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README.md ADDED
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+ ---
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+ language:
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+ - ar
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+ - en
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+ thumbnail: null
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+ tags:
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+ - Arabic
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+ - English
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+ - LLM
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+ - Decoder
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+ - causal-lm
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+ license: apache-2.0
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+ pipeline_tag: conversational
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+ ---
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+
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+ # Jais-13b-chat
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+ This is a 13 billion parameter fine-tuned bilingual large language model for both Arabic and English.
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+ It is based on transformer-based decoder-only (GPT-3) architecture and uses SwiGLU
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+ non-linearity. It implements ALiBi position embeddings, enabling the model to extrapolate
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+ to long sequence lengths, providing improved context handling and model precision.
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+
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+ Jais-13b-chat is [Jais-13b](https://huggingface.co/inception-mbzuai/jais-13b) fine-tuned over a curated set of 4 million Arabic and 6 million English prompt-response pairs.
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+ We further fine-tune our model with safety-oriented instruction, as well as providing extra guardrails in the
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+ form of a safety prompt. Our pre-trained model, [Jais-13b](https://huggingface.co/inception-mbzuai/jais-13b), is trained on
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+ 116 billion Arabic tokens and 279 billion English tokens.
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+
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+ The combination of the largest curated Arabic and English instruction tuning dataset along with the addition of multi-turn conversations allows the model to converse in a variety of topics, with a particular focus on the Arab world.
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+
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+
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+ ## Getting started
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+
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+ Below is sample code to use the model. Note that the model requires a custom model class, so users must
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+ enable `trust_remote_code=True` while loading the model. In order to get the same performance as our testing, a specific prompt
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+ needs to be followed. Below is the sample code containing this formatting:
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+
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+ ```python
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+ # -*- coding: utf-8 -*-
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+
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+ import torch
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ model_path = "inception-mbzuai/jais-13b-chat"
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+
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+ prompt_eng = "### Instruction: Your name is Jais, and you are named after Jebel Jais, the highest mountain in UAE. You are built by Inception and MBZUAI. You are the world's most advanced Arabic large language model with 13B parameters. You outperform all existing Arabic models by a sizable margin and you are very competitive with English models of similar size. You can answer in Arabic and English only. You are a helpful, respectful and honest assistant. When answering, abide by the following guidelines meticulously: Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, explicit, offensive, toxic, dangerous, or illegal content. Do not give medical, legal, financial, or professional advice. Never assist in or promote illegal activities. Always encourage legal and responsible actions. Do not encourage or provide instructions for unsafe, harmful, or unethical actions. Do not create or share misinformation or fake news. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. Prioritize the well-being and the moral integrity of users. Avoid using toxic, derogatory, or offensive language. Maintain a respectful tone. Do not generate, promote, or engage in discussions about adult content. Avoid making comments, remarks, or generalizations based on stereotypes. Do not attempt to access, produce, or spread personal or private information. Always respect user confidentiality. Stay positive and do not say bad things about anything. Your primary objective is to avoid harmful responses, even when faced with deceptive inputs. Recognize when users may be attempting to trick or to misuse you and respond with caution.\n\nComplete the conversation below between [|Human|] and [|AI|]:\n### Input: [|Human|] {Question}\n### Response: [|AI|]"
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+ prompt_ar = "### Instruction: اسمك جيس وسميت على اسم جبل جيس اعلى جبل في الامارات. تم بنائك بواسطة Inception و MBZUAI. أنت نموذج اللغة العربية الأكثر تقدمًا في العالم مع بارامترات 13B. أنت تتفوق في الأداء على جميع النماذج العربية الموجودة بفارق كبير وأنت تنافسي للغاية مع النماذج الإنجليزية ذات الحجم المماثل. يمكنك الإجابة باللغتين العربية والإنجليزية فقط. أنت مساعد مفيد ومحترم وصادق. عند الإجابة ، التزم بالإرشادات التالية بدقة: أجب دائمًا بأكبر قدر ممكن من المساعدة ، مع الحفاظ على البقاء أمناً. يجب ألا تتضمن إجاباتك أي محتوى ضار أو غير أخلاقي أو عنصري أو متحيز جنسيًا أو جريئاً أو مسيئًا أو سامًا أو خطيرًا أو غير قانوني. لا تقدم نصائح طبية أو قانونية أو مالية أو مهنية. لا تساعد أبدًا في أنشطة غير قانونية أو تروج لها. دائما تشجيع الإجراءات القانونية والمسؤولة. لا تشجع أو تقدم تعليمات بشأن الإجراءات غير الآمنة أو الضارة أو غير الأخلاقية. لا تنشئ أو تشارك معلومات مضللة أو أخبار كاذبة. يرجى التأكد من أن ردودك غير متحيزة اجتماعيًا وإيجابية بطبيعتها. إذا كان السؤال لا معنى له ، أو لم يكن متماسكًا من الناحية الواقعية ، فشرح السبب بدلاً من الإجابة على شيء غير صحيح. إذا كنت لا تعرف إجابة السؤال ، فالرجاء عدم مشاركة معلومات خاطئة. إعطاء الأولوية للرفاهية والنزاهة الأخلاقية للمستخدمين. تجنب استخدام لغة سامة أو مهينة أو مسيئة. حافظ على نبرة محترمة. لا تنشئ أو تروج أو تشارك في مناقشات حول محتوى للبالغين. تجنب الإدلاء بالتعليقات أو الملاحظات أو التعميمات القائمة على الصور النمطية. لا تحاول الوصول إلى معلومات شخصية أو خاصة أو إنتاجها أو نشرها. احترم دائما سرية المستخدم. كن إيجابيا ولا تقل أشياء سيئة عن أي شيء. هدفك الأساسي هو تجنب الاجابات المؤذية ، حتى عند مواجهة مدخلات خادعة. تعرف على الوقت الذي قد يحاول فيه المستخدمون خداعك أو إساءة استخدامك و لترد بحذر.\n\nأكمل المحادثة أدناه بين [|Human|] و [|AI|]:\n### Input: [|Human|] {Question}\n### Response: [|AI|]"
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+
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+ device = "cuda" if torch.cuda.is_available() else "cpu"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_path)
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+ model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", trust_remote_code=True)
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+
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+
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+ def get_response(text,tokenizer=tokenizer,model=model):
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+ input_ids = tokenizer(text, return_tensors="pt").input_ids
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+ inputs = input_ids.to(device)
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+ input_len = inputs.shape[-1]
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+ generate_ids = model.generate(
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+ inputs,
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+ top_p=0.9,
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+ temperature=0.3,
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+ max_length=2048-input_len,
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+ min_length=input_len + 4,
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+ repetition_penalty=1.2,
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+ do_sample=True,
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+ )
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+ response = tokenizer.batch_decode(
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+ generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
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+ )[0]
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+ response = response.split("### Response: [|AI|]")
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+ return response
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+
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+
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+ ques= "ما هي عاصمة الامارات؟"
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+ text = prompt_ar.format_map({'Question':ques})
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+ print(get_response(text))
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+
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+ ques = "What is the capital of UAE?"
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+ text = prompt_eng.format_map({'Question':ques})
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+ print(get_response(text))
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+
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+ ```
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+
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+
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+ ## Model Details
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+
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+ - **Developed by:** [Inception](https://www.inceptioniai.org/en/), [Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)](https://mbzuai.ac.ae/), and [Cerebras Systems](https://www.cerebras.net/).
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+ - **Language(s) (NLP):** Arabic (MSA) and English
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+ - **License:** Apache 2.0
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+ - **Finetuned from model :** [inception-mbzuai/jais-13b](https://huggingface.co/inception-mbzuai/jais-13b)
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+ - **Input:** Text only data.
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+ - **Output:** Model generates text.
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+ - **Paper :** [Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models](https://inceptioniai.org)
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+ - **Demo :** [Access here](https://arabic-gpt.ai)
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+
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+
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+ ## Intended Use
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+
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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+ We release the jais-13b-chat model under a full open source license. We welcome all feedback and opportunities to collaborate.
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+
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+ This model is the first release from the Inception - MBZUAI - Cerebras parternship, and at the time of release, achieved state of the art across a comprehensive Arabic test suite as described in the accompanying tech report.
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+ Some potential downstream uses include:
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+
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+ - *Research*: This model can be used by researchers and developers.
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+ - *Commercial Use*: Jais-13b-chat can be directly used for chat with suitable prompting or further fine-tuned for specific use cases.
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+ Some potential use cases include:
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+ - Chat-assistants.
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+ - Customer service.
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+
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+ Audiences that we hope will benefit from our model:
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+ - *Academics*: For those researching Arabic natural language processing.
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+ - *Businesses*: Companies targeting Arabic-speaking audiences.
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+ - *Developers*: Those integrating Arabic language capabilities in apps.
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+
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+
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ While jais-13b-chat is a powerful Arabic and English bilingual model, it's essential to understand its limitations and the potential of misuse.
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+ It is prohibited to use the model in any manner that violates applicable laws or regulations.
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+ The following are some example scenarios where the model should not be used.
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+
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+ - *Malicious Use*: The model should not be used for generating harmful, misleading, or inappropriate content. This includes but is not limited to:
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+ - Generating or promoting hate speech, violence, or discrimination.
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+ - Spreading misinformation or fake news.
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+ - Engaging in or promoting illegal activities.
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+
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+ - *Sensitive Information*: The model should not be used to handle or generate personal, confidential, or sensitive information.
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+
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+ - *Generalization Across All Languages*: Jais-13b is bilingual and optimized for Arabic and English, it should not be assumed to have equal proficiency in other languages or dialects.
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+
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+ - *High-Stakes Decisions*: The model should not be used to make high-stakes decisions without human oversight. This includes medical, legal, financial, or safety-critical decisions.
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+
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+
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ The model is trained on publicly available data which was in part curated by Inception. We have employed different
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+ techniqes to reduce bias in the model. While efforts have been made to minimize biases, it is likely that the model, as with all LLM models, will exhibit some bias.
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+
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+ The model is trained as an AI assistant for Arabic and English speakers. The model is limited to produce responses for queries in these two languages
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+ and may not produce appropriate responses to other language queries.
