, *optional`, defaults to 0): + Additional vocabulary size of the model, typically for the special "" token. Additional vocab tokens + are always trainable whereas regular vocab tokens can be frozen or not. + vocab_size (`int`, *optional*, defaults to 32000): + Vocabulary size of the Idefics model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`~IdeficsModel`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 11008): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + alpha_initializer (`str`, *optional*, defaults to `"zeros"`): + Initialization type for the alphas. + alphas_initializer_range (`float`, *optional*, defaults to 0.0): + The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross + Attention. + alpha_type (`str`, *optional*, defaults to `"float"`): + Whether the gating alphas should be vectors or single floats. + rms_norm_eps (`float`, *optional*, defaults to 1e-6): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + pad_token_id (`int`, *optional*, defaults to 0) + Padding token id. + bos_token_id (`int`, *optional*, defaults to 1) + Beginning of stream token id. + eos_token_id (`int`, *optional*, defaults to 2) + End of stream token id. + tie_word_embeddings(`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + cross_layer_interval (`int`, *optional*, default to 1) + Interval for cross attention (from text to image) layers. + qk_layer_norms (`bool`, *optional*, defaults to `False`): Whether to add layer norm after q and k + freeze_text_layers (`bool`, *optional*, defaults to `True`): Whether to freeze text layers + freeze_text_module_exceptions (`bool`, *optional*, defaults to `[]`): + Exceptions to freezing text layers when `freeze_text_layers` is `True` + freeze_lm_head (`bool`, *optional*, defaults to `False`): Whether to freeze lm head + freeze_vision_layers (`bool`, *optional*, defaults to `True`): Whether to freeze vision layers + freeze_vision_module_exceptions (`bool`, *optional*, defaults to `[]`): + Exceptions to freezing vision layers when `freeze_vision_layers` is `True` + use_resampler (`bool`, *optional*, defaults to `False`): Whether to use the Resampler + vision_config (`IdeficsVisionConfig`, *optional*): Custom vision config or dict + perceiver_config (`IdeficsPerceiverConfig`, *optional*): Custom perceiver config or dict + + Example: + + ```python + >>> from transformers import IdeficsModel, IdeficsConfig + + >>> # Initializing a Idefics idefics-9b style configuration + >>> configuration = IdeficsConfig() + + >>> # Initializing a model from the idefics-9b style configuration + >>> model = IdeficsModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + model_type = "idefics" + is_composition = False + + def __init__( + self, + vocab_size=32000, + additional_vocab_size=0, + hidden_size=4096, + intermediate_size=11008, + num_hidden_layers=32, + num_attention_heads=32, + dropout=0.0, + hidden_act="silu", + initializer_range=0.02, + alpha_initializer="zeros", + alphas_initializer_range=0.0, + alpha_type="float", + rms_norm_eps=1e-6, + use_cache=True, + pad_token_id=0, + bos_token_id=1, + eos_token_id=2, + tie_word_embeddings=False, + cross_layer_interval=1, + qk_layer_norms=False, + freeze_text_layers=True, + freeze_text_module_exceptions=[], + freeze_lm_head=False, + freeze_vision_layers=True, + freeze_vision_module_exceptions=[], + use_resampler=False, + vision_config=None, + perceiver_config=None, + **kwargs, + ): + self.vocab_size = vocab_size + self.additional_vocab_size = additional_vocab_size + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.dropout = dropout + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.alpha_initializer = alpha_initializer + self.alphas_initializer_range = alphas_initializer_range + self.alpha_type = alpha_type + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + + self.cross_layer_interval = cross_layer_interval + self.qk_layer_norms = qk_layer_norms + self.freeze_vision_layers = freeze_vision_layers + + self.freeze_text_layers = freeze_text_layers + self.freeze_text_module_exceptions = freeze_text_module_exceptions + self.freeze_vision_module_exceptions = freeze_vision_module_exceptions + self.freeze_lm_head = freeze_lm_head + + self.use_resampler = use_resampler + + if perceiver_config is None: + self.perceiver_config = IdeficsPerceiverConfig() + elif isinstance(perceiver_config, dict): + self.perceiver_config = IdeficsPerceiverConfig(**perceiver_config) + elif isinstance(perceiver_config, IdeficsPerceiverConfig): + self.perceiver_config = perceiver_config + + if vision_config is None: + self.vision_config = IdeficsVisionConfig() + elif isinstance(vision_config, dict): + self.vision_config = IdeficsVisionConfig(**vision_config) + elif isinstance(vision_config, IdeficsVisionConfig): + self.vision_config = vision_config + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + # IMPORTANT: Do not do any __init__ args-based checks in the constructor, since + # PretrainedConfig.from_dict first instantiates the class with the config dict and only then + # updates the config object with `kwargs` from from_pretrained, so during the instantiation + # of this object many attributes have default values and haven't yet been overridden. + # Do any required checks inside `from_pretrained` once the superclass' `from_pretrained` was run. diff --git a/src/transformers/models/idefics/image_processing_idefics.py b/src/transformers/models/idefics/image_processing_idefics.py new file mode 100644 index 0000000000..f870147f16 --- /dev/null +++ b/src/transformers/models/idefics/image_processing_idefics.py @@ -0,0 +1,168 @@ +# coding=utf-8 +# Copyright 2022 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. +"""Image processor class for Idefics.""" + +from typing import Callable, Dict, List, Optional, Union + +from PIL import Image + +from ...image_processing_utils import BaseImageProcessor, BatchFeature +from ...image_transforms import resize, to_channel_dimension_format +from ...image_utils import ( + ChannelDimension, + ImageInput, + PILImageResampling, + make_list_of_images, + to_numpy_array, + valid_images, +) +from ...utils import TensorType, is_torch_available + + +IDEFICS_STANDARD_MEAN = [0.48145466, 0.4578275, 0.40821073] +IDEFICS_STANDARD_STD = [0.26862954, 0.26130258, 0.27577711] + + +def convert_to_rgb(image): + # `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background + # for transparent images. The call to `alpha_composite` handles this case + if image.mode == "RGB": + return image + + image_rgba = image.convert("RGBA") + background = Image.new("RGBA", image_rgba.size, (255, 255, 255)) + alpha_composite = Image.alpha_composite(background, image_rgba) + alpha_composite = alpha_composite.convert("RGB") + return alpha_composite + + +class IdeficsImageProcessor(BaseImageProcessor): + r""" + Constructs a Idefics image processor. + + Args: + image_size (`int`, *optional*, defaults to `224`): + Resize to image size + image_num_channels (`int`, *optional*, defaults to `3`): + Number of image channels. + image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`): + Mean to use if normalizing the image. This is a float or list of floats the length of the number of + channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be + overridden by the `image_mean` parameter in the `preprocess` method. + image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`): + Standard deviation to use if normalizing the image. This is a float or list of floats the length of the + number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. + Can be overridden by the `image_std` parameter in the `preprocess` method. + """ + + model_input_names = ["pixel_values"] + + def __init__( + self, + image_size: int = 224, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + image_num_channels: Optional[int] = 3, + **kwargs, + ) -> None: + super().__init__(**kwargs) + + self.image_size = image_size + self.image_num_channels = image_num_channels + self.image_mean = image_mean + self.image_std = image_std + + def preprocess( + self, + images: ImageInput, + image_num_channels: Optional[int] = 3, + image_size: Optional[Dict[str, int]] = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + transform: Callable = None, + **kwargs, + ) -> TensorType.PYTORCH: + """ + Preprocess a batch of images. + + Args: + images (`ImageInput`): + A list of images to preprocess. + image_size (`int`, *optional*, defaults to `self.image_size`): + Resize to image size + image_num_channels (`int`, *optional*, defaults to `self.image_num_channels`): + Number of image channels. + image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`): + Mean to use if normalizing the image. This is a float or list of floats the length of the number of + channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can + be overridden by the `image_mean` parameter in the `preprocess` method. + image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`): + Standard deviation to use if normalizing the image. This is a float or list of floats the length of the + number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` + method. Can be overridden by the `image_std` parameter in the `preprocess` method. + transform (`Callable`, *optional*, defaults to `None`): + A custom transform function that accepts a single image can be passed for training. For example, + `torchvision.Compose` can be used to compose multiple transforms. If `None` - an inference mode is + assumed - and then a preset of inference-specific transforms will be applied to the images + + Returns: + a PyTorch tensor of the processed images + + """ + image_size = image_size if image_size is not None else self.image_size + image_num_channels = image_num_channels if image_num_channels is not None else self.image_num_channels + image_mean = image_mean if image_mean is not None else self.image_mean + image_std = image_std if image_std is not None else self.image_std + size = (image_size, image_size) + + if isinstance(images, list) and len(images) == 0: + return [] + + images = make_list_of_images(images) + + if not valid_images(images): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + + # For training a user needs to pass their own set of transforms as a Callable. + # For reference this is what was used in the original IDEFICS training: + # transform = transforms.Compose([ + # convert_to_rgb, + # transforms.RandomResizedCrop((size, size), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC), + # transforms.ToTensor(), + # transforms.Normalize(mean=image_mean, std=image_std), + # ]) + if transform is not None: + if not is_torch_available(): + raise ImportError("To pass in `transform` torch must be installed") + import torch + + images = [transform(x) for x in images] + return torch.stack(images) + + # for inference we do the exact transforms that were used to train IDEFICS + images = [convert_to_rgb(x) for x in images] + # further transforms expect numpy arrays + images = [to_numpy_array(x) for x in images] + images = [resize(x, size, resample=PILImageResampling.BICUBIC) for x in images] + images = [self.rescale(image=image, scale=1 / 255) for image in images] + images = [self.normalize(x, mean=image_mean, std=image_std) for x in images] + images = [to_channel_dimension_format(x, ChannelDimension.FIRST) for x in images] + # TODO: this converts to torch tensors - switch to convert_to_tensors once it becomes available + images = BatchFeature(data={"pixel_values": images}, tensor_type=TensorType.PYTORCH)["pixel_values"] + + return images diff --git a/src/transformers/models/idefics/modeling_idefics.py b/src/transformers/models/idefics/modeling_idefics.py new file mode 100644 index 0000000000..ed9b255693 --- /dev/null +++ b/src/transformers/models/idefics/modeling_idefics.py @@ -0,0 +1,1582 @@ +# coding=utf-8 +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# 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. +""" PyTorch Idefics model.""" +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss + +from ... import PreTrainedModel +from ...activations import ACT2FN +from ...modeling_outputs import ModelOutput +from ...modeling_utils import PretrainedConfig +from ...utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from .configuration_idefics import IdeficsConfig +from .perceiver import IdeficsPerceiverResampler +from .vision import IdeficsVisionTransformer + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "IdeficsConfig" + +IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "HuggingFaceM4/idefics-9b", + "HuggingFaceM4/idefics-80b", + # See all Idefics models at https://huggingface.co/models?filter=idefics +] + + +@dataclass +class IdeficsBaseModelOutputWithPast(ModelOutput): + """ + Base class for Idefics model's outputs that may also contain a past key/values (to speed up sequential decoding). + + Args: + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + + If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, + hidden_size)` is output. + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if + `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, + encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if + `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` + input) to speed up sequential decoding. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images, + sequence_length, hidden_size)`. + + image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver + """ + + last_hidden_state: torch.FloatTensor = None + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None + + +@dataclass +class IdeficsCausalLMOutputWithPast(ModelOutput): + """ + Base class for Idefics causal language model (or autoregressive) outputs. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images, + sequence_length, hidden_size)`. + + image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver + """ + + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + past_key_values: Optional[List[torch.FloatTensor]] = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None + + +def expand_inputs_for_generation( + input_ids, + expand_size=1, + is_encoder_decoder=False, + attention_mask=None, + encoder_outputs=None, + **model_kwargs, +): + expanded_return_idx = ( + torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, expand_size).view(-1).to(input_ids.device) + ) + input_ids = input_ids.index_select(0, expanded_return_idx) + model_kwargs["pixel_values"] = model_kwargs.get("pixel_values", None) + model_kwargs["image_encoder_embeddings"] = model_kwargs.get("image_encoder_embeddings", None) + model_kwargs["perceiver_embeddings"] = model_kwargs.get("perceiver_embeddings", None) + model_kwargs["image_attention_mask"] = model_kwargs.get("image_attention_mask", None) + + if "token_type_ids" in model_kwargs: + token_type_ids = model_kwargs["token_type_ids"] + model_kwargs["token_type_ids"] = token_type_ids.index_select(0, expanded_return_idx) + + if attention_mask is not None: + model_kwargs["attention_mask"] = attention_mask.index_select(0, expanded_return_idx) + + if model_kwargs["image_attention_mask"] is not None: + model_kwargs["image_attention_mask"] = model_kwargs["image_attention_mask"].index_select( + 0, expanded_return_idx + ) + + if model_kwargs["pixel_values"] is not None: + model_kwargs["pixel_values"] = model_kwargs["pixel_values"].index_select(0, expanded_return_idx) + + elif model_kwargs["image_encoder_embeddings"] is not None: + model_kwargs["image_encoder_embeddings"] = model_kwargs["image_encoder_embeddings"].index_select( + 0, expanded_return_idx + ) + + elif model_kwargs["perceiver_embeddings"] is not None: + model_kwargs["perceiver_embeddings"] = model_kwargs["perceiver_embeddings"].index_select( + 0, expanded_return_idx + ) + + return input_ids, model_kwargs + + +def update_model_kwargs_for_generation(outputs, model_kwargs): + # must have this key set to at least None + if "past_key_values" in outputs: + model_kwargs["past_key_values"] = outputs.past_key_values + else: + model_kwargs["past_key_values"] = None + + # update token_type_ids with last value + if "token_type_ids" in model_kwargs: + token_type_ids = model_kwargs["token_type_ids"] + model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1) + + # update attention masks + if "attention_mask" in model_kwargs: + attention_mask = model_kwargs["attention_mask"] + model_kwargs["attention_mask"] = torch.cat( + [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 + ) + if "image_attention_mask" in model_kwargs: + image_attention_mask = model_kwargs["image_attention_mask"] + last_mask = image_attention_mask[:, -1, :].unsqueeze(1) + model_kwargs["image_attention_mask"] = last_mask + + # Get the precomputed image_hidden_states + model_kwargs["image_hidden_states"] = outputs.image_hidden_states + + return model_kwargs + + +def prepare_inputs_for_generation(input_ids, past_key_values=None, **kwargs): + token_type_ids = kwargs.get("token_type_ids", None) + # only last token for inputs_ids if past is defined in kwargs + if past_key_values: + input_ids = input_ids[:, -1].unsqueeze(-1) + if token_type_ids is not None: + token_type_ids = token_type_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -1].unsqueeze(-1) + + pixel_values = kwargs.get("pixel_values", None) + image_encoder_embeddings = kwargs.get("image_encoder_embeddings", None) + perceiver_embeddings = kwargs.get("perceiver_embeddings", None) + image_attention_mask = kwargs.get("image_attention_mask", None) + + return { + "input_ids": input_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "position_ids": position_ids, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + "pixel_values": pixel_values, + "image_encoder_embeddings": image_encoder_embeddings, + "perceiver_embeddings": perceiver_embeddings, + "image_attention_mask": image_attention_mask, + } + + +def freeze_model(model, module_exceptions=[]): + mapping = { + "LayerNorm": nn.LayerNorm, + "Linear": nn.Linear, + "Embedding": nn.Embedding, + } + module_exceptions_mapped = [mapping[m] for m in module_exceptions] + for module in model.modules(): + if module_exceptions and any([isinstance(module, t) for t in module_exceptions_mapped]): + module.requires_grad_(True) # Explicitely setting it to true to avoid any mistakes + else: + module.requires_grad_(False) + return model + + +class IdeficsDecoupledEmbedding(nn.Embedding): + # Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html#Embedding + """ + Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings. In practise, the + regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0, + then it will create `num_additional_embeddings` additional parameters that are always trained. If + `num_additional_embeddings=0`, then the module defaults back to the regular behavior of `nn.Embedding`. + """ + + def __init__( + self, + num_embeddings, + num_additional_embeddings, + embedding_dim, + partially_freeze: Optional[bool] = False, + device=None, + dtype=None, + padding_idx=None, + **kwargs, + ) -> None: + """ + Args: + num_embeddings (`int`): + Size of the dictionary of embeddings + num_additional_embeddings (`int`): + Number of additional embeddings. Only useful when you `partially_freeze=True`. + embedding_dim (`int`): + The size of each embedding vector + partially_freeze: (`bool`, *optional*, defaults to `False`): + If `True`, the regular `weight` will be frozen. `additional_weight` is never frozen. + padding_idx (`int`, *optional*): + The padding index (needs to be less than num_embeddings) + + Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`, + `max_norm` or `norm_type`. We are not supporting these. + """ + if padding_idx is not None and padding_idx > num_embeddings: + raise ValueError(f"padding_idx must be within num_embeddings. Got {padding_idx} and {num_embeddings}") + super().__init__( + num_embeddings=num_embeddings, + embedding_dim=embedding_dim, + device=device, + dtype=dtype, + padding_idx=padding_idx, + **kwargs, + ) + self.num_embeddings = num_embeddings + self.padding_idx = padding_idx + self.num_additional_embeddings = num_additional_embeddings + self.partially_freeze = partially_freeze + + if partially_freeze: + self.weight.requires_grad_(False) + + if self.num_additional_embeddings > 0: + self.additional_embedding = nn.Embedding( + num_embeddings=self.num_additional_embeddings, + embedding_dim=embedding_dim, + device=device, + dtype=dtype, + ) + + def forward(self, input_ids): + """ + we have 2 embeddings, with different indices - one pretrained self.weight and another + self.additional_embedding.weight that is being trained. + + in order to make a lookup of the input ids, we: + 1. find out the indices of the entries belonging to the 2nd embedding + 2. extract those values while subtracting the size of the first embedding (num_embeddings), since the 2nd + embedding starts from 0 and not num_embeddings + 3. perform the 2nd embedding lookup + 4. now we handle the 1st embedding, we overwrite indices belonging to the 2nd embedding with a padding index + 5. perform the 1st embedding lookup + 6. now we overwrite the values in the 1st embedding lookup with the values of the 2nd embedding lookup + + note: for the 1st embedding lookup we could have looked up only the low indices and not do the padding, but + then we have to create a new tensor and populate it with 2 tensors that are spread out across various indices - + i.e. not a simple concat - I haven't benchmarked the complex case if it's any faster, given that seqlens are + usually relatively short it's probably not faster or if faster not by much - but might be a good idea to + measure. + + """ + if self.num_additional_embeddings == 0: + return F.embedding(input_ids, self.weight) + + # Clone so that we don't modify the original input_ids later on + input_ids = input_ids.clone() + additional_vocab_indices = torch.where(input_ids >= self.num_embeddings) + input_ids_additional_vocab = input_ids[additional_vocab_indices] + additional_embeddings = self.additional_embedding(input_ids_additional_vocab - self.num_embeddings) + + # for successful lookup replace input_ids with 0, the results of these will be discarded anyway + input_ids[additional_vocab_indices] = 0 + full_vector = F.embedding(input_ids, self.weight) + + # overwrite the records with high indices + full_vector[additional_vocab_indices] = additional_embeddings + + return full_vector + + def extra_repr(self) -> str: + return "num_embeddings={}, num_additional_embeddings={}, embedding_dim={}, partially_freeze={}".format( + self.num_embeddings, + self.num_additional_embeddings, + self.embedding_dim, + self.partially_freeze, + ) + + +class IdeficsDecoupledLinear(nn.Linear): + # Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear + """ + Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the + regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `out_additional_features` > 0, + then it will create `out_additional_features * in_features` additional parameters that are always trained. If + `out_additional_features=0`, then the module defaults back to the regular behavior of `nn.Linear`. + """ + + def __init__( + self, + in_features: int, + out_features: int, + out_additional_features: int = 0, + bias: bool = True, + partially_freeze: bool = True, + device=None, + dtype=None, + ) -> None: + """ + out_additional_features: int. Number of additional trainable dimensions. Only makes sense when + `partially_freeze=True`. partially_freeze: bool. If True, the regular `weight` will be frozen and extra + parameters (if any) will be trainable. If False, default to the regular behavior of nn.Linear. + """ + super().__init__(in_features, out_features, bias, device, dtype) + self.out_additional_features = out_additional_features + self.partially_freeze = partially_freeze + + self.in_features = in_features + self.out_features = out_features + + if partially_freeze: + self.weight.requires_grad_(False) + if bias: + self.bias.requires_grad_(False) + + if out_additional_features > 0: + self.additional_fc = nn.Linear( + in_features=in_features, + out_features=out_additional_features, + bias=bias, + device=device, + dtype=dtype, + ) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + output = F.linear(input, self.weight, self.bias) + + if self.out_additional_features > 0: + additional_features = F.linear(input, self.additional_fc.weight, self.additional_fc.bias) + output = torch.cat((output, additional_features), -1) + + return output + + def extra_repr(self) -> str: + """Overwriting `nn.Linear.extra_repr` to include new parameters.""" + return "in_features={}, out_features={}, out_additional_features={}, bias={}, partially_freeze={}".format( + self.in_features, + self.out_features, + self.out_additional_features, + self.bias is not None, + self.partially_freeze, + ) + + +# Copied from transformers.models.bart.modeling_bart._make_causal_mask +def _make_causal_mask( + input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 +): + """ + Make causal mask used for bi-directional self-attention. + """ + bsz, tgt_len = input_ids_shape + mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) + mask_cond = torch.arange(mask.size(-1), device=device) + mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) + mask = mask.to(dtype) + + if past_key_values_length > 0: + mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) + return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) + + +def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): + """ + Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. + """ + bsz, src_len = mask.size() + tgt_len = tgt_len if tgt_len is not None else src_len + + expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) + + inverted_mask = 1.0 - expanded_mask + + return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) + + +# this was adapted from LlamaRMSNorm +class IdeficsRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + IdeficsRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + + # convert into half-precision if necessary + if self.weight.