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[webui] improve webui & reasoning mode (#6778)
Former-commit-id: 45e68b9f092879dda55023ebbcd8cf4660e3045a
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@ -216,16 +216,15 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
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| [Command R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
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| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
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| [DeepSeek 2.5/3](https://huggingface.co/deepseek-ai) | 236B/685B | deepseek3 |
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| [DeepSeek R1](https://huggingface.co/deepseek-ai) | 1.5B/7B/8B/14B/32B/70B/671B | deepseekr1 |
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| [DeepSeek 2.5/3](https://huggingface.co/deepseek-ai) | 236B/671B | deepseek3 |
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| [DeepSeek R1](https://huggingface.co/deepseek-ai) | 1.5B/7B/8B/14B/32B/70B/671B | deepseek3 |
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| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
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| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
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| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
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| [GPT-2](https://huggingface.co/openai-community) | 0.1B/0.4B/0.8B/1.5B | - |
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| [Granite 3.0-3.1](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
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| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
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| [InternLM2/InternLM2.5](https://huggingface.co/internlm) | 7B/20B | intern2 |
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| [InternLM3](https://huggingface.co/internlm) | 8B | intern3 |
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| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
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| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
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| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
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| [Llama 3-3.3](https://huggingface.co/meta-llama) | 1B/3B/8B/70B | llama3 |
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@ -830,7 +829,7 @@ If you have a project that should be incorporated, please contact via email or c
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This repository is licensed under the [Apache-2.0 License](LICENSE).
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Please follow the model licenses to use the corresponding model weights: [Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [GPT-2](https://github.com/openai/gpt-2/blob/master/LICENSE) / [Granite](LICENSE) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3/Phi-4](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [Skywork](https://huggingface.co/Skywork/Skywork-13B-base/blob/main/Skywork%20Community%20License.pdf) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [TeleChat2](https://huggingface.co/Tele-AI/telechat-7B/blob/main/TeleChat%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
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Please follow the model licenses to use the corresponding model weights: [Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [GPT-2](https://github.com/openai/gpt-2/blob/master/LICENSE) / [Granite](LICENSE) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/blob/main/LICENSE) / [InternLM](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3/Phi-4](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [Skywork](https://huggingface.co/Skywork/Skywork-13B-base/blob/main/Skywork%20Community%20License.pdf) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [TeleChat2](https://huggingface.co/Tele-AI/telechat-7B/blob/main/TeleChat%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
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## Citation
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@ -218,16 +218,15 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
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| [Command R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
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| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
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| [DeepSeek 2.5/3](https://huggingface.co/deepseek-ai) | 236B/685B | deepseek3 |
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| [DeepSeek R1](https://huggingface.co/deepseek-ai) | 1.5B/7B/8B/14B/32B/70B/671B | deepseekr1 |
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| [DeepSeek 2.5/3](https://huggingface.co/deepseek-ai) | 236B/671B | deepseek3 |
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| [DeepSeek R1](https://huggingface.co/deepseek-ai) | 1.5B/7B/8B/14B/32B/70B/671B | deepseek3 |
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| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
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| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
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| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
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| [GPT-2](https://huggingface.co/openai-community) | 0.1B/0.4B/0.8B/1.5B | - |
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| [Granite 3.0-3.1](https://huggingface.co/ibm-granite) | 1B/2B/3B/8B | granite3 |
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| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
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| [InternLM2/InternLM2.5](https://huggingface.co/internlm) | 7B/20B | intern2 |
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| [InternLM3](https://huggingface.co/internlm) | 8B | intern3 |
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| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
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| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
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| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
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| [Llama 3-3.3](https://huggingface.co/meta-llama) | 1B/3B/8B/70B | llama3 |
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@ -832,7 +831,7 @@ swanlab_run_name: test_run # 可选
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本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。
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使用模型权重时，请遵循对应的模型协议：[Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [GPT-2](https://github.com/openai/gpt-2/blob/master/LICENSE) / [Granite](LICENSE) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3/Phi-4](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [Skywork](https://huggingface.co/Skywork/Skywork-13B-base/blob/main/Skywork%20Community%20License.pdf) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [TeleChat2](https://huggingface.co/Tele-AI/telechat-7B/blob/main/TeleChat%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
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使用模型权重时，请遵循对应的模型协议：[Baichuan 2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [GPT-2](https://github.com/openai/gpt-2/blob/master/LICENSE) / [Granite](LICENSE) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/blob/main/LICENSE) / [InternLM](https://github.com/InternLM/InternLM#license) / [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [Llama 2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [Llama 3](https://llama.meta.com/llama3/license/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/Phi-2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3/Phi-4](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [Skywork](https://huggingface.co/Skywork/Skywork-13B-base/blob/main/Skywork%20Community%20License.pdf) / [StarCoder 2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [TeleChat2](https://huggingface.co/Tele-AI/telechat-7B/blob/main/TeleChat%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
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## 引用
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@ -719,6 +719,13 @@ _register_template(
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format_assistant=StringFormatter(slots=["{{content}}<eoa>\n"]),
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format_system=StringFormatter(slots=["<|System|>:{{content}}\n"]),
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format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
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default_system=(
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"You are an AI assistant whose name is InternLM (书生·浦语).\n"
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"- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory "
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"(上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
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"- InternLM (书生·浦语) can understand and communicate fluently in the language "
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"chosen by the user such as English and 中文."
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),
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stop_words=["<eoa>"],
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)
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@ -729,17 +736,13 @@ _register_template(
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format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
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format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
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format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
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stop_words=["<|im_end|>"],
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)
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# copied from intern2 template
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_register_template(
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name="intern3",
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format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
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format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
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format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
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format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
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default_system=(
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"You are an AI assistant whose name is InternLM (书生·浦语).\n"
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"- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory "
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"(上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
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"- InternLM (书生·浦语) can understand and communicate fluently in the language "
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"chosen by the user such as English and 中文."
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),
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stop_words=["<|im_end|>"],
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)
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@ -105,7 +105,7 @@ def register_model_group(
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) -> None:
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for name, path in models.items():
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SUPPORTED_MODELS[name] = path
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if template is not None and (any(suffix in name for suffix in ("-Chat", "-Instruct")) or vision):
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if template is not None and (any(suffix in name for suffix in ("-Chat", "-Distill", "-Instruct")) or vision):
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DEFAULT_TEMPLATE[name] = template
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if vision:
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VISION_MODELS.add(name)
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@ -485,11 +485,11 @@ register_model_group(
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DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2.5-1210",
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DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2.5-1210",
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},
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"DeepSeek-V3-685B-Base": {
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"DeepSeek-V3-671B-Base": {
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DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V3-Base",
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DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V3-Base",
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},
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"DeepSeek-V3-685B-Chat": {
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"DeepSeek-V3-671B-Chat": {
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DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V3",
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DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V3",
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},
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@ -517,11 +517,11 @@ register_model_group(
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DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
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DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
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},
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"DeepSeek-R1-671B-Zero": {
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"DeepSeek-R1-671B-Chat-Zero": {
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DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Zero",
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DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Zero",
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},
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"DeepSeek-R1-671B": {
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},
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"DeepSeek-R1-671B-Chat": {
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DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1",
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DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1",
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},
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@ -845,20 +845,15 @@ register_model_group(
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DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b-chat",
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DownloadSource.OPENMIND: "Intern/internlm2_5-20b-chat",
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},
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},
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template="intern2",
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)
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register_model_group(
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models={
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"InternLM3-8B-Chat": {
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DownloadSource.DEFAULT: "internlm/internlm3-8b-instruct",
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DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm3-8b-instruct",
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},
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},
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template="intern3",
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template="intern2",
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)
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register_model_group(
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models={
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"Jamba-v0.1": {
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@ -36,6 +36,30 @@ if is_gradio_available():
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import gradio as gr
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def _format_response(text: str, lang: str) -> str:
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r"""
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Post-processes the response text.
