Starcoder2 model - bis (#29215)
* Copy model * changes * misc * fixes * add embed and residual dropout (#30) * misc * remove rms norm and gated MLP * remove copied mentions where its not a copy anymore * remove unused _shape * copied from mistral instead * fix copies * fix copies * add not doctested * fix * fix copyright * Update docs/source/en/model_doc/starcoder2.md Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/starcoder2/configuration_starcoder2.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/starcoder2/configuration_starcoder2.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix doc * revert some changes * add fa2 tests * fix styling nit * fix * push dummy docs --------- Co-authored-by: Joel Lamy-Poirier <joel.lamy-poirier@servicenow.com> Co-authored-by: younesbelkada <younesbelkada@gmail.com> Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
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@ -493,6 +493,7 @@ Current number of checkpoints: ** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
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1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
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1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu.
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1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with a coming soon paper.
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1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
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1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
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1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
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@ -466,6 +466,7 @@ Número actual de puntos de control: ** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
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1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
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1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu.
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1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with a coming soon paper.
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1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
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1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
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1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
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@ -487,6 +487,7 @@ Nombre actuel de points de contrôle : ** (de l'Université de Tel Aviv), publié dans l'article [Réponse à quelques questions avec peu d'exemples par la pré-sélection des spans](https://arxiv.org/abs/2101.00438) par Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
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1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (de Berkeley) a été publié dans l'article [SqueezeBERT : Que l'apprentissage automatique peut-il apprendre au traitement du langage naturel sur les réseaux neuronaux efficaces ?](https://arxiv.org/abs/2006.11316) par Forrest N. Iandola, Albert E. Shaw, Ravi Krishna et Kurt W. Keutzer.
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1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu.
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1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with a coming soon paper.
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1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (de MBZUAI) a été publié dans l'article [SwiftFormer : Attention additive efficace pour les applications de vision mobile en temps réel basées sur des transformateurs](https://arxiv.org/abs/2303.15446) par Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
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1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (de Microsoft) a été publié dans l'article [Swin Transformer : Transformateur hiérarchique de la vision utilisant des fenêtres décalées](https://arxiv.org/abs/2103.14030) par Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
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1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (de Microsoft) a été publié dans l'article [Swin Transformer V2 : Augmentation de la capacité et de la résolution](https://arxiv.org/abs/2111.09883) par Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
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@ -440,6 +440,7 @@ conda install conda-forge::transformers
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1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (तेल अवीव यूनिवर्सिटी से) साथ में पेपर [स्पैन सिलेक्शन को प्री-ट्रेनिंग करके कुछ-शॉट क्वेश्चन आंसरिंग](https://arxiv.org/abs/2101.00438) ओरि राम, युवल कर्स्टन, जोनाथन बेरेंट, अमीर ग्लोबर्सन, ओमर लेवी द्वारा।
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1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (बर्कले से) कागज के साथ [SqueezeBERT: कुशल तंत्रिका नेटवर्क के बारे में NLP को कंप्यूटर विज़न क्या सिखा सकता है?](https://arxiv.org/abs/2006.11316) फॉरेस्ट एन. इनडोला, अल्बर्ट ई. शॉ, रवि कृष्णा, और कर्ट डब्ल्यू. केटज़र द्वारा।
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1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu.
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1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with a coming soon paper.
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1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (MBZUAI से) Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. द्वाराअनुसंधान पत्र [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) के साथ जारी किया गया
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1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (माइक्रोसॉफ्ट से) साथ में कागज [स्वाइन ट्रांसफॉर्मर: शिफ्टेड विंडोज का उपयोग कर पदानुक्रमित विजन ट्रांसफॉर्मर](https://arxiv.org/abs/2103.14030) ज़ी लियू, युटोंग लिन, यू काओ, हान हू, यिक्सुआन वेई, झेंग झांग, स्टीफन लिन, बैनिंग गुओ द्वारा।
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1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (Microsoft से) साथ वाला पेपर [Swin Transformer V2: स्केलिंग अप कैपेसिटी एंड रेजोल्यूशन](https://arxiv.org/abs/2111.09883) ज़ी लियू, हान हू, युटोंग लिन, ज़ुलिआंग याओ, ज़ेंडा ज़ी, यिक्सुआन वेई, जिया निंग, यू काओ, झेंग झांग, ली डोंग, फुरु वेई, बैनिंग गुओ द्वारा।
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@ -500,6 +500,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
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1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (Tel Aviv University から), Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy から公開された研究論文: [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438)
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1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (Berkeley から) Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer から公開された研究論文: [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316)
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1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu.
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1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with a coming soon paper.
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1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (MBZUAI から) Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. から公開された研究論文 [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446)
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1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (Microsoft から) Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo から公開された研究論文: [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
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1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (Microsoft から) Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo から公開された研究論文: [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883)
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@ -415,6 +415,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
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1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (Tel Aviv University 에서) Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy 의 [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) 논문과 함께 발표했습니다.
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1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (Berkeley 에서) Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer 의 [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) 논문과 함께 발표했습니다.
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1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu.
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1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with a coming soon paper.
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1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (MBZUAI 에서 제공)은 Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.의 [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446)논문과 함께 발표했습니다.
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1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (Microsoft 에서) Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo 의 [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) 논문과 함께 발표했습니다.
