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---
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pipeline_tag: text-generation
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---
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# InternLM
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<div align="center">
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<img src="https://github.com/InternLM/InternLM/assets/22529082/b9788105-8892-4398-8b47-b513a292378e" width="200"/>
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<div> </div>
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<div align="center">
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<b><font size="5">InternLM</font></b>
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<sup>
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<a href="https://internlm.intern-ai.org.cn/">
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<i><font size="4">HOT</font></i>
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</a>
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</sup>
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<div> </div>
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</div>
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[](https://github.com/internLM/OpenCompass/)
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[💻Github Repo](https://github.com/InternLM/InternLM) • [🤔Reporting Issues](https://github.com/InternLM/InternLM/issues/new)
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</div>
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## Introduction
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InternLM has open-sourced a 7 billion parameter base model tailored for practical scenarios. The model has the following characteristics:
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- It leverages trillions of high-quality tokens for training to establish a powerful knowledge base.
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- It provides a versatile toolset for users to flexibly build their own workflows.
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## InternLM-7B
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### Performance Evaluation
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We conducted a comprehensive evaluation of InternLM using the open-source evaluation tool [OpenCompass](https://github.com/internLM/OpenCompass/). The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the [OpenCompass leaderboard](https://rank.opencompass.org.cn) for more evaluation results.
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| Datasets\Models | **InternLM-Chat-7B** | **InternLM-7B** | LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
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| -------------------- | --------------------- | ---------------- | --------- | --------- | ------------ | --------- | ---------- |
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| C-Eval(Val) | 53.2 | 53.4 | 24.2 | 42.7 | 50.9 | 28.9 | 31.2 |
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| MMLU | 50.8 | 51.0 | 35.2* | 41.5 | 46.0 | 39.7 | 47.3 |
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| AGIEval | 42.5 | 37.6 | 20.8 | 24.6 | 39.0 | 24.1 | 26.4 |
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| CommonSenseQA | 75.2 | 59.5 | 65.0 | 58.8 | 60.0 | 68.7 | 66.7 |
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| BUSTM | 74.3 | 50.6 | 48.5 | 51.3 | 55.0 | 48.8 | 62.5 |
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| CLUEWSC | 78.6 | 59.1 | 50.3 | 52.8 | 59.8 | 50.3 | 52.2 |
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| MATH | 6.4 | 7.1 | 2.8 | 3.0 | 6.6 | 2.2 | 2.8 |
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| GSM8K | 34.5 | 31.2 | 10.1 | 9.7 | 29.2 | 6.0 | 15.3 |
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| HumanEval | 14.0 | 10.4 | 14.0 | 9.2 | 9.2 | 9.2 | 11.0 |
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| RACE(High) | 76.3 | 57.4 | 46.9* | 28.1 | 66.3 | 40.7 | 54.0 |
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- The evaluation results were obtained from [OpenCompass 20230706](https://github.com/internLM/OpenCompass/) (some data marked with *, which means come from the original papers), and evaluation configuration can be found in the configuration files provided by [OpenCompass](https://github.com/internLM/OpenCompass/).
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- The evaluation data may have numerical differences due to the version iteration of [OpenCompass](https://github.com/internLM/OpenCompass/), so please refer to the latest evaluation results of [OpenCompass](https://github.com/internLM/OpenCompass/).
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**Limitations:** Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.
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### Import from Transformers
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To load the InternLM 7B Chat model using Transformers, use the following code:
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-7b", trust_remote_code=True)
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# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
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model = AutoModelForCausalLM.from_pretrained("internlm/internlm-7b", torch_dtype=torch.float16, trust_remote_code=True).cuda()
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model = model.eval()
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inputs = tokenizer(["A beautiful flower"], return_tensors="pt")
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for k,v in inputs.items():
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inputs[k] = v.cuda()
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gen_kwargs = {"max_length": 128, "top_p": 0.8, "temperature": 0.8, "do_sample": True, "repetition_penalty": 1.1}
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output = model.generate(**inputs, **gen_kwargs)
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output = tokenizer.decode(output[0].tolist(), skip_special_tokens=True)
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print(output)
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# <s> A beautiful flower box made of white rose wood. It is a perfect gift for weddings, birthdays and anniversaries.
