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  license: apache-2.0
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ datasets:
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+ - tiiuae/falcon-refinedweb
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+ language:
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+ - en
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+ inference: true
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+ widget:
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+ - text: "Hey Falcon! Any recommendations for my holidays in Abu Dhabi?"
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+ example_title: "Abu Dhabi Trip"
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+ - text: "What's the Everett interpretation of quantum mechanics?"
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+ example_title: "Q/A: Quantum & Answers"
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+ - text: "Give me a list of the top 10 dive sites you would recommend around the world."
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+ example_title: "Diving Top 10"
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+ - text: "Can you tell me more about deep-water soloing?"
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+ example_title: "Extreme sports"
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+ - text: "Can you write a short tweet about the Apache 2.0 release of our latest AI model, Falcon LLM?"
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+ example_title: "Twitter Helper"
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+ - text: "What are the responsabilities of a Chief Llama Officer?"
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+ example_title: "Trendy Jobs"
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  license: apache-2.0
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  ---
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+
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+ # ✨ Falcon-7B-Instruct
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+
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+ **Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.**
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+
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+ *Paper coming soon 😊.*
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+
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+ 🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
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+
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+ ## Why use Falcon-7B-Instruct?
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+
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+ * **You are looking for a ready-to-use chat/instruct model based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).**
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+ * **Falcon-7B is a strong base model, outperforming comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
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+ * **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
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+
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+ 💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
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+
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+ 🔥 **Looking for an even more powerful model?** [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) is Falcon-7B-Instruct's big brother!
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ import transformers
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+ import torch
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+
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+ model = "tiiuae/falcon-7b-instruct"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model)
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+ pipeline = transformers.pipeline(
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+ "text-generation",
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+ model=model,
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+ tokenizer=tokenizer,
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+ torch_dtype=torch.bfloat16,
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+ trust_remote_code=True,
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+ device_map="auto",
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+ )
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+ sequences = pipeline(
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+ "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
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+ max_length=200,
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+ do_sample=True,
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+ top_k=10,
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+ num_return_sequences=1,
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+ eos_token_id=tokenizer.eos_token_id,
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+ )
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+ for seq in sequences:
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+ print(f"Result: {seq['generated_text']}")
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+
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+ ```
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+
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+ 💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
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+
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+ For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
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+
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+ You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B-Instruct.
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+
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+
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+ # Model Card for Falcon-7B-Instruct
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ - **Developed by:** [https://www.tii.ae](https://www.tii.ae);
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+ - **Model type:** Causal decoder-only;
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+ - **Language(s) (NLP):** English and French;
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+ - **License:** Apache 2.0;
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+ - **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
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+
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+ ### Model Source
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+
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+ - **Paper:** *coming soon*.
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+
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+ ## Uses
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+
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+ ### Direct Use
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+
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+ Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets.
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+
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+ ### Out-of-Scope Use
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+
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+ Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
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+
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+ ## Bias, Risks, and Limitations
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+
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+ Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
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+
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+ ### Recommendations
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+
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+ We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use.
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+
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+ ## How to Get Started with the Model
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+
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ import transformers
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+ import torch
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+
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+ model = "tiiuae/falcon-7b-instruct"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model)
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+ pipeline = transformers.pipeline(
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+ "text-generation",
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+ model=model,
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+ tokenizer=tokenizer,
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+ torch_dtype=torch.bfloat16,
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+ trust_remote_code=True,
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+ device_map="auto",
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+ )
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+ sequences = pipeline(
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+ "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
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+ max_length=200,
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+ do_sample=True,
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+ top_k=10,
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+ num_return_sequences=1,
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+ eos_token_id=tokenizer.eos_token_id,
137
+ )
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+ for seq in sequences:
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+ print(f"Result: {seq['generated_text']}")
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+
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+ ```
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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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+ Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets.
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+
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+ | **Data source** | **Fraction** | **Tokens** | **Description** |
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+ |--------------------|--------------|------------|-----------------------------------|
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+ | [Bai ze](https://github.com/project-baize/baize-chatbot) | 65% | 164M | chat |
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+ | [GPT4All](https://github.com/nomic-ai/gpt4all) | 25% | 62M | instruct |
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+ | [GPTeacher](https://github.com/teknium1/GPTeacher) | 5% | 11M | instruct |
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+ | [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 5% | 13M | massive web crawl |
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+
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+
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+ The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
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+
159
+
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+ ## Evaluation
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+
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+ *Paper coming soon.*
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+
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+ See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
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+
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+ Note that this model variant is not optimized for NLP benchmarks.
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+
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+
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+ ## Technical Specifications
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+
171
+ For more information about pretraining, see [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
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+
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+ ### Model Architecture and Objective
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+
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+ Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
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+
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+ The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
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+
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+ * **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
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+ * **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
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+ * **Decoder-block:** parallel attention/MLP with a single layer norm.
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+
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+ | **Hyperparameter** | **Value** | **Comment** |
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+ |--------------------|-----------|----------------------------------------|
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+ | Layers | 32 | |
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+ | `d_model` | 4544 | Increased to compensate for multiquery |
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+ | `head_dim` | 64 | Reduced to optimise for FlashAttention |
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+ | Vocabulary | 65024 | |
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+ | Sequence length | 2048 | |
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+
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+ ### Compute Infrastructure
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+
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+ #### Hardware
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+
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+ Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances.
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+
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+ #### Software
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+
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+ Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
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+
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+
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+ ## Citation
203
+
204
+ *Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
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+ ```
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+ @article{falcon40b,
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+ title={{Falcon-40B}: an open large language model with state-of-the-art performance},
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+ author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
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+ year={2023}
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+ }
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+ ```
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+
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+ To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
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+
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+ ```
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+ @article{refinedweb,
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+ title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
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+ author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
219
+ journal={arXiv preprint arXiv:2306.01116},
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+ eprint={2306.01116},
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+ eprinttype = {arXiv},
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+ url={https://arxiv.org/abs/2306.01116},
223
+ year={2023}
224
+ }
225
+ ```
226
+
227
+
228
+ ## License
229
+
230
+ Falcon-7B-Instruct is made available under the Apache 2.0 license.
231
+
232
+ ## Contact
233
+ falconllm@tii.ae
config.json ADDED
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+ {
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+ "alibi": false,
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+ "apply_residual_connection_post_layernorm": false,
4
+ "architectures": [
5
+ "FalconForCausalLM"
6
+ ],
7
+ "attention_dropout": 0.0,
8
+ "auto_map": {
9
+ "AutoConfig": "configuration_falcon.FalconConfig",
10
+ "AutoModel": "modeling_falcon.FalconModel",
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+ "AutoModelForSequenceClassification": "modeling_falcon.FalconForSequenceClassification",
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+ "AutoModelForTokenClassification": "modeling_falcon.FalconForTokenClassification",
13
+ "AutoModelForQuestionAnswering": "modeling_falcon.FalconForQuestionAnswering",
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+ "AutoModelForCausalLM": "modeling_falcon.FalconForCausalLM"
15
+ },
16
+ "bias": false,
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+ "bos_token_id": 11,
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+ "eos_token_id": 11,
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+ "hidden_dropout": 0.0,
20
+ "hidden_size": 4544,
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+ "initializer_range": 0.02,
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+ "layer_norm_epsilon": 1e-05,
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+ "model_type": "falcon",
24
+ "multi_query": true,
25
+ "new_decoder_architecture": false,
26
+ "num_attention_heads": 71,
27
+ "num_hidden_layers": 32,
28
+ "parallel_attn": true,
29
+ "torch_dtype": "bfloat16",
30
+ "transformers_version": "4.27.4",
31
+ "use_cache": true,
32
+ "vocab_size": 65024
33
+ }
configuration_falcon.py ADDED
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+ # coding=utf-8
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+ # Copyright 2023 the Falcon authors and HuggingFace Inc. team. All rights reserved.