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.to(self.weight.dtype) + + return self.weight * hidden_states + + +# this was adapted from LlamaRotaryEmbedding +class IdeficsEmbedding(torch.nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): + super().__init__() + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim)) + self.register_buffer("inv_freq", inv_freq) + + # Build here to make `torch.jit.trace` work. + self.max_seq_len_cached = max_position_embeddings + t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case. + if seq_len > self.max_seq_len_cached: + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1).to(x.device) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False) + return ( + self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + ) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids): + gather_indices = position_ids[:, None, :, None] # [bs, 1, seq_len, 1] + gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3]) + cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices) + sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +# this was adapted from LlamaMLP +class IdeficsMLP(nn.Module): + def __init__( + self, + hidden_size: int, + intermediate_size: int, + hidden_act: str, + ): + super().__init__() + self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False) + self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False) + self.act_fn = ACT2FN[hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +# this was adapted from LlamaAttention +class IdeficsAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + hidden_size: int, + num_heads: int, + dropout: float = 0.0, + is_cross_attention: bool = False, + config: PretrainedConfig = None, + qk_layer_norms: bool = False, + ): + super().__init__() + self.hidden_size = hidden_size + self.num_heads = num_heads + self.head_dim = hidden_size // num_heads + self.dropout = dropout + + if (self.head_dim * num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {num_heads})." + ) + + self.is_cross_attention = is_cross_attention + + if not hasattr(nn.functional, "scaled_dot_product_attention"): + raise ValueError("this model requires pytorch 2.0 or higher") + + if self.is_cross_attention: + kv_input_dim = ( + self.hidden_size if not hasattr(config.vision_config, "embed_dim") else config.vision_config.embed_dim + ) + self.q_proj = nn.Linear( + self.hidden_size, + num_heads * self.head_dim, + bias=False, + ) + self.k_proj = nn.Linear(kv_input_dim, num_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear( + kv_input_dim, + num_heads * self.head_dim, + bias=False, + ) + else: + self.q_proj = nn.Linear( + self.hidden_size, + num_heads * self.head_dim, + bias=False, + ) + self.k_proj = nn.Linear( + self.hidden_size, + num_heads * self.head_dim, + bias=False, + ) + self.v_proj = nn.Linear( + self.hidden_size, + num_heads * self.head_dim, + bias=False, + ) + self.o_proj = nn.Linear( + num_heads * self.head_dim, + hidden_size, + bias=False, + ) + self.rotary_emb = IdeficsEmbedding(self.head_dim) + + self.qk_layer_norms = qk_layer_norms + if self.qk_layer_norms: + self.q_layer_norm = IdeficsRMSNorm(self.head_dim, eps=config.rms_norm_eps) + self.k_layer_norm = IdeficsRMSNorm(self.head_dim, eps=config.rms_norm_eps) + + 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, + key_value_states: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + # if key_value_states are provided this layer is used as a cross-attention layer + is_cross_attention = self.is_cross_attention or key_value_states is not None + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + if not is_cross_attention: + key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + else: + _, kv_len, _ = key_value_states.size() # Note that, in this case, `kv_len` == `kv_seq_len` + key_states = self.k_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = ( + self.v_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2) + ) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + if not is_cross_attention: + cos, sin = self.rotary_emb(value_states, seq_len=max(kv_seq_len, q_len)) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + # [bsz, nh, t, hd] + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + if self.qk_layer_norms: + query_states = self.q_layer_norm(query_states) + key_states = self.k_layer_norm(key_states) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + attn_output = nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=attention_mask, + dropout_p=self.dropout, + ) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + attn_weights = None + if output_attentions: + logger.warning_once( + "attn_weights are not extracted in scaled_dot_product_attention. The model returns None instead" + ) + + return attn_output, attn_weights, past_key_value + + +# this was adapted from LlamaDecoderLayer +class IdeficsDecoderLayer(nn.Module): + def __init__(self, config: IdeficsConfig): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = IdeficsAttention( + hidden_size=self.hidden_size, + num_heads=config.num_attention_heads, + dropout=config.dropout, + config=config, + ) + self.mlp = IdeficsMLP( + hidden_size=self.hidden_size, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + ) + self.input_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.dropout = config.dropout + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + 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`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +class IdeficsGatedCrossAttentionLayer(nn.Module): + def __init__(self, config: IdeficsConfig): + super().__init__() + self.hidden_size = config.hidden_size + self.cross_attn = IdeficsAttention( + hidden_size=self.hidden_size, + num_heads=config.num_attention_heads, + is_cross_attention=True, + dropout=config.dropout, + config=config, + qk_layer_norms=config.qk_layer_norms, + ) + self.mlp = IdeficsMLP( + hidden_size=self.hidden_size, + intermediate_size=config.intermediate_size, + hidden_act=config.hidden_act, + ) + self.input_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.config = config.dropout + + self.act_cross_attn = nn.Tanh() + self.act_dense = nn.Tanh() + + if config.alpha_initializer == "zeros": + if config.alpha_type == "vector": + self.alpha_cross_attn = nn.Parameter(torch.zeros(1, 1, self.hidden_size)) + self.alpha_dense = nn.Parameter(torch.zeros(1, 1, self.hidden_size)) + elif config.alpha_type == "float": + self.alpha_cross_attn = nn.Parameter(torch.zeros(1)) + self.alpha_dense = nn.Parameter(torch.zeros(1)) + else: + raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})") + + elif config.alpha_initializer == "ones": + if config.alpha_type == "vector": + self.alpha_cross_attn = nn.Parameter(torch.ones(1, 1, self.hidden_size)) + self.alpha_dense = nn.Parameter(torch.ones(1, 1, self.hidden_size)) + elif config.alpha_type == "float": + self.alpha_cross_attn = nn.Parameter(torch.ones(1)) + self.alpha_dense = nn.Parameter(torch.ones(1)) + else: + raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})") + + elif config.alpha_initializer in {"normal", "gaussian", "random"}: + if config.alpha_type == "vector": + self.alpha_cross_attn = nn.Parameter( + torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size)) + ) + self.alpha_dense = nn.Parameter( + torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1, 1, self.hidden_size)) + ) + elif config.alpha_type == "float": + self.alpha_cross_attn = nn.Parameter( + torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1)) + ) + self.alpha_dense = nn.Parameter(torch.normal(mean=0.0, std=config.alphas_initializer_range, size=(1))) + else: + raise ValueError(f"Unknown value for `alpha_type` ({config.alpha_type})") + + else: + raise NotImplementedError(f"Alpha initialization scheme {config.alpha_initializer} not yet implemented!") + + if not (hasattr(self, "alpha_cross_attn") and hasattr(self, "alpha_dense")): + raise ValueError("Alpha parameters not initialized correctly!") + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + image_hidden_states: Optional[torch.Tensor] = None, + image_attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + no_images: Optional[bool] = False, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + 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`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + no_images (`bool`, *optional*, defaults to `False`): If `True` the vision part is ignored + """ + if image_hidden_states is None: + raise ValueError( + "`image_hidden_states` is required for Idefics cross attention module which are visual features to be" + " conditioned on." + ) + + if past_key_value is not None: + raise NotImplementedError("Past key value states are not implemented for Idefics cross attention module.") + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.cross_attn( + hidden_states=hidden_states, + key_value_states=image_hidden_states, + attention_mask=image_attention_mask, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training) + # when there are no images the model is used in pure language mode + gate = 0 if no_images else 1 + hidden_states = residual + gate * self.act_cross_attn(self.alpha_cross_attn) * hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training) + hidden_states = residual + self.act_dense(self.alpha_dense) * hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +LLAMA_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 ([`IdeficsConfig`]): + 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. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class IdeficsPreTrainedModel(PreTrainedModel): + config_class = IdeficsConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["IdeficsDecoderLayer", "IdeficsGatedCrossAttentionLayer"] + + def _init_weights(self, module): + # important: this ported version of Idefics isn't meant for training from scratch - only + # inference and fine-tuning - so the proper init weights code has been removed - the m4 code + # base should be used for training from scratch and it contains the correct code. + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, IdeficsModel): + module.gradient_checkpointing = value + + +LLAMA_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) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape + `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used (see `past_key_values` input) to speed up sequential 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)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + 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 (`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. +""" + + +@add_start_docstrings( + "The bare LLaMA Model outputting raw hidden-states without any specific head on top.", + LLAMA_START_DOCSTRING, +) +class IdeficsModel(IdeficsPreTrainedModel): + """ + Transformer decoder consisting of `config.num_hidden_layers` layers. Each layer is a [`IdeficsDecoderLayer`] + + Args: + config: IdeficsConfig + """ + + def __init__(self, config: IdeficsConfig): + super().__init__(config) + self.config = config + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = IdeficsDecoupledEmbedding( + num_embeddings=config.vocab_size, + num_additional_embeddings=config.additional_vocab_size, + embedding_dim=config.hidden_size, + partially_freeze=config.freeze_text_layers, + padding_idx=self.padding_idx, + ) + + self.image_size = config.vision_config.image_size + self.vision_config = config.vision_config + self.vision_model = IdeficsVisionTransformer(config.vision_config) + + # Perceiver Resampler + if config.use_resampler: + perceiver_config = config.perceiver_config + self.perceiver_resampler = IdeficsPerceiverResampler( + config, + config.vision_config.embed_dim, + perceiver_config.resampler_depth, + perceiver_config.resampler_n_heads, + perceiver_config.resampler_head_dim, + perceiver_config.resampler_n_latents, + ) + + self.layers = nn.ModuleList([IdeficsDecoderLayer(config) for _ in range(config.num_hidden_layers)]) + + self.cross_layer_interval = config.cross_layer_interval + num_cross_layers = config.num_hidden_layers // self.cross_layer_interval + self.gated_cross_attn_layers = nn.ModuleList( + [IdeficsGatedCrossAttentionLayer(config) for _ in range(num_cross_layers)] + ) + self.gradient_checkpointing = False + + self.norm = IdeficsRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + self.freeze_relevant_params(config) + + def freeze_relevant_params(self, config=None): + if config is None: + config = self.config + + if config.freeze_text_layers: + self.freeze_text_layers(config.freeze_text_module_exceptions) + + if config.freeze_vision_layers: + freeze_model(self.vision_model, module_exceptions=config.freeze_vision_module_exceptions) + + def freeze_text_layers(self, module_exceptions=[]): + for module in [self.layers, self.norm]: + freeze_model(module, module_exceptions=module_exceptions) + + def freeze_vision_layers(self, module_exceptions=[]): + freeze_model(self.vision_model, module_exceptions=module_exceptions) + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask + def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): + # create