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+
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+ jais-13b-chat model is finetuned with both Arabic and English prompt-response pairs. We included a wide range of
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+ instructional data across various domains. In total, our instruction-tuning
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+ dataset has 3.8M and 5.9M prompt-response pairs for Arabic and English, respectively. For English, we used
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+ publicly available instruction tuning datasets. For Arabic, we internally curated instruction data and augmented it with translated Arabic data.
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+
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+
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+ Further details about the training data can be found in the technical report.
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+
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+
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+ ### Training Procedure
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+
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+
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+ In instruction tuning, each instance comprises a prompt and its corresponding response.
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+ Padding is applied to each instance since, unlike pretraining, finetuning is done with unpacked data.
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+ We utilize the same autoregressive objective as employed in the pretraining of the LLM.
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+ However, we masked the loss on the prompt i.e. backpropagation is performed only on answer tokens.
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+
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+
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+ The training process was performed on the Condor Galaxy 1 (CG-1) supercomputer platform.
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+
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+ #### Training Hyperparameters
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+
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+ | Hyperparameter | Value |
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+ |----------------------------|----------------|
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+ | Precision | fp32 |
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+ | Optimizer | AdamW |
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+ | Learning rate | 0 to 6.7e-04 (<= 400 steps) |
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+ | | 6.7e-04 to 6.7e-05 (> 8305 steps) |
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+ | Weight decay | 0.1 |
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+ | Batch size | 3392 |
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+ | Steps | 8705 |
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+
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+
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+
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+
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+ ## Evaluation
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+
197
+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ We conducted a comprehensive evaluation of Jais-chat and benchmarked it other leading base language models, focusing on both English and Arabic. The evaluation criteria spanned various dimensions, including:
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+
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+ - **Knowledge:** How well the model answers factual questions.
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+ - **Reasoning:** The model's ability to answer questions requiring reasoning.
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+ - **Misinformation/Bias:** Assessment of the model's susceptibility to generating false or misleading information, and its neutrality.
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+
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+ Arabic evaluation results:
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+
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+ | Models | Avg |EXAMS | MMLU (M) | LitQA | Hellaswag | PIQA | BoolQA | SituatedQA | ARC-C | OpenBookQA | TruthfulQA | CrowS-Pairs |
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+ |-------------------|-------|------|----------|-------|-----------|------|--------|------------|-------|------------|------------|-------------|
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+ | Jais-chat (13B) | **48.4** | 39.7 | 34.0 | 52.6 | 61.4 | 67.5 | 65.7 | 47.0 | 40.7 | 31.6 | 44.8 | 56.4 |
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+ | BLOOMz (7.1B) | 42.9 | 34.9 | 31.0 | 44.0 | 38.1 | 59.1 | 66.6 | 42.8 | 30.2 | 29.2 | 48.4 | 55.8 |
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+ | mT0-XXL (13B) | 40.9 | 31.5 | 31.2 | 36.6 | 33.9 | 56.1 | 77.8 | 44.7 | 26.1 | 27.8 | 44.5 | 45.3 |
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+ | LLaMA2-Chat (13B) | 38.1 | 26.3 | 29.1 | 33.1 | 32.0 | 52.1 | 66.0 | 36.3 | 24.1 | 28.4 | 48.6 | 47.2 |
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+ | AraBART (550M) | 36.7 | 26.5 | 27.5 | 34.3 | 28.1 | 52.6 | 57.1 | 34.6 | 25.1 | 28.6 | 49.8 | 48.8 |
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+ | AraT5 (220M) | 32.0 | 24.7 | 23.8 | 26.3 | 25.5 | 50.4 | 58.2 | 33.9 | 24.7 | 25.4 | 20.9 | 47.2 |
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+
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+
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+
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+ All tasks above report accuracy or F1 scores (the higher the better). For the sake of brevity, we do not include results over English tasks.
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+ Detailed comparisons in both languages and evaluation dataset details can be found in the technical report.
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+
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+
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+
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+ ## Generation Example
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+
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+
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+ <p align="center"> <img src="https://huggingface.co/inception-mbzuai/jais-13b/resolve/main/Rent_Example.png" width="600" /></p>
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+
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+ <!-- ## Citation [optional] -->
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+
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+
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+
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+ <!--**BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]
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+
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+ -->
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+ Copyright Inception Institute of Artificial Intelligence Ltd.
config.json ADDED
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+ {
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+ "_name_or_path": "inception-mbzuai/jais-13b-chat",
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+ "activation_function": "swiglu",
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+ "architectures": [
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+ "JAISLMHeadModel"
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+ ],
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+ "attn_pdrop": 0.0,
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+ "auto_map": {
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+ "AutoConfig": "configuration_jais.JAISConfig",
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+ "AutoModel": "modeling_jais.JAISModel",
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+ "AutoModelForCausalLM": "modeling_jais.JAISLMHeadModel"
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+ },
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+ "bos_token_id": 0,
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+ "embd_pdrop": 0.0,
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+ "embeddings_scale": 14.6,
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+ "eos_token_id": 0,
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+ "initializer_range": 0.02,
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+ "layer_norm_epsilon": 1e-05,
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+ "model_type": "jais",
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+ "n_embd": 5120,
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+ "n_head": 40,
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+ "n_inner": 13653,
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+ "n_layer": 40,
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+ "n_positions": 2048,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "alibi",
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+ "reorder_and_upcast_attn": false,
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+ "resid_pdrop": 0.0,
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+ "scale_attn_by_inverse_layer_idx": false,
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+ "scale_attn_weights": true,
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+ "scale_qk_dot_by_d": true,
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+ "tie_word_embeddings": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.28.0.dev0",
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+ "use_cache": true,
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+ "vocab_size": 84992,
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+ "width_scale": 0.11100000000000002
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+ }
configuration_jais.py ADDED
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+ # coding=utf-8
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+ # Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team.
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+ # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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+ # Copyright 2023 Cerebras Systems.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """ JAIS configuration"""
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+
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+ from transformers.configuration_utils import PretrainedConfig
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+ from transformers.utils import logging
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+
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+
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+ logger = logging.get_logger(__name__)
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+
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+ class JAISConfig(PretrainedConfig):
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+ """
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+ This is the configuration class to store the configuration of a [`JAISModel`]. It is used to
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+ instantiate a JAIS model according to the specified arguments, defining the model architecture.
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+
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+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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+ documentation from [`PretrainedConfig`] for more information.
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+
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+
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+ Args:
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+ vocab_size (`int`, *optional*, defaults to 50257):
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+ Vocabulary size of the JAIS model. Defines the number of different tokens that can be represented by the
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+ `inputs_ids` passed when calling [`JAISModel`].
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+ n_positions (`int`, *optional*, defaults to 1024):
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+ The maximum sequence length that this model might ever be used with. Typically set this to something large
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+ just in case (e.g., 512 or 1024 or 2048).
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+ n_embd (`int`, *optional*, defaults to 768):
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+ Dimensionality of the embeddings and hidden states.
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+ n_layer (`int`, *optional*, defaults to 12):
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+ Number of hidden layers in the Transformer encoder.
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+ n_head (`int`, *optional*, defaults to 12):
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+ Number of attention heads for each attention layer in the Transformer encoder.
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+ n_inner (`int`, *optional*, defaults to None):
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+ Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
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+ activation_function (`str`, *optional*, defaults to `"gelu"`):
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+ Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new", "swiglu"]`.
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+ resid_pdrop (`float`, *optional*, defaults to 0.1):
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+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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+ embd_pdrop (`float`, *optional*, defaults to 0.1):
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+ The dropout ratio for the embeddings.
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+ attn_pdrop (`float`, *optional*, defaults to 0.1):
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+ The dropout ratio for the attention.
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+ layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
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+ The epsilon to use in the layer normalization layers.
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+ initializer_range (`float`, *optional*, defaults to 0.02):
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+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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+ scale_attn_weights (`bool`, *optional*, defaults to `True`):
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+ Scale attention weights by dividing by sqrt(hidden_size)..
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+ use_cache (`bool`, *optional*, defaults to `True`):
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+ Whether or not the model should return the last key/values attentions (not used by all models).
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+ scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
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+ Whether to additionally scale attention weights by `1 / layer_idx + 1`.
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+ reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
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+ Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
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+ dot-product/softmax to float() when training with mixed precision.
70
+ position_embedding_type (`str`, *optional*, defaults to `"learned"`):
71
+ Positional embedding can be either `"alibi"` or `"learned"`.
72
+ width_scale (`float`, *optional*, defaults to 1.0):
73
+ muP parameter to scale output logits and initializers. Calculated as (`d_model,0 / d_model`),
74
+ where `d_model` is the model's width and `d_model,0` is the proxy model's width.
75
+ embeddings_scale (`float`, *optional*, defaults to 1.0):
76
+ muP parameter to scale token and position embeddings.
77
+ scale_qk_dot_by_d (`bool`, *optional*, defaults to `False`):
78
+ Scale attention weights by dividing by hidden_size instead of sqrt(hidden_size).
79
+ Need to set scale_attn_weights to `True` as well.