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Based on: https://huggingface.co/spaces/Lyte/DeepSeek-R1-Distill-Qwen-1.5B-Demo-GGUF/blob/main/app.py
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"""
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if "<think>" not in text:
|
||||
return text
|
||||
|
||||
text = text.replace("<think>", "")
|
||||
result = text.split("</think>", maxsplit=1)
|
||||
if len(result) == 1:
|
||||
summary = ALERTS["info_thinking"][lang]
|
||||
thought, answer = text, ""
|
||||
else:
|
||||
summary = ALERTS["info_thought"][lang]
|
||||
thought, answer = result
|
||||
|
||||
return (
|
||||
f"<details open><summary class='thinking-summary'><span>{summary}</span></summary>\n\n"
|
||||
f"<div class='thinking-container'>\n{thought}\n</div>\n</details>{answer}"
|
||||
)
|
||||
|
||||
|
||||
class WebChatModel(ChatModel):
|
||||
def __init__(self, manager: "Manager", demo_mode: bool = False, lazy_init: bool = True) -> None:
|
||||
self.manager = manager
|
||||
@ -124,19 +148,26 @@ class WebChatModel(ChatModel):
|
||||
torch_gc()
|
||||
yield ALERTS["info_unloaded"][lang]
|
||||
|
||||
@staticmethod
|
||||
def append(
|
||||
self,
|
||||
chatbot: List[Dict[str, str]],
|
||||
messages: List[Dict[str, str]],
|
||||
role: str,
|
||||
query: str,
|
||||
) -> Tuple[List[Dict[str, str]], List[Dict[str, str]], str]:
|
||||
r"""
|
||||
Adds the user input to chatbot.
|
||||
|
||||
Inputs: infer.chatbot, infer.messages, infer.role, infer.query
|
||||
Output: infer.chatbot, infer.messages
|
||||
"""
|
||||
return chatbot + [{"role": "user", "content": query}], messages + [{"role": role, "content": query}], ""
|
||||
|
||||
def stream(
|
||||
self,
|
||||
chatbot: List[Dict[str, str]],
|
||||
messages: List[Dict[str, str]],
|
||||
lang: str,
|
||||
system: str,
|
||||
tools: str,
|
||||
image: Optional[Any],
|
||||
@ -145,6 +176,12 @@ class WebChatModel(ChatModel):
|
||||
top_p: float,
|
||||
temperature: float,
|
||||
) -> Generator[Tuple[List[Dict[str, str]], List[Dict[str, str]]], None, None]:
|
||||
r"""
|
||||
Generates output text in stream.
|
||||
|
||||
Inputs: infer.chatbot, infer.messages, infer.system, infer.tools, infer.image, infer.video, ...
|
||||
Output: infer.chatbot, infer.messages
|
||||
"""
|
||||
chatbot.append({"role": "assistant", "content": ""})
|
||||
response = ""
|
||||
for new_text in self.stream_chat(
|
||||
@ -157,7 +194,6 @@ class WebChatModel(ChatModel):
|
||||
top_p=top_p,
|
||||
temperature=temperature,
|
||||
):
|
||||
new_text = '' if any(t in new_text for t in ('<think>', '</think>')) else new_text
|
||||
response += new_text
|
||||
if tools:
|
||||
result = self.engine.template.extract_tool(response)
|
||||
@ -166,12 +202,12 @@ class WebChatModel(ChatModel):
|
||||
|
||||
if isinstance(result, list):
|
||||
tool_calls = [{"name": tool.name, "arguments": json.loads(tool.arguments)} for tool in result]
|
||||
tool_calls = json.dumps(tool_calls, indent=4, ensure_ascii=False)
|
||||
tool_calls = json.dumps(tool_calls, ensure_ascii=False)
|
||||
output_messages = messages + [{"role": Role.FUNCTION.value, "content": tool_calls}]
|
||||
bot_text = "```json\n" + tool_calls + "\n```"
|
||||
else:
|
||||
output_messages = messages + [{"role": Role.ASSISTANT.value, "content": result}]
|
||||
bot_text = result
|
||||
bot_text = _format_response(result, lang)
|
||||
|
||||
chatbot[-1] = {"role": "assistant", "content": bot_text}
|
||||
yield chatbot, output_messages
|
||||
|
||||
@ -14,34 +14,28 @@
|
||||
|
||||
import json
|
||||
import os
|
||||
import signal
|
||||
from collections import defaultdict
|
||||
from typing import Any, Dict, Optional, Tuple
|
||||
from datetime import datetime
|
||||
from typing import Any, Dict, Optional, Union
|
||||
|
||||
from psutil import Process
|
||||
from yaml import safe_dump, safe_load
|
||||
|
||||
from ..extras import logging
|
||||
from ..extras.constants import (
|
||||
CHECKPOINT_NAMES,
|
||||
DATA_CONFIG,
|
||||
DEFAULT_TEMPLATE,
|
||||
PEFT_METHODS,
|
||||
STAGES_USE_PAIR_DATA,
|
||||
SUPPORTED_MODELS,
|
||||
TRAINING_STAGES,
|
||||
TRAINING_ARGS,
|
||||
VISION_MODELS,
|
||||
DownloadSource,
|
||||
)
|
||||
from ..extras.misc import use_modelscope, use_openmind
|
||||
from ..extras.packages import is_gradio_available
|
||||
|
||||
|
||||
if is_gradio_available():
|
||||
import gradio as gr
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
DEFAULT_CACHE_DIR = "cache"
|
||||
DEFAULT_CONFIG_DIR = "config"
|
||||
DEFAULT_DATA_DIR = "data"
|
||||
@ -49,6 +43,21 @@ DEFAULT_SAVE_DIR = "saves"
|
||||
USER_CONFIG = "user_config.yaml"
|
||||
|
||||
|
||||
def abort_process(pid: int) -> None:
|
||||
r"""
|
||||
Aborts the processes recursively in a bottom-up way.
|
||||
"""
|
||||
try:
|
||||
children = Process(pid).children()
|
||||
if children:
|
||||
for child in children:
|
||||
abort_process(child.pid)
|
||||
|
||||
os.kill(pid, signal.SIGABRT)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def get_save_dir(*paths: str) -> os.PathLike:
|
||||
r"""
|
||||
Gets the path to saved model checkpoints.
|
||||
@ -61,19 +70,19 @@ def get_save_dir(*paths: str) -> os.PathLike:
|
||||
return os.path.join(DEFAULT_SAVE_DIR, *paths)
|
||||
|
||||
|
||||
def get_config_path() -> os.PathLike:
|
||||
def _get_config_path() -> os.PathLike:
|
||||
r"""
|
||||
Gets the path to user config.
|
||||
"""
|
||||
return os.path.join(DEFAULT_CACHE_DIR, USER_CONFIG)
|
||||
|
||||
|
||||
def load_config() -> Dict[str, Any]:
|
||||
def load_config() -> Dict[str, Union[str, Dict[str, Any]]]:
|
||||
r"""
|
||||
Loads user config if exists.
|
||||
"""
|
||||
try:
|
||||
with open(get_config_path(), encoding="utf-8") as f:
|
||||
with open(_get_config_path(), encoding="utf-8") as f:
|
||||
return safe_load(f)
|
||||
except Exception:
|
||||
return {"lang": None, "last_model": None, "path_dict": {}, "cache_dir": None}
|
||||
@ -92,7 +101,7 @@ def save_config(lang: str, model_name: Optional[str] = None, model_path: Optiona
|
||||
if model_name and model_path:
|
||||
user_config["path_dict"][model_name] = model_path
|
||||
|
||||
with open(get_config_path(), "w", encoding="utf-8") as f:
|
||||
with open(_get_config_path(), "w", encoding="utf-8") as f:
|
||||
safe_dump(user_config, f)
|
||||
|
||||
|
||||
@ -120,20 +129,9 @@ def get_model_path(model_name: str) -> str:
|
||||
return model_path
|
||||
|
||||
|
||||
def get_model_info(model_name: str) -> Tuple[str, str]:
|
||||
r"""
|
||||
Gets the necessary information of this model.
