diff --git a/doc/code-docs/locales/en/LC_MESSAGES/checkpoint.po b/doc/code-docs/locales/en/LC_MESSAGES/checkpoint.po index f1d7a41..6ec16d1 100644 --- a/doc/code-docs/locales/en/LC_MESSAGES/checkpoint.po +++ b/doc/code-docs/locales/en/LC_MESSAGES/checkpoint.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: InternLM \n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-09-07 10:56+0800\n" +"POT-Creation-Date: 2023-09-11 14:25+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: en\n" @@ -19,30 +19,30 @@ msgstr "" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.12.1\n" -#: ../../source/checkpoint.rst:2 09c8645fba264cdf9a80c4b62c2bb4d1 +#: ../../source/checkpoint.rst:2 msgid "模型保存" msgstr "Model Checkpointing" -#: ../../source/checkpoint.rst:4 8b158d34631045b1afdb4fb0169b3c71 +#: ../../source/checkpoint.rst:4 msgid "" "InternLM 使用 ``internlm.utils.model_checkpoint.CheckpointManager`` " "来管理模型保存。 其中，可以 使用 ``CheckpointManager.try_save_checkpoint(train_state)`` " "来保存指定 step 的模型状态。InternLM支持启动时自动加载最新的模型备份，并在接收信号退出训练时自动进行模型备份。" msgstr "" -"InternLM uses ``internlm.utils.model_checkpoint.CheckpointManager`` to manage model checkpointing. In the implementation, " -"we use ``CheckpointManager.try_save_checkpoint(train_state)`` to checkpoint training states at specific steps. InternLM supports " -"automatic loading of latest ckpt at startup and automatic model checkpointing at signal quit." +"InternLM uses ``internlm.utils.model_checkpoint.CheckpointManager`` to " +"manage model checkpointing. In the implementation, we use " +"``CheckpointManager.try_save_checkpoint(train_state)`` to checkpoint " +"training states at specific steps. InternLM supports automatic loading of" +" latest ckpt at startup and automatic model checkpointing at signal quit." -#: ../../source/checkpoint.rst:8 a023b5a6d15749bfaa51cf2da194bda1 +#: ../../source/checkpoint.rst:8 msgid "Checkpointing" msgstr "" -#: 938575c699d1426c87e0b3f589a85d50 #: internlm.utils.model_checkpoint.CheckpointManager:1 of msgid "StorageManagerContext" msgstr "" -#: 754d6881cd034c5ebaab0f3362dd14c2 #: internlm.utils.model_checkpoint.CheckpointManager.quit_signal_handler:1 of msgid "" "Exit signal detection function, if we write the exit step in the " @@ -51,34 +51,27 @@ msgid "" "quit." msgstr "" -#: 2169f9fb4a8b40bc9bf6093894fc7a5e 6a55d2b2b24a44c8b78b40f19f4d950b -#: internlm.utils.model_checkpoint.CheckpointManager.quit_signal_handler -#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training of +#: internlm.utils.model_checkpoint.CheckpointManager.quit_signal_handler of msgid "参数" msgstr "" -#: 360a89b1591e4627ac432f4d75050354 #: internlm.utils.model_checkpoint.CheckpointManager.quit_signal_handler of msgid "返回" msgstr "" -#: 2426832f4a8a4c5481be1c940e0e7b50 #: internlm.utils.model_checkpoint.CheckpointManager.quit_signal_handler:9 of msgid "whether to quit." msgstr "" -#: 5f6842c261544a3c89f32d981b3ad755 #: internlm.utils.model_checkpoint.CheckpointManager.quit_signal_handler