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+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """ Falcon configuration"""
16
+ from transformers.configuration_utils import PretrainedConfig
17
+ from transformers.utils import logging
18
+
19
+
20
+ logger = logging.get_logger(__name__)
21
+
22
+ FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP = {
23
+ "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
24
+ "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
25
+ }
26
+
27
+
28
+ class FalconConfig(PretrainedConfig):
29
+ r"""
30
+ This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
31
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
32
+ defaults will yield a similar configuration to that of the
33
+ [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) architecture.
34
+
35
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
36
+ documentation from [`PretrainedConfig`] for more information.
37
+
38
+
39
+ Args:
40
+ vocab_size (`int`, *optional*, defaults to 65024):
41
+ Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the
42
+ `inputs_ids` passed when calling [`FalconModel`]
43
+ hidden_size (`int`, *optional*, defaults to 4544):
44
+ Dimension of the hidden representations.
45
+ num_hidden_layers (`int`, *optional*, defaults to 32):
46
+ Number of hidden layers in the Transformer decoder.
47
+ num_attention_heads (`int`, *optional*, defaults to 71):
48
+ Number of attention heads for each attention layer in the Transformer encoder.
49
+ initializer_range (`float`, *optional*, defaults to 0.02):
50
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
51
+ use_cache (`bool`, *optional*, defaults to `True`):
52
+ Whether the model should return the last key/values attentions (not used by all models). Only relevant if
53
+ `config.is_decoder=True`.
54
+ layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
55
+ The epsilon used by the layer normalization layers.
56
+ hidden_dropout (`float`, *optional*, defaults to 0.0):
57
+ The dropout probability for MLP layers.
58
+ attention_dropout (`float`, *optional*, defaults to 0.0):
59
+ The dropout probability for attention layers.
60
+ num_kv_heads (`int`, *optional*):
61
+ Number of key-value heads to use per attention layer. If unset, defaults to the same value as
62
+ `num_attention_heads`.
63
+ alibi (`bool`, *optional*, defaults to `False`):
64
+ Whether to use ALiBi positional biases during self-attention.
65
+ new_decoder_architecture (`bool`, *optional*, defaults to `False`):
66
+ Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn`
67
+ arguments are ignored, as the new decoder always uses parallel attention.
68
+ multi_query (`bool`, *optional*, defaults to `True`):
69
+ Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`.
70
+ parallel_attn (`bool`, *optional*, defaults to `True`):
71
+ Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive
72
+ instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`.
73
+ bias (`bool`, *optional*, defaults to `False`):
74
+ Whether to use bias on Linear layers.
75
+ bos_token_id (`int`, *optional*, defaults to 11):
76
+ The id of the "beginning-of-sequence" token.
77
+ eos_token_id (`int`, *optional*, defaults to 11):
78
+ The id of the "end-of-sequence" token.
79
+
80
+ Example:
81
+
82
+ ```python
83
+ >>> from transformers import FalconModel, FalconConfig
84
+
85
+ >>> # Initializing a small (2-layer) Falcon configuration
86
+ >>> configuration = FalconConfig(num_hidden_layers=2)
87
+
88
+ >>> # Initializing a model from the small configuration
89
+ >>> model = FalconModel(configuration)
90
+
91
+ >>> # Accessing the model configuration
92
+ >>> configuration = model.config
93
+ ```"""
94
+ model_type = "falcon"
95
+ keys_to_ignore_at_inference = ["past_key_values"]
96
+
97
+ def __init__(
98
+ self,
99
+ vocab_size=65024,
100
+ hidden_size=4544,
101
+ num_hidden_layers=32,
102
+ num_attention_heads=71,
103
+ layer_norm_epsilon=1e-5,
104
+ initializer_range=0.02,
105
+ use_cache=True,
106
+ hidden_dropout=0.0,
107
+ attention_dropout=0.0,
108
+ num_kv_heads=None,
109
+ alibi=False,
110
+ new_decoder_architecture=False,
111
+ multi_query=True,
112
+ parallel_attn=True,
113
+ bias=False,
114
+ bos_token_id=11,
115
+ eos_token_id=11,
116
+ **kwargs,
117
+ ):
118
+ logger.warning_once(
119
+ "\nWARNING: You are currently loading Falcon using legacy code contained in the model repository. Falcon has now been fully ported into the Hugging Face transformers library. "
120
+ "For the most up-to-date and high-performance version of the Falcon model code, please update to the latest version of transformers and then load the model "
121
+ "without the trust_remote_code=True argument.\n"
122
+ )
123
+ self.vocab_size = vocab_size
124
+ # Backward compatibility with n_embed kwarg
125
+ n_embed = kwargs.pop("n_embed", None)
126
+ self.hidden_size = hidden_size if n_embed is None else n_embed
127
+ self.num_hidden_layers = num_hidden_layers
128
+ self.num_attention_heads = num_attention_heads
129
+ self.layer_norm_epsilon = layer_norm_epsilon
130
+ self.initializer_range = initializer_range
131
+ self.use_cache = use_cache
132
+ self.hidden_dropout = hidden_dropout
133
+ self.attention_dropout = attention_dropout
134
+
135
+ self.bos_token_id = bos_token_id
136
+ self.eos_token_id = eos_token_id
137
+ self.num_kv_heads = num_attention_heads if num_kv_heads is None else num_kv_heads
138
+ self.alibi = alibi
139
+ self.new_decoder_architecture = new_decoder_architecture
140
+ self.multi_query = multi_query # Ignored when new_decoder_architecture is True
141
+ self.parallel_attn = parallel_attn
142
+ self.bias = bias
143
+
144
+ super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
145
+
146
+ @property
147
+ def head_dim(self):
148
+ return self.hidden_size // self.num_attention_heads
149
+
150
+ @property
151
+ def rotary(self):
152
+ return not self.alibi
gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.npy filter=lfs diff=lfs merge=lfs -text
15
+ *.npz filter=lfs diff=lfs merge=lfs -text
16
+ *.onnx filter=lfs diff=lfs merge=lfs -text
17
+ *.ot filter=lfs diff=lfs merge=lfs -text
18
+ *.parquet filter=lfs diff=lfs merge=lfs -text
19
+ *.pb filter=lfs diff=lfs merge=lfs -text
20
+ *.pickle filter=lfs diff=lfs merge=lfs -text
21
+ *.pkl filter=lfs diff=lfs merge=lfs -text
22
+ *.pt filter=lfs diff=lfs merge=lfs -text
23
+ *.pth filter=lfs diff=lfs merge=lfs -text
24
+ *.rar filter=lfs diff=lfs merge=lfs -text
25
+ *.safetensors filter=lfs diff=lfs merge=lfs -text
26
+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
27
+ *.tar.* filter=lfs diff=lfs merge=lfs -text
28
+ *.tflite filter=lfs diff=lfs merge=lfs -text
29
+ *.tgz filter=lfs diff=lfs merge=lfs -text
30
+ *.wasm filter=lfs diff=lfs merge=lfs -text
31
+ *.xz filter=lfs diff=lfs merge=lfs -text
32
+ *.zip filter=lfs diff=lfs merge=lfs -text
33
+ *.zst filter=lfs diff=lfs merge=lfs -text
34
+ *tfevents* filter=lfs diff=lfs merge=lfs -text
handler.py ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+
3
+ from typing import Any, Dict
4
+ from transformers import AutoModelForCausalLM, AutoTokenizer
5
+
6
+
7
+ class EndpointHandler:
8
+ def __init__(self, path=""):
9
+ # load model and tokenizer from path
10
+ self.tokenizer = AutoTokenizer.from_pretrained(path)
11
+ self.model = AutoModelForCausalLM.from_pretrained(
12
+ path, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True
13
+ )
14
+ self.device = "cuda" if torch.cuda.is_available() else "cpu"
15
+
16
+ def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
17
+ # process input
18
+ inputs = data.pop("inputs", data)
19
+ parameters = data.pop("parameters", None)
20
+
21
+ # preprocess
22
+ inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device)
23
+
24
+ # pass inputs with all kwargs in data
25
+ if parameters is not None:
26
+ outputs = self.model.generate(**inputs, **parameters)
27
+ else:
28
+ outputs = self.model.generate(**inputs)
29
+
30
+ # postprocess the prediction
31
+ prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
32
+
33
+ return [{"generated_text": prediction}]
modeling_falcon.py ADDED
@@ -0,0 +1,1262 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2023 the Falcon authors and HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """PyTorch Falcon model."""