|
||||
|
||||
Returns:
|
||||
model_path (str)
|
||||
template (str)
|
||||
"""
|
||||
return get_model_path(model_name), get_template(model_name)
|
||||
|
||||
|
||||
def get_template(model_name: str) -> str:
|
||||
r"""
|
||||
Gets the template name if the model is a chat model.
|
||||
Gets the template name if the model is a chat/distill/instruct model.
|
||||
"""
|
||||
return DEFAULT_TEMPLATE.get(model_name, "default")
|
||||
|
||||
@ -145,24 +143,11 @@ def get_visual(model_name: str) -> bool:
|
||||
return model_name in VISION_MODELS
|
||||
|
||||
|
||||
def list_checkpoints(model_name: str, finetuning_type: str) -> "gr.Dropdown":
|
||||
def get_time() -> str:
|
||||
r"""
|
||||
Lists all available checkpoints.
|
||||
Gets current date and time.
|
||||
"""
|
||||
checkpoints = []
|
||||
if model_name:
|
||||
save_dir = get_save_dir(model_name, finetuning_type)
|
||||
if save_dir and os.path.isdir(save_dir):
|
||||
for checkpoint in os.listdir(save_dir):
|
||||
if os.path.isdir(os.path.join(save_dir, checkpoint)) and any(
|
||||
os.path.isfile(os.path.join(save_dir, checkpoint, name)) for name in CHECKPOINT_NAMES
|
||||
):
|
||||
checkpoints.append(checkpoint)
|
||||
|
||||
if finetuning_type in PEFT_METHODS:
|
||||
return gr.Dropdown(value=[], choices=checkpoints, multiselect=True)
|
||||
else:
|
||||
return gr.Dropdown(value=None, choices=checkpoints, multiselect=False)
|
||||
return datetime.now().strftime(r"%Y-%m-%d-%H-%M-%S")
|
||||
|
||||
|
||||
def load_dataset_info(dataset_dir: str) -> Dict[str, Dict[str, Any]]:
|
||||
@ -181,11 +166,135 @@ def load_dataset_info(dataset_dir: str) -> Dict[str, Dict[str, Any]]:
|
||||
return {}
|
||||
|
||||
|
||||
def list_datasets(dataset_dir: str = None, training_stage: str = list(TRAINING_STAGES.keys())[0]) -> "gr.Dropdown":
|
||||
def load_args(config_path: str) -> Optional[Dict[str, Any]]:
|
||||
r"""
|
||||
Lists all available datasets in the dataset dir for the training stage.
|
||||
Loads the training configuration from config path.
|
||||
"""
|
||||
dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR)
|
||||
ranking = TRAINING_STAGES[training_stage] in STAGES_USE_PAIR_DATA
|
||||
datasets = [k for k, v in dataset_info.items() if v.get("ranking", False) == ranking]
|
||||
return gr.Dropdown(choices=datasets)
|
||||
try:
|
||||
with open(config_path, encoding="utf-8") as f:
|
||||
return safe_load(f)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def save_args(config_path: str, config_dict: Dict[str, Any]) -> None:
|
||||
r"""
|
||||
Saves the training configuration to config path.
|
||||
"""
|
||||
with open(config_path, "w", encoding="utf-8") as f:
|
||||
safe_dump(config_dict, f)
|
||||
|
||||
|
||||
def _clean_cmd(args: Dict[str, Any]) -> Dict[str, Any]:
|
||||
r"""
|
||||
Removes args with NoneType or False or empty string value.
|
||||
"""
|
||||
no_skip_keys = ["packing"]
|
||||
return {k: v for k, v in args.items() if (k in no_skip_keys) or (v is not None and v is not False and v != "")}
|
||||
|
||||
|
||||
def gen_cmd(args: Dict[str, Any]) -> str:
|
||||
r"""
|
||||
Generates CLI commands for previewing.
|
||||
"""
|
||||
cmd_lines = ["llamafactory-cli train "]
|
||||
for k, v in _clean_cmd(args).items():
|
||||
if isinstance(v, dict):
|
||||
cmd_lines.append(f" --{k} {json.dumps(v, ensure_ascii=False)} ")
|
||||
elif isinstance(v, list):
|
||||
cmd_lines.append(f" --{k} {' '.join(map(str, v))} ")
|
||||
else:
|
||||
cmd_lines.append(f" --{k} {str(v)} ")
|
||||
|
||||
if os.name == "nt":
|
||||
cmd_text = "`\n".join(cmd_lines)
|
||||
else:
|
||||
cmd_text = "\\\n".join(cmd_lines)
|
||||
|
||||
cmd_text = f"```bash\n{cmd_text}\n```"
|
||||
return cmd_text
|
||||
|
||||
|
||||
def save_cmd(args: Dict[str, Any]) -> str:
|
||||
r"""
|
||||
Saves CLI commands to launch training.
|
||||
"""
|
||||
output_dir = args["output_dir"]
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
with open(os.path.join(output_dir, TRAINING_ARGS), "w", encoding="utf-8") as f:
|
||||
safe_dump(_clean_cmd(args), f)
|
||||
|
||||
return os.path.join(output_dir, TRAINING_ARGS)
|
||||
|
||||
|
||||
def load_eval_results(path: os.PathLike) -> str:
|
||||
r"""
|
||||
Gets scores after evaluation.
|
||||
"""
|
||||
with open(path, encoding="utf-8") as f:
|
||||
result = json.dumps(json.load(f), indent=4)
|
||||
|
||||
return f"```json\n{result}\n```\n"
|
||||
|
||||
|
||||
def create_ds_config() -> None:
|
||||
r"""
|
||||
Creates deepspeed config in the current directory.
|
||||
"""
|
||||
os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
|
||||
ds_config = {
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": "auto",
|
||||
"zero_allow_untested_optimizer": True,
|
||||
"fp16": {
|
||||
"enabled": "auto",
|
||||
"loss_scale": 0,
|
||||
"loss_scale_window": 1000,
|
||||
"initial_scale_power": 16,
|
||||
"hysteresis": 2,
|
||||
"min_loss_scale": 1,
|
||||
},
|
||||
"bf16": {"enabled": "auto"},
|
||||
}
|
||||
offload_config = {
|
||||
"device": "cpu",
|
||||
"pin_memory": True,
|
||||
}
|
||||
ds_config["zero_optimization"] = {
|
||||
"stage": 2,
|
||||
"allgather_partitions": True,
|
||||
"allgather_bucket_size": 5e8,
|
||||
"overlap_comm": True,
|
||||
"reduce_scatter": True,
|
||||
"reduce_bucket_size": 5e8,
|
||||
"contiguous_gradients": True,
|
||||
"round_robin_gradients": True,
|
||||
}
|
||||
with open(os.path.join(DEFAULT_CACHE_DIR, "ds_z2_config.json"), "w", encoding="utf-8") as f:
|
||||
json.dump(ds_config, f, indent=2)
|
||||
|
||||
ds_config["zero_optimization"]["offload_optimizer"] = offload_config
|
||||
with open(os.path.join(DEFAULT_CACHE_DIR, "ds_z2_offload_config.json"), "w", encoding="utf-8") as f:
|
||||
json.dump(ds_config, f, indent=2)
|
||||
|
||||
ds_config["zero_optimization"] = {
|
||||
"stage": 3,
|
||||
"overlap_comm": True,
|
||||
"contiguous_gradients": True,
|
||||
"sub_group_size": 1e9,
|
||||
"reduce_bucket_size": "auto",
|
||||
"stage3_prefetch_bucket_size": "auto",
|
||||
"stage3_param_persistence_threshold": "auto",
|
||||
"stage3_max_live_parameters": 1e9,
|
||||
"stage3_max_reuse_distance": 1e9,
|
||||
"stage3_gather_16bit_weights_on_model_save": True,
|
||||
}
|
||||
with open(os.path.join(DEFAULT_CACHE_DIR, "ds_z3_config.json"), "w", encoding="utf-8") as f:
|
||||
json.dump(ds_config, f, indent=2)
|
||||
|
||||
ds_config["zero_optimization"]["offload_optimizer"] = offload_config
|
||||
ds_config["zero_optimization"]["offload_param"] = offload_config
|
||||
with open(os.path.join(DEFAULT_CACHE_DIR, "ds_z3_offload_config.json"), "w", encoding="utf-8") as f:
|
||||
json.dump(ds_config, f, indent=2)
|
||||
|
||||
@ -12,11 +12,12 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import json
|
||||
from typing import TYPE_CHECKING, Dict, Tuple
|
||||
|
||||
from ...data import Role
|
||||
from ...extras.packages import is_gradio_available
|
||||
from ..utils import check_json_schema
|
||||
from ..locales import ALERTS
|
||||
|
||||
|
||||
if is_gradio_available():
|
||||
@ -29,9 +30,27 @@ if TYPE_CHECKING:
|
||||
from ..engine import Engine
|
||||
|
||||
|
||||
def check_json_schema(text: str, lang: str) -> None:
|
||||
r"""
|
||||
Checks if the json schema is valid.