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# All the roses are from our farm Roses Flanders. Therefor you know that these flowers last much longer than those in store or online!</s>
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```
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## Open Source License
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The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow **free** commercial usage. To apply for a commercial license, please fill in the [application form (English)](https://wj.qq.com/s2/12727483/5dba/)/[申请表（中文）](https://wj.qq.com/s2/12725412/f7c1/). For other questions or collaborations, please contact <internlm@pjlab.org.cn>.
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## 简介
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InternLM ，即书生·浦语大模型，包含面向实用场景的70亿参数基础模型 （InternLM-7B）。模型具有以下特点：
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- 使用上万亿高质量预料，建立模型超强知识体系；
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- 通用工具调用能力，支持用户灵活自助搭建流程；
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## InternLM-7B
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### 性能评测
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我们使用开源评测工具 [OpenCompass](https://github.com/internLM/OpenCompass/) 从学科综合能力、语言能力、知识能力、推理能力、理解能力五大能力维度对InternLM开展全面评测，部分评测结果如下表所示，欢迎访问[ OpenCompass 榜单 ](https://rank.opencompass.org.cn)获取更多的评测结果。
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| 数据集\模型 | **InternLM-Chat-7B** | **InternLM-7B** | LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
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| -------------------- | --------------------- | ---------------- | --------- | --------- | ------------ | --------- | ---------- |
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| C-Eval(Val) | 53.2 | 53.4 | 24.2 | 42.7 | 50.9 | 28.9 | 31.2 |
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| MMLU | 50.8 | 51.0 | 35.2* | 41.5 | 46.0 | 39.7 | 47.3 |
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| AGIEval | 42.5 | 37.6 | 20.8 | 24.6 | 39.0 | 24.1 | 26.4 |
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| CommonSenseQA | 75.2 | 59.5 | 65.0 | 58.8 | 60.0 | 68.7 | 66.7 |
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| BUSTM | 74.3 | 50.6 | 48.5 | 51.3 | 55.0 | 48.8 | 62.5 |
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| CLUEWSC | 78.6 | 59.1 | 50.3 | 52.8 | 59.8 | 50.3 | 52.2 |
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| MATH | 6.4 | 7.1 | 2.8 | 3.0 | 6.6 | 2.2 | 2.8 |
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| GSM8K | 34.5 | 31.2 | 10.1 | 9.7 | 29.2 | 6.0 | 15.3 |
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| HumanEval | 14.0 | 10.4 | 14.0 | 9.2 | 9.2 | 9.2 | 11.0 |
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| RACE(High) | 76.3 | 57.4 | 46.9* | 28.1 | 66.3 | 40.7 | 54.0 |
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- 以上评测结果基于 [OpenCompass 20230706](https://github.com/internLM/OpenCompass/) 获得（部分数据标注`*`代表数据来自原始论文），具体测试细节可参见 [OpenCompass](https://github.com/internLM/OpenCompass/) 中提供的配置文件。
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- 评测数据会因 [OpenCompass](https://github.com/internLM/OpenCompass/) 的版本迭代而存在数值差异，请以 [OpenCompass](https://github.com/internLM/OpenCompass/) 最新版的评测结果为主。
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**局限性：** 尽管在训练过程中我们非常注重模型的安全性，尽力促使模型输出符合伦理和法律要求的文本，但受限于模型大小以及概率生成范式，模型可能会产生各种不符合预期的输出，例如回复内容包含偏见、歧视等有害内容，请勿传播这些内容。由于传播不良信息导致的任何后果，本项目不承担责任。
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### 通过 Transformers 加载
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通过以下的代码加载 InternLM 7B Chat 模型
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-7b", trust_remote_code=True)
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# `torch_dtype=torch.float16` 可以令模型以 float16 精度加载，否则 transformers 会将模型加载为 float32，有可能导致显存不足
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model = AutoModelForCausalLM.from_pretrained("internlm/internlm-7b", torch_dtype=torch.float16, trust_remote_code=True).cuda()
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model = model.eval()
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inputs = tokenizer(["来到美丽的大自然，我们发现"], return_tensors="pt")
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for k,v in inputs.items():
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inputs[k] = v.cuda()
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gen_kwargs = {"max_length": 128, "top_p": 0.8, "temperature": 0.8, "do_sample": True, "repetition_penalty": 1.1}
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output = model.generate(**inputs, **gen_kwargs)
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output = tokenizer.decode(output[0].tolist(), skip_special_tokens=True)
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print(output)
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# 来到美丽的大自然，我们发现各种各样的花千奇百怪。有的颜色鲜艳亮丽,使人感觉生机勃勃；有的是红色的花瓣儿粉嫩嫩的像少女害羞的脸庞一样让人爱不释手．有的小巧玲珑; 还有的花瓣粗大看似枯黄实则暗藏玄机！
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# 不同的花卉有不同的“脾气”,它们都有着属于自己的故事和人生道理.这些鲜花都是大自然中最为原始的物种,每一朵都绽放出别样的美令人陶醉、着迷!