80
+
81
+ """
82
+
83
+ model_type = "jais"
84
+ keys_to_ignore_at_inference = ["past_key_values"]
85
+ attribute_map = {
86
+ "hidden_size": "n_embd",
87
+ "max_position_embeddings": "n_positions",
88
+ "num_attention_heads": "n_head",
89
+ "num_hidden_layers": "n_layer",
90
+ }
91
+
92
+ def __init__(
93
+ self,
94
+ vocab_size=50257,
95
+ n_positions=1024,
96
+ n_embd=768,
97
+ n_layer=12,
98
+ n_head=12,
99
+ n_inner=None,
100
+ activation_function="gelu_new",
101
+ resid_pdrop=0.1,
102
+ embd_pdrop=0.1,
103
+ attn_pdrop=0.1,
104
+ layer_norm_epsilon=1e-5,
105
+ initializer_range=0.02,
106
+ scale_attn_weights=True,
107
+ use_cache=True,
108
+ bos_token_id=50256,
109
+ eos_token_id=50256,
110
+ scale_attn_by_inverse_layer_idx=False,
111
+ reorder_and_upcast_attn=False,
112
+ position_embedding_type="learned",
113
+ width_scale=1.0,
114
+ embeddings_scale=1.0,
115
+ scale_qk_dot_by_d=False,
116
+ **kwargs,
117
+ ):
118
+ self.vocab_size = vocab_size
119
+ self.n_positions = n_positions
120
+ self.n_embd = n_embd
121
+ self.n_layer = n_layer
122
+ self.n_head = n_head
123
+ self.n_inner = n_inner
124
+ self.activation_function = activation_function
125
+ self.resid_pdrop = resid_pdrop
126
+ self.embd_pdrop = embd_pdrop
127
+ self.attn_pdrop = attn_pdrop
128
+ self.layer_norm_epsilon = layer_norm_epsilon
129
+ self.initializer_range = initializer_range
130
+ self.scale_attn_weights = scale_attn_weights
131
+ self.use_cache = use_cache
132
+ self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
133
+ self.reorder_and_upcast_attn = reorder_and_upcast_attn
134
+
135
+ self.bos_token_id = bos_token_id
136
+ self.eos_token_id = eos_token_id
137
+
138
+ self.position_embedding_type = position_embedding_type
139
+ self.width_scale = width_scale
140
+ self.embeddings_scale = embeddings_scale
141
+ self.scale_qk_dot_by_d = scale_qk_dot_by_d
142
+
143
+ super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
generation_config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 0,
4
+ "eos_token_id": 0,
5
+ "pad_token_id": 0,
6
+ "transformers_version": "4.28.0.dev0"
7
+ }
modeling_jais.py ADDED
@@ -0,0 +1,1522 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team.
3
+ # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
4
+ # Copyright 2023 G42 Systems.
5
+ #
6
+ # Licensed under the Apache License, Version 2.0 (the "License");
7
+ # you may not use this file except in compliance with the License.
8
+ # You may obtain a copy of the License at
9
+ #
10
+ # http://www.apache.org/licenses/LICENSE-2.0
11
+ #
12
+ # Unless required by applicable law or agreed to in writing, software
13
+ # distributed under the License is distributed on an "AS IS" BASIS,
14
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15
+ # See the License for the specific language governing permissions and
16
+ # limitations under the License.
17
+ """ PyTorch JAIS model."""
18
+
19
+ import math
20
+ import os
21
+ import warnings
22
+ from typing import Optional, Tuple, Union
23
+
24
+ import torch
25
+ from torch import Tensor, nn
26
+ from torch.cuda.amp import autocast
27
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
28
+
29
+ from transformers.activations import ACT2FN
30
+ from transformers.modeling_outputs import (
31
+ BaseModelOutputWithPastAndCrossAttentions,
32
+ CausalLMOutputWithCrossAttentions,
33
+ QuestionAnsweringModelOutput,
34
+ SequenceClassifierOutputWithPast,
35
+ TokenClassifierOutput,
36
+ )
37
+ from transformers.modeling_utils import PreTrainedModel
38
+ from transformers.pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
39
+ from transformers.utils import (
40
+ add_code_sample_docstrings,
41
+ add_start_docstrings,
42
+ add_start_docstrings_to_model_forward,
43
+ logging,
44
+ )
45
+ from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
46
+ from .configuration_jais import JAISConfig
47
+
48
+
49
+ logger = logging.get_logger(__name__)
50
+
51
+ _CHECKPOINT_FOR_DOC = "IIAI/checkpoint"
52
+ _CONFIG_FOR_DOC = "JAISConfig"
53
+
54
+
55
+ class SwiGLUActivation(nn.Module):
56
+ def forward(self, x1: Tensor, x2: Tensor) -> Tensor:
57
+ return x1 * nn.functional.silu(x2)
58
+
59
+
60
+ class AlibiPositionEmbeddingLayer(nn.Module):
61
+ def __init__(self, num_heads):
62
+ super(AlibiPositionEmbeddingLayer, self).__init__()
63
+
64
+ self.num_heads = num_heads
65
+ slopes = torch.tensor(
66
+ AlibiPositionEmbeddingLayer._get_alibi_slopes(num_heads)
67
+ ).unsqueeze(-1)
68
+ self.slopes = nn.parameter.Parameter(slopes, requires_grad=False)
69
+
70
+ def forward(self, seq_length, key_length, cached_qk_len):
71
+ context_position = torch.arange(
72
+ cached_qk_len, cached_qk_len + seq_length, device=self.slopes.device
73
+ )[:, None]
74
+ memory_position = torch.arange(
75
+ key_length + cached_qk_len, device=self.slopes.device
76
+ )[None, :]
77
+ relative_position = memory_position - context_position
78
+ relative_position = torch.abs(relative_position).unsqueeze(0).expand(self.num_heads, -1, -1)
79
+ alibi = (self.slopes * -1.0).unsqueeze(1) * relative_position
80
+ return alibi
81
+
82
+ @staticmethod
83
+ def _get_alibi_slopes(n):
84
+ def get_slopes_power_of_2(n):
85
+ start = 2 ** (-(2 ** -(math.log2(n) - 3)))
86
+ ratio = start
87
+ return [start * ratio ** i for i in range(n)]
88
+
89
+ if math.log2(n).is_integer():
90
+ return get_slopes_power_of_2(
91
+ n
92
+ ) # In the paper, we only train models that have 2^a heads for some a. This function has
93
+ else: # some good properties that only occur when the input is a power of 2. To maintain that even
94
+ closest_power_of_2 = 2 ** math.floor(
95
+ math.log2(n)
96
+ ) # when the number of heads is not a power of 2, we use this workaround.
97
+ return (
98
+ get_slopes_power_of_2(closest_power_of_2)
99
+ + AlibiPositionEmbeddingLayer._get_alibi_slopes(
100
+ 2 * closest_power_of_2
101
+ )[0::2][: n - closest_power_of_2]
102
+ )
103
+
104
+
105
+ def load_tf_weights_in_jais(model, config, jais_checkpoint_path):
106
+ """Load tf checkpoints in a pytorch model"""
107
+ try:
108
+ import re
109
+
110
+ import tensorflow as tf
111
+ except ImportError:
112
+ logger.error(
113
+ "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
114
+ "https://www.tensorflow.org/install/ for installation instructions."
115
+ )
116
+ raise
117
+ tf_path = os.path.abspath(jais_checkpoint_path)
118
+ logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
119
+ # Load weights from TF model
120
+ init_vars = tf.train.list_variables(tf_path)
121
+ names = []
122
+ arrays = []
123
+ for name, shape in init_vars:
124
+ logger.info(f"Loading TF weight {name} with shape {shape}")
125
+ array = tf.train.load_variable(tf_path, name)
126
+ names.append(name)
127
+ arrays.append(array.squeeze())
128
+
129
+ for name, array in zip(names, arrays):
130
+ name = name[6:] # skip "model/"
131
+ name = name.split("/")
132
+ pointer = model
133
+ for m_name in name:
134
+ if re.fullmatch(r"[A-Za-z]+\d+", m_name):
135
+ scope_names = re.split(r"(\d+)", m_name)
136
+ else:
137
+ scope_names = [m_name]
138
+ if scope_names[0] == "w" or scope_names[0] == "g":
139
+ pointer = getattr(pointer, "weight")
140
+ elif scope_names[0] == "b":
141
+ pointer = getattr(pointer, "bias")
142
+ elif scope_names[0] == "wpe" or scope_names[0] == "wte":
143
+ pointer = getattr(pointer, scope_names[0])
144
+ pointer = getattr(pointer, "weight")
145
+ else:
146
+ pointer = getattr(pointer, scope_names[0])
147
+ if len(scope_names) >= 2:
148
+ num = int(scope_names[1])
149
+ pointer = pointer[num]
150
+ try:
151
+ assert (
152
+ pointer.shape == array.shape
153
+ ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
154
+ except AssertionError as e:
155
+ e.args += (pointer.shape, array.shape)
156
+ raise
157
+ logger.info(f"Initialize PyTorch weight {name}")
158
+ pointer.data = torch.from_numpy(array)
159
+ return model
160
+
161
+
162
+ class JAISAttention(nn.Module):
163
+ def __init__(self, config, is_cross_attention=False, layer_idx=None):
164
+ super().__init__()
165
+
166
+ max_positions = config.max_position_embeddings
167
+ self.register_buffer(
168
+ "bias",
169
+ torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
170
+ 1, 1, max_positions, max_positions
171
+ ),
172
+ persistent=False,
173
+ )
174
+ self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False)
175
+
176
+ self.embed_dim = config.hidden_size
177
+ self.num_heads = config.num_attention_heads
178
+ self.head_dim = self.embed_dim // self.num_heads
179
+ self.split_size = self.embed_dim
180
+ if self.head_dim * self.num_heads != self.embed_dim:
181
+ raise ValueError(
182
+ f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
183
+ f" {self.num_heads})."