16
+
17
+ import math
18
+ from typing import Optional, Tuple, Union
19
+
20
+ import torch
21
+ import torch.utils.checkpoint
22
+ from torch import nn
23
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
24
+ from torch.nn import functional as F
25
+
26
+ from transformers.modeling_outputs import (
27
+ BaseModelOutputWithPastAndCrossAttentions,
28
+ CausalLMOutputWithCrossAttentions,
29
+ QuestionAnsweringModelOutput,
30
+ SequenceClassifierOutputWithPast,
31
+ TokenClassifierOutput,
32
+ )
33
+ from transformers.modeling_utils import PreTrainedModel
34
+ from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
35
+ from .configuration_falcon import FalconConfig
36
+
37
+
38
+ logger = logging.get_logger(__name__)
39
+
40
+ FALCON_PRETRAINED_MODEL_ARCHIVE_LIST = [
41
+ "tiiuae/falcon-40b",
42
+ "tiiuae/falcon-40b-instruct",
43
+ "tiiuae/falcon-7b",
44
+ "tiiuae/falcon-7b-instruct",
45
+ "tiiuae/falcon-rw-7b",
46
+ "tiiuae/falcon-rw-1b",
47
+ ]
48
+ _CHECKPOINT_FOR_DOC = "Rocketknight1/falcon-rw-1b"
49
+ _CONFIG_FOR_DOC = "FalconConfig"
50
+
51
+
52
+ # NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations.
53
+ # In order not to degrade the quality of our HF-port, we keep these characteristics in the final model.
54
+ class FalconLinear(nn.Linear):
55
+ def forward(self, input: torch.Tensor) -> torch.Tensor:
56
+ hidden_states = input @ self.weight.T
57
+ if self.bias is None:
58
+ return hidden_states
59
+ return hidden_states + self.bias
60
+
61
+
62
+ # rotary pos emb helpers (torch.jit.script does not seem to support staticmethod...)
63
+ def rotate_half(x):
64
+ x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
65
+ return torch.cat((-x2, x1), dim=-1)
66
+
67
+
68
+ class FalconRotaryEmbedding(nn.Module):
69
+ """Implementation of RotaryEmbedding from GPT-NeoX.
70
+ This implementation is designed to operate on queries and keys that are compatible with `[batch_size,
71
+ n_heads_per_partition, seq_len, head_dim]` (e.g. MinGPTAttention format).
72
+ """
73
+
74
+ def __init__(self, head_dim: int, base=10000):
75
+ super().__init__()
76
+ inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2).float() / head_dim))
77
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
78
+ self.head_dim = head_dim
79
+ self.seq_len_cached = -1
80
+ self.cos_cached: torch.Tensor | None = None
81
+ self.sin_cached: torch.Tensor | None = None
82
+
83
+ def cos_sin(self, seq_len: int, past_key_values_length: int, device="cpu", dtype=torch.bfloat16) -> torch.Tensor:
84
+ total_length = seq_len + past_key_values_length
85
+ if total_length > self.seq_len_cached:
86
+ self.seq_len_cached = total_length
87
+ t = torch.arange(total_length, device=device, dtype=self.inv_freq.dtype)
88
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
89
+ emb = torch.cat((freqs, freqs), dim=-1).to(device)
90
+
91
+ if dtype in [torch.float16, torch.bfloat16]:
92
+ emb = emb.float()
93
+
94
+ self.cos_cached = emb.cos()[None, :, :]
95
+ self.sin_cached = emb.sin()[None, :, :]
96
+
97
+ self.cos_cached = self.cos_cached.type(dtype)
98
+ self.sin_cached = self.sin_cached.type(dtype)
99
+
100
+ return (
101
+ self.cos_cached[:, past_key_values_length : seq_len + past_key_values_length],
102
+ self.sin_cached[:, past_key_values_length : seq_len + past_key_values_length],
103
+ )
104
+
105
+ def forward(self, query, key, past_key_values_length=0):
106
+ batch, seq_len, head_dim = query.shape
107
+ cos, sin = self.cos_sin(seq_len, past_key_values_length, query.device, query.dtype)
108
+ return (query * cos) + (rotate_half(query) * sin), (key * cos) + (rotate_half(key) * sin)
109
+
110
+
111
+ def _make_causal_mask(
112
+ input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
113
+ ) -> torch.BoolTensor:
114
+ """
115
+ Make causal mask used for self-attention. This mask does not take the existing attention mask into account - it
116
+ just blocks tokens from attending forwards in the sequence. The output shape will be `[batch_size, 1,
117
+ target_length, target_length+past_key_values_length]`.
118
+ """
119
+ batch_size, target_length = input_ids_shape
120
+
121
+ mask = torch.triu(torch.ones((target_length, target_length), dtype=torch.bool, device=device), diagonal=1)
122
+ # If past_key_values_length is 0 this is an empty tensor and the concatenation is a no-op.
123
+ # This code style is an unfortunate consequence of getting your TF engineer to port models; doing it this
124
+ # way avoids a data-dependent conditional, which will help me when I have to port this to XLA later.
125
+ past_mask = torch.zeros((target_length, past_key_values_length), dtype=torch.bool, device=device)
126
+ mask = torch.cat([past_mask, mask], dim=-1)
127
+ expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length)
128
+ return expanded_mask
129
+
130
+
131
+ def _expand_mask(mask: torch.Tensor, past_key_values_length: int) -> torch.BoolTensor:
132
+ """
133
+ Expands attention_mask from `[batch_size, seq_length]` to `[batch_size, 1, seq_length, seq_length + past_length]`.
134
+ """
135
+ batch_size, total_length = mask.shape
136
+ seq_length = total_length - past_key_values_length if past_key_values_length is not None else total_length
137
+
138
+ expanded_mask = ~(mask[:, None, None, :].to(torch.bool))
139
+ return expanded_mask.expand(batch_size, 1, seq_length, total_length)
140
+
141
+
142
+ def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
143
+ batch_size, seq_length = attention_mask.shape
144
+ closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
145
+ base = torch.tensor(
146
+ 2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
147
+ )
148
+ powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32)
149
+ slopes = torch.pow(base, powers)
150
+
151
+ if closest_power_of_2 != num_heads:
152
+ extra_base = torch.tensor(
153
+ 2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
154
+ )
155
+ num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
156
+ extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32)
157
+ slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
158
+
159
+ # Note: alibi will added to the attention bias that will be applied to the query, key product of attention
160
+ # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
161
+ # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
162
+ # => the query_length dimension will then be broadcasted correctly
163
+ # This is more or less identical to T5's relative position bias:
164
+ # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
165
+ arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
166
+ alibi = slopes[..., None].bfloat16() * arange_tensor
167
+ return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
168
+
169
+
170
+ # Copied from transformers.models.bloom.modeling_bloom.dropout_add
171
+ def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
172
+ """
173
+ Dropout add function
174
+
175
+ Args:
176
+ x (`torch.tensor`, *required*):
177
+ input tensor
178
+ residual (`torch.tensor`, *required*):
179
+ residual tensor
180
+ prob (`float`, *required*):
181
+ dropout probability
182
+ training (`bool`, *required*):
183
+ training mode
184
+ """
185
+ out = F.dropout(x, p=prob, training=training)
186
+ out = residual + out
187
+ return out
188
+
189
+
190
+ class FalconAttention(nn.Module):
191
+ def __init__(self, config: FalconConfig):
192
+ super().__init__()
193
+
194
+ self.hidden_size = config.hidden_size
195
+ self.num_heads = config.num_attention_heads
196
+ self.head_dim = self.hidden_size // self.num_heads
197
+ self.split_size = self.hidden_size
198
+ self.hidden_dropout = config.hidden_dropout
199
+
200
+ if self.head_dim * self.num_heads != self.hidden_size:
201
+ raise ValueError(
202
+ f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
203
+ f" {self.num_heads})."