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1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (Microsoft 에서) Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo 의 [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) 논문과 함께 발표했습니다.
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@ -439,6 +439,7 @@ conda install conda-forge::transformers
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1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (来自 Tel Aviv University) 伴随论文 [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) 由 Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy 发布。
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1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (来自 Berkeley) 伴随论文 [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) 由 Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer 发布。
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1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu.
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1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with a coming soon paper.
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1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (来自 MBZUAI) 伴随论文 [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) 由 Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan 发布。
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1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (来自 Microsoft) 伴随论文 [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) 由 Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo 发布。
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1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (来自 Microsoft) 伴随论文 [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) 由 Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo 发布。
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@ -451,6 +451,7 @@ conda install conda-forge::transformers
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1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University) released with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
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1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
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1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu.
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1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with a coming soon paper.
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1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
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1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
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1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
|
||||
|
|
|
|||
|
|
@ -484,6 +484,8 @@
|
|||
title: SqueezeBERT
|
||||
- local: model_doc/stablelm
|
||||
title: StableLm
|
||||
- local: model_doc/starcoder2
|
||||
title: Starcoder2
|
||||
- local: model_doc/switch_transformers
|
||||
title: SwitchTransformers
|
||||
- local: model_doc/t5
|
||||
|
|
|
|||
|
|
@ -261,6 +261,7 @@ Flax), PyTorch, and/or TensorFlow.
|
|||
| [Splinter](model_doc/splinter) | ✅ | ❌ | ❌ |
|
||||
| [SqueezeBERT](model_doc/squeezebert) | ✅ | ❌ | ❌ |
|
||||
| [StableLm](model_doc/stablelm) | ✅ | ❌ | ❌ |
|
||||
| [Starcoder2](model_doc/starcoder2) | ✅ | ❌ | ❌ |
|
||||
| [SwiftFormer](model_doc/swiftformer) | ✅ | ❌ | ❌ |
|
||||
| [Swin Transformer](model_doc/swin) | ✅ | ✅ | ❌ |
|
||||
| [Swin Transformer V2](model_doc/swinv2) | ✅ | ❌ | ❌ |
|
||||
|
|
|
|||
|
|
@ -0,0 +1,43 @@
|
|||
<!--Copyright 2024 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.
|
||||
|
||||
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
||||
rendered properly in your Markdown viewer.
|
||||
|
||||
-->
|
||||
|
||||
# Starcoder2
|
||||
|
||||
## Overview
|
||||
|
||||
Starcoder2 has been released with the paper [Stacoder-2](https://drive.google.com/file/d/17iGn3c-sYNiLyRSY-A85QOzgzGnGiVI3/view) by BigCode team.
|
||||
|
||||
Documentation page about the model is coming soon
|
||||
|
||||
|
||||
## Starcoder2Config
|
||||
|
||||
[[autodoc]] Starcoder2Config
|
||||
|
||||
## Starcoder2Model
|
||||
|
||||
[[autodoc]] Starcoder2Model
|
||||
- forward
|
||||
|
||||
## Starcoder2ForCausalLM
|
||||
|
||||
[[autodoc]] Starcoder2ForCausalLM
|
||||
- forward
|
||||
|
||||
## Starcoder2ForSequenceClassification
|
||||
|
||||
[[autodoc]] Starcoder2ForSequenceClassification
|
||||
- forward
|
||||
|
|
@ -54,6 +54,7 @@ FlashAttention-2 is currently supported for the following architectures:
|
|||
* [OPT](https://huggingface.co/docs/transformers/model_doc/opt#transformers.OPTModel)
|
||||
* [Phi](https://huggingface.co/docs/transformers/model_doc/phi#transformers.PhiModel)
|
||||
* [StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm#transformers.StableLmModel)
|
||||
* [Starcoder2](https://huggingface.co/docs/transformers/model_doc/starcoder2#transformers.Starcoder2Model)
|
||||
* [Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2#transformers.Qwen2Model)
|
||||
* [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperModel)
|
||||
|
||||
|
|
@ -180,6 +181,7 @@ For now, Transformers supports SDPA inference and training for the following arc
|
|||