|
||||
"""
|
||||
try:
|
||||
tools = json.loads(text)
|
||||
if tools:
|
||||
assert isinstance(tools, list)
|
||||
for tool in tools:
|
||||
if "name" not in tool:
|
||||
raise NotImplementedError("Name not found.")
|
||||
except NotImplementedError:
|
||||
gr.Warning(ALERTS["err_tool_name"][lang])
|
||||
except Exception:
|
||||
gr.Warning(ALERTS["err_json_schema"][lang])
|
||||
|
||||
|
||||
def create_chat_box(
|
||||
engine: "Engine", visible: bool = False
|
||||
) -> Tuple["Component", "Component", Dict[str, "Component"]]:
|
||||
lang = engine.manager.get_elem_by_id("top.lang")
|
||||
with gr.Column(visible=visible) as chat_box:
|
||||
chatbot = gr.Chatbot(type="messages", show_copy_button=True)
|
||||
messages = gr.State([])
|
||||
@ -67,7 +86,7 @@ def create_chat_box(
|
||||
[chatbot, messages, query],
|
||||
).then(
|
||||
engine.chatter.stream,
|
||||
[chatbot, messages, system, tools, image, video, max_new_tokens, top_p, temperature],
|
||||
[chatbot, messages, lang, system, tools, image, video, max_new_tokens, top_p, temperature],
|
||||
[chatbot, messages],
|
||||
)
|
||||
clear_btn.click(lambda: ([], []), outputs=[chatbot, messages])
|
||||
|
||||
@ -40,6 +40,9 @@ def next_page(page_index: int, total_num: int) -> int:
|
||||
|
||||
|
||||
def can_preview(dataset_dir: str, dataset: list) -> "gr.Button":
|
||||
r"""
|
||||
Checks if the dataset is a local dataset.
|
||||
"""
|
||||
try:
|
||||
with open(os.path.join(dataset_dir, DATA_CONFIG), encoding="utf-8") as f:
|
||||
dataset_info = json.load(f)
|
||||
@ -67,6 +70,9 @@ def _load_data_file(file_path: str) -> List[Any]:
|
||||
|
||||
|
||||
def get_preview(dataset_dir: str, dataset: list, page_index: int) -> Tuple[int, list, "gr.Column"]:
|
||||
r"""
|
||||
Gets the preview samples from the dataset.
|
||||
"""
|
||||
with open(os.path.join(dataset_dir, DATA_CONFIG), encoding="utf-8") as f:
|
||||
dataset_info = json.load(f)
|
||||
|
||||
|
||||
@ -15,7 +15,8 @@
|
||||
from typing import TYPE_CHECKING, Dict
|
||||
|
||||
from ...extras.packages import is_gradio_available
|
||||
from ..common import DEFAULT_DATA_DIR, list_datasets
|
||||
from ..common import DEFAULT_DATA_DIR
|
||||
from ..control import list_datasets
|
||||
from .data import create_preview_box
|
||||
|
||||
|
||||
|
||||
@ -17,8 +17,8 @@ from typing import TYPE_CHECKING, Dict
|
||||
from ...data import TEMPLATES
|
||||
from ...extras.constants import METHODS, SUPPORTED_MODELS
|
||||
from ...extras.packages import is_gradio_available
|
||||
from ..common import get_model_info, list_checkpoints, save_config
|
||||
from ..utils import can_quantize, can_quantize_to
|
||||
from ..common import save_config
|
||||
from ..control import can_quantize, can_quantize_to, get_model_info, list_checkpoints
|
||||
|
||||
|
||||
if is_gradio_available():
|
||||
|
||||
@ -19,8 +19,8 @@ from transformers.trainer_utils import SchedulerType
|
||||
from ...extras.constants import TRAINING_STAGES
|
||||
from ...extras.misc import get_device_count
|
||||
from ...extras.packages import is_gradio_available
|
||||
from ..common import DEFAULT_DATA_DIR, list_checkpoints, list_datasets
|
||||
from ..utils import change_stage, list_config_paths, list_output_dirs
|
||||
from ..common import DEFAULT_DATA_DIR
|
||||
from ..control import change_stage, list_checkpoints, list_config_paths, list_datasets, list_output_dirs
|
||||
from .data import create_preview_box
|
||||
|
||||
|
||||
|
||||
201
src/llamafactory/webui/control.py
Normal file
201
src/llamafactory/webui/control.py
Normal file
@ -0,0 +1,201 @@
|
||||
# Copyright 2024 the LlamaFactory team.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import json
|
||||
import os
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
from transformers.trainer_utils import get_last_checkpoint
|
||||
|
||||
from ..extras.constants import (
|
||||
CHECKPOINT_NAMES,
|
||||
PEFT_METHODS,
|
||||
RUNNING_LOG,
|
||||
STAGES_USE_PAIR_DATA,
|
||||
TRAINER_LOG,
|
||||
TRAINING_STAGES,
|
||||
)
|
||||
from ..extras.packages import is_gradio_available, is_matplotlib_available
|
||||
from ..extras.ploting import gen_loss_plot
|
||||
from ..model import QuantizationMethod
|
||||
from .common import DEFAULT_CONFIG_DIR, DEFAULT_DATA_DIR, get_model_path, get_save_dir, get_template, load_dataset_info
|
||||
|
||||
|
||||
if is_gradio_available():
|
||||
import gradio as gr
|
||||
|
||||
|
||||
def can_quantize(finetuning_type: str) -> "gr.Dropdown":
|
||||
r"""
|
||||
Judges if the quantization is available in this finetuning type.
|
||||
|
||||
Inputs: top.finetuning_type
|
||||
Outputs: top.quantization_bit
|
||||
"""
|
||||
if finetuning_type not in PEFT_METHODS:
|
||||
return gr.Dropdown(value="none", interactive=False)
|
||||
else:
|
||||
return gr.Dropdown(interactive=True)
|
||||
|
||||
|
||||
def can_quantize_to(quantization_method: str) -> "gr.Dropdown":
|
||||
r"""
|
||||
Gets the available quantization bits.
|
||||
|
||||
Inputs: top.quantization_method
|
||||
Outputs: top.quantization_bit
|
||||
"""
|
||||
if quantization_method == QuantizationMethod.BITS_AND_BYTES.value:
|
||||
available_bits = ["none", "8", "4"]
|
||||
elif quantization_method == QuantizationMethod.HQQ.value:
|
||||
available_bits = ["none", "8", "6", "5", "4", "3", "2", "1"]
|
||||
elif quantization_method == QuantizationMethod.EETQ.value:
|
||||
available_bits = ["none", "8"]
|
||||
|
||||
return gr.Dropdown(choices=available_bits)
|
||||
|
||||
|
||||
def change_stage(training_stage: str = list(TRAINING_STAGES.keys())[0]) -> Tuple[List[str], bool]:
|
||||
r"""
|
||||
Modifys states after changing the training stage.