204
+ )
205
+
206
+ self.maybe_rotary = FalconRotaryEmbedding(config.head_dim) if config.rotary else lambda q, k, t: (q, k)
207
+
208
+ # Layer-wise attention scaling
209
+ self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
210
+ self.beta = self.inv_norm_factor
211
+ if config.new_decoder_architecture:
212
+ qkv_out_dim = (config.num_kv_heads * 2 + config.num_attention_heads) * self.head_dim
213
+ elif config.multi_query:
214
+ qkv_out_dim = self.hidden_size + 2 * self.head_dim
215
+ else:
216
+ qkv_out_dim = 3 * self.hidden_size
217
+ self.query_key_value = FalconLinear(self.hidden_size, qkv_out_dim, bias=config.bias)
218
+ self.new_decoder_architecture = config.new_decoder_architecture
219
+ self.multi_query = config.multi_query
220
+ self.dense = FalconLinear(self.hidden_size, self.hidden_size, bias=config.bias)
221
+ self.attention_dropout = nn.Dropout(config.attention_dropout)
222
+ self.num_kv_heads = config.num_kv_heads if (self.new_decoder_architecture or not self.multi_query) else 1
223
+
224
+ def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
225
+ """
226
+ Split the last dimension into (num_heads, head_dim), results share same memory storage as `fused_qkv`
227
+
228
+ Args:
229
+ fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]
230
+
231
+ Returns:
232
+ query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
233
+ value: [batch_size, seq_length, num_heads, head_dim]
234
+ """
235
+ if self.new_decoder_architecture:
236
+ batch, seq_len, _ = fused_qkv.shape
237
+ qkv = fused_qkv.view(batch, seq_len, -1, self.num_heads // self.num_kv_heads + 2, self.head_dim)
238
+ query = qkv[:, :, :, :-2]
239
+ key = qkv[:, :, :, [-2]]
240
+ value = qkv[:, :, :, [-1]]
241
+ key = torch.broadcast_to(key, query.shape)
242
+ value = torch.broadcast_to(value, query.shape)
243
+
244
+ query, key, value = [x.flatten(2, 3) for x in (query, key, value)]
245
+ return query, key, value
246
+ elif not self.multi_query:
247
+ batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
248
+ fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
249
+ return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
250
+ else:
251
+ batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
252
+ fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
253
+ return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
254
+
255
+ # Copied from transformers.models.bloom.modeling_bloom.BloomAttention._merge_heads
256
+ def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
257
+ """
258
+ Merge heads together over the last dimenstion
259
+
260
+ Args:
261
+ x (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim]
262
+
263
+ Returns:
264
+ torch.tensor: [batch_size, seq_length, num_heads * head_dim]
265
+ """
266
+ # What we want to achieve is:
267
+ # batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
268
+ batch_size_and_num_heads, seq_length, _ = x.shape
269
+ batch_size = batch_size_and_num_heads // self.num_heads
270
+
271
+ # First view to decompose the batch size
272
+ # batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
273
+ x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
274
+
275
+ # batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
276
+ x = x.permute(0, 2, 1, 3)
277
+
278
+ # batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
279
+ return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
280
+
281
+ def forward(
282
+ self,
283
+ hidden_states: torch.Tensor,
284
+ alibi: Optional[torch.Tensor],
285
+ attention_mask: torch.Tensor,
286
+ layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
287
+ head_mask: Optional[torch.Tensor] = None,
288
+ use_cache: bool = False,
289
+ output_attentions: bool = False,
290
+ ):
291
+ fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
292
+ num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
293
+ # 3 x [batch_size, seq_length, num_heads, head_dim]
294
+ (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
295
+
296
+ batch_size, query_length, _, _ = query_layer.shape
297
+
298
+ query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, query_length, self.head_dim)
299
+ key_layer = key_layer.transpose(1, 2).reshape(
300
+ batch_size * num_kv_heads,
301
+ query_length,
302
+ self.head_dim,
303
+ )
304
+ value_layer = value_layer.transpose(1, 2).reshape(batch_size * num_kv_heads, query_length, self.head_dim)
305
+
306
+ past_kv_length = 0 if layer_past is None else layer_past[0].shape[1]
307
+ query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length)
308
+
309
+ if layer_past is not None:
310
+ past_key, past_value = layer_past
311
+ # concatenate along seq_length dimension:
312
+ # - key: [batch_size * self.num_heads, kv_length, head_dim]
313
+ # - value: [batch_size * self.num_heads, kv_length, head_dim]
314
+ key_layer = torch.cat((past_key, key_layer), dim=1)
315
+ value_layer = torch.cat((past_value, value_layer), dim=1)
316
+
317
+ _, kv_length, _ = key_layer.shape
318
+ if use_cache:
319
+ present = (key_layer, value_layer)
320
+ else:
321
+ present = None
322
+
323
+ attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, float("-1e9")).to(query_layer.dtype)
324
+
325
+ query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
326
+ key_layer_ = key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
327
+ value_layer_ = value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
328
+
329
+ if alibi is None:
330
+ if output_attentions:
331
+ # F.scaled_dot_product_attention doesn't return the attention weights, so we have
332
+ # to do it by hand if we want them
333
+ attention_scores = query_layer_ @ key_layer_.transpose(-1, -2)
334
+ attention_scores /= math.sqrt(self.head_dim)
335
+
336
+ attention_scores = F.softmax(
337
+ attention_scores + attention_mask_float, dim=-1, dtype=hidden_states.dtype
338
+ )
339
+ attn_output = attention_scores @ value_layer_
340
+ else:
341
+ attn_output = F.scaled_dot_product_attention(
342
+ query_layer_, key_layer_, value_layer_, attention_mask_float, 0.0, is_causal=False
343
+ )
344
+ attention_scores = None
345
+
346
+ attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
347
+ attn_output = attn_output.permute(0, 2, 1, 3)
348
+ attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
349
+
350
+ output_tensor = self.dense(attn_output)
351
+
352
+ if output_attentions:
353
+ return output_tensor, present, attention_scores
354
+ else:
355
+ return output_tensor, present
356
+
357
+ else:
358
+ matmul_result = query_layer_ @ key_layer_.transpose(-1, -2)
359
+
360
+ # change view to [batch_size, num_heads, q_length, kv_length]
361
+ attention_scores = matmul_result.view(batch_size, self.num_heads, query_length, kv_length)
362
+
363
+ # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
364
+ input_dtype = attention_scores.dtype
365
+ # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
366
+ if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
367
+ attention_scores = attention_scores.to(torch.float32)
368
+ # Matt (HF) note: We could possibly use F.scaled_dot_product_attention here too, by
369
+ # adding (alibi * self.inv_norm_factor) to attention_mask_float. I think this would be mathematically
370
+ # equivalent and more performant, but there might be a numerical difference. If you're reading this
371
+ # and you'd like to experiment and maybe file a PR, feel free!