causal mask + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + combined_attention_mask = None + if input_shape[-1] > 1: + combined_attention_mask = _make_causal_mask( + input_shape, + inputs_embeds.dtype, + device=inputs_embeds.device, + past_key_values_length=past_key_values_length, + ) + + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( + inputs_embeds.device + ) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask + ) + + return combined_attention_mask + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + image_encoder_embeddings: Optional[torch.FloatTensor] = None, + perceiver_embeddings: Optional[torch.FloatTensor] = None, + image_attention_mask: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, IdeficsBaseModelOutputWithPast]: + device = input_ids.device if input_ids is not None else inputs_embeds.device + + 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 + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + seq_length_with_past = seq_length + past_key_values_length = 0 + + if past_key_values is not None: + past_key_values_length = past_key_values[0][0].shape[2] + seq_length_with_past = seq_length_with_past + past_key_values_length + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + elif position_ids is None: + device = input_ids.device if input_ids is not None else inputs_embeds.device + position_ids = torch.arange( + past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device + ) + position_ids = position_ids.unsqueeze(0).view(-1, seq_length) + else: + position_ids = position_ids.view(-1, seq_length).long() + + no_images = False + if (pixel_values, image_encoder_embeddings, perceiver_embeddings).count(None) != 2: + raise ValueError( + "Exactly 1 of pixel_values, image_encoder_embeddings or perceiver_embeddings has to be not-None." + ) + + elif pixel_values is not None: + no_images = len(torch.nonzero(pixel_values)) == 0 + pixel_values = pixel_values.to(dtype=self.dtype, device=device) # fp16 compatibility + batch_size, num_images = pixel_values.shape[:2] + pixel_values = pixel_values.contiguous().view(batch_size * num_images, *pixel_values.shape[2:]) + + # Get sequence from the vision encoder + image_hidden_states = self.vision_model(pixel_values=pixel_values).last_hidden_state + + elif image_encoder_embeddings is not None: + batch_size, num_images, image_seq_len, image_hidden_size = image_encoder_embeddings.size() + image_hidden_states = image_encoder_embeddings.to(dtype=self.dtype, device=input_ids.device) + image_hidden_states = image_hidden_states.view(batch_size * num_images, image_seq_len, image_hidden_size) + + if self.config.use_resampler: + if perceiver_embeddings is None: + perceiver_embeddings = self.perceiver_resampler(image_hidden_states) + image_seq_len, image_hidden_size = perceiver_embeddings.size(1), perceiver_embeddings.size(2) + else: + batch_size, num_images, image_seq_len, image_hidden_size = perceiver_embeddings.size() + image_hidden_states = perceiver_embeddings + elif perceiver_embeddings is None: + image_seq_len, image_hidden_size = image_hidden_states.size(1), image_hidden_states.size(2) + else: + raise ValueError("If `perceiver_embeddings` are passed, use_resampler should be True") + + image_hidden_states = image_hidden_states.view(batch_size, num_images * image_seq_len, image_hidden_size) + # # Hack to use the model in full language modeling mode + # image_attention_mask = torch.zeros(batch_size, seq_length, 1, dtype=torch.long, device=image_hidden_states.device) + # Make image_attention_mask compatible with hidden states + text_seq_len = image_attention_mask.size(1) + image_attention_mask = image_attention_mask.unsqueeze(-1) + image_attention_mask = image_attention_mask.repeat(1, 1, 1, image_seq_len) + image_attention_mask = image_attention_mask.view(batch_size, text_seq_len, num_images * image_seq_len) + + if image_hidden_states is not None: + image_batch_size, image_sequence_length, _ = image_hidden_states.size() + image_hidden_shape = (image_batch_size, image_sequence_length) + if image_attention_mask is None: + image_attention_mask = torch.ones(image_hidden_shape, device=device) + image_attention_mask = self.invert_attention_mask(image_attention_mask) + else: + image_attention_mask = None + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + # embed positions + if attention_mask is None: + attention_mask = torch.ones( + (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device + ) + attention_mask = self._prepare_decoder_attention_mask( + attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length + ) + + hidden_states = inputs_embeds + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = () if use_cache else None + + for idx, decoder_layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states += (hidden_states,) + + past_key_value = past_key_values[idx] if past_key_values is not None else None + + def vblock( + main_block, + hidden_states, + attention_mask, + position_ids, + past_key_value, + image_hidden_states, + image_attention_mask, + output_attentions, + use_cache, + no_images, + layer_idx, + cross_layer_interval, + gated_cross_attn_layers, + ): + # TODO(ls): Add cross attention values to respective lists + if layer_idx % cross_layer_interval == 0: + xblock = gated_cross_attn_layers[layer_idx // cross_layer_interval] + outputs = xblock( + hidden_states, + attention_mask=attention_mask, + image_hidden_states=image_hidden_states, + image_attention_mask=image_attention_mask, + output_attentions=output_attentions, + use_cache=use_cache, + past_key_value=None, # not implemented + no_images=no_images, + ) + hidden_states = outputs[0] + + layer_outputs = main_block( + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + ) + + return layer_outputs + + if self.gradient_checkpointing and self.training: + past_key_value = None + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + layer_outputs = torch.utils.checkpoint.checkpoint( + vblock, + decoder_layer, + hidden_states, + attention_mask, + position_ids, + past_key_value, + image_hidden_states, + image_attention_mask, + output_attentions, + use_cache, + no_images, + idx, + self.cross_layer_interval, + self.gated_cross_attn_layers, + ) + else: + layer_outputs = vblock( + decoder_layer, + hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + image_hidden_states=image_hidden_states, + image_attention_mask=image_attention_mask, + output_attentions=output_attentions, + use_cache=use_cache, + no_images=no_images, + layer_idx=idx, + cross_layer_interval=self.cross_layer_interval, + gated_cross_attn_layers=self.gated_cross_attn_layers, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + image_hidden_states = image_hidden_states.view(batch_size, num_images, image_seq_len, image_hidden_size) + if not return_dict: + return tuple( + v + for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, image_hidden_states] + if v is not None + ) + return IdeficsBaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + image_hidden_states=image_hidden_states, + ) + + +class IdeficsForVisionText2Text(IdeficsPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"lm_head.weight"] + _tied_weights_keys = ["model.embed_tokens.weight", "lm_head.weight"] + + def __init__(self, config, vision_model=None): + super().__init__(config) + self.model = IdeficsModel(config) + + self.lm_head = IdeficsDecoupledLinear( + in_features=config.hidden_size, + out_features=config.vocab_size, + out_additional_features=config.additional_vocab_size, + bias=False, + partially_freeze=config.freeze_lm_head, + ) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def tie_weights(self): + """ + Overwrite `transformers.modeling_utils.PreTrainedModel.tie_weights` to handle the case of + IdeficsDecoupledLinear and IdeficsDecoupledEmbedding. + """ + output_embeddings = self.get_output_embeddings() + input_embeddings = self.get_input_embeddings() + + if getattr(self.config, "tie_word_embeddings", True): + output_embeddings.weight = input_embeddings.weight + if input_embeddings.num_additional_embeddings > 0: + assert output_embeddings.out_additional_features == input_embeddings.num_additional_embeddings + output_embeddings.additional_fc.weight = input_embeddings.additional_embedding.weight + + if hasattr(output_embeddings, "out_features") and hasattr(input_embeddings, "num_embeddings"): + output_embeddings.out_features = input_embeddings.num_embeddings + if hasattr(output_embeddings, "out_additional_features") and hasattr( + input_embeddings, "num_additional_embeddings" + ): + output_embeddings.out_additional_features = input_embeddings.num_additional_embeddings + + @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=IdeficsCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + image_encoder_embeddings: Optional[torch.FloatTensor] = None, + perceiver_embeddings: Optional[torch.FloatTensor] = None, + image_attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, IdeficsCausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, LlamaForCausalLM + + >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) + >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) + + >>> prompt = "Hey, are you consciours? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." + ```""" + + 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 + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + pixel_values=pixel_values, + image_encoder_embeddings=image_encoder_embeddings, + perceiver_embeddings=perceiver_embeddings, + image_attention_mask=image_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + if attention_mask is not None: + shift_attention_mask = attention_mask[..., 1:] + shift_logits = logits[..., :-1, :][shift_attention_mask != 0].contiguous() + shift_labels = labels[..., 1:][shift_attention_mask != 0].contiguous() + else: + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return IdeficsCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=outputs.image_hidden_states, + ) + + def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs): + image_hidden_states = kwargs.pop("image_hidden_states", None) + if image_hidden_states is not None: + if self.config.use_resampler: + kwargs["perceiver_embeddings"] = image_hidden_states + else: + kwargs["image_encoder_embeddings"] = image_hidden_states + kwargs["pixel_values"] = None + inputs = prepare_inputs_for_generation(input_ids, past=past, **kwargs) + unwanted_kwargs = ["token_type_ids"] + for kwarg in unwanted_kwargs: + inputs.pop(kwarg, None) + return inputs + + @staticmethod + def _expand_inputs_for_generation( + *args, + **model_kwargs, + ): + return expand_inputs_for_generation(*args, **model_kwargs) + + @staticmethod + def _update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder): + return update_model_kwargs_for_generation(outputs, model_kwargs) + + @staticmethod + def _reorder_cache(past, beam_idx): + reordered_past = () + for layer_past in past: + reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) + return reordered_past diff --git a/src/transformers/models/idefics/perceiver.py b/src/transformers/models/idefics/perceiver.py new file mode 100644 index 0000000000..888c5b0bb9 --- /dev/null +++ b/src/transformers/models/idefics/perceiver.py @@ -0,0 +1,188 @@ +# This code was adapted from https://github.com/lucidrains/flamingo-pytorch licensed under the MIT License. +# +# MIT License +# +# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + + +""" + +Generic interface to various configurations of the Perceiver Resampler, that simply takes in a series of (potentially +time-indexed) contextual embeddings, and "resamples" (compresses) them down to a pre-specified number of latents! Note +that the Perceiver in general resamples based solely off the *long-range* context; there's a nice opportunity here to +prime the Perceiver Resampler with say a single layer's worth of language embeddings (the target domain), and use that +to softly "retrieve & compress" what we need --> this would be a novel contribution we should explore. + +References: + - DeepMind's Flamingo: https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model + - Code borrowed w/ love from: https://github.com/lucidrains/flamingo-pytorch + +""" +from typing import Optional, Tuple + +import torch +import torch.nn as nn + +from .configuration_idefics import IdeficsConfig + + +class IdeficsPerceiverResampler(nn.Module): + def __init__( + self, config: IdeficsConfig, embed_dim: int, depth: int, n_heads: int, head_dim: int, n_latents: int + ) -> None: + """ + Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a ResNet or ViT or + MAE) of a given dimension, performs `depth` blocks of cross-attention with a fixed `n_latents` inputs, then + returns a Tensor of shape [bsz, n_latents, embed_dim]. :param embed_dim: Dimensionality of embeddings being fed + to the Perceiver Resampler (also dimensionality of latent embeddings *returned* by the Perceiver Resampler. + Could be e.g., VIT embed_dim, ResNet pool dim, and so on. + + Args: + config (`IdeficsConfig`): config object + embed_dim (`int`): The size of each embedding vector + depth (`int`): Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3). + n_heads (`int`): Number of heads in each Transformer block (for multi-headed self-attention). + head_dim (`int`): Dimensionality of each head projection in the Transformer block. + n_latents (`int`): + Number of latent embeddings to resample ("compress") the input sequence to (usually < 128). + + """ + super().