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```
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## 开源许可证
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本仓库的代码依照 Apache-2.0 协议开源。模型权重对学术研究完全开放，也可申请免费的商业使用授权（[申请表](https://wj.qq.com/s2/12725412/f7c1/)）。其他问题与合作请联系 <internlm@pjlab.org.cn>。
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{
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"architectures": [
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"InternLMForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_internlm.InternLMConfig",
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"AutoModel": "modeling_internlm.InternLMForCausalLM",
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"AutoModelForCausalLM": "modeling_internlm.InternLMForCausalLM"
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},
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"bias": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 2048,
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"model_type": "internlm",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"pad_token_id": 2,
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"rms_norm_eps": 1e-06,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.29.2",
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"use_cache": true,
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"vocab_size": 103168,
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"rotary": {
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"base": 10000,
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"type": "dynamic"
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}
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}
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configuration_internlm.py
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# coding=utf-8
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# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
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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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""" InternLM model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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INTERNLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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# Modified from transformers.model.llama.configuration_llama.LlamaConfig
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class InternLMConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate
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an InternLM model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the InternLM-7B.
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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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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
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Vocabulary size of the InternLM model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`InternLMModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 11008):
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||||||
|
Dimension of the MLP representations.
|
||||||
|
num_hidden_layers (`int`, *optional*, defaults to 32):
|
||||||
|
Number of hidden layers in the Transformer encoder.
|
||||||
|
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||||
|
Number of attention heads for each attention layer in the Transformer encoder.
|
||||||
|
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||||
|
The non-linear activation function (function or string) in the decoder.
|
||||||
|
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
||||||
|
The maximum sequence length that this model might ever be used with. Typically set this to something large
|
||||||
|
just in case (e.g., 512 or 1024 or 2048).
|
||||||
|
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||||
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||||
|
rms_norm_eps (`float`, *optional*, defaults to 1e-12):
|
||||||
|
The epsilon used by the rms normalization layers.
|
||||||
|
use_cache (`bool`, *optional*, defaults to `True`):
|
||||||
|
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||||
|
relevant if `config.is_decoder=True`.