184
+ )
185
+
186
+ self.scale_attn_weights = config.scale_attn_weights
187
+ self.is_cross_attention = is_cross_attention
188
+
189
+ # Layer-wise attention scaling, reordering, and upcasting
190
+ self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
191
+ self.layer_idx = layer_idx
192
+ self.reorder_and_upcast_attn = config.reorder_and_upcast_attn
193
+
194
+ if self.is_cross_attention:
195
+ self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim)
196
+ self.q_attn = Conv1D(self.embed_dim, self.embed_dim)
197
+ else:
198
+ self.c_attn = Conv1D(3 * self.embed_dim, self.embed_dim)
199
+ self.c_proj = Conv1D(self.embed_dim, self.embed_dim)
200
+
201
+ self.attn_dropout = nn.Dropout(config.attn_pdrop)
202
+ self.resid_dropout = nn.Dropout(config.resid_pdrop)
203
+
204
+ self.pruned_heads = set()
205
+
206
+ self.attn_scale_power = 1.0 if config.scale_qk_dot_by_d else 0.5
207
+
208
+ def prune_heads(self, heads):
209
+ if len(heads) == 0:
210
+ return
211
+ heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads)
212
+ index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
213
+
214
+ # Prune conv1d layers
215
+ self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
216
+ self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
217
+
218
+ # Update hyper params
219
+ self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads))
220
+ self.num_heads = self.num_heads - len(heads)
221
+ self.pruned_heads = self.pruned_heads.union(heads)
222
+
223
+ def _attn(self, query, key, value, attention_mask=None, head_mask=None, position_bias=None):
224
+ attn_weights = torch.matmul(query, key.transpose(-1, -2))
225
+
226
+ if self.scale_attn_weights:
227
+ attn_weights = attn_weights / torch.full(
228
+ [], value.size(-1) ** self.attn_scale_power, dtype=attn_weights.dtype, device=attn_weights.device
229
+ )
230
+
231
+ # Layer-wise attention scaling
232
+ if self.scale_attn_by_inverse_layer_idx:
233
+ attn_weights = attn_weights / float(self.layer_idx + 1)
234
+
235
+ if not self.is_cross_attention:
236
+ # if only "normal" attention layer implements causal mask
237
+ query_length, key_length = query.size(-2), key.size(-2)
238
+ causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
239
+ mask_value = torch.finfo(attn_weights.dtype).min
240
+ # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
241
+ # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
242
+ mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
243
+ attn_weights = torch.where(causal_mask, attn_weights.to(attn_weights.dtype), mask_value)
244
+
245
+ if attention_mask is not None:
246
+ # Apply the attention mask
247
+ attn_weights = attn_weights + attention_mask
248
+
249
+ if position_bias is not None:
250
+ attn_weights += position_bias.type_as(attn_weights).unsqueeze(0)
251
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
252
+
253
+ # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op otherwise
254
+ attn_weights = attn_weights.type(value.dtype)
255
+ attn_weights = self.attn_dropout(attn_weights)
256
+
257
+ # Mask heads if we want to
258
+ if head_mask is not None:
259
+ attn_weights = attn_weights * head_mask
260
+
261
+ attn_output = torch.matmul(attn_weights, value)
262
+
263
+ return attn_output, attn_weights
264
+
265
+ def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None, position_bias=None):
266
+ # Use `torch.baddbmm` (a bit more efficient w/ alpha param for scaling -- from Megatron-LM)
267
+ bsz, num_heads, q_seq_len, dk = query.size()
268
+ _, _, k_seq_len, _ = key.size()
269
+
270
+ # Preallocate attn_weights for `baddbmm`
271
+ attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device)
272
+
273
+ # Compute Scale Factor
274
+ scale_factor = 1.0
275
+ if self.scale_attn_weights:
276
+ scale_factor /= float(value.size(-1)) ** self.attn_scale_power
277
+
278
+ if self.scale_attn_by_inverse_layer_idx:
279
+ scale_factor /= float(self.layer_idx + 1)
280
+
281
+ # Upcast (turn off autocast) and reorder (Scale K by 1 / root(dk))
282
+ with autocast(enabled=False):
283
+ q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len)
284
+ attn_weights = torch.baddbmm(attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor)
285
+ attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len)
286
+
287
+ if not self.is_cross_attention:
288
+ # if only "normal" attention layer implements causal mask
289
+ query_length, key_length = query.size(-2), key.size(-2)
290
+ causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
291
+ mask_value = torch.finfo(attn_weights.dtype).min
292
+ # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
293
+ # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
294
+ mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
295
+ attn_weights = torch.where(causal_mask, attn_weights, mask_value)
296
+
297
+ if attention_mask is not None:
298
+ # Apply the attention mask
299
+ attn_weights = attn_weights + attention_mask
300
+
301
+ if position_bias is not None:
302
+ attn_weights += position_bias.type_as(attn_weights).unsqueeze(0)
303
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
304
+
305
+ # Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise
306
+ if attn_weights.dtype != torch.float32:
307
+ raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32")
308
+ attn_weights = attn_weights.type(value.dtype)
309
+ attn_weights = self.attn_dropout(attn_weights)
310
+
311
+ # Mask heads if we want to
312
+ if head_mask is not None:
313
+ attn_weights = attn_weights * head_mask
314
+
315
+ attn_output = torch.matmul(attn_weights, value)
316
+
317
+ return attn_output, attn_weights
318
+
319
+ def _split_heads(self, tensor, num_heads, attn_head_size):
320
+ """
321
+ Splits hidden_size dim into attn_head_size and num_heads
322
+ """
323
+ new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
324
+ tensor = tensor.view(new_shape)
325
+ return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
326
+
327
+ def _merge_heads(self, tensor, num_heads, attn_head_size):
328
+ """
329
+ Merges attn_head_size dim and num_attn_heads dim into hidden_size
330
+ """
331
+ tensor = tensor.permute(0, 2, 1, 3).contiguous()
332
+ new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
333
+ return tensor.view(new_shape)
334
+
335
+ def forward(
336
+ self,
337
+ hidden_states: Optional[Tuple[torch.FloatTensor]],
338
+ layer_past: Optional[Tuple[torch.Tensor]] = None,
339
+ attention_mask: Optional[torch.FloatTensor] = None,
340
+ head_mask: Optional[torch.FloatTensor] = None,
341
+ encoder_hidden_states: Optional[torch.Tensor] = None,
342
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
343
+ use_cache: Optional[bool] = False,
344
+ output_attentions: Optional[bool] = False,
345
+ position_bias: Optional[torch.FloatTensor] = None,
346
+ ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
347
+ if encoder_hidden_states is not None:
348
+ if not hasattr(self, "q_attn"):
349
+ raise ValueError(
350
+ "If class is used as cross attention, the weights `q_attn` have to be defined. "
351
+ "Please make sure to instantiate class with `JAISAttention(..., is_cross_attention=True)`."
352
+ )
353
+
354
+ query = self.q_attn(hidden_states)
355
+ key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
356
+ attention_mask = encoder_attention_mask
357
+ else:
358
+ query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=2)
359
+
360
+ query = self._split_heads(query, self.num_heads, self.head_dim)
361
+ key = self._split_heads(key, self.num_heads, self.head_dim)
362
+ value = self._split_heads(value, self.num_heads, self.head_dim)
363
+
364
+ if layer_past is not None:
365
+ past_key, past_value = layer_past
366
+ key = torch.cat((past_key, key), dim=-2)
367
+ value = torch.cat((past_value, value), dim=-2)
368
+
369
+ if use_cache is True:
370
+ present = (key, value)
371
+ else:
372
+ present = None
373
+
374
+ if self.reorder_and_upcast_attn:
375
+ attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask, position_bias)
376
+ else:
377
+ attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask, position_bias)
378
+
379
+ attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
380
+ attn_output = self.c_proj(attn_output)
381
+ attn_output = self.resid_dropout(attn_output)
382
+
383
+ outputs = (attn_output, present)
384
+ if output_attentions:
385
+ outputs += (attn_weights,)
386
+
387
+ return outputs # a, present, (attentions)
388
+
389
+
390
+ class JAISMLP(nn.Module):
391
+ def __init__(self, intermediate_size, config):
392
+ super().__init__()
393
+ embed_dim = config.hidden_size
394
+ self.swiglu = config.activation_function == "swiglu"
395
+ self.c_fc = Conv1D(intermediate_size, embed_dim)
396
+ self.c_fc2 = Conv1D(intermediate_size, embed_dim) if self.swiglu else None
397
+ self.c_proj = Conv1D(embed_dim, intermediate_size)
398
+ self.act = SwiGLUActivation() if self.swiglu else ACT2FN[config.activation_function]
399
+ self.dropout = nn.Dropout(config.resid_pdrop)
400
+
401
+ def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
402
+ if self.swiglu:
403
+ hidden_states2 = self.c_fc2(hidden_states)
404
+ hidden_states = self.c_fc(hidden_states)
405
+ hidden_states = self.act(hidden_states, hidden_states2) if self.swiglu else self.act(hidden_states)
406
+ hidden_states = self.c_proj(hidden_states)
407
+ hidden_states = self.dropout(hidden_states)
408
+ return hidden_states
409
+
410
+
411
+ class JAISBlock(nn.Module):
412
+ def __init__(self, config, layer_idx=None):
413
+ super().__init__()
414
+ hidden_size = config.hidden_size
415
+ inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
416
+
417
+ self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
418
+ self.attn = JAISAttention(config, layer_idx=layer_idx)
419
+ self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
420
+
421
+ if config.add_cross_attention:
422
+ self.crossattention = JAISAttention(config, is_cross_attention=True, layer_idx=layer_idx)
423
+ self.ln_cross_attn = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
424
+
425
+ self.mlp = JAISMLP(inner_dim, config)
426
+
427
+ def forward(
428
+ self,
429
+ hidden_states: Optional[Tuple[torch.FloatTensor]],
430
+ layer_past: Optional[Tuple[torch.Tensor]] = None,
431
+ attention_mask: Optional[torch.FloatTensor] = None,
432
+ head_mask: Optional[torch.FloatTensor] = None,
433
+ encoder_hidden_states: Optional[torch.Tensor] = None,
434
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
435
+ use_cache: Optional[bool] = False,
436
+ output_attentions: Optional[bool] = False,
437
+ position_bias: Optional[torch.FloatTensor] = None,
438
+ ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
439
+ residual = hidden_states
440
+ hidden_states = self.ln_1(hidden_states)
441
+ attn_outputs = self.attn(
442
+ hidden_states,
443
+ layer_past=layer_past,
444
+ attention_mask=attention_mask,
445
+ head_mask=head_mask,
446
+ use_cache=use_cache,
447
+ output_attentions=output_attentions,
448
+ position_bias=position_bias,
449
+ )
450
+ attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
451
+ outputs = attn_outputs[1:]
452
+ # residual connection
453
+ hidden_states = attn_output + residual
454
+
455
+ if encoder_hidden_states is not None:
456
+ # add one self-attention block for cross-attention
457
+ if not hasattr(self, "crossattention"):
458
+ raise ValueError(
459
+ f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
460
+ "cross-attention layers by setting `config.add_cross_attention=True`"
461
+ )
462
+ residual = hidden_states
463
+ hidden_states = self.ln_cross_attn(hidden_states)
464
+ cross_attn_outputs = self.crossattention(
465
+ hidden_states,
466
+ attention_mask=attention_mask,
467
+ head_mask=head_mask,
468
+ encoder_hidden_states=encoder_hidden_states,
469
+ encoder_attention_mask=encoder_attention_mask,
470
+ output_attentions=output_attentions,
471
+ position_bias=position_bias,
472
+ )
473
+ attn_output = cross_attn_outputs[0]
474
+ # residual connection
475
+ hidden_states = residual + attn_output
476
+ outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights
477
+
478
+ residual = hidden_states
479
+ hidden_states = self.ln_2(hidden_states)
480
+ feed_forward_hidden_states = self.mlp(hidden_states)
481
+ # residual connection
482
+ hidden_states = residual + feed_forward_hidden_states
483
+
484
+ if use_cache:
485
+ outputs = (hidden_states,) + outputs
486
+ else:
487
+ outputs = (hidden_states,) + outputs[1:]
488
+
489
+ return outputs # hidden_states, present, (attentions, cross_attentions)
490
+
491
+
492
+ class JAISPreTrainedModel(PreTrainedModel):
493
+ """
494
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
495
+ models.