__init__() + self.embed_dim, self.n_heads, self.head_dim, self.n_latents = embed_dim, n_heads, head_dim, n_latents + self.qk_layer_norms = config.perceiver_config.qk_layer_norms_perceiver + + # Create Latents for Perceiver + self.latents = nn.Parameter(torch.randn(self.n_latents, self.embed_dim), requires_grad=True) + + self.intermediate_dim = ( + self.embed_dim * 4 + if not hasattr(config.vision_config, "embed_dim") + else config.vision_config.embed_dim * 4 + ) + # Create Transformer Blocks + self.blocks = nn.ModuleList( + [ + nn.ModuleList( + [ + IdeficsPerceiverAttention(self.embed_dim, self.n_heads, self.head_dim, self.qk_layer_norms), + IdeficsMLP(self.intermediate_dim, config), + ] + ) + for _ in range(depth) + ] + ) + self.layer_norm = nn.LayerNorm(self.embed_dim) + + def forward(self, context: torch.Tensor) -> torch.Tensor: + """Resample arbitrary length context & *compress* down to self.n_latents latent embeddings""" + # einsum.repeat(self.latents, "seq embed -> bsz seq embed", bsz=context.shape[0]) + latents = self.latents.repeat(context.shape[0], 1, 1) + + # Feed through Perceiver Attention blocks... + for attn, ff in self.blocks: + latents = attn(context, latents) + latents + latents = ff(latents) + latents + + return self.layer_norm(latents) + + +class IdeficsPerceiverAttention(nn.Module): + def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool) -> None: + """Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`""" + super().__init__() + self.embed_dim, self.n_heads, self.head_dim = embed_dim, n_heads, head_dim + self.qk_layer_norms = qk_layer_norms + # Normalization & Scaling + self.context_layer_norm = nn.LayerNorm(self.embed_dim) + self.latents_layer_norm = nn.LayerNorm(self.embed_dim) + if self.qk_layer_norms: + self.q_layer_norm = nn.LayerNorm(self.head_dim) + self.k_layer_norm = nn.LayerNorm(self.head_dim) + + self.qk_scale = self.head_dim**-0.5 + + # Q, K, V Projection (no bias -- detail from Perceiver/Flamingo Papers). + self.q_proj = nn.Linear(self.embed_dim, self.n_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(self.embed_dim, self.n_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(self.embed_dim, self.n_heads * self.head_dim, bias=False) + + self.output_proj = nn.Linear(self.n_heads * self.head_dim, embed_dim, bias=False) + + def forward(self, context: torch.Tensor, latents: torch.Tensor) -> torch.Tensor: + """ + Runs Perceiver Self-Attention, with special (context, latents) appended along the `seq` dimension! + + Args: + context (`torch.Tensor`): + Tensor of shape `[bsz, seq, embed_dim]` representing long-form context to resample. + latents (`torch.Tensor`): + Tensor of shape `[bsz, n_latents, embed_dim]` representing fixed length latents to compress to. + + Returns: + `torch.Tensor`: Tensor of shape `[bsz, n_latents, embed_dim]` representing attention over latents w/ cross + from context. + """ + context = self.context_layer_norm(context) + latents = self.latents_layer_norm(latents) + batch_size, seq_length, embed_dim = context.shape[:3] + + # Query, Key, Value Projections --> Note that in Flamingo, latents are *concatenated* with context prior to attn! + # Note: This results in queries w/ `seq = n_latents`, and keys, values with `seq = len(context) + n_latents` + q = self.q_proj(latents) + k = self.k_proj(torch.cat([context, latents], dim=-2)) + v = self.v_proj(torch.cat([context, latents], dim=-2)) + + # Multiheaded Self-Attention w/ stable softmax (subtract per-row max -- `amax` -- before softmax call) + # =>> `attn` should be a 2D matrix of shape [n_latents x (context + n_latents)] + # einsum.rearrange(x, "bsz seq (heads embed) -> bsz heads seq embed", heads=self.n_heads) + q, k, v = [x.reshape(batch_size, x.shape[1], self.n_heads, self.head_dim).transpose(1, 2) for x in (q, k, v)] + + if self.qk_layer_norms: + q = self.q_layer_norm(q) + k = self.k_layer_norm(k) + + scores = torch.einsum("... i d, ... j d -> ... i j", q * self.qk_scale, k) + stabilized_scores = scores - (scores.amax(dim=-1, keepdim=True).detach()) + attn = stabilized_scores.softmax(dim=-1) + + # Attend & project back to output... + resampled = torch.einsum("... i j, ... j d -> ... i d", attn, v) + # einsum.rearrange(resampled, "bsz heads seq embed -> bsz seq (heads embed)", heads=self.n_heads) + return self.output_proj(resampled.transpose(1, 2).flatten(-2)) + + +class IdeficsMLP(nn.Module): + def __init__(self, intermediate_size, config: IdeficsConfig): + """Simple MLP block with intermediate_size and embedding size""" + super().__init__() + self.embed_dim = config.vision_config.embed_dim + self.ln = nn.LayerNorm(self.embed_dim) + self.fc = nn.Linear(self.embed_dim, intermediate_size, bias=False) + self.act = nn.ReLU() + self.c_proj = nn.Linear(intermediate_size, self.embed_dim, bias=False) + + def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor: + hidden_states = self.ln(hidden_states) + hidden_states = self.fc(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.c_proj(hidden_states) + + return hidden_states diff --git a/src/transformers/models/idefics/processing_idefics.py b/src/transformers/models/idefics/processing_idefics.py new file mode 100644 index 0000000000..c1d8485c53 --- /dev/null +++ b/src/transformers/models/idefics/processing_idefics.py @@ -0,0 +1,412 @@ +# coding=utf-8 +# Copyright 2022 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. +""" +Processor class for IDEFICS. +""" + +from typing import Callable, List, Optional, Union +from urllib.parse import urlparse + +from ...feature_extraction_utils import BatchFeature +from ...processing_utils import ProcessorMixin +from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy +from ...utils import TensorType, is_torch_available + + +if is_torch_available(): + import torch + + +IMAGE_TOKEN = "" + + +# copied from m4.training.packing +def incremental_to_binary_attention_mask(incremental_mask, num_classes=-1): + # This function converts: [-1, 0, 1] => [[0, 0], [1, 0], [0, 1]] + + # If any of images index are more than num_classes, set them to -1. + # Words after the max number of images allowed have been seen don't attend on anything + if num_classes != -1: + incremental_mask[incremental_mask >= num_classes] = -1 + + negatives = incremental_mask == -1 + incremental_mask[negatives] = 0 + attn_mask = torch.nn.functional.one_hot(incremental_mask, num_classes=num_classes) + attn_mask[negatives, :] = 0 + return attn_mask + + +# copied from m4.training.packing +def image_attention_mask_for_packed_input_ids(input_ids, tokenizer): + image_attention_mask = torch.full_like(input_ids, fill_value=-1) + next_image_attention_mask = torch.full_like(input_ids, fill_value=-1) + image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN) + eod_token_id = tokenizer.eos_token_id + for batch_idx in range(input_ids.size(0)): + count = -1 + seen_eod = False + for idx, token_id in enumerate(input_ids[batch_idx]): + if token_id == image_token_id: + count += 1 + image_attention_mask[batch_idx][idx] = count + seen_eod = False + else: + image_attention_mask[batch_idx][idx] = count + + if seen_eod: + image_attention_mask[batch_idx][idx] = -1 + + if token_id == eod_token_id: + seen_eod = True + + for batch_idx in range(input_ids.size(0)): + count = -1 + seen_eod = False + for idx in range(input_ids[batch_idx].size(0) - 1, -1, -1): + token_id = input_ids[batch_idx][idx] + if token_id == image_token_id: + count += 1 + next_image_attention_mask[batch_idx][idx] = count + seen_eod = False + else: + next_image_attention_mask[batch_idx][idx] = count + + if token_id == eod_token_id: + seen_eod = True + + if seen_eod: + next_image_attention_mask[batch_idx][idx] = -1 + + non_negative_indices = next_image_attention_mask[batch_idx] != -1 + next_image_attention_mask[batch_idx][non_negative_indices] -= count + next_image_attention_mask[batch_idx][non_negative_indices] *= -1 + + return image_attention_mask, next_image_attention_mask + + +def is_url(string): + """Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately + invalidated the url""" + if " " in string: + return False + result = urlparse(string) + return all([result.scheme, result.netloc]) + + +class IdeficsProcessor(ProcessorMixin): + r""" + Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor. + + [`IdeficsProcessor`] offers all the functionalities of [`IdeficsImageProcessor`] and [`LlamaTokenizerFast`]. See + the docstring of [`~IdeficsProcessor.__call__`] and [`~IdeficsProcessor.decode`] for more information. + + Args: + image_processor (`IdeficsImageProcessor`): + An instance of [`IdeficsImageProcessor`]. The image processor is a required input. + tokenizer (`LlamaTokenizerFast`): + An instance of [`LlamaTokenizerFast`]. The tokenizer is a required input. + image_size (`int`, *optional*, defaults to 224): Image size (assuming a square image) + """ + attributes = ["image_processor", "tokenizer"] + image_processor_class = "IdeficsImageProcessor" + tokenizer_class = "LlamaTokenizerFast" + + def __init__(self, image_processor, tokenizer=None, image_size=224, add_end_of_utterance_token=None, **kwargs): + if image_processor is None: + raise ValueError("You need to specify an `image_processor`.") + if tokenizer is None: + raise ValueError("You need to specify a `tokenizer`.") + + super().__init__(image_processor, tokenizer) + self.current_processor = self.image_processor + self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN) + + self.default_image_dims = ( + self.image_processor.image_num_channels, + self.image_processor.image_size, + self.image_processor.image_size, + ) + + self.tokenizer_was_trained_with_end_of_utterance_token = ( + True + if "" in self.tokenizer.special_tokens_map.get("additional_special_tokens", []) + else False + ) + + def __call__( + self, + prompts: Union[List[TextInput], List[List[TextInput]]], + padding: Union[bool, str, PaddingStrategy] = False, + truncation: Union[bool, str, TruncationStrategy] = None, + max_length: Optional[int] = None, + transform: Callable = None, + add_eos_token=False, + add_end_of_utterance_token=None, + debug=False, + return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, + ) -> BatchEncoding: + """This method takes batched or non-batched prompts made of text and images and converts them into prompts that + the model was trained on and prepares the image pixel values for the model to process. + + Args: + prompts (`Union[List[TextInput], [List[List[TextInput]]]]`): + either a single prompt or a batched list of prompts - see the detailed description immediately after + the end of the arguments doc section. + padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): + Select a strategy to pad the returned sequences (according to the model's padding side and padding + index) among: + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + max_length (`int`, *optional*): + Maximum length of the returned list and optionally padding length (see above). + truncation (`bool`, *optional*): + Activates truncation to cut input sequences longer than `max_length` to `max_length`. + transform (`Callable`, *optional*): + A custom transform function that accepts a single image can be passed for training. For example, + `torchvision.Compose` can be used to compose multiple functions. If `None` a preset inference-specific + set of transforms will be applied to the images + add_eos_token (`bool`, *optional*, defaults to `False`): + Adds `eos_token` at the end of the final prompt if True` + add_end_of_utterance_token (`bool`, *optional*) + Whether to automatically add `` after each prompt's text input (unless followed by an + image). If `None` the tokenizer will be checked instead and if this token is found in + `additional_special_tokens` then the value will be `True`. + debug (`bool`, *optional*, defaults to `False`): + `True` value will help debug prompt generation by dumping useful information + return_tensors (`str` or `TensorType`, *optional*, defaults to `TensorType.PYTORCH`): + The type of tensors to return. Can be one of: + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + + Returns: + a dict with entries: `input_ids`, `attention_mask`, `pixel_values`, `image_attention_mask` which can be + directly passed to `model.generate` + + Detailed explanation: + + Each entry in `prompts` is either a text to be passed as is or an image that will be processed. + + An image can be either an image object (`PIL.Image`) or a url from which the image can be retrieved. + + When the processor encounters an image it'll inject `` + entry into the prompt. + + Example: + + ```python + checkpoint = "HuggingFaceM4/idefics-9b" + processor = AutoProcessor.from_pretrained(checkpoint) + url = "https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg" + img = processor.image_processor.fetch_images([url])[0] + + prompts = [ + "User:", + img, + "Describe this image.