|
||||
|
||||
Inputs: train.training_stage
|
||||
Outputs: train.dataset, train.packing
|
||||
"""
|
||||
return [], TRAINING_STAGES[training_stage] == "pt"
|
||||
|
||||
|
||||
def get_model_info(model_name: str) -> Tuple[str, str]:
|
||||
r"""
|
||||
Gets the necessary information of this model.
|
||||
|
||||
Inputs: top.model_name
|
||||
Outputs: top.model_path, top.template
|
||||
"""
|
||||
return get_model_path(model_name), get_template(model_name)
|
||||
|
||||
|
||||
def get_trainer_info(output_path: os.PathLike, do_train: bool) -> Tuple[str, "gr.Slider", Optional["gr.Plot"]]:
|
||||
r"""
|
||||
Gets training infomation for monitor.
|
||||
|
||||
If do_train is True:
|
||||
Inputs: train.output_path
|
||||
Outputs: train.output_box, train.progress_bar, train.loss_viewer
|
||||
If do_train is False:
|
||||
Inputs: eval.output_path
|
||||
Outputs: eval.output_box, eval.progress_bar, None
|
||||
"""
|
||||
running_log = ""
|
||||
running_progress = gr.Slider(visible=False)
|
||||
running_loss = None
|
||||
|
||||
running_log_path = os.path.join(output_path, RUNNING_LOG)
|
||||
if os.path.isfile(running_log_path):
|
||||
with open(running_log_path, encoding="utf-8") as f:
|
||||
running_log = f.read()
|
||||
|
||||
trainer_log_path = os.path.join(output_path, TRAINER_LOG)
|
||||
if os.path.isfile(trainer_log_path):
|
||||
trainer_log: List[Dict[str, Any]] = []
|
||||
with open(trainer_log_path, encoding="utf-8") as f:
|
||||
for line in f:
|
||||
trainer_log.append(json.loads(line))
|
||||
|
||||
if len(trainer_log) != 0:
|
||||
latest_log = trainer_log[-1]
|
||||
percentage = latest_log["percentage"]
|
||||
label = "Running {:d}/{:d}: {} < {}".format(
|
||||
latest_log["current_steps"],
|
||||
latest_log["total_steps"],
|
||||
latest_log["elapsed_time"],
|
||||
latest_log["remaining_time"],
|
||||
)
|
||||
running_progress = gr.Slider(label=label, value=percentage, visible=True)
|
||||
|
||||
if do_train and is_matplotlib_available():
|
||||
running_loss = gr.Plot(gen_loss_plot(trainer_log))
|
||||
|
||||
return running_log, running_progress, running_loss
|
||||
|
||||
|
||||
def list_checkpoints(model_name: str, finetuning_type: str) -> "gr.Dropdown":
|
||||
r"""
|
||||
Lists all available checkpoints.
|
||||
|
||||
Inputs: top.model_name, top.finetuning_type
|
||||
Outputs: top.checkpoint_path
|
||||
"""
|
||||
checkpoints = []
|
||||
if model_name:
|
||||
save_dir = get_save_dir(model_name, finetuning_type)
|
||||
if save_dir and os.path.isdir(save_dir):
|
||||
for checkpoint in os.listdir(save_dir):
|
||||
if os.path.isdir(os.path.join(save_dir, checkpoint)) and any(
|
||||
os.path.isfile(os.path.join(save_dir, checkpoint, name)) for name in CHECKPOINT_NAMES
|
||||
):
|
||||
checkpoints.append(checkpoint)
|
||||
|
||||
if finetuning_type in PEFT_METHODS:
|
||||
return gr.Dropdown(value=[], choices=checkpoints, multiselect=True)
|
||||
else:
|
||||
return gr.Dropdown(value=None, choices=checkpoints, multiselect=False)
|
||||
|
||||
|
||||
def list_config_paths(current_time: str) -> "gr.Dropdown":
|
||||
r"""
|
||||
Lists all the saved configuration files.
|
||||
|
||||
Inputs: train.current_time
|
||||
Outputs: train.config_path
|
||||
"""
|
||||
config_files = [f"{current_time}.yaml"]
|
||||
if os.path.isdir(DEFAULT_CONFIG_DIR):
|
||||
for file_name in os.listdir(DEFAULT_CONFIG_DIR):
|
||||
if file_name.endswith(".yaml") and file_name not in config_files:
|
||||
config_files.append(file_name)
|
||||
|
||||
return gr.Dropdown(choices=config_files)
|
||||
|
||||
|
||||
def list_datasets(dataset_dir: str = None, training_stage: str = list(TRAINING_STAGES.keys())[0]) -> "gr.Dropdown":
|
||||
r"""
|
||||
Lists all available datasets in the dataset dir for the training stage.
|
||||
|
||||
Inputs: *.dataset_dir, *.training_stage
|
||||
Outputs: *.dataset
|
||||
"""
|
||||
dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR)
|
||||
ranking = TRAINING_STAGES[training_stage] in STAGES_USE_PAIR_DATA
|
||||
datasets = [k for k, v in dataset_info.items() if v.get("ranking", False) == ranking]
|
||||
return gr.Dropdown(choices=datasets)
|
||||
|
||||
|
||||
def list_output_dirs(model_name: Optional[str], finetuning_type: str, current_time: str) -> "gr.Dropdown":
|
||||
r"""
|
||||
Lists all the directories that can resume from.
|
||||
|
||||
Inputs: top.model_name, top.finetuning_type, train.current_time
|
||||
Outputs: train.output_dir
|
||||
"""
|
||||
output_dirs = [f"train_{current_time}"]
|
||||
if model_name:
|
||||
save_dir = get_save_dir(model_name, finetuning_type)
|
||||
if save_dir and os.path.isdir(save_dir):
|
||||
for folder in os.listdir(save_dir):
|
||||
output_dir = os.path.join(save_dir, folder)
|
||||
if os.path.isdir(output_dir) and get_last_checkpoint(output_dir) is not None:
|
||||
output_dirs.append(folder)
|
||||
|
||||
return gr.Dropdown(choices=output_dirs)
|
||||
@ -20,6 +20,29 @@ CSS = r"""
|
||||
border-radius: 100vh !important;
|
||||
}
|
||||
|
||||
.thinking-summary {
|
||||
padding: 8px !important;
|
||||
}
|
||||
|
||||
.thinking-summary span {
|
||||
border: 1px solid #e0e0e0 !important;
|
||||
border-radius: 4px !important;
|
||||
padding: 4px !important;
|
||||
cursor: pointer !important;
|
||||
font-size: 14px !important;
|
||||
background: #333333 !important;
|
||||
}
|
||||
|
||||
.thinking-container {
|
||||
border-left: 2px solid #a6a6a6 !important;
|
||||
padding-left: 10px !important;
|
||||
margin: 4px 0 !important;
|
||||
}
|
||||
|
||||
.thinking-container p {
|
||||
color: #a6a6a6 !important;
|
||||
}
|
||||
|
||||
.modal-box {
|
||||
position: fixed !important;
|
||||
top: 50%;
|
||||
|
||||
@ -15,11 +15,10 @@
|
||||
from typing import TYPE_CHECKING, Any, Dict
|
||||
|
||||
from .chatter import WebChatModel
|
||||
from .common import load_config
|
||||
from .common import create_ds_config, get_time, load_config
|
||||
from .locales import LOCALES
|
||||
from .manager import Manager
|
||||
from .runner import Runner
|
||||
from .utils import create_ds_config, get_time
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
@ -27,6 +26,10 @@ if TYPE_CHECKING:
|
||||
|
||||
|
||||
class Engine:
|
||||
r"""
|
||||
A general engine to control the behaviors of Web UI.
|
||||
"""
|
||||
|
||||
def __init__(self, demo_mode: bool = False, pure_chat: bool = False) -> None:
|
||||
self.demo_mode = demo_mode
|
||||
self.pure_chat = pure_chat
|
||||
@ -38,7 +41,7 @@ class Engine:
|
||||
|
||||
def _update_component(self, input_dict: Dict[str, Dict[str, Any]]) -> Dict["Component", "Component"]:
|
||||
r"""
|
||||
Gets the dict to update the components.