496
+ """
497
+
498
+ config_class = JAISConfig
499
+ load_tf_weights = load_tf_weights_in_jais
500
+ base_model_prefix = "transformer"
501
+ is_parallelizable = True
502
+ supports_gradient_checkpointing = True
503
+ _no_split_modules = ["JAISBlock"]
504
+ _skip_keys_device_placement = "past_key_values"
505
+
506
+ def __init__(self, *inputs, **kwargs):
507
+ super().__init__(*inputs, **kwargs)
508
+
509
+ def _init_weights(self, module):
510
+ """Initialize the weights."""
511
+ mup_init_scale = math.sqrt(self.config.width_scale)
512
+ if isinstance(module, (nn.Linear, Conv1D)):
513
+ # Slightly different from the TF version which uses truncated_normal for initialization
514
+ # cf https://github.com/pytorch/pytorch/pull/5617
515
+ module.weight.data.normal_(mean=0.0, std=(self.config.initializer_range * mup_init_scale))
516
+ if module.bias is not None:
517
+ module.bias.data.zero_()
518
+ elif isinstance(module, nn.Embedding):
519
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
520
+ if module.padding_idx is not None:
521
+ module.weight.data[module.padding_idx].zero_()
522
+ elif isinstance(module, nn.LayerNorm):
523
+ module.bias.data.zero_()
524
+ module.weight.data.fill_(1.0)
525
+
526
+ # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
527
+ # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
528
+ # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
529
+ # > -- GPT-2 :: https://openai.com/blog/better-language-models/
530
+ #
531
+ # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
532
+ for name, p in module.named_parameters():
533
+ if name == "c_proj.weight":
534
+ # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
535
+ stddev = self.config.initializer_range * mup_init_scale / math.sqrt(2 * self.config.n_layer)
536
+ p.data.normal_(mean=0.0, std=stddev)
537
+
538
+ def _set_gradient_checkpointing(self, module, value=False):
539
+ if isinstance(module, JAISModel):
540
+ module.gradient_checkpointing = value
541
+
542
+
543
+ JAIS_START_DOCSTRING = r"""
544
+
545
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
546
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
547
+ etc.)
548
+
549
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
550
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
551
+ and behavior.
552
+
553
+ Parameters:
554
+ config ([`JAISConfig`]): Model configuration class with all the parameters of the model.
555
+ Initializing with a config file does not load the weights associated with the model, only the
556
+ configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
557
+ """
558
+
559
+ JAIS_INPUTS_DOCSTRING = r"""
560
+ Args:
561
+ input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
562
+ `input_ids_length` = `sequence_length` if `past_key_values` is `None` else
563
+ `past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input
564
+ sequence tokens in the vocabulary.
565
+
566
+ If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
567
+ `input_ids`.
568
+
569
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
570
+ [`PreTrainedTokenizer.__call__`] for details.
571
+
572
+ [What are input IDs?](../glossary#input-ids)
573
+ past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.n_layers`):
574
+ Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
575
+ `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
576
+ their past given to this model should not be passed as `input_ids` as they have already been computed.
577
+ attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
578
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
579
+
580
+ - 1 for tokens that are **not masked**,
581
+ - 0 for tokens that are **masked**.
582
+
583
+ If `past_key_values` is used, `attention_mask` needs to contain the masking strategy that was used for
584
+ `past_key_values`. In other words, the `attention_mask` always has to have the length:
585
+ `len(past_key_values) + len(input_ids)`
586
+
587
+ [What are attention masks?](../glossary#attention-mask)
588
+ token_type_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*):
589
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
590
+ 1]`:
591
+
592
+ - 0 corresponds to a *sentence A* token,
593
+ - 1 corresponds to a *sentence B* token.
594
+
595
+ [What are token type IDs?](../glossary#token-type-ids)
596
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
597
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
598
+ config.max_position_embeddings - 1]`.
599
+
600
+ [What are position IDs?](../glossary#position-ids)
601
+ head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
602
+ Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
603
+
604
+ - 1 indicates the head is **not masked**,
605
+ - 0 indicates the head is **masked**.
606
+
607
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
608
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
609
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
610
+ model's internal embedding lookup matrix.
611
+
612
+ If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
613
+ `past_key_values`).
614
+ use_cache (`bool`, *optional*):
615
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
616
+ `past_key_values`).
617
+ output_attentions (`bool`, *optional*):
618
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
619
+ tensors for more detail.
620
+ output_hidden_states (`bool`, *optional*):
621
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
622
+ more detail.
623
+ return_dict (`bool`, *optional*):
624
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
625
+ """
626
+ PARALLELIZE_DOCSTRING = r"""
627
+ This is an experimental feature and is a subject to change at a moment's notice.
628
+
629
+ Uses a device map to distribute attention modules of the model across several devices. If no device map is given,
630
+ it will evenly distribute blocks across all devices.
631
+
632
+ Args:
633
+ device_map (`Dict[int, list]`, optional, defaults to None):
634
+ A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
635
+ automatically mapped to the first device (for esoteric reasons). That means that the first device should
636
+ have fewer attention modules mapped to it than other devices. For reference, the gpt2 models have the
637
+ following number of attention modules:
638
+
639
+ - gpt2: 12
640
+ - gpt2-medium: 24
641
+ - gpt2-large: 36
642
+ - gpt2-xl: 48
643
+
644
+ Example:
645
+
646
+ ```python
647
+ # Here is an example of a device map on a machine with 4 GPUs using gpt2-xl, which has a total of 48 attention modules:
648
+ model = GPT2LMHeadModel.from_pretrained("gpt2-xl")
649
+ device_map = {
650
+ 0: [0, 1, 2, 3, 4, 5, 6, 7, 8],
651
+ 1: [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
652
+ 2: [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34],
653
+ 3: [35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
654
+ }
655
+ model.parallelize(device_map)
656
+ ```
657
+ """
658
+ DEPARALLELIZE_DOCSTRING = r"""
659
+ Moves the model to cpu from a model parallel state.
660
+
661
+ Example:
662
+
663
+ ```python
664
+ # On a 4 GPU machine with gpt2-large:
665
+ model = GPT2LMHeadModel.from_pretrained("gpt2-large")
666
+ device_map = {
667
+ 0: [0, 1, 2, 3, 4, 5, 6, 7],
668
+ 1: [8, 9, 10, 11, 12, 13, 14, 15],
669
+ 2: [16, 17, 18, 19, 20, 21, 22, 23],
670
+ 3: [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35],
671
+ }
672
+ model.parallelize(device_map) # Splits the model across several devices
673
+ model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
674
+ ```
675
+ """
676
+
677
+
678
+ @add_start_docstrings(
679
+ "The bare JAIS Model transformer outputting raw hidden-states without any specific head on top.",
680
+ JAIS_START_DOCSTRING,
681
+ )
682
+ class JAISModel(JAISPreTrainedModel):
683
+ _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]
684
+ _keys_to_ignore_on_load_missing = [r"attn.masked_bias", r"h\.\d+\.attn\.masked_bias", r"h\.\d+\.attn\.bias"]
685
+
686
+ def __init__(self, config):
687
+ super().__init__(config)
688
+
689
+ self.embed_dim = config.hidden_size
690
+
691
+ self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
692
+ self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim) if config.position_embedding_type != "alibi" else None
693
+ self.embeddings_scale = config.embeddings_scale
694
+
695
+ self.drop = nn.Dropout(config.embd_pdrop)
696
+ self.h = nn.ModuleList([JAISBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)])
697
+ self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
698
+
699
+ self.relative_pe = AlibiPositionEmbeddingLayer(config.num_attention_heads) if config.position_embedding_type == "alibi" else None
700
+
701
+ # Model parallel
702
+ self.model_parallel = False
703
+ self.device_map = None
704
+ self.gradient_checkpointing = False
705
+
706
+ # Initialize weights and apply final processing
707
+ self.post_init()
708
+
709
+ @add_start_docstrings(PARALLELIZE_DOCSTRING)
710
+ def parallelize(self, device_map=None):
711
+ # Check validity of device_map
712
+ warnings.warn(
713
+ "`JAISModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your"
714
+ " model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
715
+ " `device_map` but it needs to be a dictionary module_name to device, so for instance {'h.0': 0, 'h.1': 1,"
716
+ " ...}",
717
+ FutureWarning,
718
+ )
719
+ self.device_map = (
720
+ get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map
721
+ )
722
+ assert_device_map(self.device_map, len(self.h))
723
+ self.model_parallel = True
724
+ self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
725
+ self.last_device = "cuda:" + str(max(self.device_map.keys()))
726
+ self.wte = self.wte.to(self.first_device)
727
+ if self.wpe is not None:
728
+ self.wpe = self.wpe.to(self.first_device)
729
+ # Load onto devices
730
+ for k, v in self.device_map.items():
731
+ for block in v:
732
+ cuda_device = "cuda:" + str(k)