\nAssistant: An image of two kittens in grass.\n", + "User:", + "https://hips.hearstapps.com/hmg-prod/images/dog-puns-1581708208.jpg", + "Describe this image.\nAssistant:", + ] + + inputs = processor(prompts, return_tensors="pt") + generated_ids = model.generate(**inputs, max_length=100) + generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + ``` + + In this example the `prompts` will be converted into: + + ``` + User:Describe this image. + Assistant: An image of two kittens in grass. + User:Describe this image. + Assistant:' + ``` + + and the two images will be massaged using [`IdeficsImageProcessor.__call__`] method and placed inside the + `pixel_values` dict entry of the return value. + + This example also examplifies that images can be passed as objects or as text urls. It can be seen that the + first image is passed as object and the second one as a url. + + To do training do: + + ```python + image_transform = transforms.Compose( + [ + transforms.RandomResizedCrop( + (w, h), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC + ), + transforms.ToTensor(), + transforms.Normalize(mean=self.image_mean, std=self.image_std), + ] + ) + inputs = processor(prompts, transform=image_transform, return_tensors="pt") + ``` + + In order to help debug prompt generation enable `debug=True` which will show you what's happening. + + """ + + # if the value isn't overriden by the user, check if the tokenizer was trained with this token and then use it + if add_end_of_utterance_token is None: + add_end_of_utterance_token = self.tokenizer_was_trained_with_end_of_utterance_token + + # turn non-batched prompts into batched + if not any(isinstance(i, list) for i in prompts): + prompts = [prompts] + + fake_token = "" + image_token = "" + end_of_utterance_token = "" + + def image_tokens(last_was_image): + if last_was_image: + return image_token + fake_token + else: + return fake_token + image_token + fake_token + + all_texts = [] + all_images = [] + for sample in prompts: + # the model was trained on samples starting with + full_text = f"{self.tokenizer.bos_token}" + + # an image can either be an image object in the item or the url, everything else is a verbatim prompt text + image_objects = [] + last_was_image = False + last_was_text = False + for i, item in enumerate(sample): + if i > 0: + last_was_text = True if not last_was_image else False + + if isinstance(item, str): + item = item.strip(" ") + if is_url(item): + image = self.image_processor.fetch_images(item) + full_text += image_tokens(last_was_image) + image_objects.append(image) + last_was_image = True + else: + # we add end_of_utterance_token between each subsequent text prompts (but not at the last one!) + if add_end_of_utterance_token and last_was_text: + full_text += end_of_utterance_token + full_text += item + last_was_image = False + else: + # must be an image obj + full_text += image_tokens(last_was_image) + image_objects.append(item) + last_was_image = True + + if add_eos_token: + full_text += self.tokenizer.eos_token + + if debug is True: + print(f"{full_text=}") + + image_objects = self.image_processor(image_objects, transform=transform) + + text_encoding = self.tokenizer( + text=full_text, + add_special_tokens=False, + padding=padding, + truncation=truncation, + max_length=max_length, + ) + + all_texts.append(text_encoding["input_ids"]) + all_images.append(image_objects) + + max_seq_len = max(len(x) for x in all_texts) + + # max_num_images has to be at least 1 even when there are no images + max_num_images = max(len(x) for x in all_images) + max_num_images = max(1, max_num_images) + + at_least_one_image = sum(len(x) for x in all_images) > 0 + output_input_ids = [] + output_images = [] + output_attention_masks = [] + for text, images in zip(all_texts, all_images): + padded_input_ids = [self.tokenizer.pad_token_id] * max_seq_len + unpadded_seq_len = len(text) + start = max_seq_len - unpadded_seq_len + padded_input_ids[start:] = text[:max_seq_len] + + attention_mask = torch.zeros((max_seq_len,), dtype=torch.long) + attention_mask[start:] = 1 + + image_count = padded_input_ids.count(self.image_token_id) + local_max_num_images = min(image_count, max_num_images) + + current_images = images[:local_max_num_images] + + if len(current_images) > 0: + padded_image_tensor = torch.zeros(max_num_images, *current_images.size()[1:]) + padded_image_tensor[: current_images.size(0)] = current_images + else: + padded_image_tensor = torch.zeros(max_num_images, *self.default_image_dims) + + output_images.append(padded_image_tensor) + output_input_ids.append(torch.tensor(padded_input_ids)) + + output_attention_masks.append(attention_mask) + + output_input_ids = torch.stack(output_input_ids) + output_images = torch.stack(output_images) + output_attention_masks = torch.stack(output_attention_masks) + + if at_least_one_image: + image_attention_mask, _ = image_attention_mask_for_packed_input_ids(output_input_ids, self.tokenizer) + image_attention_mask = incremental_to_binary_attention_mask( + image_attention_mask, num_classes=max_num_images + ) + else: + # in full language mode we set the image mask to all-0s + image_attention_mask = torch.zeros( + output_input_ids.shape[0], output_input_ids.shape[1], 1, dtype=torch.bool + ) + + return BatchFeature( + data={ + "input_ids": output_input_ids, + "attention_mask": output_attention_masks, + "pixel_values": output_images, + "image_attention_mask": image_attention_mask, + } + ) + + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + @property + 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/src/transformers/models/idefics/vision.py b/src/transformers/models/idefics/vision.py new file mode 100644 index 0000000000..614de18c1d --- /dev/null +++ b/src/transformers/models/idefics/vision.py @@ -0,0 +1,433 @@ +# coding=utf-8 +# Copyright 2021 The OpenAI Team 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. +""" PyTorch IdeficsVision model: a copy of CLIPVisionModel using a simpler config object""" + + +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn + +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling +from ...utils import ( + ModelOutput, + logging, +) +from .configuration_idefics import IdeficsVisionConfig + + +logger = logging.get_logger(__name__) + + +@dataclass +class IdeficsVisionModelOutput(ModelOutput): + """ + Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. + + Args: + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): + The image embeddings obtained by applying the projection layer to the pooler_output. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + image_embeds: Optional[torch.FloatTensor] = None + last_hidden_state: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->Idefics +class IdeficsVisionEmbeddings(nn.Module): + def __init__(self, config: IdeficsVisionConfig): + 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(self.embed_dim)) + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + bias=False, + ) + + self.num_patches = (self.image_size // self.patch_size) ** 2 + self.num_positions = self.num_patches + 1 + self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) + self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) + + 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.to(dtype=target_dtype)) # shape = [*, width, grid, grid] + patch_embeds = patch_embeds.flatten(2).transpose(1, 2) + + class_embeds = self.class_embedding.expand(batch_size, 1, -1) + embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + embeddings = embeddings + self.position_embedding(self.position_ids) + return embeddings + + +# Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->IdeficsVision +class IdeficsVisionAttention(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 = config.attention_dropout + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.out_proj = 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, + attention_mask: Optional[torch.Tensor] = None, + causal_attention_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() + + # get query proj + query_states = self.q_proj(hidden_states) * self.scale + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_states = value_states.view(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {attn_weights.size()}" + ) + + # apply the causal_attention_mask first + if causal_attention_mask is not None: + if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" + f" {causal_attention_mask.size()}" + ) + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if output_attentions: + # this operation is a bit akward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(bsz, tgt_len, embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped + + +# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->IdeficsVision +class IdeficsVisionMLP(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.clip.modeling_clip.CLIPEncoderLayer with CLIP->IdeficsVision +class IdeficsVisionEncoderLayer(nn.Module): + def __init__(self, config: IdeficsVisionConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.self_attn = IdeficsVisionAttention(config) + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = IdeficsVisionMLP(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, + causal_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, + attention_mask=attention_mask, + causal_attention_mask=causal_attention_mask, + output_attentions=output_attentions, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->IdeficsVision +class IdeficsVisionEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`IdeficsVisionEncoderLayer`]. + + Args: + config: IdeficsVisionConfig + """ + + def __init__(self, config: IdeficsVisionConfig): + super().__init__() + self.config = config + self.layers = nn.ModuleList([IdeficsVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + inputs_embeds, + attention_mask: Optional[torch.Tensor] = None, + causal_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)`): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + 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) + causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Causal mask for the text model. 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, + causal_attention_mask, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + causal_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 + ) + + +# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer +class IdeficsVisionTransformer(nn.Module): + def __init__(self, config: IdeficsVisionConfig): + super().__init__() + self.config = config + embed_dim = config.hidden_size + + self.embeddings = IdeficsVisionEmbeddings(config) + self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + self.encoder = IdeficsVisionEncoder(config) + self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + + # copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward + 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) + hidden_states = self.pre_layrnorm(hidden_states) + + 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] + 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, + ) diff --git a/src/transformers/utils/dummy_pt_objects.py b/src/transformers/utils/dummy_pt_objects.py index b399568814..af4ea05837 100644 --- a/src/transformers/utils/dummy_pt_objects.py +++ b/src/transformers/utils/dummy_pt_objects.py @@ -3942,6 +3942,37 @@ class IBertPreTrainedModel(metaclass=DummyObject): requires_backends(self, ["torch"]) +IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST = None + + +class IdeficsForVisionText2Text(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class IdeficsModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class IdeficsPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class IdeficsProcessor(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST = None diff --git a/src/transformers/utils/dummy_vision_objects.py b/src/transformers/utils/dummy_vision_objects.py index a2457989ab..134a853eec 100644 --- a/src/transformers/utils/dummy_vision_objects.py +++ b/src/transformers/utils/dummy_vision_objects.py @@ -233,6 +233,13 @@ class GLPNImageProcessor(metaclass=DummyObject): requires_backends(self, ["vision"]) +class IdeficsImageProcessor(metaclass=DummyObject): + _backends = ["vision"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["vision"]) + + class ImageGPTFeatureExtractor(metaclass=DummyObject): _backends = ["vision"] diff --git a/tests/models/idefics/__init__.py b/tests/models/idefics/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/models/idefics/test_image_processing_idefics.py b/tests/models/idefics/test_image_processing_idefics.py new file mode 100644 index 0000000000..6c682ce4a8 --- /dev/null +++ b/tests/models/idefics/test_image_processing_idefics.py @@ -0,0 +1,203 @@ +# coding=utf-8 +# Copyright 2021 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import