of msgid "返回类型" msgstr "" -#: 1392da84b6e645bcb8dab605e1231fdc #: internlm.utils.model_checkpoint.CheckpointManager.wait_async_upload_finish:1 #: of msgid "wait for all checkpoint uploads to be completed" msgstr "" -#: d1774593e9c94608b49b10504bfbc38b #: internlm.utils.model_checkpoint.CheckpointManager.query_latest_snapshot_step_boto3:1 #: of msgid "" @@ -86,38 +79,25 @@ msgid "" "found, None will return." msgstr "" -#: a3abbbd2bd574872892d908ab248e804 -#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training:1 of -msgid "Attempt to restore the training state of the last ckpt." -msgstr "" - -#: de021d1eb6d54955a2850c11c0191710 -#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training:3 of -msgid "lr_scheduler object." -msgstr "" - -#: 20be15854f2e420a9d96c86b5869bfa6 -#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training:5 of -msgid "optimizer object." -msgstr "" - -#: 68f69086c5054acc8aca15c8a764acc5 -#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training:7 of -msgid "learning rate." -msgstr "" - -#: 5d34d34a972d4abeab4bda3e49ee157b -#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training:9 of -msgid "traing states." -msgstr "" - -#: 82ebb67afaa748ecabc4cef598d7fc30 -#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training:11 of -msgid "traning dataloader object" -msgstr "" - -#: 0c95dfcd712749279daca78166bb4326 #: internlm.utils.model_checkpoint.CheckpointManager.save_checkpoint:1 of msgid "Save checkpoint to the given folder path." msgstr "" +#~ msgid "Attempt to restore the training state of the last ckpt." +#~ msgstr "" + +#~ msgid "lr_scheduler object." +#~ msgstr "" + +#~ msgid "optimizer object." +#~ msgstr "" + +#~ msgid "learning rate." +#~ msgstr "" + +#~ msgid "traing states." +#~ msgstr "" + +#~ msgid "traning dataloader object" +#~ msgstr "" + diff --git a/doc/code-docs/locales/en/LC_MESSAGES/usage.po b/doc/code-docs/locales/en/LC_MESSAGES/usage.po index 2297f8f..868a25f 100644 --- a/doc/code-docs/locales/en/LC_MESSAGES/usage.po +++ b/doc/code-docs/locales/en/LC_MESSAGES/usage.po @@ -8,7 +8,7 @@ msgid "" msgstr "" "Project-Id-Version: InternLM \n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2023-09-07 14:15+0800\n" +"POT-Creation-Date: 2023-09-11 14:25+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language: en\n" @@ -19,11 +19,11 @@ msgstr "" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.12.1\n" -#: ../../../usage.md:2 a64aaaa1525e4e01b0ddcebc42c24bbd +#: ../../../usage.md:2 msgid "使用教程" msgstr "Quickstart Guide" -#: ../../../usage.md:4 f1b40737fb584d889b82c7f55b652977 +#: ../../../usage.md:4 msgid "" "启动一个 Demo " "模型训练，需要进行三项准备，**安装**，**数据集准备**和**模型训练配置**。