|
||||||
|
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
|
||||||
|
Whether to tie weight embeddings
|
||||||
|
Example:
|
||||||
|
```python
|
||||||
|
>>> from transformers import InternLMModel, InternLMConfig
|
||||||
|
>>> # Initializing a InternLM internlm-7b style configuration
|
||||||
|
>>> configuration = InternLMConfig()
|
||||||
|
>>> # Initializing a model from the internlm-7b style configuration
|
||||||
|
>>> model = InternLMModel(configuration)
|
||||||
|
>>> # Accessing the model configuration
|
||||||
|
>>> configuration = model.config
|
||||||
|
```"""
|
||||||
|
model_type = "internlm"
|
||||||
|
_auto_class = "AutoConfig"
|
||||||
|
|
||||||
|
def __init__( # pylint: disable=W0102
|
||||||
|
self,
|
||||||
|
vocab_size=103168,
|
||||||
|
hidden_size=4096,
|
||||||
|
intermediate_size=11008,
|
||||||
|
num_hidden_layers=32,
|
||||||
|
num_attention_heads=32,
|
||||||
|
hidden_act="silu",
|
||||||
|
max_position_embeddings=2048,
|
||||||
|
initializer_range=0.02,
|
||||||
|
rms_norm_eps=1e-6,
|
||||||
|
use_cache=True,
|
||||||
|
pad_token_id=0,
|
||||||
|
bos_token_id=1,
|
||||||
|
eos_token_id=2,
|
||||||
|
tie_word_embeddings=False,
|
||||||
|
bias=True,
|
||||||
|
rotary={"base": 10000, "type": "dynamic"}, # pylint: disable=W0102
|
||||||
|
attn_implementation="eager",
|
||||||
|
**kwargs,
|
||||||
|
):
|
||||||
|
self.vocab_size = vocab_size
|
||||||
|
self.max_position_embeddings = max_position_embeddings
|
||||||
|
self.hidden_size = hidden_size
|
||||||
|
self.intermediate_size = intermediate_size
|
||||||
|
self.num_hidden_layers = num_hidden_layers
|
||||||
|
self.num_attention_heads = num_attention_heads
|
||||||
|
self.hidden_act = hidden_act
|
||||||
|
self.initializer_range = initializer_range
|
||||||
|
self.rms_norm_eps = rms_norm_eps
|
||||||
|
self.use_cache = use_cache
|
||||||
|
self.bias = bias
|
||||||
|
self.rotary = rotary
|
||||||
|
self.attn_implementation = attn_implementation
|
||||||
|
if self.attn_implementation is None:
|
||||||
|
self.attn_implementation = "eager"
|
||||||
|
super().__init__(
|
||||||
|
pad_token_id=pad_token_id,
|
||||||
|
bos_token_id=bos_token_id,
|
||||||
|
eos_token_id=eos_token_id,
|
||||||
|
tie_word_embeddings=tie_word_embeddings,
|
||||||
|
**kwargs,
|
||||||
|
)
|
||||||
7
generation_config.json
Normal file
7
generation_config.json
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"bos_token_id": 1,
|
||||||
|
"eos_token_id": 2,
|
||||||
|
"pad_token_id": 2,
|
||||||
|
"transformers_version": "4.29.2"
|
||||||
|
}
|
||||||
1304
modeling_internlm.py
Normal file
1304
modeling_internlm.py
Normal file
File diff suppressed because it is too large
Load Diff
3
pytorch_model-00001-of-00008.bin
Normal file
3
pytorch_model-00001-of-00008.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:942d9bd57a467fe6774ee4c73201981a8bb395c44d3e7a19716a803186ce9538
|
||||||
|
size 1969370847
|
||||||
3
pytorch_model-00002-of-00008.bin
Normal file
3
pytorch_model-00002-of-00008.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:9b797a32db6710bedd5af42b941f33fdd44f3597c025ac93b7862ce833177d63
|
||||||
|
size 1933844137
|
||||||
3
pytorch_model-00003-of-00008.bin
Normal file
3
pytorch_model-00003-of-00008.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:1ef5d6a2e889e2ad4cae9269d54e573be882851e3f7bcef603f2c8214be2e62d
|
||||||
|
size 1933844201
|
||||||
3
pytorch_model-00004-of-00008.bin
Normal file
3
pytorch_model-00004-of-00008.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:0816ef2610486858c85f320afa8e016e32ecda64b4ff1bf1d217d62359842d24
|
||||||
|
size 1990458181
|
||||||
3
pytorch_model-00005-of-00008.bin
Normal file
3
pytorch_model-00005-of-00008.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:ce4a9e081f73cb186ee13401e376d592d6903c8c2ed8ef8cc2f811b93f42a209
|
||||||
|
size 1990458775
|
||||||
3
pytorch_model-00006-of-00008.bin
Normal file
3
pytorch_model-00006-of-00008.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:48ee372fe7e219a3780a6780111dda956f2fd2c023a8619c5284de6ca4212fae
|
||||||
|
size 1990458775
|
||||||
3
pytorch_model-00007-of-00008.bin
Normal file
3
pytorch_model-00007-of-00008.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:884dac6a4d76562e38b27c48e15bad64ecec24f06ef0f1306c52937a12e6a016
|
||||||
|
size 1990467305
|
||||||
3
pytorch_model-00008-of-00008.bin
Normal file
3
pytorch_model-00008-of-00008.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:779901110578aa9824f3ce506c9211ef94e7cdb2b82dbee105e06ff55e0098b6
|
||||||
|
size 845153194
|
||||||
458
pytorch_model.bin.index.json
Normal file
458
pytorch_model.bin.index.json
Normal file
@@ -0,0 +1,458 @@
|
|||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"total_size": 14643904512
|
||||||
|
},
|
||||||
|
"weight_map": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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||||||