* [Mistral](https://huggingface.co/docs/transformers/model_doc/mistral#transformers.MistralModel)
|
||||
* [Mixtral](https://huggingface.co/docs/transformers/model_doc/mixtral#transformers.MixtralModel)
|
||||
* [StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm#transformers.StableLmModel)
|
||||
* [Starcoder2](https://huggingface.co/docs/transformers/model_doc/starcoder2#transformers.Starcoder2Model)
|
||||
* [Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2#transformers.Qwen2Model)
|
||||
|
||||
<Tip>
|
||||
|
|
|
|||
|
|
@ -37,7 +37,8 @@ You can finetune other architectures for causal language modeling following the
|
|||
Choose one of the following architectures:
|
||||
|
||||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
[BART](../model_doc/bart), [BERT](../model_doc/bert), [Bert Generation](../model_doc/bert-generation), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CodeLlama](../model_doc/code_llama), [CodeGen](../model_doc/codegen), [CPM-Ant](../model_doc/cpmant), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [Falcon](../model_doc/falcon), [Fuyu](../model_doc/fuyu), [Gemma](../model_doc/gemma), [GIT](../model_doc/git), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT NeoX Japanese](../model_doc/gpt_neox_japanese), [GPT-J](../model_doc/gptj), [LLaMA](../model_doc/llama), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [Mistral](../model_doc/mistral), [Mixtral](../model_doc/mixtral), [MPT](../model_doc/mpt), [MusicGen](../model_doc/musicgen), [MVP](../model_doc/mvp), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Pegasus](../model_doc/pegasus), [Persimmon](../model_doc/persimmon), [Phi](../model_doc/phi), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [QDQBert](../model_doc/qdqbert), [Qwen2](../model_doc/qwen2), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [RWKV](../model_doc/rwkv), [Speech2Text2](../model_doc/speech_to_text_2), [StableLm](../model_doc/stablelm), [Transformer-XL](../model_doc/transfo-xl), [TrOCR](../model_doc/trocr), [Whisper](../model_doc/whisper), [XGLM](../model_doc/xglm), [XLM](../model_doc/xlm), [XLM-ProphetNet](../model_doc/xlm-prophetnet), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod)
|
||||
[BART](../model_doc/bart), [BERT](../model_doc/bert), [Bert Generation](../model_doc/bert-generation), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CodeLlama](../model_doc/code_llama), [CodeGen](../model_doc/codegen), [CPM-Ant](../model_doc/cpmant), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [Falcon](../model_doc/falcon), [Fuyu](../model_doc/fuyu), [Gemma](../model_doc/gemma), [GIT](../model_doc/git), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT NeoX Japanese](../model_doc/gpt_neox_japanese), [GPT-J](../model_doc/gptj), [LLaMA](../model_doc/llama), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [Mistral](../model_doc/mistral), [Mixtral](../model_doc/mixtral), [MPT](../model_doc/mpt), [MusicGen](../model_doc/musicgen), [MVP](../model_doc/mvp), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Pegasus](../model_doc/pegasus), [Persimmon](../model_doc/persimmon), [Phi](../model_doc/phi), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [QDQBert](../model_doc/qdqbert), [Qwen2](../model_doc/qwen2), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [RWKV](../model_doc/rwkv), [Speech2Text2](../model_doc/speech_to_text_2), [StableLm](../model_doc/stablelm), [Starcoder2](../model_doc/starcoder2), [Transformer-XL](../model_doc/transfo-xl), [TrOCR](../model_doc/trocr), [Whisper](../model_doc/whisper), [XGLM](../model_doc/xglm), [XLM](../model_doc/xlm), [XLM-ProphetNet](../model_doc/xlm-prophetnet), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod)
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ The task illustrated in this tutorial is supported by the following model archit
|
|||
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
|
||||
|
||||
|
||||
[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [CodeLlama](../model_doc/code_llama), [ConvBERT](../model_doc/convbert), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [Falcon](../model_doc/falcon), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [Gemma](../model_doc/gemma), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [LLaMA](../model_doc/llama), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [Mistral](../model_doc/mistral), [Mixtral](../model_doc/mixtral), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MPT](../model_doc/mpt), [MRA](../model_doc/mra), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Perceiver](../model_doc/perceiver), [Persimmon](../model_doc/persimmon), [Phi](../model_doc/phi), [PLBart](../model_doc/plbart), [QDQBert](../model_doc/qdqbert), [Qwen2](../model_doc/qwen2), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [StableLm](../model_doc/stablelm), [T5](../model_doc/t5), [TAPAS](../model_doc/tapas), [Transformer-XL](../model_doc/transfo-xl), [UMT5](../model_doc/umt5), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [CodeLlama](../model_doc/code_llama), [ConvBERT](../model_doc/convbert), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [Falcon](../model_doc/falcon), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [Gemma](../model_doc/gemma), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [LLaMA](../model_doc/llama), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [Mistral](../model_doc/mistral), [Mixtral](../model_doc/mixtral), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MPT](../model_doc/mpt), [MRA](../model_doc/mra), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Perceiver](../model_doc/perceiver), [Persimmon](../model_doc/persimmon), [Phi](../model_doc/phi), [PLBart](../model_doc/plbart), [QDQBert](../model_doc/qdqbert), [Qwen2](../model_doc/qwen2), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [StableLm](../model_doc/stablelm), [Starcoder2](../model_doc/starcoder2), [T5](../model_doc/t5), [TAPAS](../model_doc/tapas), [Transformer-XL](../model_doc/transfo-xl), [UMT5](../model_doc/umt5), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -809,6 +809,7 @@ _import_structure = {
|
|||
"SqueezeBertTokenizer",
|
||||
],
|
||||
"models.stablelm": ["STABLELM_PRETRAINED_CONFIG_ARCHIVE_MAP", "StableLmConfig"],
|
||||
"models.starcoder2": ["STARCODER2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Starcoder2Config"],
|
||||
"models.swiftformer": [
|
||||
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
|
||||
"SwiftFormerConfig",
|
||||
|
|
@ -3282,6 +3283,14 @@ else:
|
|||
"StableLmPreTrainedModel",
|
||||
]
|
||||
)
|
||||
_import_structure["models.starcoder2"].extend(
|
||||
[
|
||||
"Starcoder2ForCausalLM",
|
||||
"Starcoder2ForSequenceClassification",
|
||||
"Starcoder2Model",
|
||||
"Starcoder2PreTrainedModel",
|
||||
]
|
||||
)
|
||||
_import_structure["models.swiftformer"].extend(
|
||||
[
|
||||
"SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
|
||||
|
|
@ -5584,6 +5593,7 @@ if TYPE_CHECKING:
|
|||
SqueezeBertTokenizer,
|
||||
)
|
||||
from .models.stablelm import STABLELM_PRETRAINED_CONFIG_ARCHIVE_MAP, StableLmConfig
|
||||
from .models.starcoder2 import STARCODER2_PRETRAINED_CONFIG_ARCHIVE_MAP, Starcoder2Config
|
||||
from .models.swiftformer import (
|
||||
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
|
||||
SwiftFormerConfig,
|
||||
|
|
@ -7717,6 +7727,12 @@ if TYPE_CHECKING:
|
|||
StableLmModel,
|
||||
StableLmPreTrainedModel,
|
||||
)
|
||||
from .models.starcoder2 import (
|
||||
Starcoder2ForCausalLM,
|
||||
Starcoder2ForSequenceClassification,
|
||||
Starcoder2Model,
|
||||
Starcoder2PreTrainedModel,
|
||||
)
|
||||
from .models.swiftformer import (
|
||||
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
SwiftFormerForImageClassification,
|
||||
|
|
|
|||
|
|
@ -205,6 +205,7 @@ from . import (
|
|||
splinter,
|
||||
squeezebert,
|
||||
stablelm,
|
||||
starcoder2,
|
||||
swiftformer,
|
||||
swin,
|
||||
swin2sr,
|
||||
|
|
|
|||
|
|
@ -214,6 +214,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
|
|||
("splinter", "SplinterConfig"),
|
||||
("squeezebert", "SqueezeBertConfig"),
|
||||
("stablelm", "StableLmConfig"),
|
||||
("starcoder2", "Starcoder2Config"),
|
||||
("swiftformer", "SwiftFormerConfig"),
|
||||
("swin", "SwinConfig"),
|
||||
("swin2sr", "Swin2SRConfig"),
|
||||
|
|
@ -439,6 +440,7 @@ CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
|
|||
("splinter", "SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("squeezebert", "SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("stablelm", "STABLELM_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("starcoder2", "STARCODER2_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("swiftformer", "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("swin", "SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("swin2sr", "SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
|
|
@ -694,6 +696,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
|
|||
("splinter", "Splinter"),
|
||||
("squeezebert", "SqueezeBERT"),
|
||||
("stablelm", "StableLm"),
|
||||
("starcoder2", "Starcoder2"),
|
||||
("swiftformer", "SwiftFormer"),
|
||||
("swin", "Swin Transformer"),
|
||||
("swin2sr", "Swin2SR"),
|
||||
|
|
|
|||
|
|
@ -203,6 +203,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
|
|||
("splinter", "SplinterModel"),
|
||||
("squeezebert", "SqueezeBertModel"),
|
||||
("stablelm", "StableLmModel"),
|
||||
("starcoder2", "Starcoder2Model"),
|
||||
("swiftformer", "SwiftFormerModel"),
|
||||
("swin", "SwinModel"),
|
||||
("swin2sr", "Swin2SRModel"),
|
||||
|
|
@ -465,6 +466,7 @@ MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
|
|||
("rwkv", "RwkvForCausalLM"),
|
||||
("speech_to_text_2", "Speech2Text2ForCausalLM"),
|
||||
("stablelm", "StableLmForCausalLM"),
|
||||
("starcoder2", "Starcoder2ForCausalLM"),
|
||||
("transfo-xl", "TransfoXLLMHeadModel"),
|
||||
("trocr", "TrOCRForCausalLM"),
|
||||
("whisper", "WhisperForCausalLM"),
|
||||
|
|
@ -865,6 +867,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
|
|||
("roformer", "RoFormerForSequenceClassification"),
|
||||
("squeezebert", "SqueezeBertForSequenceClassification"),
|
||||
("stablelm", "StableLmForSequenceClassification"),
|
||||
("starcoder2", "Starcoder2ForSequenceClassification"),
|
||||
("t5", "T5ForSequenceClassification"),
|
||||
("tapas", "TapasForSequenceClassification"),
|
||||
("transfo-xl", "TransfoXLForSequenceClassification"),
|
||||
|
|
|
|||
|
|
@ -399,6 +399,7 @@ else:
|
|||
("SqueezeBertTokenizer", "SqueezeBertTokenizerFast" if is_tokenizers_available() else None),