接下来，首先会介绍数据准备相关的操作，再简要描述模型训练配置相关的内容。" @@ -33,21 +33,21 @@ msgstr "" "configuration**. In this guide, we will first cover the steps for dataset" " preparation and then briefly describe the model training configuration." -#: ../../../usage.md:6 b35abe307c2f4d23866fff828308ebf2 +#: ../../../usage.md:6 msgid "安装" msgstr "Installation" -#: ../../../usage.md:7 64a8c1f5f71c45519e636aa7edba10bc +#: ../../../usage.md:7 msgid "请参考[安装文档](./install.md)进行安装。" msgstr "" "Please refer to the [installation guide](./install.md) for instructions " "on how to install the necessary dependencies." -#: ../../../usage.md:9 bd96714d12ee415794dea5a4578bd8cd +#: ../../../usage.md:9 msgid "数据准备 （预训练）" msgstr "Dataset Preparation (Pre-training)" -#: ../../../usage.md:11 5a0b39fb9da94e96b87db40d1f231a0c +#: ../../../usage.md:11 msgid "InternLM训练任务的数据集包括一系列的`bin`和`meta`文件。使用`tokenizer`从原始文本文件生成训练用数据集。通过在`tools/tokenizer.py`中指定模型参数路径的方式来导入tokenizer模型。目前提供`V7_sft.model`来生成tokens。若想使用不同的模型，可直接修改`tokernizer.py`中的模型参数路径。" msgstr "" "The dataset for the InternLM training task includes a series of `bin` and" @@ -58,7 +58,7 @@ msgstr "" "different model, you can directly modify the model parameter path in " "`tokenizer.py`." -#: ../../../usage.md:13 3cef8126b8784af48d81cc140322909e +#: ../../../usage.md:13 msgid "可以运行以下命令生成原始数据对应的`bin`和`meta`文件，其中参数`text_input_path`表示原始文本数据路径，目前支持`txt`、`json`和`jsonl`三种输入格式，`bin_output_path`表示生成的`bin`文件的保存路径。" msgstr "" "You can run the following command to generate `bin` and `meta` files " @@ -67,30 +67,30 @@ msgstr "" "`txt`, `json`, and `jsonl` formats, while `bin_output_path` represents " "the save path of the generated `bin` files." -#: ../../../usage.md:18 107ff2280da14cb6a27f4e9857186333 +#: ../../../usage.md:18 msgid "下面是一个数据处理的例子：" msgstr "Here is an example of data processing:" -#: ../../../usage.md:20 c11a9860263c4e2288a561f3435fa706 +#: ../../../usage.md:20 msgid "给定一个包含原始数据集的文件`raw_data.txt`，原始数据集如下所示：" msgstr "" "Given a file `raw_data.txt` containing the raw dataset, the raw dataset " "is shown below:" -#: ../../../usage.md:27 4012599b42ab47bd979d2a0b79ca1147 +#: ../../../usage.md:27 msgid "可以通过运行以下命令来生成`bin`和`meta`文件：" msgstr "" "You can generate the `bin` and `meta` files by running the following " "command:" -#: ../../../usage.md:32 cca91b6cf53a4082932dd34ea4b7f954 +#: ../../../usage.md:32 msgid "需要注意的是，生成的`bin`文件需要保存在`cn`或者`en`或者`code`或者`ja`或者`ar`或者`kaoshi`这六个目录下，以区分数据集的类型。" msgstr "" "It should be noted that the generated `bin` files need to be saved in one" " of the following directories: `cn`, `en`, `code`, `ja`, `ar`, or " "`kaoshi`, depending on the type of dataset." -#: ../../../usage.md:34 417312ca1e35479e811953f777e3565a +#: ../../../usage.md:34 msgid "其中，`cn`表示中文数据集；`en`表示英文数据集；`code`表示代码数据集；`ja`表示日语数据集；`ar`表示阿拉伯语数据集；`kaoshi`表示考试数据集。" msgstr "" "Here, `cn` represents the Chinese dataset, `en` represents the English " @@ -98,22 +98,22 @@ msgstr "" " dataset, `ar` represents the Arabic dataset, and `kaoshi` represents the" " exam dataset." -#: ../../../usage.md:36 79c21f8e89b34499ba4e25e20593ec28 +#: ../../../usage.md:36 msgid "生成的bin文件的格式如下：" msgstr "The format of the generated `bin` files is as follows:" -#: ../../../usage.md:42 26388d996c4e4116bc216be9bc007f62 +#: ../../../usage.md:42 msgid "`bin`文件中的每一行均对应原始数据集中的每一个句子，表示每个句子的`token`（下文将用sequence指定）。" msgstr "" "Each line in the `bin` file corresponds to each sentence in the original " "dataset, representing the tokens of each sentence (referred to as " "sequence below)." -#: ../../../usage.md:44 b39148a85ee64a349975d26282fbe59b +#: ../../../usage.md:44 msgid "生成的`meta`文件的格式如下：" msgstr "The format of the generated `meta` file is as follows:" -#: ../../../usage.md:48 175a6007197a40568535f945672e5df2 +#: ../../../usage.md:48 msgid "" "在`meta`文件中，每个元组对应着`bin`文件中每一个`sequence`的元信息。其中，元组的第一个元素表示每个`sequence`在所有`sequence`中的`starting" " index`，第二个元素表示每个`sequence`中有多少个`tokens`。" @@ -123,7 +123,7 @@ msgstr "" "index` of each `sequence` among all `sequences`, and the second element " "indicates the number of `tokens` for each `sequence`." -#: ../../../usage.md:50 46874a3de3924837979f9949f1237e39 +#: ../../../usage.md:50 msgid "" "例如，对于第一个`sequence`，`starting index`为 0，有 11 " "个`tokens`；对于第二个`sequence`，由于第一个`sequence`转换为`string`后的长度为`89`，因此它的`starting" @@ -132,17 +132,17 @@ msgstr "" "For example, the first `sequence` starts at index 0 and has 16 `tokens`. " "The second `sequence` starts at index 110 and has 24 `tokens`." -#: ../../../usage.md:52 25ea049fa411408b8856e7aa657835ab +#: ../../../usage.md:52 msgid "`json`和`jsonl`类型的文件的`bin`和`meta`文件格式和`txt`一致，此处不再赘叙。" msgstr "" "The `bin` and `meta` file formats for `json` and `jsonl` type files are " "the same as for `txt`, so we won't go over them here." -#: ../../../usage.md:54 bc52f959cb57494483a181e843014ed1 +#: ../../../usage.md:54 msgid "数据准备 （微调）" msgstr "Data Preparation (Fine-tuning)" -#: ../../../usage.md:56 73c74620c2994486acc747ba0c7f0b46 +#: ../../../usage.md:56 msgid "" "微调任务的数据集格式与预训练任务保持一致，生成的数据格式为一系列的`bin`和`meta`文件。以下以 Alpaca " "数据集为例，介绍微调的数据准备流程。" @@ -152,7 +152,7 @@ msgstr "" "the Alpaca dataset as an example to explain the data preparation process " "for fine-tuning." -#: ../../../usage.md:58 75f0e22d10ca413389ec8b947ae6141f +#: ../../../usage.md:58 msgid "" "下载 [Alpaca 数据集](https://github.com/tatsu-" "lab/stanford_alpaca/blob/main/alpaca_data.json)" @@ -160,87 +160,86 @@ msgstr "" "Download the [Alpaca dataset](https://github.com/tatsu-" "lab/stanford_alpaca/blob/main/alpaca_data.json)." -#: ../../../usage.md:60 667606fcea454af48353a5b40f82fc46 +#: ../../../usage.md:60 msgid "对 Alpaca 数据进行 tokenize，使用以下命令" msgstr "Tokenize the Alpaca dataset using the following command:" -#: ../../../usage.md:66 60283b9237c8462ea37288b8ece79081 +#: ../../../usage.md:66 msgid "建议用户参考 alpaca_tokenizer.py 编写新的脚本对自己的数据集进行 tokenize" msgstr "" "It is recommended that users refer to alpaca_tokenizer.py to write new " "scripts to tokenize their own datasets" -#: ../../../usage.md:68 cdf45a4de9874e9fb65f7104dcee3c61 +#: ../../../usage.md:68 msgid "训练配置" msgstr "Training Configuration" -#: ../../../usage.md:70 7c42ebc23246450cbc1270e1461b16f6 -msgid "以 7B Demo 的配置文件`configs/7B_sft.py`为例，介绍启动一个模型训练所需要进行的数据、模型和并行等相关的配置。" +#: ../../../usage.md:70 +#, fuzzy +msgid "以 7B Demo 的配置文件`configs/7B_sft.py`为例：" msgstr "" "Taking the configuration file `configs/7B_sft.py` for the 7B demo as an " -"example, let's discuss the data, model, and parallel configurations " +"example," + +#: ../../../usage.md:237 +msgid "接下来将详细介绍启动一个模型训练所需要进行的数据、模型、并行和监控等相关的配置。" +msgstr "let's discuss the data, model, parallel and monitoring configurations " "required to start a model training." -#: ../../../usage.md:72 247cfe98a7f44c2293aa2e2351f1ea69 +#: ../../../usage.md:239 msgid "数据配置" msgstr "Data Configuration" -#: ../../../usage.md:73 31327e7dce5848778db5361b3fbded1c +#: ../../../usage.md:240 msgid "数据相关的关键参数配置及释义如下所示：" msgstr "Here are the key parameters and their explanations for data configuration:" -#: ../../../usage.md:88 4d2608136fef4141bd6e47f78b8591b2 +#: ../../../usage.md:255 msgid "![pack_into_one](./imgs/pack_into_one.png)" msgstr "" -#: ../../../usage.md:88 c5acb028f2694712b2af788a864d5927 +#: ../../../usage.md:255 msgid "pack_into_one" msgstr "" -#: ../../../usage.md:91 db6b9ce8e8294952845893dd7aad098f +#: ../../../usage.md:258 msgid "目前支持传入数据集文件路径`train_folder`，且要求文件格式如下：" msgstr "" "Currently, it supports passing the dataset file path `train_folder`, and " "the file format is required to be as follows:" -#: ../../../usage.md:98 f22536fc3dfa4552a103a7cb57a20f92 +#: ../../../usage.md:265 msgid "数据集的详细内容可参考``数据准备``模块相关的介绍。" msgstr "" "For detailed information about the dataset, please refer to the \"Data " "Preparation\" section." -#: ../../../usage.md:100 bc4f0b06e9c24730a7a831b7aca417e2 +#: ../../../usage.md:267 msgid "模型配置" msgstr "Model Configuration" -#: ../../../usage.md:102 ecf278a0a851496fae2e49c436e59368 +#: ../../../usage.md:269 msgid "如果在启动训练时要加载模型 `checkpoint`，可进行如下相关配置：" msgstr "" "If you want to load a model checkpoint when starting the training, you " "can configure it as follows:" -#: ../../../usage.md:115 38244aba74294067a4019d0777621746 +#: ../../../usage.md:282 msgid "注意：" msgstr "Note:" -#: ../../../usage.md:116 19d1eb0a797f4bd9a702a00e525d7753 -msgid "`load_model_only_folder`与`load_ckpt_folder`不能同时设置" -msgstr "" -"`load_model_only_folder` and `load_ckpt_folder` cannot be set at the same" -" time." - -#: ../../../usage.md:117 3ea27a1f6be044a3959890be69311b24 +#: ../../../usage.md:283 msgid "路径若以 `local:` 为前缀，则存储在本地文件系统；若以 `boto3:` 为前缀，则存储在远程 oss 上" msgstr "" "If the path starts with `local:`, it means the file is stored in the " "local file system. If it starts with `boto3:`, it means the file is " "stored in the remote OSS." -#: ../../../usage.md:119 1d6381b4cfff41d8bdd5347e8a135869 +#: ../../../usage.md:285 msgid "模型相关关键参数配置如下所示：" msgstr "The configuration for the model is as follows:" -#: ../../../usage.md:143 1026791c9f054576857ef1930db6b167 +#: ../../../usage.md:309 msgid "注意：用户可自定义模型类型名和模型结构，并配置相对应的模型参数。