|
||||
Updates gradio components according to the (elem_id, properties) mapping.
|
||||
"""
|
||||
output_dict: Dict["Component", "Component"] = {}
|
||||
for elem_id, elem_attr in input_dict.items():
|
||||
@ -48,9 +51,11 @@ class Engine:
|
||||
return output_dict
|
||||
|
||||
def resume(self):
|
||||
user_config = load_config() if not self.demo_mode else {}
|
||||
r"""
|
||||
Gets the initial value of gradio components and restores training status if necessary.
|
||||
"""
|
||||
user_config = load_config() if not self.demo_mode else {} # do not use config in demo mode
|
||||
lang = user_config.get("lang", None) or "en"
|
||||
|
||||
init_dict = {"top.lang": {"value": lang}, "infer.chat_box": {"visible": self.chatter.loaded}}
|
||||
|
||||
if not self.pure_chat:
|
||||
@ -74,6 +79,9 @@ class Engine:
|
||||
yield self._update_component({"eval.resume_btn": {"value": True}})
|
||||
|
||||
def change_lang(self, lang: str):
|
||||
r"""
|
||||
Updates the displayed language of gradio components.
|
||||
"""
|
||||
return {
|
||||
elem: elem.__class__(**LOCALES[elem_name][lang])
|
||||
for elem_name, elem in self.manager.get_elem_iter()
|
||||
|
||||
@ -2786,6 +2786,20 @@ ALERTS = {
|
||||
"ko": "모델이 언로드되었습니다.",
|
||||
"ja": "モデルがアンロードされました。",
|
||||
},
|
||||
"info_thinking": {
|
||||
"en": "🌀 Thinking...",
|
||||
"ru": "🌀 Думаю...",
|
||||
"zh": "🌀 思考中...",
|
||||
"ko": "🌀 생각 중...",
|
||||
"ja": "🌀 考えています...",
|
||||
},
|
||||
"info_thought": {
|
||||
"en": "✅ Thought",
|
||||
"ru": "✅ Думать закончено",
|
||||
"zh": "✅ 思考完成",
|
||||
"ko": "✅ 생각이 완료되었습니다",
|
||||
"ja": "✅ 思考完了",
|
||||
},
|
||||
"info_exporting": {
|
||||
"en": "Exporting model...",
|
||||
"ru": "Экспорт модели...",
|
||||
|
||||
@ -20,6 +20,10 @@ if TYPE_CHECKING:
|
||||
|
||||
|
||||
class Manager:
|
||||
r"""
|
||||
A class to manage all the gradio components in Web UI.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._id_to_elem: Dict[str, "Component"] = {}
|
||||
self._elem_to_id: Dict["Component", str] = {}
|
||||
|
||||
@ -24,9 +24,20 @@ from transformers.utils import is_torch_npu_available
|
||||
from ..extras.constants import LLAMABOARD_CONFIG, PEFT_METHODS, TRAINING_STAGES
|
||||
from ..extras.misc import is_gpu_or_npu_available, torch_gc, use_ray
|
||||
from ..extras.packages import is_gradio_available, is_transformers_version_equal_to_4_46
|
||||
from .common import DEFAULT_CACHE_DIR, DEFAULT_CONFIG_DIR, get_save_dir, load_config
|
||||
from .common import (
|
||||
DEFAULT_CACHE_DIR,
|
||||
DEFAULT_CONFIG_DIR,
|
||||
abort_process,
|
||||
gen_cmd,
|
||||
get_save_dir,
|
||||
load_args,
|
||||
load_config,
|
||||
load_eval_results,
|
||||
save_args,
|
||||
save_cmd,
|
||||
)
|
||||
from .control import get_trainer_info
|
||||
from .locales import ALERTS, LOCALES
|
||||
from .utils import abort_process, gen_cmd, get_eval_results, get_trainer_info, load_args, save_args, save_cmd
|
||||
|
||||
|
||||
if is_gradio_available():
|
||||
@ -40,6 +51,10 @@ if TYPE_CHECKING:
|
||||
|
||||
|
||||
class Runner:
|
||||
r"""
|
||||
A class to manage the running status of the trainers.
|
||||
"""
|
||||
|
||||
def __init__(self, manager: "Manager", demo_mode: bool = False) -> None:
|
||||
self.manager = manager
|
||||
self.demo_mode = demo_mode
|
||||
@ -57,6 +72,9 @@ class Runner:
|
||||
abort_process(self.trainer.pid)
|
||||
|
||||
def _initialize(self, data: Dict["Component", Any], do_train: bool, from_preview: bool) -> str:
|
||||
r"""
|
||||
Validates the configuration.
|
||||
"""
|
||||
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
|
||||
lang, model_name, model_path = get("top.lang"), get("top.model_name"), get("top.model_path")
|
||||
dataset = get("train.dataset") if do_train else get("eval.dataset")
|
||||
@ -98,6 +116,9 @@ class Runner:
|
||||
return ""
|
||||
|
||||
def _finalize(self, lang: str, finish_info: str) -> str:
|
||||
r"""
|
||||
Cleans the cached memory and resets the runner.
|
||||
"""
|
||||
finish_info = ALERTS["info_aborted"][lang] if self.aborted else finish_info
|
||||
gr.Info(finish_info)
|
||||
self.trainer = None
|
||||
@ -108,6 +129,9 @@ class Runner:
|
||||
return finish_info
|
||||
|
||||
def _parse_train_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
|
||||
r"""
|
||||
Builds and validates the training arguments.
|
||||
"""
|
||||
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
|
||||
model_name, finetuning_type = get("top.model_name"), get("top.finetuning_type")
|
||||
user_config = load_config()
|
||||
@ -268,6 +292,9 @@ class Runner:
|
||||
return args
|
||||
|
||||
def _parse_eval_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
|
||||
r"""
|
||||
Builds and validates the evaluation arguments.
|
||||
"""
|
||||
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
|
||||
model_name, finetuning_type = get("top.model_name"), get("top.finetuning_type")
|
||||
user_config = load_config()
|
||||
@ -319,6 +346,9 @@ class Runner:
|
||||
return args
|
||||
|
||||
def _preview(self, data: Dict["Component", Any], do_train: bool) -> Generator[Dict["Component", str], None, None]:
|
||||
r"""
|
||||
Previews the training commands.
|
||||
"""
|
||||
output_box = self.manager.get_elem_by_id("{}.output_box".format("train" if do_train else "eval"))
|
||||
error = self._initialize(data, do_train, from_preview=True)
|
||||
if error:
|
||||
@ -329,6 +359,9 @@ class Runner:
|
||||
yield {output_box: gen_cmd(args)}
|
||||
|
||||
def _launch(self, data: Dict["Component", Any], do_train: bool) -> Generator[Dict["Component", Any], None, None]:
|
||||
r"""
|
||||
Starts the training process.
|
||||
"""
|
||||
output_box = self.manager.get_elem_by_id("{}.output_box".format("train" if do_train else "eval"))
|
||||
error = self._initialize(data, do_train, from_preview=False)
|
||||
if error:
|
||||
@ -339,7 +372,7 @@ class Runner:
|
||||
args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)
|
||||
|
||||
os.makedirs(args["output_dir"], exist_ok=True)
|
||||
save_args(os.path.join(args["output_dir"], LLAMABOARD_CONFIG), self._form_config_dict(data))
|
||||
save_args(os.path.join(args["output_dir"], LLAMABOARD_CONFIG), self._build_config_dict(data))
|
||||
|
||||
env = deepcopy(os.environ)
|
||||
env["LLAMABOARD_ENABLED"] = "1"
|
||||
@ -350,7 +383,10 @@ class Runner:
|
||||
self.trainer = Popen(["llamafactory-cli", "train", save_cmd(args)], env=env)
|
||||
yield from self.monitor()
|
||||
|
||||
def _form_config_dict(self, data: Dict["Component", Any]) -> Dict[str, Any]:
|
||||
def _build_config_dict(self, data: Dict["Component", Any]) -> Dict[str, Any]:
|
||||
r"""
|
||||
Builds a dictionary containing the current training configuration.