372
+ attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
373
+ attention_logits *= self.inv_norm_factor
374
+ attention_probs = F.softmax(attention_logits + attention_mask_float, dim=-1, dtype=hidden_states.dtype)
375
+ # [batch_size, num_heads, q_length, kv_length]
376
+ attention_probs = self.attention_dropout(attention_probs)
377
+
378
+ if head_mask is not None:
379
+ attention_probs = attention_probs * head_mask
380
+
381
+ # change view [batch_size, num_heads, q_length, kv_length]
382
+ attention_probs_reshaped = attention_probs.view(batch_size, self.num_heads, query_length, kv_length)
383
+
384
+ # matmul: [batch_size * num_heads, q_length, head_dim]
385
+ context_layer = (attention_probs_reshaped @ value_layer_).flatten(0, 1)
386
+
387
+ # change view [batch_size, num_heads, q_length, head_dim]
388
+ context_layer = self._merge_heads(context_layer)
389
+
390
+ output_tensor = self.dense(context_layer)
391
+
392
+ if output_attentions:
393
+ return output_tensor, present, attention_probs
394
+ else:
395
+ return output_tensor, present
396
+
397
+
398
+ class FalconMLP(nn.Module):
399
+ def __init__(self, config: FalconConfig):
400
+ super().__init__()
401
+ hidden_size = config.hidden_size
402
+
403
+ self.dense_h_to_4h = FalconLinear(hidden_size, 4 * hidden_size, bias=config.bias)
404
+ self.act = nn.GELU()
405
+ self.dense_4h_to_h = FalconLinear(4 * hidden_size, hidden_size, bias=config.bias)
406
+ self.hidden_dropout = config.hidden_dropout
407
+
408
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
409
+ x = self.act(self.dense_h_to_4h(x))
410
+ x = self.dense_4h_to_h(x)
411
+ return x
412
+
413
+
414
+ class FalconDecoderLayer(nn.Module):
415
+ def __init__(self, config: FalconConfig):
416
+ super().__init__()
417
+ hidden_size = config.hidden_size
418
+ self.num_heads = config.num_attention_heads
419
+ self.self_attention = FalconAttention(config)
420
+ self.mlp = FalconMLP(config)
421
+ self.hidden_dropout = config.hidden_dropout
422
+ self.config = config
423
+
424
+ if config.new_decoder_architecture:
425
+ # The layer norm before self-attention
426
+ self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
427
+ # The layer norm before the MLP
428
+ self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
429
+ else:
430
+ self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
431
+ if not config.parallel_attn:
432
+ self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
433
+
434
+ def forward(
435
+ self,
436
+ hidden_states: torch.Tensor,
437
+ alibi: Optional[torch.Tensor],
438
+ attention_mask: torch.Tensor,
439
+ layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
440
+ head_mask: Optional[torch.Tensor] = None,
441
+ use_cache: bool = False,
442
+ output_attentions: bool = False,
443
+ ):
444
+ residual = hidden_states
445
+
446
+ if self.config.new_decoder_architecture:
447
+ attention_layernorm_out = self.ln_attn(hidden_states)
448
+ mlp_layernorm_out = self.ln_mlp(hidden_states)
449
+ else:
450
+ attention_layernorm_out = self.input_layernorm(hidden_states)
451
+
452
+ # Self attention.
453
+ attn_outputs = self.self_attention(
454
+ attention_layernorm_out,
455
+ layer_past=layer_past,
456
+ attention_mask=attention_mask,
457
+ alibi=alibi,
458
+ head_mask=head_mask,
459
+ use_cache=use_cache,
460
+ output_attentions=output_attentions,
461
+ )
462
+
463
+ attention_output = attn_outputs[0]
464
+
465
+ if not self.config.new_decoder_architecture:
466
+ if self.config.parallel_attn:
467
+ mlp_layernorm_out = attention_layernorm_out
468
+ else:
469
+ residual = dropout_add(
470
+ attention_output, residual, self.config.attention_dropout, training=self.training
471
+ )
472
+ mlp_layernorm_out = self.post_attention_layernorm(residual)
473
+
474
+ outputs = attn_outputs[1:]
475
+
476
+ # MLP.
477
+ mlp_output = self.mlp(mlp_layernorm_out)
478
+
479
+ if self.config.new_decoder_architecture or self.config.parallel_attn:
480
+ mlp_output += attention_output
481
+
482
+ output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training)
483
+
484
+ if use_cache:
485
+ outputs = (output,) + outputs
486
+ else:
487
+ outputs = (output,) + outputs[1:]
488
+
489
+ return outputs # hidden_states, present, attentions
490
+
491
+
492
+ FALCON_START_DOCSTRING = r"""
493
+
494
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
495
+ library implements for all its model (such as downloading or saving, resizing the input embeddings etc.)
496
+
497
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
498
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
499
+ and behavior.
500
+
501
+ Parameters:
502
+ config ([`FalconConfig`]): Model configuration class with all the parameters of the model.
503
+ Initializing with a config file does not load the weights associated with the model, only the
504
+ configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
505
+ """
506
+
507
+ FALCON_INPUTS_DOCSTRING = r"""
508
+ Args:
509
+ input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
510
+ `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][0].shape[2]`
511
+ (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
512
+
513
+ If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
514
+ `input_ids`.
515
+
516
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
517
+ [`PreTrainedTokenizer.__call__`] for details.
518
+
519
+ [What are input IDs?](../glossary#input-ids)
520
+ past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.num_hidden_layers`):
521
+ Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see
522
+ `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have
523
+ their past given to this model should not be passed as `input_ids` as they have already been computed.
524
+
525
+ Each element of `past_key_values` is a tuple (past_key, past_value):
526
+ - past_key: [batch_size * num_heads, head_dim, kv_length]
527
+ - past_value: [batch_size * num_heads, kv_length, head_dim]
528
+ attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
529
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
530
+
531
+ - 1 for tokens that are **not masked**,
532
+ - 0 for tokens that are **masked**.
533
+
534
+ [What are attention masks?](../glossary#attention-mask)
535
+ head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
536
+ Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
537
+
538
+ - 1 indicates the head is **not masked**,
539
+ - 0 indicates the head is **masked**.
540
+
541
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
542
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
543
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
544
+ model's internal embedding lookup matrix.
545
+
546
+ If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see
547
+ `past_key_values`).
548
+ use_cache (`bool`, *optional*):
549
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
550
+ `past_key_values`).
551
+ output_attentions (`bool`, *optional*):
552
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
553
+ tensors for more detail.
554
+ output_hidden_states (`bool`, *optional*):
555
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
556
+ more detail.
557
+ return_dict (`bool`, *optional*):
558
+ Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
559
+ """
560
+
561
+
562
+ class FalconPreTrainedModel(PreTrainedModel):
563
+ """
564
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
565
+ models.
566
+ """
567
+
568
+ config_class = FalconConfig
569
+ base_model_prefix = "transformer"
570
+ supports_gradient_checkpointing = True
571
+ _no_split_modules = ["FalconDecoderLayer"]
572
+
573
+ def __init__(self, *inputs, **kwargs):
574
+ super().__init__(*inputs, **kwargs)
575
+
576
+ def _init_weights(self, module: nn.Module):
577
+ """Initialize the weights."""
578
+ if isinstance(module, nn.Linear) or isinstance(module, FalconLinear):
579
+ # Slightly different from the TF version which uses truncated_normal for initialization
580
+ # cf https://github.com/pytorch/pytorch/pull/5617
581
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
582
+ if module.bias is not None:
583
+ module.bias.data.zero_()
584
+ elif isinstance(module, nn.Embedding):
585
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
586
+ if module.padding_idx is not None:
587
+ module.weight.data[module.padding_idx].zero_()
588
+ elif isinstance(module, LayerNorm):
589
+ module.bias.data.zero_()
590
+ module.weight.data.fill_(1.0)
591
+
592
+ # Copied from transformers.models.bloom.modeling_bloom.BloomPreTrainedModel._set_gradient_checkpointing with BloomModel->FalconModel
593
+ def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False):
594
+ if isinstance(module, FalconModel):
595
+ module.gradient_checkpointing = value
596
+
597
+ @staticmethod
598
+ def _convert_cache_to_standard_format(
599
+ past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
600
+ ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
601
+ """
602
+ Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size,
603
+ num_heads, ...]))
604
+ """
605
+ batch_size_times_num_heads, kv_length, head_dim = past_key_value[0][0].shape
606
+ # [batch_size * self.num_heads, kv_length, head_dim] -> [batch_size, num_heads, kv_length, head_dim]
607
+ # Note that don't want to use self.num_attention_heads because the number of heads may vary depending
608
+ # on whether we use multi_query attention.