unittest + +from transformers.testing_utils import require_torch, require_torchvision, require_vision +from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available + +from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs + + +if is_torch_available(): + import torch + +if is_torchvision_available(): + import torchvision.transforms as transforms + +if is_vision_available(): + from PIL import Image + + from transformers import IdeficsImageProcessor + + +class IdeficsImageProcessingTester(unittest.TestCase): + def __init__( + self, + parent, + batch_size=7, + num_channels=3, + image_size=18, + min_resolution=30, + max_resolution=400, + size=None, + image_mean=[0.48145466, 0.4578275, 0.40821073], + image_std=[0.26862954, 0.26130258, 0.27577711], + ): + size = size if size is not None else {"shortest_edge": 30} + self.parent = parent + self.batch_size = batch_size + self.num_channels = num_channels + self.image_size = image_size + self.min_resolution = min_resolution + self.max_resolution = max_resolution + # self.size = size + self.image_mean = image_mean + self.image_std = image_std + + def prepare_image_processor_dict(self): + return { + "image_mean": self.image_mean, + "image_std": self.image_std, + "image_size": self.image_size, + } + + def get_expected_values(self, image_inputs, batched=False): + """ + This function computes the expected height and width when providing images to IdeficsImageProcessor, + assuming do_resize is set to True with a scalar size and size_divisor. + """ + if not batched: + size = self.image_size + image = image_inputs[0] + if isinstance(image, Image.Image): + w, h = image.size + else: + h, w = image.shape[1], image.shape[2] + scale = size / min(w, h) + if h < w: + newh, neww = size, scale * w + else: + newh, neww = scale * h, size + + max_size = int((1333 / 800) * size) + if max(newh, neww) > max_size: + scale = max_size / max(newh, neww) + newh = newh * scale + neww = neww * scale + + newh, neww = int(newh + 0.5), int(neww + 0.5) + expected_height, expected_width = ( + newh // self.size_divisor * self.size_divisor, + neww // self.size_divisor * self.size_divisor, + ) + + else: + expected_values = [] + for image in image_inputs: + expected_height, expected_width = self.get_expected_values([image]) + expected_values.append((expected_height, expected_width)) + expected_height = max(expected_values, key=lambda item: item[0])[0] + expected_width = max(expected_values, key=lambda item: item[1])[1] + + return expected_height, expected_width + + def expected_output_image_shape(self, images): + height, width = self.get_expected_values(images, batched=True) + return (self.num_channels, height, width) + + def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): + return prepare_image_inputs( + batch_size=self.batch_size, + num_channels=self.num_channels, + min_resolution=self.min_resolution, + max_resolution=self.max_resolution, + equal_resolution=equal_resolution, + numpify=numpify, + torchify=torchify, + ) + + +@require_torch +@require_vision +class IdeficsImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): + image_processing_class = IdeficsImageProcessor if is_vision_available() else None + + def setUp(self): + self.image_processor_tester = IdeficsImageProcessingTester(self) + + @property + def image_processor_dict(self): + return self.image_processor_tester.prepare_image_processor_dict() + + def test_image_processor_properties(self): + image_processing = self.image_processing_class(**self.image_processor_dict) + self.assertTrue(hasattr(image_processing, "image_mean")) + self.assertTrue(hasattr(image_processing, "image_std")) + self.assertTrue(hasattr(image_processing, "image_size")) + + def test_image_processor_from_dict_with_kwargs(self): + image_processor = self.image_processing_class.from_dict(self.image_processor_dict) + self.assertNotEqual(image_processor.image_size, 30) + + image_processor = self.image_processing_class.from_dict(self.image_processor_dict, image_size=42) + self.assertEqual(image_processor.image_size, 42) + + @require_torchvision + def test_torchvision_numpy_transforms_equivalency(self): + # as we had to reimplement the torchvision transforms using transformers utils we must check + # they both do the same + + image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False) + image_processor = self.image_processing_class(**self.image_processor_dict) + + print(image_inputs) + + def convert_to_rgb(image): + # `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background + # for transparent images. The call to `alpha_composite` handles this case + if image.mode == "RGB": + return image + + image_rgba = image.convert("RGBA") + background = Image.new("RGBA", image_rgba.size, (255, 255, 255)) + alpha_composite = Image.alpha_composite(background, image_rgba) + alpha_composite = alpha_composite.convert("RGB") + return alpha_composite + + image_size = image_processor.image_size + image_mean = image_processor.image_mean + image_std = image_processor.image_std + + transform = transforms.Compose( + [ + convert_to_rgb, + transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC), + transforms.ToTensor(), + transforms.Normalize(mean=image_mean, std=image_std), + ] + ) + + pixel_values_transform_implied = image_processor(image_inputs, transform=None) + pixel_values_transform_supplied = image_processor(image_inputs, transform=transform) + + torch.testing.assert_close(pixel_values_transform_implied, pixel_values_transform_supplied, rtol=0.0, atol=0.0) + + @unittest.skip("not supported") + def test_call_numpy(self): + pass + + @unittest.skip("not supported") + def test_call_numpy_4_channels(self): + pass + + @unittest.skip("not supported") + def test_call_pil(self): + pass + + @unittest.skip("not supported") + def test_call_pytorch(self): + pass diff --git a/tests/models/idefics/test_modeling_idefics.py b/tests/models/idefics/test_modeling_idefics.py new file mode 100644 index 0000000000..f593b5d600 --- /dev/null +++ b/tests/models/idefics/test_modeling_idefics.py @@ -0,0 +1,474 @@ +# 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. +""" Testing suite for the PyTorch Idefics model. """ + +import unittest + +from transformers import IdeficsConfig, is_torch_available, is_vision_available +from transformers.testing_utils import TestCasePlus, require_torch, require_vision, slow, torch_device +from transformers.utils import cached_property + +from ...test_configuration_common import ConfigTester +from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask +from ...test_pipeline_mixin import PipelineTesterMixin + + +if is_torch_available(): + import torch + + from transformers import IdeficsForVisionText2Text, IdeficsModel, IdeficsProcessor + from transformers.models.idefics.configuration_idefics import IdeficsPerceiverConfig, IdeficsVisionConfig + from transformers.models.idefics.modeling_idefics import IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST + from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0 +else: + is_torch_greater_or_equal_than_2_0 = False + +if is_vision_available(): + from PIL import Image + + +class IdeficsModelTester: + def __init__( + self, + parent, + batch_size=1, + seq_length=7, + image_size=30, + patch_size=2, + num_channels=3, + is_training=True, + use_input_mask=True, + use_token_type_ids=True, + use_labels=True, + vocab_size=99, + hidden_size=32, + num_hidden_layers=5, + num_attention_heads=4, + intermediate_size=37, + hidden_act="gelu", + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + max_position_embeddings=512, + type_vocab_size=16, + type_sequence_label_size=2, + initializer_range=0.02, + num_labels=3, + scope=None, + modality_type_vocab_size=2, + add_multiple_images=False, + num_images=-1, + vision_embed_dim=32, + vision_patch_size=2, + vision_image_size=30, + vision_num_attention_heads=4, + vision_num_hidden_layers=5, + vision_intermediate_size=37, + perceiver_qk_layer_norms_perceiver=False, + perceiver_resampler_depth=2, + perceiver_resampler_head_dim=8, + perceiver_resampler_n_heads=2, + perceiver_resampler_n_latents=16, + ): + self.parent = parent + self.batch_size = batch_size + self.seq_length = seq_length + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.is_training = is_training + self.use_input_mask = use_input_mask + self.use_token_type_ids = use_token_type_ids + self.use_labels = use_labels + 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.intermediate_size = intermediate_size + self.hidden_act = hidden_act + self.hidden_dropout_prob = hidden_dropout_prob + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.max_position_embeddings = max_position_embeddings + self.type_vocab_size = type_vocab_size + self.type_sequence_label_size = type_sequence_label_size + self.initializer_range = initializer_range + self.num_labels = num_labels + self.scope = scope + self.modality_type_vocab_size = modality_type_vocab_size + self.add_multiple_images = add_multiple_images + self.num_images = num_images + + self.vision_embed_dim = vision_embed_dim + self.vision_patch_size = vision_patch_size + self.vision_image_size = vision_image_size + self.vision_num_attention_heads = vision_num_attention_heads + self.vision_num_hidden_layers = vision_num_hidden_layers + self.vision_intermediate_size = vision_intermediate_size + + self.vision_config = IdeficsVisionConfig( + embed_dim=self.vision_embed_dim, + patch_size=self.vision_patch_size, + image_size=self.vision_image_size, + num_attention_heads=self.vision_num_attention_heads, + num_hidden_layers=self.vision_num_hidden_layers, + intermediate_size=self.vision_intermediate_size, + ) + + self.perceiver_qk_layer_norms_perceiver = perceiver_qk_layer_norms_perceiver + self.perceiver_resampler_depth = perceiver_resampler_depth + self.perceiver_resampler_head_dim = perceiver_resampler_head_dim + self.perceiver_resampler_n_heads = perceiver_resampler_n_heads + self.perceiver_resampler_n_latents = perceiver_resampler_n_latents + + self.perceiver_config = IdeficsPerceiverConfig( + qk_layer_norms_perceiver=self.perceiver_qk_layer_norms_perceiver, + resampler_depth=self.perceiver_resampler_depth, + resampler_head_dim=self.perceiver_resampler_head_dim, + resampler_n_heads=self.perceiver_resampler_n_heads, + resampler_n_latents=self.perceiver_resampler_n_latents, + ) + + # we set the expected sequence length (which is used in several tests) + # this is equal to the seq length of the text tokens + number of image patches + 1 for the CLS token + self.expected_seq_len = self.seq_length + (self.image_size // self.patch_size) ** 2 + 1 + + def prepare_config_and_inputs(self): + self.seq_length = 42 + + input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) + + num_images = 2 if self.add_multiple_images else 1 + pixel_values = floats_tensor( + [self.batch_size, num_images, self.num_channels, self.image_size, self.image_size] + ) + input_mask = None + if self.use_input_mask: + input_mask = random_attention_mask([self.batch_size, self.seq_length]) + + image_attention_mask = random_attention_mask([self.batch_size, self.seq_length, num_images]) + + config = self.get_config() + + return (config, input_ids, input_mask, pixel_values, image_attention_mask) + + def get_config(self): + return IdeficsConfig( + image_size=self.image_size, + patch_size=self.patch_size, + num_channels=self.num_channels, + vocab_size=self.vocab_size, + hidden_size=self.hidden_size, + num_hidden_layers=self.num_hidden_layers, + num_attention_heads=self.num_attention_heads, + intermediate_size=self.intermediate_size, + hidden_act=self.hidden_act, + hidden_dropout_prob=self.hidden_dropout_prob, + attention_probs_dropout_prob=self.attention_probs_dropout_prob, + max_position_embeddings=self.max_position_embeddings, + type_vocab_size=self.type_vocab_size, + is_decoder=False, + initializer_range=self.initializer_range, + num_labels=self.num_labels, + modality_type_vocab_size=self.modality_type_vocab_size, + num_images=self.num_images, + vision_config=self.vision_config, + ) + + def create_and_check_model( + self, + config, + input_ids, + input_mask, + pixel_values, + image_attention_mask, + ): + model = IdeficsModel(config=config) + model.to(torch_device) + model.eval() + result = model( + input_ids, attention_mask=input_mask, pixel_values=pixel_values, image_attention_mask=image_attention_mask + ) + self.parent.assertEqual( + result.last_hidden_state.shape, (self.batch_size, input_ids.shape[1], self.hidden_size) + ) + + def prepare_config_and_inputs_for_common(self): + config_and_inputs = self.prepare_config_and_inputs() + ( + config, + input_ids, + input_mask, + pixel_values, + image_attention_mask, + ) = config_and_inputs + inputs_dict = { + "input_ids": input_ids, + "attention_mask": input_mask, + "pixel_values": pixel_values, + "image_attention_mask": image_attention_mask, + } + return config, inputs_dict + + def prepare_pixel_values(self): + return floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) + + +@unittest.skipIf(not is_torch_greater_or_equal_than_2_0, reason="pytorch 