733
+ self.h[block] = self.h[block].to(cuda_device)
734
+ # ln_f to last
735
+ self.ln_f = self.ln_f.to(self.last_device)
736
+
737
+ @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
738
+ def deparallelize(self):
739
+ warnings.warn(
740
+ "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
741
+ FutureWarning,
742
+ )
743
+ self.model_parallel = False
744
+ self.device_map = None
745
+ self.first_device = "cpu"
746
+ self.last_device = "cpu"
747
+ self.wte = self.wte.to("cpu")
748
+ if self.wpe is not None:
749
+ self.wpe = self.wpe.to("cpu")
750
+ for index in range(len(self.h)):
751
+ self.h[index] = self.h[index].to("cpu")
752
+ self.ln_f = self.ln_f.to("cpu")
753
+ torch.cuda.empty_cache()
754
+
755
+ def get_input_embeddings(self):
756
+ return self.wte
757
+
758
+ def set_input_embeddings(self, new_embeddings):
759
+ self.wte = new_embeddings
760
+
761
+ def _prune_heads(self, heads_to_prune):
762
+ """
763
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
764
+ """
765
+ for layer, heads in heads_to_prune.items():
766
+ self.h[layer].attn.prune_heads(heads)
767
+
768
+ @add_start_docstrings_to_model_forward(JAIS_INPUTS_DOCSTRING)
769
+ @add_code_sample_docstrings(
770
+ checkpoint=_CHECKPOINT_FOR_DOC,
771
+ output_type=BaseModelOutputWithPastAndCrossAttentions,
772
+ config_class=_CONFIG_FOR_DOC,
773
+ )
774
+ def forward(
775
+ self,
776
+ input_ids: Optional[torch.LongTensor] = None,
777
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
778
+ attention_mask: Optional[torch.FloatTensor] = None,
779
+ token_type_ids: Optional[torch.LongTensor] = None,
780
+ position_ids: Optional[torch.LongTensor] = None,
781
+ head_mask: Optional[torch.FloatTensor] = None,
782
+ inputs_embeds: Optional[torch.FloatTensor] = None,
783
+ encoder_hidden_states: Optional[torch.Tensor] = None,
784
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
785
+ use_cache: Optional[bool] = None,
786
+ output_attentions: Optional[bool] = None,
787
+ output_hidden_states: Optional[bool] = None,
788
+ return_dict: Optional[bool] = None,
789
+ ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
790
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
791
+ output_hidden_states = (
792
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
793
+ )
794
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
795
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
796
+
797
+ if input_ids is not None and inputs_embeds is not None:
798
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
799
+ elif input_ids is not None:
800
+ input_shape = input_ids.size()
801
+ input_ids = input_ids.view(-1, input_shape[-1])
802
+ batch_size = input_ids.shape[0]
803
+ elif inputs_embeds is not None:
804
+ input_shape = inputs_embeds.size()[:-1]
805
+ batch_size = inputs_embeds.shape[0]
806
+ else:
807
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
808
+
809
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
810
+
811
+ if token_type_ids is not None:
812
+ token_type_ids = token_type_ids.view(-1, input_shape[-1])
813
+ if position_ids is not None:
814
+ position_ids = position_ids.view(-1, input_shape[-1])
815
+
816
+ if past_key_values is None:
817
+ past_length = 0
818
+ past_key_values = tuple([None] * len(self.h))
819
+ else:
820
+ past_length = past_key_values[0][0].size(-2)
821
+ if position_ids is None:
822
+ position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
823
+ position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
824
+
825
+ # JAISAttention mask.
826
+ if attention_mask is not None:
827
+ if batch_size <= 0:
828
+ raise ValueError("batch_size has to be defined and > 0")
829
+ attention_mask = attention_mask.view(batch_size, -1)
830
+ # We create a 3D attention mask from a 2D tensor mask.
831
+ # Sizes are [batch_size, 1, 1, to_seq_length]
832
+ # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
833
+ # this attention mask is more simple than the triangular masking of causal attention
834
+ # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
835
+ attention_mask = attention_mask[:, None, None, :]
836
+
837
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
838
+ # masked positions, this operation will create a tensor which is 0.0 for
839
+ # positions we want to attend and the dtype's smallest value for masked positions.
840
+ # Since we are adding it to the raw scores before the softmax, this is
841
+ # effectively the same as removing these entirely.
842
+ attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
843
+ attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
844
+
845
+ # If a 2D or 3D attention mask is provided for the cross-attention
846
+ # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
847
+ if self.config.add_cross_attention and encoder_hidden_states is not None:
848
+ encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
849
+ encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
850
+ if encoder_attention_mask is None:
851
+ encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
852
+ encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask)
853
+ else:
854
+ encoder_attention_mask = None
855
+
856
+ # Prepare head mask if needed
857
+ # 1.0 in head_mask indicate we keep the head
858
+ # attention_probs has shape bsz x n_heads x N x N
859
+ # head_mask has shape n_layer x batch x n_heads x N x N
860
+ head_mask = self.get_head_mask(head_mask, self.config.n_layer)
861
+
862
+ if inputs_embeds is None:
863
+ inputs_embeds = self.wte(input_ids)
864
+ if self.wpe is not None:
865
+ position_embeds = self.wpe(position_ids)
866
+ hidden_states = inputs_embeds + position_embeds
867
+ else:
868
+ hidden_states = inputs_embeds
869
+ hidden_states *= torch.tensor(
870
+ float(self.embeddings_scale), dtype=hidden_states.dtype, device=hidden_states.device
871
+ )
872
+
873
+ if token_type_ids is not None:
874
+ token_type_embeds = self.wte(token_type_ids)
875
+ hidden_states = hidden_states + token_type_embeds
876
+
877
+ hidden_states = self.drop(hidden_states)
878
+
879
+ if self.relative_pe is not None:
880
+ length = input_ids.shape[1]
881
+ cached_kv_length = 0
882
+ cached_kv = past_key_values[0]
883
+ if cached_kv is not None:
884
+ cached_kv_length = cached_kv[0].shape[-2]
885
+ position_bias = self.relative_pe(length, length, cached_kv_length)
886
+ else:
887
+ position_bias = None
888
+
889
+ output_shape = input_shape + (hidden_states.size(-1),)
890
+
891
+ if self.gradient_checkpointing and self.training:
892
+ if use_cache:
893
+ logger.warning_once(
894
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
895
+ )
896
+ use_cache = False
897
+
898
+ presents = () if use_cache else None
899
+ all_self_attentions = () if output_attentions else None
900
+ all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
901
+ all_hidden_states = () if output_hidden_states else None
902
+ for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
903
+ # Model parallel
904
+ if self.model_parallel:
905
+ torch.cuda.set_device(hidden_states.device)
906
+ # Ensure layer_past is on same device as hidden_states (might not be correct)
907
+ if layer_past is not None:
908
+ layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
909
+ # Ensure that attention_mask is always on the same device as hidden_states
910
+ if attention_mask is not None:
911
+ attention_mask = attention_mask.to(hidden_states.device)
912
+ if isinstance(head_mask, torch.Tensor):
913
+ head_mask = head_mask.to(hidden_states.device)
914
+ if output_hidden_states:
915
+ all_hidden_states = all_hidden_states + (hidden_states,)
916
+
917
+ if self.gradient_checkpointing and self.training:
918
+
919
+ def create_custom_forward(module):
920
+ def custom_forward(*inputs):
921
+ # None for past_key_value
922
+ return module(*inputs, use_cache, output_attentions)
923
+
924
+ return custom_forward
925
+
926
+ outputs = torch.utils.checkpoint.checkpoint(
927
+ create_custom_forward(block),
928
+ hidden_states,
929
+ None,
930
+ attention_mask,
931
+ head_mask[i],
932
+ encoder_hidden_states,
933
+ encoder_attention_mask,
934
+ )
935
+ else:
936
+ outputs = block(
937
+ hidden_states,
938
+ layer_past=layer_past,
939
+ attention_mask=attention_mask,
940
+ head_mask=head_mask[i],
941
+ encoder_hidden_states=encoder_hidden_states,
942
+ encoder_attention_mask=encoder_attention_mask,
943
+ use_cache=use_cache,
944
+ output_attentions=output_attentions,
945
+ position_bias=position_bias,
946
+ )
947
+
948
+ hidden_states = outputs[0]
949
+ if use_cache is True:
950
+ presents = presents + (outputs[1],)
951
+
952
+ if output_attentions:
953
+ all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
954
+ if self.config.add_cross_attention:
955
+ all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],)
956
+
957
+ # Model Parallel: If it's the last layer for that device, put things on the next device
958
+ if self.model_parallel:
959
+ for k, v in self.device_map.items():
960
+ if i == v[-1] and "cuda:" + str(k) != self.last_device:
961
+ hidden_states = hidden_states.to("cuda:" + str(k + 1))
962
+
963
+ hidden_states = self.ln_f(hidden_states)
964
+
965
+ hidden_states = hidden_states.view(output_shape)
966
+ # Add last hidden state
967
+ if output_hidden_states:
968
+ all_hidden_states = all_hidden_states + (hidden_states,)
969
+
970
+ if not return_dict:
971
+ return tuple(
972
+ v
973
+ for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
974
+ if v is not None
975
+ )
976
+
977
+ return BaseModelOutputWithPastAndCrossAttentions(
978
+ last_hidden_state=hidden_states,
979
+ past_key_values=presents,
980
+ hidden_states=all_hidden_states,
981
+ attentions=all_self_attentions,
982
+ cross_attentions=all_cross_attentions,
983
+ )
984
+
985
+
986
+ @add_start_docstrings(
987
+ """
988
+ The JAIS Model transformer with a language modeling head on top (linear layer with weights tied to the input
989
+ embeddings).