|
||||
),
|
||||
("stablelm", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)),
|
||||
("starcoder2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),
|
||||
(
|
||||
"switch_transformers",
|
||||
(
|
||||
|
|
|
|||
|
|
@ -0,0 +1,62 @@
|
|||
# Copyright 2024 BigCode and 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.
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from ...utils import (
|
||||
OptionalDependencyNotAvailable,
|
||||
_LazyModule,
|
||||
is_torch_available,
|
||||
)
|
||||
|
||||
|
||||
_import_structure = {
|
||||
"configuration_starcoder2": ["STARCODER2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Starcoder2Config"],
|
||||
}
|
||||
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["modeling_starcoder2"] = [
|
||||
"Starcoder2ForCausalLM",
|
||||
"Starcoder2Model",
|
||||
"Starcoder2PreTrainedModel",
|
||||
"Starcoder2ForSequenceClassification",
|
||||
]
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .configuration_starcoder2 import STARCODER2_PRETRAINED_CONFIG_ARCHIVE_MAP, Starcoder2Config
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
from .modeling_starcoder2 import (
|
||||
Starcoder2ForCausalLM,
|
||||
Starcoder2ForSequenceClassification,
|
||||
Starcoder2Model,
|
||||
Starcoder2PreTrainedModel,
|
||||
)
|
||||
|
||||
|
||||
else:
|
||||
import sys
|
||||
|
||||
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
|
||||
|
|
@ -0,0 +1,147 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 BigCode and 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.
|
||||
""" Starcoder2 model configuration"""
|
||||
|
||||
from ...configuration_utils import PretrainedConfig
|
||||
from ...utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
STARCODER2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
|
||||
|
||||
|
||||
class Starcoder2Config(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`Starcoder2Model`]. It is used to instantiate a
|
||||
Starcoder2 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 [bigcode/starcoder2-7b_16k](https://huggingface.co/bigcode/starcoder2-7b_16k) model.
|
||||
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
|
||||
Args:
|
||||
vocab_size (`int`, *optional*, defaults to 49152):
|
||||
Vocabulary size of the Starcoder2 model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`Starcoder2Model`]
|
||||
hidden_size (`int`, *optional*, defaults to 3072):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 12288):
|
||||
Dimension of the MLP representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 30):
|
||||
Number of hidden layers in the Transformer encoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 24):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
num_key_value_heads (`int`, *optional*, defaults to 2):
|
||||
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
||||
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
||||
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
||||
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
||||
by meanpooling all the original heads within that group. For more details checkout [this
|
||||
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 4096):
|
||||
The maximum sequence length that this model might ever be used with. Starcoder2's sliding window attention
|
||||
allows sequence of up to 4096*32 tokens.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
norm_epsilon (`float`, *optional*, defaults to 1e-05):
|
||||
Epsilon value for the layer norm
|
||||
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`.
|
||||
bos_token_id (`int`, *optional*, defaults to 50256):
|
||||
The id of the "beginning-of-sequence" token.
|
||||
eos_token_id (`int`, *optional*, defaults to 50256):
|
||||
The id of the "end-of-sequence" token.
|
||||
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||||
The base period of the RoPE embeddings.
|
||||
sliding_window (`int`, *optional*):
|
||||
Sliding window attention window size. If not specified, will default to `None` (no sliding window).
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
residual_dropout (`float`, *optional*, defaults to 0.0):
|
||||
Residual connection dropout value.
|
||||
embedding_dropout (`float`, *optional*, defaults to 0.0):
|
||||
Embedding dropout.
|
||||
use_bias (`bool`, *optional*, defaults to `True`):
|
||||
Whether to use bias term on linear layers of the model.
|
||||
|
||||
|
||||
```python
|
||||
>>> from transformers import Starcoder2Model, Starcoder2Config
|
||||
|
||||
>>> # Initializing a Starcoder2 7B style configuration
|
||||
>>> configuration = Starcoder2Config()
|
||||
|
||||
>>> # Initializing a model from the Starcoder2 7B style configuration
|
||||
>>> model = Starcoder2Model(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = "starcoder2"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=49152,
|
||||
hidden_size=3072,
|
||||
intermediate_size=12288,
|
||||
num_hidden_layers=30,
|
||||
num_attention_heads=24,
|
||||
num_key_value_heads=2,
|
||||
hidden_act="gelu_pytorch_tanh",
|
||||
max_position_embeddings=4096,
|
||||
initializer_range=0.018042,
|
||||
norm_epsilon=1e-5,
|
||||
use_cache=True,
|
||||
bos_token_id=50256,
|
||||
eos_token_id=50256,
|
||||
rope_theta=10000.0,
|
||||
sliding_window=None,
|
||||
attention_dropout=0.0,
|
||||
residual_dropout=0.0,
|
||||
embedding_dropout=0.0,
|
||||
use_bias=True,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
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.sliding_window = sliding_window
|
||||
self.use_bias = use_bias
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.hidden_act = hidden_act
|
||||
self.initializer_range = initializer_range
|
||||
self.norm_epsilon = norm_epsilon
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.attention_dropout = attention_dropout
|
||||
self.residual_dropout = residual_dropout
|
||||
self.embedding_dropout = embedding_dropout
|
||||
|
||||
super().__init__(
|
||||
bos_token_id=bos_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
**kwargs,
|
||||
)
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -7895,6 +7895,34 @@ class StableLmPreTrainedModel(metaclass=DummyObject):