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||||||
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||||||
|
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||||||
|
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||||||
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||||||
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||||||
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|
||||||
|
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|
||||||
|
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|
||||||
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||||||
|
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|
||||||
|
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||||||
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||||||
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|
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|
||||||
|
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|
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|
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|
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|
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|
||||||
|
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|
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|
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|
||||||
|
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|
||||||
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|
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||||||
|
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|
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|
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|
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||||||
|
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|
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|
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|
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|
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|
||||||
|
"model.layers.8.self_attn.v_proj.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.8.self_attn.v_proj.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.input_layernorm.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.mlp.down_proj.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.mlp.gate_proj.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.mlp.up_proj.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.post_attention_layernorm.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.self_attn.k_proj.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.self_attn.k_proj.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.self_attn.o_proj.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.self_attn.o_proj.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.self_attn.q_proj.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.self_attn.q_proj.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.self_attn.rotary_emb.inv_freq": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.self_attn.v_proj.bias": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.layers.9.self_attn.v_proj.weight": "pytorch_model-00003-of-00008.bin",
|
||||||
|
"model.norm.weight": "pytorch_model-00007-of-00008.bin"
|
||||||
|
}
|
||||||
|
}
|
||||||
6
special_tokens_map.json
Normal file
6
special_tokens_map.json
Normal file
@@ -0,0 +1,6 @@
|
|||||||
|
{
|
||||||
|
"bos_token": "<s>",
|
||||||
|
"eos_token": "</s>",
|
||||||
|
"pad_token": "</s>",
|
||||||
|
"unk_token": "<unk>"
|
||||||
|
}
|
||||||
237
tokenization_internlm.py
Normal file
237
tokenization_internlm.py
Normal file
@@ -0,0 +1,237 @@
|
|||||||
|
# coding=utf-8
|
||||||
|
# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
|
||||||
|
#
|
||||||
|
# This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
|
||||||
|
"""Tokenization classes for InternLM."""
|
||||||
|
import os
|
||||||
|
from shutil import copyfile
|
||||||
|
from typing import Any, Dict, List, Optional, Tuple
|
||||||
|
|
||||||
|
import sentencepiece as spm
|
||||||
|
|
||||||
|
from transformers.tokenization_utils import PreTrainedTokenizer
|
||||||
|
from transformers.utils import logging
|
||||||
|
|
||||||
|
|
||||||
|
logger = logging.get_logger(__name__)
|
||||||
|
|
||||||
|
VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
|
||||||
|
|
||||||
|
PRETRAINED_VOCAB_FILES_MAP = {}
|
||||||
|
|
||||||
|
# Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer -> InternLM2Tokenizer
|
||||||
|
class InternLMTokenizer(PreTrainedTokenizer):
|
||||||
|
"""
|
||||||
|
Construct a InternLM tokenizer. Based on byte-level Byte-Pair-Encoding.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
vocab_file (`str`):
|
||||||
|
Path to the vocabulary file.