通过`utils/registry.py`下的`MODEL_INITIALIZER`对象进行模型初始化函数接口注册，在训练主函数`train.py`中初始化模型时，可通过`model_type`配置获取指定的模型初始化接口函数。" msgstr "" "Note: Users can customize the model type name and model structure, and " @@ -251,7 +250,7 @@ msgstr "" "interface function can be obtained through the `model_type` " "configuration." -#: ../../../usage.md:145 34823bcbe7754190bc9747758c1aad0c +#: ../../../usage.md:311 msgid "" "*如果基于 InternLM 7B继续训练，可以参考 " "[ModelZoo](https://github.com/InternLM/InternLM/tree/main#model-zoo) 中 " @@ -261,21 +260,21 @@ msgstr "" "OpenXLab [ModelZoo](https://github.com/InternLM/InternLM/tree/main#model-" "zoo) to download weights*." -#: ../../../usage.md:147 4cabc928f8884cd38a6bb683b3bfade3 +#: ../../../usage.md:313 msgid "并行配置" msgstr "Parallel Configuration" -#: ../../../usage.md:149 f97ade07340340959345e73567bae793 +#: ../../../usage.md:315 msgid "训练并行配置样例如下：" msgstr "Training parallel configuration example:" -#: ../../../usage.md:158 87fb5a4e4a4047ee8a9b8bb43915636d +#: ../../../usage.md:324 msgid "zero1：zero 并行策略，分如下三种情况，默认值为 -1" msgstr "" "zero1: zero parallel strategy, divided into the following three cases, " "default value is -1" -#: ../../../usage.md:159 58dc08e2c52e4aaba99b4fbb6cf2e8b4 +#: ../../../usage.md:325 #, fuzzy msgid "当`zero1 <= 0`，则 zero1 进程组的大小等于数据并行进程组的大小，因此优化器状态参数将在数据并行范围内分配" msgstr "" @@ -283,57 +282,57 @@ msgstr "" "size of the data parallel process group, so the optimizer state " "parameters will be split within the data parallel range." -#: ../../../usage.md:160 67e2ebd795d840b29fd1d684a068e90d +#: ../../../usage.md:326 #, fuzzy msgid "当`zero1 == 1`，则不使用 zero1 ，所有数据并行组保留完整的优化器状态参数" msgstr "" -"When `zero1 == 1`, zero1 is not used, and all data parallel groups retain " -"the complete optimizer state parameters." +"When `zero1 == 1`, zero1 is not used, and all data parallel groups retain" +" the complete optimizer state parameters." -#: ../../../usage.md:161 7caedfc943514b9b83090b858ef6d163 +#: ../../../usage.md:327 #, fuzzy msgid "当`zero1 > 1`且`zero1 <= data_parallel_world_size`，则 zero1 进程组是数据并行进程组的子集" msgstr "" -"When `zero1 > 1` and `zero1 <= data_parallel_world_size`, the zero1 process" -" group is a subset of the data parallel process group." +"When `zero1 > 1` and `zero1 <= data_parallel_world_size`, the zero1 " +"process group is a subset of the data parallel process group." -#: ../../../usage.md:162 b38d3a1f72d543c6a44728fb6babea6b +#: ../../../usage.md:328 msgid "tensor：张量并行大小，通常是每个节点的 GPU 数量，默认值为 1" msgstr "" "tensor: tensor parallel size, usually the number of GPUs per node, " "default is 1" -#: ../../../usage.md:163 237ac76df68f4a999396dad37c5495c3 +#: ../../../usage.md:329 msgid "pipeline：流水线并行策略" msgstr "pipeline: pipeline parallel strategy" -#: ../../../usage.md:164 c8c38f6ab2ea432eb9ebbb62618ca33e +#: ../../../usage.md:330 msgid "size：流水线并行大小，默认值为 1" msgstr "size: pipeline parallel size, the default value is 1" -#: ../../../usage.md:165 b9158818e72e49acbdd52ad317cb80df +#: ../../../usage.md:331 msgid "interleaved_overlap：bool 类型，交错式调度时，开启或关闭通信优化，默认值为关闭" msgstr "" "interleaved_overlap: bool type, when interleaved scheduling, enable or " "disable communication