|
|||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class Starcoder2ForCausalLM(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class Starcoder2ForSequenceClassification(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class Starcoder2Model(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class Starcoder2PreTrainedModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,549 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 BigCode and 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 Starcoder2 model. """
|
||||
|
||||
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import pytest
|
||||
|
||||
from transformers import Starcoder2Config, is_torch_available
|
||||
from transformers.testing_utils import (
|
||||
require_bitsandbytes,
|
||||
require_flash_attn,
|
||||
require_torch,
|
||||
require_torch_gpu,
|
||||
slow,
|
||||
torch_device,
|
||||
)
|
||||
|
||||
from ...generation.test_utils import GenerationTesterMixin
|
||||
from ...test_configuration_common import ConfigTester
|
||||
from ...test_modeling_common import ModelTesterMixin, ids_tensor
|
||||
from ...test_pipeline_mixin import PipelineTesterMixin
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
from transformers import (
|
||||
AutoTokenizer,
|
||||
Starcoder2ForCausalLM,
|
||||
Starcoder2ForSequenceClassification,
|
||||
Starcoder2Model,
|
||||
)
|
||||
|
||||
|
||||
# Copied from transformers.tests.models.mistral.test_modeling_mistral.Starcoder2ModelTester with Mistral->Starcoder2
|
||||
class Starcoder2ModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=13,
|
||||
seq_length=7,
|
||||
is_training=True,
|
||||
use_input_mask=True,
|
||||
use_token_type_ids=False,
|
||||
use_labels=True,
|
||||
vocab_size=99,
|
||||
hidden_size=32,
|
||||
num_hidden_layers=2,
|
||||
num_attention_heads=4,
|
||||
num_key_value_heads=2,
|
||||
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,
|
||||
num_choices=4,
|
||||
pad_token_id=0,
|
||||
scope=None,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.seq_length = seq_length
|
||||
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.num_key_value_heads = num_key_value_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.num_choices = num_choices
|
||||
self.pad_token_id = pad_token_id
|
||||
self.scope = scope
|
||||
|
||||
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs
|
||||
def prepare_config_and_inputs(self):
|
||||
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
||||
|
||||
input_mask = None
|
||||
if self.use_input_mask:
|
||||
input_mask = torch.tril(torch.ones(self.batch_size, self.seq_length)).to(torch_device)
|
||||
|
||||
token_type_ids = None
|
||||
if self.use_token_type_ids:
|
||||
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
|
||||
|
||||
sequence_labels = None
|
||||
token_labels = None
|
||||
choice_labels = None
|
||||
if self.use_labels:
|
||||
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
|
||||
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
|
||||
choice_labels = ids_tensor([self.batch_size], self.num_choices)
|
||||
|
||||
config = self.get_config()
|
||||
|
||||
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
|
||||
# Ignore copy
|
||||
def get_config(self):
|
||||
return Starcoder2Config(
|
||||
vocab_size=self.vocab_size,
|
||||
hidden_size=self.hidden_size,
|
||||
num_hidden_layers=self.num_hidden_layers,
|
||||
num_attention_heads=self.num_attention_heads,
|
||||
num_key_value_heads=self.num_key_value_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,
|
||||
pad_token_id=self.pad_token_id,
|
||||
eos_token_id=self.pad_token_id,
|
||||
bos_token_id=self.pad_token_id,
|
||||
)
|
||||
|
||||
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->Starcoder2
|
||||
def create_and_check_model(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = Starcoder2Model(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=input_mask)
|
||||
result = model(input_ids)
|
||||
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
|
||||
|
||||
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model_as_decoder with Llama->Starcoder2
|
||||
def create_and_check_model_as_decoder(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
):
|
||||
config.add_cross_attention = True
|
||||
model = Starcoder2Model(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
)
|
||||
result = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
)
|
||||
result = model(input_ids, attention_mask=input_mask)
|
||||
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
|
||||
|
||||
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_for_causal_lm with Llama->Starcoder2
|
||||
def create_and_check_for_causal_lm(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
):
|
||||
model = Starcoder2ForCausalLM(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
|
||||
|
||||
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_decoder_model_past_large_inputs with Llama->Starcoder2
|
||||
def create_and_check_decoder_model_past_large_inputs(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
):
|
||||
config.is_decoder = True
|
||||
config.add_cross_attention = True
|
||||
model = Starcoder2ForCausalLM(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
# first forward pass
|
||||
outputs = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
use_cache=True,
|
||||
)
|
||||
past_key_values = outputs.past_key_values
|
||||
|
||||
# create hypothetical multiple next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
|
||||
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
|
||||
|
||||
# append to next input_ids and
|
||||
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
||||
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
|
||||
|
||||
output_from_no_past = model(
|
||||
next_input_ids,
|
||||
attention_mask=next_attention_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
output_hidden_states=True,
|
||||
)["hidden_states"][0]
|
||||
output_from_past = model(
|
||||
next_tokens,
|
||||
attention_mask=next_attention_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