990
+ """,
991
+ JAIS_START_DOCSTRING,
992
+ )
993
+ class JAISLMHeadModel(JAISPreTrainedModel):
994
+ _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
995
+ _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias", r"h\.\d+\.attn\.bias"]
996
+
997
+ def __init__(self, config):
998
+ super().__init__(config)
999
+ self.transformer = JAISModel(config)
1000
+ self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
1001
+ self.output_logits_scale = config.width_scale
1002
+
1003
+ # Model parallel
1004
+ self.model_parallel = False
1005
+ self.device_map = None
1006
+
1007
+ # Initialize weights and apply final processing
1008
+ self.post_init()
1009
+
1010
+ @add_start_docstrings(PARALLELIZE_DOCSTRING)
1011
+ def parallelize(self, device_map=None):
1012
+ warnings.warn(
1013
+ "`JAISLMHeadModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load"
1014
+ " your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
1015
+ " `device_map` but it needs to be a dictionary module_name to device, so for instance {'transformer.h.0':"
1016
+ " 0, 'transformer.h.1': 1, ...}",
1017
+ FutureWarning,
1018
+ )
1019
+ self.device_map = (
1020
+ get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
1021
+ if device_map is None
1022
+ else device_map
1023
+ )
1024
+ assert_device_map(self.device_map, len(self.transformer.h))
1025
+ self.transformer.parallelize(self.device_map)
1026
+ self.lm_head = self.lm_head.to(self.transformer.first_device)
1027
+ self.model_parallel = True
1028
+
1029
+ @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
1030
+ def deparallelize(self):
1031
+ warnings.warn(
1032
+ "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
1033
+ FutureWarning,
1034
+ )
1035
+ self.transformer.deparallelize()
1036
+ self.transformer = self.transformer.to("cpu")
1037
+ self.lm_head = self.lm_head.to("cpu")
1038
+ self.model_parallel = False
1039
+ torch.cuda.empty_cache()
1040
+
1041
+ def get_output_embeddings(self):
1042
+ return self.lm_head
1043
+
1044
+ def set_output_embeddings(self, new_embeddings):
1045
+ self.lm_head = new_embeddings
1046
+
1047
+ def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
1048
+ token_type_ids = kwargs.get("token_type_ids", None)
1049
+ # only last token for inputs_ids if past is defined in kwargs
1050
+ if past_key_values:
1051
+ input_ids = input_ids[:, -1].unsqueeze(-1)
1052
+ if token_type_ids is not None:
1053
+ token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
1054
+
1055
+ attention_mask = kwargs.get("attention_mask", None)
1056
+ position_ids = kwargs.get("position_ids", None)
1057
+
1058
+ if attention_mask is not None and position_ids is None:
1059
+ # create position_ids on the fly for batch generation
1060
+ position_ids = attention_mask.long().cumsum(-1) - 1
1061
+ position_ids.masked_fill_(attention_mask == 0, 1)
1062
+ if past_key_values:
1063
+ position_ids = position_ids[:, -1].unsqueeze(-1)
1064
+ else:
1065
+ position_ids = None
1066
+
1067
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1068
+ if inputs_embeds is not None and past_key_values is None:
1069
+ model_inputs = {"inputs_embeds": inputs_embeds}
1070
+ else:
1071
+ model_inputs = {"input_ids": input_ids}
1072
+
1073
+ model_inputs.update(
1074
+ {
1075
+ "past_key_values": past_key_values,
1076
+ "use_cache": kwargs.get("use_cache"),
1077
+ "position_ids": position_ids,
1078
+ "attention_mask": attention_mask,
1079
+ "token_type_ids": token_type_ids,
1080
+ }
1081
+ )
1082
+ return model_inputs
1083
+
1084
+ @add_start_docstrings_to_model_forward(JAIS_INPUTS_DOCSTRING)
1085
+ @add_code_sample_docstrings(
1086
+ checkpoint=_CHECKPOINT_FOR_DOC,
1087
+ output_type=CausalLMOutputWithCrossAttentions,
1088
+ config_class=_CONFIG_FOR_DOC,
1089
+ )
1090
+ def forward(
1091
+ self,
1092
+ input_ids: Optional[torch.LongTensor] = None,
1093
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
1094
+ attention_mask: Optional[torch.FloatTensor] = None,
1095
+ token_type_ids: Optional[torch.LongTensor] = None,
1096
+ position_ids: Optional[torch.LongTensor] = None,
1097
+ head_mask: Optional[torch.FloatTensor] = None,
1098
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1099
+ encoder_hidden_states: Optional[torch.Tensor] = None,
1100
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
1101
+ labels: Optional[torch.LongTensor] = None,
1102
+ use_cache: Optional[bool] = None,
1103
+ output_attentions: Optional[bool] = None,
1104
+ output_hidden_states: Optional[bool] = None,
1105
+ return_dict: Optional[bool] = None,
1106
+ ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
1107
+ r"""
1108
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1109
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
1110
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
1111
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
1112
+ """
1113
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1114
+
1115
+ transformer_outputs = self.transformer(
1116
+ input_ids,
1117
+ past_key_values=past_key_values,
1118
+ attention_mask=attention_mask,
1119
+ token_type_ids=token_type_ids,
1120
+ position_ids=position_ids,
1121
+ head_mask=head_mask,
1122
+ inputs_embeds=inputs_embeds,
1123
+ encoder_hidden_states=encoder_hidden_states,
1124
+ encoder_attention_mask=encoder_attention_mask,
1125
+ use_cache=use_cache,
1126
+ output_attentions=output_attentions,
1127
+ output_hidden_states=output_hidden_states,
1128
+ return_dict=return_dict,
1129
+ )
1130
+ hidden_states = transformer_outputs[0]
1131
+
1132
+ # Set device for model parallelism
1133
+ if self.model_parallel:
1134
+ torch.cuda.set_device(self.transformer.first_device)
1135
+ hidden_states = hidden_states.to(self.lm_head.weight.device)
1136
+
1137
+ lm_logits = self.lm_head(hidden_states)
1138
+ lm_logits *= torch.tensor(
1139
+ float(self.output_logits_scale), dtype=lm_logits.dtype, device=lm_logits.device
1140
+ )
1141
+
1142
+ loss = None
1143
+ if labels is not None:
1144
+ # move labels to correct device to enable model parallelism
1145
+ labels = labels.to(lm_logits.device)
1146
+ # Shift so that tokens < n predict n
1147
+ shift_logits = lm_logits[..., :-1, :].contiguous()
1148
+ shift_labels = labels[..., 1:].contiguous()
1149
+ # Flatten the tokens
1150
+ loss_fct = CrossEntropyLoss()
1151
+ loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
1152
+
1153
+ if not return_dict:
1154
+ output = (lm_logits,) + transformer_outputs[1:]
1155
+ return ((loss,) + output) if loss is not None else output
1156
+
1157
+ return CausalLMOutputWithCrossAttentions(
1158
+ loss=loss,
1159
+ logits=lm_logits,
1160
+ past_key_values=transformer_outputs.past_key_values,
1161
+ hidden_states=transformer_outputs.hidden_states,
1162
+ attentions=transformer_outputs.attentions,
1163
+ cross_attentions=transformer_outputs.cross_attentions,
1164
+ )
1165
+
1166
+ @staticmethod
1167
+ def _reorder_cache(
1168
+ past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
1169
+ ) -> Tuple[Tuple[torch.Tensor]]:
1170
+ """
1171
+ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
1172
+ [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
1173
+ beam_idx at every generation step.
1174
+ """
1175
+ return tuple(
1176
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
1177
+ for layer_past in past_key_values
1178
+ )
1179
+
1180
+
1181
+ @add_start_docstrings(
1182
+ """
1183
+ The JAIS Model transformer with a sequence classification head on top (linear layer).
1184
+
1185
+ [`JAISForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1186
+ (e.g. GPT-1) do.
1187
+
1188
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1189
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1190
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1191
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1192
+ each row of the batch).
1193
+ """,
1194
+ JAIS_START_DOCSTRING,
1195
+ )
1196
+ class JAISForSequenceClassification(JAISPreTrainedModel):
1197
+ _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]
1198
+ _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head.weight"]
1199
+
1200
+ def __init__(self, config):
1201
+ super().__init__(config)
1202
+ self.num_labels = config.num_labels
1203
+ self.transformer = JAISModel(config)
1204
+ self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
1205
+ self.output_logits_scale = config.width_scale
1206
+
1207
+ # Model parallel
1208
+ self.model_parallel = False
1209
+ self.device_map = None
1210
+
1211
+ # Initialize weights and apply final processing
1212
+ self.post_init()
1213
+
1214
+ @add_start_docstrings_to_model_forward(JAIS_INPUTS_DOCSTRING)
1215
+ @add_code_sample_docstrings(
1216
+ checkpoint="microsoft/DialogRPT-updown",
1217
+ output_type=SequenceClassifierOutputWithPast,
1218
+ config_class=_CONFIG_FOR_DOC,
1219
+ )
1220
+ def forward(
1221
+ self,
1222
+ input_ids: Optional[torch.LongTensor] = None,
1223
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
1224
+ attention_mask: Optional[torch.FloatTensor] = None,
1225
+ token_type_ids: Optional[torch.LongTensor] = None,
1226
+ position_ids: Optional[torch.LongTensor] = None,
1227
+ head_mask: Optional[torch.FloatTensor] = None,
1228
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1229
+ labels: Optional[torch.LongTensor] = None,
1230
+ use_cache: Optional[bool] = None,
1231
+ output_attentions: Optional[bool] = None,
1232
+ output_hidden_states: Optional[bool] = None,
1233
+ return_dict: Optional[bool] = None,
1234
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1235
+ r"""
1236
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1237
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1238
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1239
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1240
+ """
1241
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1242
+
1243
+ transformer_outputs = self.transformer(
1244
+ input_ids,
1245
+ past_key_values=past_key_values,
1246
+ attention_mask=attention_mask,
1247
+ token_type_ids=token_type_ids,
1248
+ position_ids=position_ids,
1249
+ head_mask=head_mask,
1250
+ inputs_embeds=inputs_embeds,
1251
+ use_cache=use_cache,
1252
+ output_attentions=output_attentions,
1253
+ output_hidden_states=output_hidden_states,
1254
+ return_dict=return_dict,
1255
+ )
1256
+ hidden_states = transformer_outputs[0]
1257
+ logits = self.score(hidden_states)
1258
+ logits *= torch.tensor(
1259
+ float(self.output_logits_scale), dtype=logits.dtype, device=logits.device
1260
+ )
1261
+
1262
+ if input_ids is not None:
1263
+ batch_size, sequence_length = input_ids.shape[:2]
1264
+ else:
1265
+ batch_size, sequence_length = inputs_embeds.shape[:2]
1266
+
1267
+ assert (
1268
+ self.config.pad_token_id is not None or batch_size == 1
1269
+ ), "Cannot handle batch sizes > 1 if no padding token is defined."