609
+ num_heads = batch_size_times_num_heads // batch_size
610
+ return tuple(
611
+ (
612
+ layer_past[0].view(batch_size, num_heads, kv_length, head_dim),
613
+ layer_past[1].view(batch_size, num_heads, kv_length, head_dim),
614
+ )
615
+ for layer_past in past_key_value
616
+ )
617
+
618
+ @staticmethod
619
+ def _convert_to_rw_cache(
620
+ past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]]
621
+ ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
622
+ batch_size, num_heads, kv_length, head_dim = past_key_value[0][0].shape
623
+ batch_size_times_num_heads = batch_size * num_heads
624
+ # [batch_size, num_heads, kv_length, head_dim] -> [batch_size * num_heads, kv_length, head_dim]
625
+ return tuple(
626
+ (
627
+ layer_past[0].view(batch_size_times_num_heads, kv_length, head_dim),
628
+ layer_past[1].view(batch_size_times_num_heads, kv_length, head_dim),
629
+ )
630
+ for layer_past in past_key_value
631
+ )
632
+
633
+
634
+ @add_start_docstrings(
635
+ "The bare Falcon Model transformer outputting raw hidden-states without any specific head on top.",
636
+ FALCON_START_DOCSTRING,
637
+ )
638
+ class FalconModel(FalconPreTrainedModel):
639
+ def __init__(self, config: FalconConfig):
640
+ super().__init__(config)
641
+
642
+ self.embed_dim = config.hidden_size
643
+ self.num_heads = config.num_attention_heads
644
+ self.use_alibi = config.alibi
645
+
646
+ # Embedding + LN Embedding
647
+ self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
648
+
649
+ # Transformer blocks
650
+ self.h = nn.ModuleList([FalconDecoderLayer(config) for _ in range(config.num_hidden_layers)])
651
+
652
+ # Final Layer Norm
653
+ self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
654
+
655
+ self.gradient_checkpointing = False
656
+
657
+ # Initialize weights and apply final processing
658
+ self.post_init()
659
+
660
+ def get_input_embeddings(self):
661
+ return self.word_embeddings
662
+
663
+ @staticmethod
664
+ def _prepare_attn_mask(
665
+ attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
666
+ ) -> torch.BoolTensor:
667
+ # Create a causal mask
668
+ # The attention mask we receive as input should cover the whole extended sequence, including any past
669
+ # cache, so its shape should be [batch_size, seq_length + past_key_values_length]
670
+ # The output shape will be [batch_size, 1, seq_length, seq_length + past_key_values_length]
671
+ if input_shape[1] + past_key_values_length != attention_mask.shape[1]:
672
+ raise ValueError(
673
+ "Attention mask shape should be (batch_size, seq_length + past_key_values_length)"
674
+ f" but is {attention_mask.shape} with input_ids shape {input_shape} and past length"
675
+ f" {past_key_values_length}."
676
+ )
677
+ combined_attention_mask = None
678
+ device = attention_mask.device
679
+ _, seq_length = input_shape
680
+
681
+ if seq_length > 1:
682
+ combined_attention_mask = _make_causal_mask(
683
+ input_shape, device=device, past_key_values_length=past_key_values_length
684
+ )
685
+
686
+ # [batch_size, seq_length + past_key_values_length] -> [batch_size, 1, seq_length, seq_length + past_key_values_length]
687
+ expanded_attn_mask = _expand_mask(attention_mask, past_key_values_length=past_key_values_length)
688
+ combined_attention_mask = (
689
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
690
+ )
691
+
692
+ return combined_attention_mask
693
+
694
+ def set_input_embeddings(self, new_embeddings: torch.Tensor):
695
+ self.word_embeddings = new_embeddings
696
+
697
+ @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
698
+ @add_code_sample_docstrings(
699
+ checkpoint=_CHECKPOINT_FOR_DOC,
700
+ output_type=BaseModelOutputWithPastAndCrossAttentions,
701
+ config_class=_CONFIG_FOR_DOC,
702
+ )
703
+ def forward(
704
+ self,
705
+ input_ids: Optional[torch.LongTensor] = None,
706
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
707
+ attention_mask: Optional[torch.Tensor] = None,
708
+ head_mask: Optional[torch.LongTensor] = None,
709
+ inputs_embeds: Optional[torch.LongTensor] = None,
710
+ use_cache: Optional[bool] = None,
711
+ output_attentions: Optional[bool] = None,
712
+ output_hidden_states: Optional[bool] = None,
713
+ return_dict: Optional[bool] = None,
714
+ ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
715
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
716
+ output_hidden_states = (
717
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
718
+ )
719
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
720
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
721
+
722
+ if input_ids is not None and inputs_embeds is not None:
723
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
724
+ elif input_ids is not None:
725
+ batch_size, seq_length = input_ids.shape
726
+ elif inputs_embeds is not None:
727
+ batch_size, seq_length, _ = inputs_embeds.shape
728
+ else:
729
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
730
+
731
+ if past_key_values is None:
732
+ past_key_values = tuple([None] * len(self.h))
733
+ else:
734
+ past_key_values = self._convert_to_rw_cache(past_key_values)
735
+
736
+ # Prepare head mask if needed
737
+ # 1.0 in head_mask indicate we keep the head
738
+ # attention_probs has shape batch_size x num_heads x N x N
739
+ # head_mask has shape n_layer x batch x num_heads x N x N
740
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
741
+
742
+ if inputs_embeds is None:
743
+ inputs_embeds = self.word_embeddings(input_ids)
744
+
745
+ hidden_states = inputs_embeds
746
+
747
+ presents = () if use_cache else None
748
+ all_self_attentions = () if output_attentions else None
749
+ all_hidden_states = () if output_hidden_states else None
750
+
751
+ # Compute alibi tensor: check build_alibi_tensor documentation
752
+ past_key_values_length = 0
753
+ if past_key_values[0] is not None:
754
+ past_key_values_length = past_key_values[0][0].shape[1] # 1 because RW-cache, not standard format
755
+ if attention_mask is None:
756
+ attention_mask = torch.ones((batch_size, seq_length + past_key_values_length), device=hidden_states.device)
757
+ else:
758
+ attention_mask = attention_mask.to(hidden_states.device)
759
+
760
+ if self.use_alibi:
761
+ alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
762
+ else:
763
+ alibi = None
764
+
765
+ causal_mask = self._prepare_attn_mask(
766
+ attention_mask,
767
+ input_shape=(batch_size, seq_length),
768
+ past_key_values_length=past_key_values_length,
769
+ )
770
+
771
+ for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
772
+ if output_hidden_states:
773
+ all_hidden_states = all_hidden_states + (hidden_states,)
774
+
775
+ if self.gradient_checkpointing and self.training:
776
+ if use_cache:
777
+ logger.warning(
778
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
779
+ )
780
+ use_cache = False
781
+
782
+ def create_custom_forward(module):
783
+ def custom_forward(*inputs):
784
+ # None for past_key_value
785
+ return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
786
+
787
+ return custom_forward
788
+
789
+ outputs = torch.utils.checkpoint.checkpoint(
790
+ create_custom_forward(block),
791
+ hidden_states,
792
+ alibi,
793
+ causal_mask,
794
+ head_mask[i],
795
+ )
796
+ else:
797
+ outputs = block(
798
+ hidden_states,
799
+ layer_past=layer_past,
800
+ attention_mask=causal_mask,
801
+ head_mask=head_mask[i],
802
+ use_cache=use_cache,
803
+ output_attentions=output_attentions,
804
+ alibi=alibi,
805
+ )
806
+
807
+ hidden_states = outputs[0]
808
+ if use_cache is True:
809
+ presents = presents + (outputs[1],)
810
+
811
+ if output_attentions:
812
+ all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
813
+
814
+ # Add last hidden state
815
+ hidden_states = self.ln_f(hidden_states)
816
+
817
+ if output_hidden_states:
818
+ all_hidden_states = all_hidden_states + (hidden_states,)
819
+
820
+ if presents is not None:
821
+ presents = self._convert_cache_to_standard_format(presents, batch_size)
822
+
823
+ if not return_dict:
824
+ return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
825
+
826
+ return BaseModelOutputWithPastAndCrossAttentions(
827
+ last_hidden_state=hidden_states,
828
+ past_key_values=presents,
829
+ hidden_states=all_hidden_states,
830
+ attentions=all_self_attentions,
831
+ )
832
+
833
+
834
+ @add_start_docstrings(
835
+ "The Falcon Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).",
836
+ FALCON_START_DOCSTRING,
837
+ )
838
+ class FalconForCausalLM(FalconPreTrainedModel):
839
+ _tied_weights_keys = ["lm_head.weight"]
840
+
841
+ def __init__(self, config: FalconConfig):
842
+ super().__init__(config)
843
+ self.transformer = FalconModel(config)
844
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
845
+
846
+ # Initialize weights and apply final processing
847
+ self.post_init()
848
+
849