output_hidden_states=True,
|
||||
)["hidden_states"][0]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
|
||||
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
|
||||
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
|
||||
|
||||
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
|
||||
|
||||
# test that outputs are equal for slice
|
||||
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
|
||||
|
||||
# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = config_and_inputs
|
||||
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
|
||||
# Copied from transformers.tests.models.mistral.test_modeling_mistral.MistralModelTest with Mistral->Starcoder2
|
||||
class Starcoder2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (
|
||||
(Starcoder2Model, Starcoder2ForCausalLM, Starcoder2ForSequenceClassification) if is_torch_available() else ()
|
||||
)
|
||||
all_generative_model_classes = (Starcoder2ForCausalLM,) if is_torch_available() else ()
|
||||
pipeline_model_mapping = (
|
||||
{
|
||||
"feature-extraction": Starcoder2Model,
|
||||
"text-classification": Starcoder2ForSequenceClassification,
|
||||
"text-generation": Starcoder2ForCausalLM,
|
||||
"zero-shot": Starcoder2ForSequenceClassification,
|
||||
}
|
||||
if is_torch_available()
|
||||
else {}
|
||||
)
|
||||
test_headmasking = False
|
||||
test_pruning = False
|
||||
|
||||
# TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146
|
||||
def is_pipeline_test_to_skip(
|
||||
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
|
||||
):
|
||||
return True
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = Starcoder2ModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=Starcoder2Config, 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_model_various_embeddings(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
for type in ["absolute", "relative_key", "relative_key_query"]:
|
||||
config_and_inputs[0].position_embedding_type = type
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
def test_Starcoder2_sequence_classification_model(self):
|
||||
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
print(config)
|
||||
config.num_labels = 3
|
||||
input_ids = input_dict["input_ids"]
|
||||
attention_mask = input_ids.ne(1).to(torch_device)
|
||||
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
|
||||
model = Starcoder2ForSequenceClassification(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
|
||||
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
|
||||
|
||||
def test_Starcoder2_sequence_classification_model_for_single_label(self):
|
||||
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.num_labels = 3
|
||||
config.problem_type = "single_label_classification"
|
||||
input_ids = input_dict["input_ids"]
|
||||
attention_mask = input_ids.ne(1).to(torch_device)
|
||||
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
|
||||
model = Starcoder2ForSequenceClassification(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
|
||||
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
|
||||
|
||||
def test_Starcoder2_sequence_classification_model_for_multi_label(self):
|
||||
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.num_labels = 3
|
||||
config.problem_type = "multi_label_classification"
|
||||
input_ids = input_dict["input_ids"]
|
||||
attention_mask = input_ids.ne(1).to(torch_device)
|
||||
sequence_labels = ids_tensor(
|
||||
[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
|
||||
).to(torch.float)
|
||||
model = Starcoder2ForSequenceClassification(config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
|
||||
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
|
||||
|
||||
@unittest.skip("Starcoder2 buffers include complex numbers, which breaks this test")
|
||||
def test_save_load_fast_init_from_base(self):
|
||||
pass
|
||||
|
||||
@unittest.skip("Starcoder2 uses GQA on all models so the KV cache is a non standard format")
|
||||
def test_past_key_values_format(self):
|
||||
pass
|
||||
|
||||
@require_flash_attn
|
||||
@require_torch_gpu
|
||||
@pytest.mark.flash_attn_test
|
||||
@slow
|
||||
def test_flash_attn_2_generate_padding_right(self):
|
||||
import torch
|
||||
|
||||
for model_class in self.all_generative_model_classes:
|
||||
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
model = model_class(config)
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
model.save_pretrained(tmpdirname)
|
||||
model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to(
|
||||
torch_device
|
||||
)
|
||||
|
||||
dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device)
|
||||
dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [1, 1, 1, 0]]).to(torch_device)
|
||||
|
||||
model.generate(dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False)
|
||||
|
||||
model = model_class.from_pretrained(
|
||||
tmpdirname,
|
||||
torch_dtype=torch.float16,
|
||||
attn_implementation="flash_attention_2",
|
||||
low_cpu_mem_usage=True,
|
||||
).to(torch_device)
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
_ = model.generate(
|
||||
dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False
|
||||
)
|
||||
|
||||
@require_flash_attn
|
||||
@require_torch_gpu
|
||||
@pytest.mark.flash_attn_test
|
||||
@slow
|
||||
def test_flash_attn_2_generate_use_cache(self):
|
||||
import torch
|
||||
|
||||
max_new_tokens = 30
|
||||
|
||||
for model_class in self.all_generative_model_classes:
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
dummy_input = inputs_dict[model_class.main_input_name]
|
||||
if dummy_input.dtype in [torch.float32, torch.bfloat16]:
|
||||
dummy_input = dummy_input.to(torch.float16)
|
||||
|
||||
# make sure that all models have enough positions for generation
|
||||
if hasattr(config, "max_position_embeddings"):
|
||||
config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1
|
||||
|
||||
model = model_class(config)
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdirname:
|
||||
model.save_pretrained(tmpdirname)
|
||||
|
||||
dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input))