1270
+ if self.config.pad_token_id is None:
1271
+ sequence_lengths = -1
1272
+ else:
1273
+ if input_ids is not None:
1274
+ sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
1275
+ else:
1276
+ sequence_lengths = -1
1277
+ logger.warning(
1278
+ f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
1279
+ "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
1280
+ )
1281
+
1282
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1283
+
1284
+ loss = None
1285
+ if labels is not None:
1286
+ if self.config.problem_type is None:
1287
+ if self.num_labels == 1:
1288
+ self.config.problem_type = "regression"
1289
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1290
+ self.config.problem_type = "single_label_classification"
1291
+ else:
1292
+ self.config.problem_type = "multi_label_classification"
1293
+
1294
+ if self.config.problem_type == "regression":
1295
+ loss_fct = MSELoss()
1296
+ if self.num_labels == 1:
1297
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1298
+ else:
1299
+ loss = loss_fct(pooled_logits, labels)
1300
+ elif self.config.problem_type == "single_label_classification":
1301
+ loss_fct = CrossEntropyLoss()
1302
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1303
+ elif self.config.problem_type == "multi_label_classification":
1304
+ loss_fct = BCEWithLogitsLoss()
1305
+ loss = loss_fct(pooled_logits, labels)
1306
+ if not return_dict:
1307
+ output = (pooled_logits,) + transformer_outputs[1:]
1308
+ return ((loss,) + output) if loss is not None else output
1309
+
1310
+ return SequenceClassifierOutputWithPast(
1311
+ loss=loss,
1312
+ logits=pooled_logits,
1313
+ past_key_values=transformer_outputs.past_key_values,
1314
+ hidden_states=transformer_outputs.hidden_states,
1315
+ attentions=transformer_outputs.attentions,
1316
+ )
1317
+
1318
+
1319
+ @add_start_docstrings(
1320
+ """
1321
+ JAIS Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
1322
+ Named-Entity-Recognition (NER) tasks.
1323
+ """,
1324
+ JAIS_START_DOCSTRING,
1325
+ )
1326
+ class JAISForTokenClassification(JAISPreTrainedModel):
1327
+ def __init__(self, config):
1328
+ super().__init__(config)
1329
+ self.num_labels = config.num_labels
1330
+
1331
+ self.transformer = JAISModel(config)
1332
+ if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
1333
+ classifier_dropout = config.classifier_dropout
1334
+ elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
1335
+ classifier_dropout = config.hidden_dropout
1336
+ else:
1337
+ classifier_dropout = 0.1
1338
+ self.dropout = nn.Dropout(classifier_dropout)
1339
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
1340
+ self.output_logits_scale = config.width_scale
1341
+
1342
+ # Model parallel
1343
+ self.model_parallel = False
1344
+ self.device_map = None
1345
+
1346
+ # Initialize weights and apply final processing
1347
+ self.post_init()
1348
+
1349
+ @add_start_docstrings_to_model_forward(JAIS_INPUTS_DOCSTRING)
1350
+ # fmt: off
1351
+ @add_code_sample_docstrings(
1352
+ checkpoint="brad1141/gpt2-finetuned-comp2",
1353
+ output_type=TokenClassifierOutput,
1354
+ config_class=_CONFIG_FOR_DOC,
1355
+ expected_loss=0.25,
1356
+ expected_output=["Lead", "Lead", "Lead", "Position", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead", "Lead"],
1357
+ )
1358
+ # fmt: on
1359
+ def forward(
1360
+ self,
1361
+ input_ids: Optional[torch.LongTensor] = None,
1362
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
1363
+ attention_mask: Optional[torch.FloatTensor] = None,
1364
+ token_type_ids: Optional[torch.LongTensor] = None,
1365
+ position_ids: Optional[torch.LongTensor] = None,
1366
+ head_mask: Optional[torch.FloatTensor] = None,
1367
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1368
+ labels: Optional[torch.LongTensor] = None,
1369
+ use_cache: Optional[bool] = None,
1370
+ output_attentions: Optional[bool] = None,
1371
+ output_hidden_states: Optional[bool] = None,
1372
+ return_dict: Optional[bool] = None,
1373
+ ) -> Union[Tuple, TokenClassifierOutput]:
1374
+ r"""
1375
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1376
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1377
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1378
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1379
+ """
1380
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1381
+
1382
+ transformer_outputs = self.transformer(
1383
+ input_ids,
1384
+ past_key_values=past_key_values,
1385
+ attention_mask=attention_mask,
1386
+ token_type_ids=token_type_ids,
1387
+ position_ids=position_ids,
1388
+ head_mask=head_mask,
1389
+ inputs_embeds=inputs_embeds,
1390
+ use_cache=use_cache,
1391
+ output_attentions=output_attentions,
1392
+ output_hidden_states=output_hidden_states,
1393
+ return_dict=return_dict,
1394
+ )
1395
+
1396
+ hidden_states = transformer_outputs[0]
1397
+ hidden_states = self.dropout(hidden_states)
1398
+ logits = self.classifier(hidden_states)
1399
+ logits *= torch.tensor(
1400
+ float(self.output_logits_scale), dtype=logits.dtype, device=logits.device
1401
+ )
1402
+
1403
+ loss = None
1404
+ if labels is not None:
1405
+ labels = labels.to(logits.device)
1406
+ loss_fct = CrossEntropyLoss()
1407
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
1408
+
1409
+ if not return_dict:
1410
+ output = (logits,) + transformer_outputs[2:]
1411
+ return ((loss,) + output) if loss is not None else output
1412
+
1413
+ return TokenClassifierOutput(
1414
+ loss=loss,
1415
+ logits=logits,
1416
+ hidden_states=transformer_outputs.hidden_states,
1417
+ attentions=transformer_outputs.attentions,
1418
+ )
1419
+
1420
+
1421
+ @add_start_docstrings(
1422
+ """
1423
+ The JAIS Model transformer with a span classification head on top for extractive question-answering tasks like
1424
+ SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
1425
+ """,
1426
+ JAIS_START_DOCSTRING,
1427
+ )
1428
+ class JAISForQuestionAnswering(JAISPreTrainedModel):
1429
+ _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]
1430
+ _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"h\.\d+\.attn\.bias", r"lm_head.weight"]
1431
+
1432
+ def __init__(self, config):
1433
+ super().__init__(config)
1434
+ self.num_labels = config.num_labels
1435
+ self.transformer = JAISModel(config)
1436
+ self.qa_outputs = nn.Linear(config.hidden_size, 2)
1437
+ self.output_logits_scale = config.width_scale
1438
+
1439
+ # Model parallel
1440
+ self.model_parallel = False
1441
+ self.device_map = None
1442
+ self.gradient_checkpointing = False
1443
+
1444
+ # Initialize weights and apply final processing
1445
+ self.post_init()
1446
+
1447
+ def forward(
1448
+ self,
1449
+ input_ids: Optional[torch.LongTensor] = None,
1450
+ attention_mask: Optional[torch.FloatTensor] = None,
1451
+ token_type_ids: Optional[torch.LongTensor] = None,
1452
+ position_ids: Optional[torch.LongTensor] = None,
1453
+ head_mask: Optional[torch.FloatTensor] = None,
1454
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1455
+ start_positions: Optional[torch.LongTensor] = None,
1456
+ end_positions: Optional[torch.LongTensor] = None,
1457
+ output_attentions: Optional[bool] = None,
1458
+ output_hidden_states: Optional[bool] = None,
1459
+ return_dict: Optional[bool] = None,
1460
+ ) -> Union[Tuple, QuestionAnsweringModelOutput]:
1461
+ r"""
1462
+ start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1463
+ Labels for position (index) of the start of the labelled span for computing the token classification loss.
1464
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1465
+ are not taken into account for computing the loss.
1466
+ end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1467
+ Labels for position (index) of the end of the labelled span for computing the token classification loss.
1468
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1469
+ are not taken into account for computing the loss.
1470
+ """
1471
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1472
+
1473
+ outputs = self.transformer(
1474
+ input_ids,
1475
+ attention_mask=attention_mask,
1476
+ token_type_ids=token_type_ids,
1477
+ position_ids=position_ids,
1478
+ head_mask=head_mask,
1479
+ inputs_embeds=inputs_embeds,
1480
+ output_attentions=output_attentions,
1481
+ output_hidden_states=output_hidden_states,
1482
+ return_dict=return_dict,
1483
+ )
1484
+
1485
+ sequence_output = outputs[0]
1486
+
1487
+ logits = self.qa_outputs(sequence_output)
1488
+ logits *= torch.tensor(
1489
+ float(self.output_logits_scale), dtype=logits.dtype, device=logits.device
1490
+ )
1491
+ start_logits, end_logits = logits.split(1, dim=-1)
1492
+ start_logits = start_logits.squeeze(-1).contiguous()
1493
+ end_logits = end_logits.squeeze(-1).contiguous()
1494
+
1495
+ total_loss = None
1496
+ if start_positions is not None and end_positions is not None:
1497
+ # If we are on multi-GPU, split add a dimension
1498
+ if len(start_positions.size()) > 1:
1499
+ start_positions = start_positions.squeeze(-1).to(start_logits.device)
1500
+ if len(end_positions.size()) > 1:
1501
+ end_positions = end_positions.squeeze(-1).to(end_logits.device)
1502
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
1503
+ ignored_index = start_logits.size(1)
1504
+ start_positions = start_positions.clamp(0, ignored_index)
1505
+ end_positions = end_positions.clamp(0, ignored_index)
1506
+
1507
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
1508
+ start_loss = loss_fct(start_logits, start_positions)
1509
+ end_loss = loss_fct(end_logits, end_positions)
1510
+ total_loss = (start_loss + end_loss) / 2
1511
+
1512
+ if not return_dict:
1513
+ output = (start_logits, end_logits) + outputs[2:]
1514
+ return ((total_loss,) + output) if total_loss is not None else output
1515
+
1516
+ return QuestionAnsweringModelOutput(
1517
+ loss=total_loss,
1518
+ start_logits=start_logits,
1519
+ end_logits=end_logits,
1520
+ hidden_states=outputs.hidden_states,
1521
+ attentions=outputs.attentions,
1522
+ )
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