|
||||||
|
"""
|
||||||
|
|
||||||
|
vocab_files_names = VOCAB_FILES_NAMES
|
||||||
|
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
||||||
|
model_input_names = ["input_ids", "attention_mask"]
|
||||||
|
_auto_class = "AutoTokenizer"
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
vocab_file,
|
||||||
|
unk_token="<unk>",
|
||||||
|
bos_token="<s>",
|
||||||
|
eos_token="</s>",
|
||||||
|
pad_token="</s>",
|
||||||
|
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
||||||
|
add_bos_token=True,
|
||||||
|
add_eos_token=False,
|
||||||
|
decode_with_prefix_space=False,
|
||||||
|
clean_up_tokenization_spaces=False,
|
||||||
|
**kwargs,
|
||||||
|
):
|
||||||
|
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
||||||
|
self.vocab_file = vocab_file
|
||||||
|
self.add_bos_token = add_bos_token
|
||||||
|
self.add_eos_token = add_eos_token
|
||||||
|
self.decode_with_prefix_space = decode_with_prefix_space
|
||||||
|
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
||||||
|
self.sp_model.Load(vocab_file)
|
||||||
|
self._no_prefix_space_tokens = None
|
||||||
|
super().__init__(
|
||||||
|
bos_token=bos_token,
|
||||||
|
eos_token=eos_token,
|
||||||
|
unk_token=unk_token,
|
||||||
|
pad_token=pad_token,
|
||||||
|
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
||||||
|
**kwargs,
|
||||||
|
)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def no_prefix_space_tokens(self):
|
||||||
|
if self._no_prefix_space_tokens is None:
|
||||||
|
vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
|
||||||
|
self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
|
||||||
|
return self._no_prefix_space_tokens
|
||||||
|
|
||||||
|
@property
|
||||||
|
def vocab_size(self):
|
||||||
|
"""Returns vocab size"""
|
||||||
|
return self.sp_model.get_piece_size()
|
||||||
|
|
||||||
|
@property
|
||||||
|
def bos_token_id(self) -> Optional[int]:
|
||||||
|
return self.sp_model.bos_id()
|
||||||
|
|
||||||
|
@property
|
||||||
|
def eos_token_id(self) -> Optional[int]:
|
||||||
|
return self.sp_model.eos_id()
|
||||||
|
|
||||||
|
def get_vocab(self):
|
||||||
|
"""Returns vocab as a dict"""
|
||||||
|
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
||||||
|
vocab.update(self.added_tokens_encoder)
|
||||||
|
return vocab
|
||||||
|
|
||||||
|
def _tokenize(self, text):
|
||||||
|
"""Returns a tokenized string."""
|
||||||
|
return self.sp_model.encode(text, out_type=str)
|
||||||
|
|
||||||
|
def _convert_token_to_id(self, token):
|
||||||
|
"""Converts a token (str) in an id using the vocab."""
|
||||||
|
return self.sp_model.piece_to_id(token)
|
||||||
|
|
||||||
|
def _convert_id_to_token(self, index):
|
||||||
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
||||||
|
token = self.sp_model.IdToPiece(index)
|
||||||
|
return token
|
||||||
|
|
||||||
|
def _maybe_add_prefix_space(self, tokens, decoded):
|
||||||
|
if tokens and tokens[0] not in self.no_prefix_space_tokens:
|
||||||
|
return " " + decoded
|
||||||
|
else:
|
||||||
|
return decoded
|
||||||
|
|
||||||
|
def convert_tokens_to_string(self, tokens):
|
||||||
|
"""Converts a sequence of tokens (string) in a single string."""
|
||||||
|
current_sub_tokens = []
|
||||||
|
out_string = ""
|
||||||
|
prev_is_special = False
|
||||||
|
for token in tokens:
|
||||||
|
# make sure that special tokens are not decoded using sentencepiece model
|
||||||
|
if token in self.all_special_tokens:
|
||||||
|
if not prev_is_special:
|
||||||
|
out_string += " "
|
||||||
|
out_string += self.sp_model.decode(current_sub_tokens) + token
|
||||||
|
prev_is_special = True
|
||||||
|
current_sub_tokens = []
|
||||||
|
else:
|
||||||
|
current_sub_tokens.append(token)
|
||||||
|
prev_is_special = False
|
||||||
|
out_string += self.sp_model.decode(current_sub_tokens)
|
||||||
|
out_string = self.clean_up_tokenization(out_string)
|
||||||
|
out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
|
||||||
|
return out_string[1:]
|
||||||
|
|
||||||
|
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||||||
|
"""
|
||||||
|
Save the vocabulary and special tokens file to a directory.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
save_directory (`str`):
|
||||||
|
The directory in which to save the vocabulary.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
`Tuple(str)`: Paths to the files saved.