|
||||
"""
|
||||
config_dict = {}
|
||||
skip_ids = ["top.lang", "top.model_path", "train.output_dir", "train.config_path"]
|
||||
for elem, value in data.items():
|
||||
@ -373,6 +409,9 @@ class Runner:
|
||||
yield from self._launch(data, do_train=False)
|
||||
|
||||
def monitor(self):
|
||||
r"""
|
||||
Monitors the training progress and logs.
|
||||
"""
|
||||
self.aborted = False
|
||||
self.running = True
|
||||
|
||||
@ -416,7 +455,7 @@ class Runner:
|
||||
finish_info = ALERTS["err_failed"][lang]
|
||||
else:
|
||||
if os.path.exists(os.path.join(output_path, "all_results.json")) or use_ray():
|
||||
finish_info = get_eval_results(os.path.join(output_path, "all_results.json"))
|
||||
finish_info = load_eval_results(os.path.join(output_path, "all_results.json"))
|
||||
else:
|
||||
finish_info = ALERTS["err_failed"][lang]
|
||||
|
||||
@ -427,6 +466,9 @@ class Runner:
|
||||
yield return_dict
|
||||
|
||||
def save_args(self, data):
|
||||
r"""
|
||||
Saves the training configuration to config path.
|
||||
"""
|
||||
output_box = self.manager.get_elem_by_id("train.output_box")
|
||||
error = self._initialize(data, do_train=True, from_preview=True)
|
||||
if error:
|
||||
@ -438,10 +480,13 @@ class Runner:
|
||||
os.makedirs(DEFAULT_CONFIG_DIR, exist_ok=True)
|
||||
save_path = os.path.join(DEFAULT_CONFIG_DIR, config_path)
|
||||
|
||||
save_args(save_path, self._form_config_dict(data))
|
||||
save_args(save_path, self._build_config_dict(data))
|
||||
return {output_box: ALERTS["info_config_saved"][lang] + save_path}
|
||||
|
||||
def load_args(self, lang: str, config_path: str):
|
||||
r"""
|
||||
Loads the training configuration from config path.
|
||||
"""
|
||||
output_box = self.manager.get_elem_by_id("train.output_box")
|
||||
config_dict = load_args(os.path.join(DEFAULT_CONFIG_DIR, config_path))
|
||||
if config_dict is None:
|
||||
@ -455,6 +500,9 @@ class Runner:
|
||||
return output_dict
|
||||
|
||||
def check_output_dir(self, lang: str, model_name: str, finetuning_type: str, output_dir: str):
|
||||
r"""
|
||||
Restore the training status if output_dir exists.
|
||||
"""
|
||||
output_box = self.manager.get_elem_by_id("train.output_box")
|
||||
output_dict: Dict["Component", Any] = {output_box: LOCALES["output_box"][lang]["value"]}
|
||||
if model_name and output_dir and os.path.isdir(get_save_dir(model_name, finetuning_type, output_dir)):
|
||||
|
||||
@ -1,304 +0,0 @@
|
||||
# Copyright 2024 the LlamaFactory team.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import json
|
||||
import os
|
||||
import signal
|
||||
from datetime import datetime
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import psutil
|
||||
from transformers.trainer_utils import get_last_checkpoint
|
||||
from yaml import safe_dump, safe_load
|
||||
|
||||
from ..extras.constants import PEFT_METHODS, RUNNING_LOG, TRAINER_LOG, TRAINING_ARGS, TRAINING_STAGES
|
||||
from ..extras.packages import is_gradio_available, is_matplotlib_available
|
||||
from ..extras.ploting import gen_loss_plot
|
||||
from ..model import QuantizationMethod
|
||||
from .common import DEFAULT_CACHE_DIR, DEFAULT_CONFIG_DIR, get_save_dir
|
||||
from .locales import ALERTS
|
||||
|
||||
|
||||
if is_gradio_available():
|
||||
import gradio as gr
|
||||
|
||||
|
||||
def abort_process(pid: int) -> None:
|
||||
r"""
|
||||
Aborts the processes recursively in a bottom-up way.
|
||||
"""
|
||||
try:
|
||||
children = psutil.Process(pid).children()
|
||||
if children:
|
||||
for child in children:
|
||||
abort_process(child.pid)
|
||||
|
||||
os.kill(pid, signal.SIGABRT)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def can_quantize(finetuning_type: str) -> "gr.Dropdown":
|
||||
r"""
|
||||
Judges if the quantization is available in this finetuning type.
|
||||
"""
|
||||
if finetuning_type not in PEFT_METHODS:
|
||||
return gr.Dropdown(value="none", interactive=False)
|
||||
else:
|
||||
return gr.Dropdown(interactive=True)
|
||||
|
||||
|
||||
def can_quantize_to(quantization_method: str) -> "gr.Dropdown":
|
||||
r"""
|
||||
Returns the available quantization bits.
|
||||
"""
|
||||
if quantization_method == QuantizationMethod.BITS_AND_BYTES.value:
|
||||
available_bits = ["none", "8", "4"]
|
||||
elif quantization_method == QuantizationMethod.HQQ.value:
|
||||
available_bits = ["none", "8", "6", "5", "4", "3", "2", "1"]
|
||||
elif quantization_method == QuantizationMethod.EETQ.value:
|
||||
available_bits = ["none", "8"]
|
||||
|
||||
return gr.Dropdown(choices=available_bits)
|
||||
|
||||
|
||||
def change_stage(training_stage: str = list(TRAINING_STAGES.keys())[0]) -> Tuple[List[str], bool]:
|
||||
r"""
|
||||
Modifys states after changing the training stage.
|
||||
"""
|
||||
return [], TRAINING_STAGES[training_stage] == "pt"
|
||||
|
||||
|
||||
def check_json_schema(text: str, lang: str) -> None:
|
||||
r"""
|
||||
Checks if the json schema is valid.
|
||||
"""
|
||||
try:
|
||||
tools = json.loads(text)
|
||||
if tools:
|
||||
assert isinstance(tools, list)
|
||||
for tool in tools:
|
||||
if "name" not in tool:
|
||||
raise NotImplementedError("Name not found.")
|
||||
except NotImplementedError:
|
||||
gr.Warning(ALERTS["err_tool_name"][lang])
|
||||
except Exception:
|
||||
gr.Warning(ALERTS["err_json_schema"][lang])
|
||||
|
||||
|
||||
def clean_cmd(args: Dict[str, Any]) -> Dict[str, Any]:
|
||||
r"""
|
||||
Removes args with NoneType or False or empty string value.
|
||||
"""
|
||||
no_skip_keys = ["packing"]
|
||||
return {k: v for k, v in args.items() if (k in no_skip_keys) or (v is not None and v is not False and v != "")}
|
||||
|
||||
|
||||
def gen_cmd(args: Dict[str, Any]) -> str:
|
||||
r"""
|
||||
Generates arguments for previewing.
|
||||
"""
|
||||
cmd_lines = ["llamafactory-cli train "]
|
||||
for k, v in clean_cmd(args).items():
|
||||
if isinstance(v, dict):
|
||||
cmd_lines.append(f" --{k} {json.dumps(v, ensure_ascii=False)} ")
|
||||
elif isinstance(v, list):
|
||||
cmd_lines.append(f" --{k} {' '.join(map(str, v))} ")
|
||||
else:
|
||||
cmd_lines.append(f" --{k} {str(v)} ")
|
||||
|
||||
if os.name == "nt":
|
||||
cmd_text = "`\n".join(cmd_lines)
|
||||
else:
|
||||
cmd_text = "\\\n".join(cmd_lines)
|
||||
|
||||
cmd_text = f"```bash\n{cmd_text}\n```"
|
||||
return cmd_text
|
||||
|
||||
|
||||
def save_cmd(args: Dict[str, Any]) -> str:
|
||||
r"""
|
||||
Saves arguments to launch training.