+ def get_output_embeddings(self):
850
+ return self.lm_head
851
+
852
+ def set_output_embeddings(self, new_embeddings: torch.Tensor):
853
+ self.lm_head = new_embeddings
854
+
855
+ def prepare_inputs_for_generation(
856
+ self,
857
+ input_ids: torch.LongTensor,
858
+ past_key_values: Optional[torch.Tensor] = None,
859
+ attention_mask: Optional[torch.Tensor] = None,
860
+ **kwargs,
861
+ ) -> dict:
862
+ if past_key_values is not None:
863
+ input_ids = input_ids[:, -1:]
864
+
865
+ return {
866
+ "input_ids": input_ids,
867
+ "past_key_values": past_key_values,
868
+ "use_cache": kwargs.get("use_cache"),
869
+ "attention_mask": attention_mask,
870
+ }
871
+
872
+ @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
873
+ @add_code_sample_docstrings(
874
+ checkpoint=_CHECKPOINT_FOR_DOC,
875
+ output_type=CausalLMOutputWithCrossAttentions,
876
+ config_class=_CONFIG_FOR_DOC,
877
+ )
878
+ def forward(
879
+ self,
880
+ input_ids: Optional[torch.LongTensor] = None,
881
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
882
+ attention_mask: Optional[torch.Tensor] = None,
883
+ head_mask: Optional[torch.Tensor] = None,
884
+ inputs_embeds: Optional[torch.Tensor] = None,
885
+ labels: Optional[torch.Tensor] = None,
886
+ use_cache: Optional[bool] = None,
887
+ output_attentions: Optional[bool] = None,
888
+ output_hidden_states: Optional[bool] = None,
889
+ return_dict: Optional[bool] = None,
890
+ ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
891
+ r"""
892
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
893
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
894
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
895
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
896
+ """
897
+
898
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
899
+
900
+ transformer_outputs = self.transformer(
901
+ input_ids,
902
+ past_key_values=past_key_values,
903
+ attention_mask=attention_mask,
904
+ head_mask=head_mask,
905
+ inputs_embeds=inputs_embeds,
906
+ use_cache=use_cache,
907
+ output_attentions=output_attentions,
908
+ output_hidden_states=output_hidden_states,
909
+ return_dict=return_dict,
910
+ )
911
+ hidden_states = transformer_outputs[0]
912
+
913
+ lm_logits = self.lm_head(hidden_states)
914
+
915
+ loss = None
916
+ if labels is not None:
917
+ # Shift so that tokens < n predict n
918
+ shift_logits = lm_logits[..., :-1, :].contiguous()
919
+ shift_labels = labels[..., 1:].contiguous()
920
+ batch_size, seq_length, vocab_size = shift_logits.shape
921
+ # Flatten the tokens
922
+ loss_fct = CrossEntropyLoss()
923
+ loss = loss_fct(
924
+ shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
925
+ )
926
+
927
+ if not return_dict:
928
+ output = (lm_logits,) + transformer_outputs[1:]
929
+ return ((loss,) + output) if loss is not None else output
930
+
931
+ return CausalLMOutputWithCrossAttentions(
932
+ loss=loss,
933
+ logits=lm_logits,
934
+ past_key_values=transformer_outputs.past_key_values,
935
+ hidden_states=transformer_outputs.hidden_states,
936
+ attentions=transformer_outputs.attentions,
937
+ )
938
+
939
+ def _reorder_cache(
940
+ self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
941
+ ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
942
+ """
943
+ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
944
+ [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
945
+ beam_idx at every generation step.
946
+
947
+ Output shares the same memory storage as `past`.
948
+ """
949
+
950
+ # Get a copy of `beam_idx` on all the devices where we need those indices.
951
+ device_to_beam_idx = {
952
+ past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
953
+ }
954
+ reordered_past = tuple(
955
+ (
956
+ layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
957
+ layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
958
+ )
959
+ for layer_past in past
960
+ )
961
+ return reordered_past
962
+
963
+
964
+ @add_start_docstrings(
965
+ """
966
+ The Falcon Model transformer with a sequence classification head on top (linear layer).
967
+
968
+ [`FalconForSequenceClassification`] uses the last token in order to do the classification, as other causal models
969
+ (e.g. GPT-1) do.
970
+
971
+ Since it does classification on the last token, it requires to know the position of the last token. If a
972
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
973
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
974
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
975
+ each row of the batch).
976
+ """,
977
+ FALCON_START_DOCSTRING,
978
+ )
979
+ class FalconForSequenceClassification(FalconPreTrainedModel):
980
+ def __init__(self, config: FalconConfig):
981
+ super().__init__(config)
982
+ self.num_labels = config.num_labels
983
+ self.transformer = FalconModel(config)
984
+ self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
985
+
986
+ # Initialize weights and apply final processing
987
+ self.post_init()
988
+
989
+ @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
990
+ @add_code_sample_docstrings(
991
+ checkpoint=_CHECKPOINT_FOR_DOC,
992
+ output_type=SequenceClassifierOutputWithPast,
993
+ config_class=_CONFIG_FOR_DOC,
994
+ )
995
+ def forward(
996
+ self,
997
+ input_ids: Optional[torch.LongTensor] = None,
998
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
999
+ attention_mask: Optional[torch.Tensor] = None,
1000
+ head_mask: Optional[torch.Tensor] = None,
1001
+ inputs_embeds: Optional[torch.Tensor] = None,
1002
+ labels: Optional[torch.Tensor] = None,
1003
+ use_cache: Optional[bool] = None,
1004
+ output_attentions: Optional[bool] = None,
1005
+ output_hidden_states: Optional[bool] = None,
1006
+ return_dict: Optional[bool] = None,
1007
+ ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
1008
+ r"""
1009
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1010
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1011
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1012
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1013
+ """
1014
+
1015
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1016
+
1017
+ transformer_outputs = self.transformer(
1018
+ input_ids,
1019
+ past_key_values=past_key_values,
1020
+ attention_mask=attention_mask,
1021
+ head_mask=head_mask,
1022
+ inputs_embeds=inputs_embeds,
1023
+ use_cache=use_cache,
1024
+ output_attentions=output_attentions,
1025
+ output_hidden_states=output_hidden_states,
1026
+ return_dict=return_dict,
1027
+ )
1028
+
1029
+ hidden_states = transformer_outputs[0]
1030
+ logits = self.score(hidden_states)
1031
+
1032
+ if input_ids is not None:
1033
+ batch_size = input_ids.shape[0]
1034
+ else:
1035
+ batch_size = inputs_embeds.shape[0]
1036
+
1037
+ if self.config.pad_token_id is None and batch_size != 1:
1038
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1039
+ if self.config.pad_token_id is None:
1040
+ sequence_lengths = -1
1041
+ else:
1042
+ if input_ids is not None:
1043
+ sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1
1044
+ else:
1045
+ sequence_lengths = -1
1046
+ logger.warning(
1047
+ f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
1048
+ "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
1049
+ )
1050
+
1051
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1052
+
1053
+ loss = None
1054
+ if labels is not None:
1055
+ if self.config.problem_type is None:
1056
+ if self.num_labels == 1:
1057
+ self.config.problem_type = "regression"
1058
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1059
+ self.config.problem_type = "single_label_classification"
1060
+ else:
1061
+ self.config.problem_type = "multi_label_classification"
1062
+
1063
+ if self.config.problem_type == "regression":
1064
+ loss_fct = MSELoss()
1065
+ if self.num_labels == 1:
1066
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1067
+ else:
1068
+ loss = loss_fct(pooled_logits, labels)
1069
+ elif self.config.problem_type == "single_label_classification":
1070
+ loss_fct = CrossEntropyLoss()
1071
+ loss = loss_fct(pooled_logits, labels)
1072
+ elif self.config.problem_type == "multi_label_classification":
1073
+ loss_fct = BCEWithLogitsLoss()
1074
+ loss = loss_fct(pooled_logits, labels)
1075
+ if not return_dict:
1076
+ output = (pooled_logits,) + transformer_outputs[1:]
1077
+ return ((loss,) + output) if loss is not None else output
1078
+
1079
+ return SequenceClassifierOutputWithPast(
1080
+ loss=loss,
1081
+ logits=pooled_logits,
1082
+ past_key_values=transformer_outputs.past_key_values,
1083
+ hidden_states=transformer_outputs.hidden_states,
1084
+ attentions=transformer_outputs.attentions,
1085
+ )
1086
+
1087
+
1088
+ @add_start_docstrings(
1089
+ """
1090
+ Falcon Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
1091
+ Named-Entity-Recognition (NER) tasks.