|
||||
# NOTE: Starcoder2 apparently does not support right padding + use_cache with FA2.
|
||||
dummy_attention_mask[:, -1] = 1
|
||||
|
||||
model = model_class.from_pretrained(
|
||||
tmpdirname,
|
||||
torch_dtype=torch.float16,
|
||||
attn_implementation="flash_attention_2",
|
||||
low_cpu_mem_usage=True,
|
||||
).to(torch_device)
|
||||
|
||||
# Just test that a large cache works as expected
|
||||
_ = model.generate(
|
||||
dummy_input,
|
||||
attention_mask=dummy_attention_mask,
|
||||
max_new_tokens=max_new_tokens,
|
||||
do_sample=False,
|
||||
use_cache=True,
|
||||
)
|
||||
|
||||
@require_flash_attn
|
||||
@require_torch_gpu
|
||||
@pytest.mark.flash_attn_test
|
||||
@slow
|
||||
def test_flash_attn_2_inference_padding_right(self):
|
||||
self.skipTest("Starcoder2 flash attention does not support right padding")
|
||||
|
||||
|
||||
@slow
|
||||
@require_torch_gpu
|
||||
class Starcoder2IntegrationTest(unittest.TestCase):
|
||||
def test_starcoder2_batched_generation_sdpa(self):
|
||||
EXPECTED_TEXT = [
|
||||
"Hello my name is Younes and I am a student at the University of Liverpool. I am currently studying for my MSc in Computer Science. I am interested in the field of Machine Learning and I am currently working on",
|
||||
"def hello_world():\n\treturn 'Hello World!'\n\n@app.route('/hello/<name>')\ndef hello_name(name):\n\treturn 'Hello %s!' % name\n\n@app",
|
||||
]
|
||||
model_id = "bigcode/starcoder2-7b_16k"
|
||||
|
||||
model = Starcoder2ForCausalLM.from_pretrained(
|
||||
model_id, torch_dtype=torch.float16, device_map="auto", attn_implementation="sdpa"
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
|
||||
text = ["Hello my name is Younes and", "def hello_world():"]
|
||||
inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
|
||||
|
||||
output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
|
||||
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
|
||||
self.assertEqual(EXPECTED_TEXT, output_text)
|
||||
|
||||
def test_starcoder2_batched_generation_eager(self):
|
||||
EXPECTED_TEXT = [
|
||||
"Hello my name is Younes and I am a student at the University of Liverpool. I am currently studying for my MSc in Computer Science. I am interested in the field of Machine Learning and I am currently working on",
|
||||
"def hello_world():\n\treturn 'Hello World!'\n\n@app.route('/hello/<name>')\ndef hello_name(name):\n\treturn 'Hello %s!' % name\n\n@app",
|
||||
]
|
||||
model_id = "bigcode/starcoder2-7b_16k"
|
||||
|
||||
model = Starcoder2ForCausalLM.from_pretrained(
|
||||
model_id, torch_dtype=torch.float16, device_map="auto", attn_implementation="eager"
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
|
||||
text = ["Hello my name is Younes and", "def hello_world():"]
|
||||
inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
|
||||
|
||||
output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
|
||||
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
|
||||
self.assertEqual(EXPECTED_TEXT, output_text)
|
||||
|
||||
@require_flash_attn
|
||||
def test_starcoder2_batched_generation_fa2(self):
|
||||
EXPECTED_TEXT = [
|
||||
"Hello my name is Younes and I am a student at the University of Liverpool. I am currently studying for my MSc in Computer Science. I am interested in the field of Machine Learning and I am currently working on",
|
||||
"def hello_world():\n\treturn 'Hello World!'\n\n@app.route('/hello/<name>')\ndef hello_name(name):\n\treturn 'Hello %s!' % name\n\n@app",
|
||||
]
|
||||
model_id = "bigcode/starcoder2-7b_16k"
|
||||
|
||||
model = Starcoder2ForCausalLM.from_pretrained(
|
||||
model_id, torch_dtype=torch.float16, device_map="auto", attn_implementation="flash_attention_2"
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
|
||||
text = ["Hello my name is Younes and", "def hello_world():"]
|
||||
inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
|
||||
|
||||
output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
|
||||
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
|
||||
self.assertEqual(EXPECTED_TEXT, output_text)
|
||||
|
||||
@require_bitsandbytes
|
||||
def test_starcoder2_batched_generation_4bit(self):
|
||||
EXPECTED_TEXT = [
|
||||
'Hello my name is Younes and I am a student at the University of Maryland. I am currently working on a project that is related to the topic of "How to make a game". I am currently working on a project',
|
||||
'def hello_world():\n\treturn "Hello World"\n\n@app.route(\'/hello/<name>\')\ndef hello_name(name):\n\treturn "Hello " + name\n\n@app.route',
|
||||
]
|
||||
model_id = "bigcode/starcoder2-7b_16k"
|
||||
|
||||
model = Starcoder2ForCausalLM.from_pretrained(model_id, load_in_4bit=True)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
|
||||
text = ["Hello my name is Younes and", "def hello_world():"]
|
||||
inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
|
||||
|
||||
output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
|
||||
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
|
||||
self.assertEqual(EXPECTED_TEXT, output_text)
|
||||
|
|
@ -809,6 +809,7 @@ src/transformers/models/splinter/configuration_splinter.py
|
|||
src/transformers/models/splinter/modeling_splinter.py
|
||||
src/transformers/models/squeezebert/modeling_squeezebert.py
|
||||
src/transformers/models/stablelm/modeling_stablelm.py
|
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src/transformers/models/starcoder2/modeling_starcoder2.py
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src/transformers/models/swiftformer/configuration_swiftformer.py
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src/transformers/models/swiftformer/convert_swiftformer_original_to_hf.py
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src/transformers/models/swiftformer/modeling_swiftformer.py
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Reference in New Issue