2.0 or higher is required") +@require_torch +class IdeficsModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): + all_model_classes = (IdeficsModel, IdeficsForVisionText2Text) if is_torch_available() else () + pipeline_model_mapping = {} + test_pruning = False + test_headmasking = False + test_torchscript = False + + def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): + inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) + # XXX: IdeficsForVisionText2TextTest has no MODEL_FOR group yet, but it should be the same + # as MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, so for now manually changing to do the right thing + # as super won't do it + if return_labels: + inputs_dict["labels"] = torch.zeros( + (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device + ) + + return inputs_dict + + def test_model_outputs_equivalence(self): + try: + orig = self.all_model_classes + # IdeficsModel.forward doesn't have labels input arg - only IdeficsForVisionText2Text does + self.all_model_classes = (IdeficsForVisionText2Text,) if is_torch_available() else () + super().test_model_outputs_equivalence() + finally: + self.all_model_classes = orig + + def setUp(self): + self.model_tester = IdeficsModelTester(self) + self.config_tester = ConfigTester(self, config_class=IdeficsConfig, hidden_size=37) + + def test_config(self): + self.config_tester.run_common_tests() + + def test_model(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_model(*config_and_inputs) + + def test_training(self): + if not self.model_tester.is_training: + return + + for model_class in self.all_model_classes: + # IdeficsModel does not support training, users should use + # IdeficsForVisionText2Text for this purpose + if model_class == IdeficsModel: + return + + config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() + config.return_dict = True + + model = model_class(config) + model.to(torch_device) + model.train() + inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) + for k, v in inputs.items(): + print(k, v.shape) + loss = model(**inputs).loss + loss.backward() + + def test_training_gradient_checkpointing(self): + if not self.model_tester.is_training: + return + + for model_class in self.all_model_classes: + # IdeficsModel does not support training, users should use + # IdeficsForVisionText2Text for this purpose + if model_class == IdeficsModel: + return + + config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() + config.use_cache = False + config.return_dict = True + + model = model_class(config) + model.to(torch_device) + model.gradient_checkpointing_enable() + model.train() + inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) + loss = model(**inputs).loss + loss.backward() + + @unittest.skip(reason="""IDEFICS does not support retaining the gradients of the hidden states and attention""") + def test_retain_grad_hidden_states_attentions(self): + return + + def test_attention_outputs(self): + config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() + config.return_dict = True + + for model_class in self.all_model_classes: + inputs_dict["output_attentions"] = True + inputs_dict["output_hidden_states"] = False + config.return_dict = True + model = model_class(config) + model.to(torch_device) + model.eval() + with torch.no_grad(): + outputs = model(**self._prepare_for_class(inputs_dict, model_class)) + attentions = outputs.attentions + + self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) + + # check that output_attentions also work using config + del inputs_dict["output_attentions"] + config.output_attentions = True + model = model_class(config) + model.to(torch_device) + model.eval() + with torch.no_grad(): + outputs = model(**self._prepare_for_class(inputs_dict, model_class)) + attentions = outputs.attentions + # IDEFICS does not support outputting attention score becuase it uses SDPA under the hood + self.assertTrue(attentions[0] is None) + out_len = len(outputs) + + # Check attention is always last and order is fine + inputs_dict["output_attentions"] = True + inputs_dict["output_hidden_states"] = True + model = model_class(config) + model.to(torch_device) + model.eval() + with torch.no_grad(): + outputs = model(**self._prepare_for_class(inputs_dict, model_class)) + + self.assertEqual(out_len + 1, len(outputs)) + + self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions + + self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) + # IDEFICS does not support outputting attention score becuase it uses SDPA under the hood + self.assertTrue(self_attentions[0] is None) + + def test_hidden_states_output(self): + def check_hidden_states_output(inputs_dict, config, model_class): + model = model_class(config) + model.to(torch_device) + model.eval() + + with torch.no_grad(): + outputs = model(**self._prepare_for_class(inputs_dict, model_class)) + + hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states + + expected_num_layers = getattr( + self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 + ) + self.assertEqual(len(hidden_states), expected_num_layers) + + seq_length = self.model_tester.seq_length + + self.assertListEqual( + list(hidden_states[0].shape[-2:]), + [seq_length, self.model_tester.hidden_size], + ) + + config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() + + for model_class in self.all_model_classes: + inputs_dict["output_hidden_states"] = True + check_hidden_states_output(inputs_dict, config, model_class) + + # check that output_hidden_states also work using config + del inputs_dict["output_hidden_states"] + config.output_hidden_states = True + + check_hidden_states_output(inputs_dict, config, model_class) + + @slow + def test_model_from_pretrained(self): + for model_name in IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: + model = IdeficsModel.from_pretrained(model_name) + self.assertIsNotNone(model) + + +@unittest.skipIf(not is_torch_greater_or_equal_than_2_0, reason="pytorch 2.0 or higher is required") +@require_torch +class IdeficsForVisionText2TextTest(IdeficsModelTest, unittest.TestCase): + all_model_classes = (IdeficsForVisionText2Text,) if is_torch_available() else () + + def setUp(self): + self.model_tester = IdeficsModelTester( + self, modality_type_vocab_size=3, add_multiple_images=True, num_images=2 + ) + self.config_tester = ConfigTester(self, config_class=IdeficsConfig, hidden_size=37) + + @unittest.skip("We only test the model that takes in multiple images") + def test_model(self): + pass + + @unittest.skip("We only test the model that takes in multiple images") + def test_for_token_classification(self): + pass + + @unittest.skip(reason="""IDEFICS does not support retaining the gradients of the hidden states and attention""") + def test_retain_grad_hidden_states_attentions(self): + pass + + +@unittest.skipIf(not is_torch_greater_or_equal_than_2_0, reason="pytorch 2.0 or higher is required") +@require_torch +@require_vision +class IdeficsModelIntegrationTest(TestCasePlus): + @cached_property + def default_processor(self): + return IdeficsProcessor.from_pretrained("HuggingFaceM4/idefics-9b") if is_vision_available() else None + + @slow + def test_inference_natural_language_visual_reasoning(self): + cat_image_path = self.tests_dir / "fixtures/tests_samples/COCO/000000039769.png" + cats_image_obj = Image.open(cat_image_path) # 2 cats + dogs_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image1.jpeg" + + prompts = [ + [ + "User:", + dogs_image_url, + "Describe this image.\nAssistant: An image of two dogs.\n", + "User:", + cats_image_obj, + "Describe this image.\nAssistant:", + ], + [ + "User:", + cats_image_obj, + "Describe this image.\nAssistant: An image of two kittens.\n", + "User:", + dogs_image_url, + "Describe this image.\nAssistant:", + ], + ] + + model = IdeficsForVisionText2Text.from_pretrained("HuggingFaceM4/idefics-9b").to(torch_device) + processor = self.default_processor + inputs = processor(prompts, return_tensors="pt").to(torch_device) + generated_ids = model.generate(**inputs, max_length=100) + generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) + + # keep for debugging + for i, t in enumerate(generated_text): + t = bytes(t, "utf-8").decode("unicode_escape") + print(f"{i}:\n{t}\n") + + self.assertIn("image of two cats", generated_text[0]) + self.assertIn("image of two dogs", generated_text[1]) diff --git a/tests/models/idefics/test_processor_idefics.py b/tests/models/idefics/test_processor_idefics.py new file mode 100644 index 0000000000..4ad11d31ae --- /dev/null +++ b/tests/models/idefics/test_processor_idefics.py @@ -0,0 +1,154 @@ +# Copyright 2022 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. + +import numpy as np + +from transformers.testing_utils import TestCasePlus, require_torch, require_vision +from transformers.utils import is_torch_available, is_vision_available + + +if is_torch_available(): + import torch + +if is_vision_available(): + from PIL import Image + + from transformers import ( + AutoProcessor, + IdeficsImageProcessor, + IdeficsProcessor, + LlamaTokenizerFast, + PreTrainedTokenizerFast, + ) + + +@require_torch +@require_vision +class IdeficsProcessorTest(TestCasePlus): + def setUp(self): + super().setUp() + + self.checkpoint_path = self.get_auto_remove_tmp_dir() + + image_processor = IdeficsImageProcessor() + tokenizer = LlamaTokenizerFast.from_pretrained("HuggingFaceM4/tiny-random-idefics") + + processor = IdeficsProcessor(image_processor, tokenizer) + + processor.save_pretrained(self.checkpoint_path) + + self.input_keys = ["pixel_values", "input_ids", "attention_mask", "image_attention_mask"] + + def get_tokenizer(self, **kwargs): + return AutoProcessor.from_pretrained(self.checkpoint_path, **kwargs).tokenizer + + def get_image_processor(self, **kwargs): + return AutoProcessor.from_pretrained(self.checkpoint_path, **kwargs).image_processor + + def prepare_prompts(self): + """This function prepares a list of PIL images""" + + num_images = 2 + images = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8) for x in range(num_images)] + images = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in images] + + # print([type(x) for x in images]) + # die + + prompts = [ + # text and 1 image + [ + "User:", + images[0], + "Describe this image.\nAssistant:", + ], + # text and images + [ + "User:", + images[0], + "Describe this image.\nAssistant: An image of two dogs.\n", + "User:", + images[1], + "Describe this image.\nAssistant:", + ], + # only text + [ + "User:", + "Describe this image.\nAssistant: An image of two kittens.\n", + "User:", + "Describe this image.\nAssistant:", + ], + # only images + [ + images[0], + images[1], + ], + ] + + return prompts + + def test_save_load_pretrained_additional_features(self): + processor = IdeficsProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()) + processor.save_pretrained(self.checkpoint_path) + + tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") + image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0) + + processor = IdeficsProcessor.from_pretrained( + self.checkpoint_path, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0 + ) + + self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) + self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast) + + self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) + self.assertIsInstance(processor.image_processor, IdeficsImageProcessor) + + def test_processor(self): + image_processor = self.get_image_processor() + tokenizer = self.get_tokenizer() + + processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor) + + prompts = self.prepare_prompts() + + # test that all prompts succeeded + input_processor = processor(prompts, return_tensors="pt") + for key in self.input_keys: + assert torch.is_tensor(input_processor[key]) + + def test_tokenizer_decode(self): + image_processor = self.get_image_processor() + tokenizer = self.get_tokenizer() + + processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor) + + predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] + + decoded_processor = processor.batch_decode(predicted_ids) + decoded_tok = tokenizer.batch_decode(predicted_ids) + + self.assertListEqual(decoded_tok, decoded_processor) + + def test_model_input_names(self): + image_processor = self.get_image_processor() + tokenizer = self.get_tokenizer() + + processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor) + prompts = self.prepare_prompts() + + inputs = processor(prompts) + + # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask'] + self.assertSetEqual(set(inputs.keys()), set(self.input_keys)) diff --git a/utils/check_config_attributes.py b/utils/check_config_attributes.py index dd54fe1d88..e0d4a01695 100644 --- a/utils/check_config_attributes.py +++ b/utils/check_config_attributes.py @@ -121,6 +121,9 @@ SPECIAL_CASES_TO_ALLOW.update( "JukeboxPriorConfig": True, # TODO: @Younes (for `is_decoder`) "Pix2StructTextConfig": True, + "IdeficsConfig": True, + "IdeficsVisionConfig": True, + "IdeficsPerceiverConfig": True, } )