|
||||||
|
"""
|
||||||
|
if not os.path.isdir(save_directory):
|
||||||
|
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
||||||
|
return
|
||||||
|
out_vocab_file = os.path.join(
|
||||||
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||||||
|
)
|
||||||
|
|
||||||
|
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
||||||
|
copyfile(self.vocab_file, out_vocab_file)
|
||||||
|
elif not os.path.isfile(self.vocab_file):
|
||||||
|
with open(out_vocab_file, "wb") as fi:
|
||||||
|
content_spiece_model = self.sp_model.serialized_model_proto()
|
||||||
|
fi.write(content_spiece_model)
|
||||||
|
|
||||||
|
return (out_vocab_file,)
|
||||||
|
|
||||||
|
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
||||||
|
if self.add_bos_token:
|
||||||
|
bos_token_ids = [self.bos_token_id]
|
||||||
|
else:
|
||||||
|
bos_token_ids = []
|
||||||
|
|
||||||
|
output = bos_token_ids + token_ids_0
|
||||||
|
|
||||||
|
if token_ids_1 is not None:
|
||||||
|
output = output + token_ids_1
|
||||||
|
|
||||||
|
if self.add_eos_token:
|
||||||
|
output = output + [self.eos_token_id]
|
||||||
|
|
||||||
|
return output
|
||||||
|
|
||||||
|
def get_special_tokens_mask(
|
||||||
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
||||||
|
) -> List[int]:
|
||||||
|
"""
|
||||||
|
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
||||||
|
special tokens using the tokenizer `prepare_for_model` method.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
token_ids_0 (`List[int]`):
|
||||||
|
List of IDs.
|
||||||
|
token_ids_1 (`List[int]`, *optional*):
|
||||||
|
Optional second list of IDs for sequence pairs.
|
||||||
|
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
||||||
|
Whether or not the token list is already formatted with special tokens for the model.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
||||||
|
"""
|
||||||
|
if already_has_special_tokens:
|
||||||
|
return super().get_special_tokens_mask(
|
||||||
|
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
||||||
|
)
|
||||||
|
|
||||||
|
if token_ids_1 is None:
|
||||||
|
return [1] + ([0] * len(token_ids_0)) + [1]
|
||||||
|
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
|
||||||
|
|
||||||
|
def create_token_type_ids_from_sequences(
|
||||||
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||||||
|
) -> List[int]:
|
||||||
|
"""
|
||||||
|
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
|
||||||
|
use of token type ids, therefore a list of zeros is returned.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
token_ids_0 (`List[int]`):
|
||||||
|
List of IDs.
|
||||||
|
token_ids_1 (`List[int]`, *optional*):
|
||||||
|
Optional second list of IDs for sequence pairs.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
`List[int]`: List of zeros.
|
||||||
|
"""
|
||||||
|
eos = [self.eos_token_id]
|
||||||
|
|
||||||
|
if token_ids_1 is None:
|
||||||
|
return len(token_ids_0 + eos) * [0]
|
||||||
|
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
|
||||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:aab622d98c98677a1a51f969e25765154487bf3e85c7819db105db2fcacba83f
|
||||||
|
size 1658691
|
||||||
15
tokenizer_config.json
Normal file
15
tokenizer_config.json
Normal file
@@ -0,0 +1,15 @@
|
|||||||
|
{
|
||||||
|
"auto_map": {
|
||||||
|
"AutoTokenizer": [
|
||||||
|
"tokenization_internlm.InternLMTokenizer",
|
||||||
|
null
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"bos_token": "<s>",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "</s>",
|
||||||
|
"model_max_length": 1000000000000000019884624838656,
|
||||||
|
"pad_token": "</s>",
|
||||||
|
"tokenizer_class": "InternLMTokenizer",
|
||||||
|
"unk_token": "<unk>"
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user