|
||||
"""
|
||||
output_dir = args["output_dir"]
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
with open(os.path.join(output_dir, TRAINING_ARGS), "w", encoding="utf-8") as f:
|
||||
safe_dump(clean_cmd(args), f)
|
||||
|
||||
return os.path.join(output_dir, TRAINING_ARGS)
|
||||
|
||||
|
||||
def get_eval_results(path: os.PathLike) -> str:
|
||||
r"""
|
||||
Gets scores after evaluation.
|
||||
"""
|
||||
with open(path, encoding="utf-8") as f:
|
||||
result = json.dumps(json.load(f), indent=4)
|
||||
return f"```json\n{result}\n```\n"
|
||||
|
||||
|
||||
def get_time() -> str:
|
||||
r"""
|
||||
Gets current date and time.
|
||||
"""
|
||||
return datetime.now().strftime(r"%Y-%m-%d-%H-%M-%S")
|
||||
|
||||
|
||||
def get_trainer_info(output_path: os.PathLike, do_train: bool) -> Tuple[str, "gr.Slider", Optional["gr.Plot"]]:
|
||||
r"""
|
||||
Gets training infomation for monitor.
|
||||
"""
|
||||
running_log = ""
|
||||
running_progress = gr.Slider(visible=False)
|
||||
running_loss = None
|
||||
|
||||
running_log_path = os.path.join(output_path, RUNNING_LOG)
|
||||
if os.path.isfile(running_log_path):
|
||||
with open(running_log_path, encoding="utf-8") as f:
|
||||
running_log = f.read()
|
||||
|
||||
trainer_log_path = os.path.join(output_path, TRAINER_LOG)
|
||||
if os.path.isfile(trainer_log_path):
|
||||
trainer_log: List[Dict[str, Any]] = []
|
||||
with open(trainer_log_path, encoding="utf-8") as f:
|
||||
for line in f:
|
||||
trainer_log.append(json.loads(line))
|
||||
|
||||
if len(trainer_log) != 0:
|
||||
latest_log = trainer_log[-1]
|
||||
percentage = latest_log["percentage"]
|
||||
label = "Running {:d}/{:d}: {} < {}".format(
|
||||
latest_log["current_steps"],
|
||||
latest_log["total_steps"],
|
||||
latest_log["elapsed_time"],
|
||||
latest_log["remaining_time"],
|
||||
)
|
||||
running_progress = gr.Slider(label=label, value=percentage, visible=True)
|
||||
|
||||
if do_train and is_matplotlib_available():
|
||||
running_loss = gr.Plot(gen_loss_plot(trainer_log))
|
||||
|
||||
return running_log, running_progress, running_loss
|
||||
|
||||
|
||||
def load_args(config_path: str) -> Optional[Dict[str, Any]]:
|
||||
r"""
|
||||
Loads saved arguments.
|
||||
"""
|
||||
try:
|
||||
with open(config_path, encoding="utf-8") as f:
|
||||
return safe_load(f)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def save_args(config_path: str, config_dict: Dict[str, Any]):
|
||||
r"""
|
||||
Saves arguments.
|
||||
"""
|
||||
with open(config_path, "w", encoding="utf-8") as f:
|
||||
safe_dump(config_dict, f)
|
||||
|
||||
|
||||
def list_config_paths(current_time: str) -> "gr.Dropdown":
|
||||
r"""
|
||||
Lists all the saved configuration files.
|
||||
"""
|
||||
config_files = [f"{current_time}.yaml"]
|
||||
if os.path.isdir(DEFAULT_CONFIG_DIR):
|
||||
for file_name in os.listdir(DEFAULT_CONFIG_DIR):
|
||||
if file_name.endswith(".yaml") and file_name not in config_files:
|
||||
config_files.append(file_name)
|
||||
|
||||
return gr.Dropdown(choices=config_files)
|
||||
|
||||
|
||||
def list_output_dirs(model_name: Optional[str], finetuning_type: str, current_time: str) -> "gr.Dropdown":
|
||||
r"""
|
||||
Lists all the directories that can resume from.
|
||||
"""
|
||||
output_dirs = [f"train_{current_time}"]
|
||||
if model_name:
|
||||
save_dir = get_save_dir(model_name, finetuning_type)
|
||||
if save_dir and os.path.isdir(save_dir):
|
||||
for folder in os.listdir(save_dir):
|
||||
output_dir = os.path.join(save_dir, folder)
|
||||
if os.path.isdir(output_dir) and get_last_checkpoint(output_dir) is not None:
|
||||
output_dirs.append(folder)
|
||||
|
||||
return gr.Dropdown(choices=output_dirs)
|
||||
|
||||
|
||||
def create_ds_config() -> None:
|
||||
r"""
|
||||
Creates deepspeed config.
|
||||
"""
|
||||
os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
|
||||
ds_config = {
|
||||
"train_batch_size": "auto",
|
||||
"train_micro_batch_size_per_gpu": "auto",
|
||||
"gradient_accumulation_steps": "auto",
|
||||
"gradient_clipping": "auto",
|
||||
"zero_allow_untested_optimizer": True,
|
||||
"fp16": {
|
||||
"enabled": "auto",
|
||||
"loss_scale": 0,
|
||||
"loss_scale_window": 1000,
|
||||
"initial_scale_power": 16,
|
||||
"hysteresis": 2,
|
||||
"min_loss_scale": 1,
|
||||
},
|
||||
"bf16": {"enabled": "auto"},
|
||||
}
|
||||
offload_config = {
|
||||
"device": "cpu",
|
||||
"pin_memory": True,
|
||||
}
|
||||
ds_config["zero_optimization"] = {
|
||||
"stage": 2,
|
||||
"allgather_partitions": True,
|
||||
"allgather_bucket_size": 5e8,
|
||||
"overlap_comm": True,
|
||||
"reduce_scatter": True,
|
||||
"reduce_bucket_size": 5e8,
|
||||
"contiguous_gradients": True,
|
||||
"round_robin_gradients": True,
|
||||
}
|
||||
with open(os.path.join(DEFAULT_CACHE_DIR, "ds_z2_config.json"), "w", encoding="utf-8") as f:
|
||||
json.dump(ds_config, f, indent=2)
|
||||
|
||||
ds_config["zero_optimization"]["offload_optimizer"] = offload_config
|
||||
with open(os.path.join(DEFAULT_CACHE_DIR, "ds_z2_offload_config.json"), "w", encoding="utf-8") as f:
|
||||
json.dump(ds_config, f, indent=2)
|
||||
|
||||
ds_config["zero_optimization"] = {
|
||||
"stage": 3,
|
||||
"overlap_comm": True,
|
||||
"contiguous_gradients": True,
|
||||
"sub_group_size": 1e9,
|
||||
"reduce_bucket_size": "auto",
|
||||
"stage3_prefetch_bucket_size": "auto",
|
||||
"stage3_param_persistence_threshold": "auto",
|
||||
"stage3_max_live_parameters": 1e9,
|
||||
"stage3_max_reuse_distance": 1e9,
|
||||
"stage3_gather_16bit_weights_on_model_save": True,
|
||||
}
|
||||
with open(os.path.join(DEFAULT_CACHE_DIR, "ds_z3_config.json"), "w", encoding="utf-8") as f:
|
||||
json.dump(ds_config, f, indent=2)
|
||||
|
||||
ds_config["zero_optimization"]["offload_optimizer"] = offload_config
|
||||
ds_config["zero_optimization"]["offload_param"] = offload_config
|
||||
with open(os.path.join(DEFAULT_CACHE_DIR, "ds_z3_offload_config.json"), "w", encoding="utf-8") as f:
|
||||
json.dump(ds_config, f, indent=2)
|
||||
Loading…
x
Reference in New Issue
Block a user