1092
+ """,
1093
+ FALCON_START_DOCSTRING,
1094
+ )
1095
+ class FalconForTokenClassification(FalconPreTrainedModel):
1096
+ def __init__(self, config: FalconConfig):
1097
+ super().__init__(config)
1098
+ self.num_labels = config.num_labels
1099
+
1100
+ self.transformer = FalconModel(config)
1101
+ if getattr(config, "classifier_dropout", None) is not None:
1102
+ classifier_dropout = config.classifier_dropout
1103
+ elif getattr(config, "hidden_dropout", None) is not None:
1104
+ classifier_dropout = config.hidden_dropout
1105
+ else:
1106
+ classifier_dropout = 0.1
1107
+ self.dropout = nn.Dropout(classifier_dropout)
1108
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
1109
+
1110
+ # Initialize weights and apply final processing
1111
+ self.post_init()
1112
+
1113
+ @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
1114
+ @add_code_sample_docstrings(
1115
+ checkpoint=_CHECKPOINT_FOR_DOC,
1116
+ output_type=TokenClassifierOutput,
1117
+ config_class=_CONFIG_FOR_DOC,
1118
+ )
1119
+ def forward(
1120
+ self,
1121
+ input_ids: Optional[torch.LongTensor] = None,
1122
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
1123
+ attention_mask: Optional[torch.Tensor] = None,
1124
+ head_mask: Optional[torch.Tensor] = None,
1125
+ inputs_embeds: Optional[torch.Tensor] = None,
1126
+ labels: Optional[torch.Tensor] = None,
1127
+ use_cache: Optional[bool] = None,
1128
+ output_attentions: Optional[bool] = None,
1129
+ output_hidden_states: Optional[bool] = None,
1130
+ return_dict: Optional[bool] = None,
1131
+ ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
1132
+ r"""
1133
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1134
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1135
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1136
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1137
+ """
1138
+
1139
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1140
+
1141
+ transformer_outputs = self.transformer(
1142
+ input_ids,
1143
+ past_key_values=past_key_values,
1144
+ attention_mask=attention_mask,
1145
+ head_mask=head_mask,
1146
+ inputs_embeds=inputs_embeds,
1147
+ use_cache=use_cache,
1148
+ output_attentions=output_attentions,
1149
+ output_hidden_states=output_hidden_states,
1150
+ return_dict=return_dict,
1151
+ )
1152
+
1153
+ hidden_states = transformer_outputs[0]
1154
+ hidden_states = self.dropout(hidden_states)
1155
+ logits = self.classifier(hidden_states)
1156
+
1157
+ loss = None
1158
+ if labels is not None:
1159
+ batch_size, seq_length = labels.shape
1160
+ loss_fct = CrossEntropyLoss()
1161
+ loss = loss_fct(
1162
+ logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
1163
+ )
1164
+
1165
+ if not return_dict:
1166
+ output = (logits,) + transformer_outputs[2:]
1167
+ return ((loss,) + output) if loss is not None else output
1168
+
1169
+ return TokenClassifierOutput(
1170
+ loss=loss,
1171
+ logits=logits,
1172
+ hidden_states=transformer_outputs.hidden_states,
1173
+ attentions=transformer_outputs.attentions,
1174
+ )
1175
+
1176
+
1177
+ @add_start_docstrings(
1178
+ """
1179
+ The Falcon Model transformer with a span classification head on top for extractive question-answering tasks like
1180
+ SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
1181
+ """,
1182
+ FALCON_START_DOCSTRING,
1183
+ )
1184
+ class FalconForQuestionAnswering(FalconPreTrainedModel):
1185
+ def __init__(self, config):
1186
+ super().__init__(config)
1187
+ self.transformer = FalconModel(config)
1188
+ self.qa_outputs = nn.Linear(config.hidden_size, 2)
1189
+
1190
+ # Initialize weights and apply final processing
1191
+ self.post_init()
1192
+
1193
+ @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
1194
+ def forward(
1195
+ self,
1196
+ input_ids: Optional[torch.LongTensor] = None,
1197
+ attention_mask: Optional[torch.FloatTensor] = None,
1198
+ head_mask: Optional[torch.FloatTensor] = None,
1199
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1200
+ start_positions: Optional[torch.LongTensor] = None,
1201
+ end_positions: Optional[torch.LongTensor] = None,
1202
+ output_attentions: Optional[bool] = None,
1203
+ output_hidden_states: Optional[bool] = None,
1204
+ return_dict: Optional[bool] = None,
1205
+ ) -> Union[Tuple, QuestionAnsweringModelOutput]:
1206
+ r"""
1207
+ start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1208
+ Labels for position (index) of the start of the labelled span for computing the token classification loss.
1209
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1210
+ are not taken into account for computing the loss.
1211
+ end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1212
+ Labels for position (index) of the end of the labelled span for computing the token classification loss.
1213
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1214
+ are not taken into account for computing the loss.
1215
+ """
1216
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1217
+
1218
+ outputs = self.transformer(
1219
+ input_ids,
1220
+ attention_mask=attention_mask,
1221
+ head_mask=head_mask,
1222
+ inputs_embeds=inputs_embeds,
1223
+ output_attentions=output_attentions,
1224
+ output_hidden_states=output_hidden_states,
1225
+ return_dict=return_dict,
1226
+ )
1227
+
1228
+ sequence_output = outputs[0]
1229
+
1230
+ logits = self.qa_outputs(sequence_output)
1231
+ start_logits, end_logits = logits.split(1, dim=-1)
1232
+ start_logits = start_logits.squeeze(-1).contiguous()
1233
+ end_logits = end_logits.squeeze(-1).contiguous()
1234
+
1235
+ total_loss = None
1236
+ if start_positions is not None and end_positions is not None:
1237
+ # If we are on multi-GPU, split add a dimension
1238
+ if len(start_positions.size()) > 1:
1239
+ start_positions = start_positions.squeeze(-1)
1240
+ if len(end_positions.size()) > 1:
1241
+ end_positions = end_positions.squeeze(-1)
1242
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
1243
+ ignored_index = start_logits.size(1)
1244
+ start_positions = start_positions.clamp(0, ignored_index)
1245
+ end_positions = end_positions.clamp(0, ignored_index)
1246
+
1247
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
1248
+ start_loss = loss_fct(start_logits, start_positions)
1249
+ end_loss = loss_fct(end_logits, end_positions)
1250
+ total_loss = (start_loss + end_loss) / 2
1251
+
1252
+ if not return_dict:
1253
+ output = (start_logits, end_logits) + outputs[2:]
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+ return ((total_loss,) + output) if total_loss is not None else output
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+
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+ return QuestionAnsweringModelOutput(
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+ loss=total_loss,
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+ start_logits=start_logits,
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+ end_logits=end_logits,
1260
+ hidden_states=outputs.hidden_states,
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+ attentions=outputs.attentions,
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+ )
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