optimization, the default value is False" -#: ../../../usage.md:166 28e4d48661ff4f80aff788fdda604433 +#: ../../../usage.md:332 msgid "sequence_parallel：是否开启序列化并行，默认值为 False" msgstr "" "sequence_parallel: Whether to enable sequence parallelism, the default " "value is False" -#: ../../../usage.md:168 27528ab826824d2280506460e1f2f7bd +#: ../../../usage.md:334 msgid "注意：`数据并行大小 = 总的 GPU 数目 / 流水线并行大小 / 张量并行大小`" msgstr "" "Note: `Data parallel size = Total number of GPUs / Pipeline parallel size" " / Tensor parallel size`" -#: ../../../usage.md:170 5a7af23cec604f1d9096a5ab81993c87 +#: ../../../usage.md:336 msgid "启动训练" msgstr "Start Training" -#: ../../../usage.md:172 795e51542ed84cea83b63c5233bb88bc +#: ../../../usage.md:338 msgid "完成了以上数据集准备和相关训练配置后，可启动 Demo 训练。接下来分别以 slurm 和 torch 环境为例，介绍训练启动方式。" msgstr "" "After completing the data preparation and relevant training " @@ -341,25 +340,30 @@ msgstr "" "following examples demonstrate how to start the training in both slurm " "and torch environments." -#: ../../../usage.md:174 96402cbe443044c0a0a1695c9847140b +#: ../../../usage.md:340 msgid "若在 slurm 上启动分布式运行环境，多节点 16 卡的运行命令如下所示：" msgstr "" "If you want to start distributed training on slurm with 16 GPUs across " "multiple nodes, use the following command:" -#: ../../../usage.md:179 c569e60401a6471eb9af2473acc4d5a6 +#: ../../../usage.md:345 msgid "若在 torch 上启动分布式运行环境，单节点 8 卡的运行命令如下所示：" msgstr "" "If you want to start distributed training on torch with 8 GPUs on a " "single node, use the following command:" -#: ../../../usage.md:184 a045a060d0734aab9d894aed553cef34 +#: ../../../usage.md:350 msgid "运行结果" msgstr "Training Results" -#: ../../../usage.md:186 c68e8dfa259647c7a6e6e0c0446b0b18 +#: ../../../usage.md:352 msgid "以 slurm 上单机 8 卡的 Demo 训练配置为例，训练结果日志展示如下：" msgstr "" "Taking the configuration of the demo training on a single machine with 8 " "GPUs on slurm as an example, the training result log is shown below:" +#~ msgid "`load_model_only_folder`与`load_ckpt_folder`不能同时设置" +#~ msgstr "" +#~ "`load_model_only_folder` and `load_ckpt_folder` " +#~ "cannot be set at the same time." + diff --git a/doc/en/usage.md b/doc/en/usage.md index d115fb1..864ead6 100644 --- a/doc/en/usage.md +++ b/doc/en/usage.md @@ -74,7 +74,173 @@ It is recommended that users refer to alpaca_tokenizer.py to write new scripts t ### Training Configuration -Taking the configuration file `configs/7B_sft.py` for the 7B demo as an example, let's discuss the data, model, and parallel configurations required to start a model training. +Taking the configuration file `configs/7B_sft.py` for the 7B demo as an example, let's discuss the data, model, parallel and monitoring configurations required to start a model training. +```python +JOB_NAME = "7b_train" +DO_ALERT = False + +SEQ_LEN = 2048 +HIDDEN_SIZE = 4096 +NUM_ATTENTION_HEAD = 32 +MLP_RATIO = 8 / 3 +NUM_LAYER = 32 +VOCAB_SIZE = 103168 + +MODEL_ONLY_FOLDER = "local:llm_ckpts/xxxx" +# Ckpt folder format: +# fs: 'local:/mnt/nfs/XXX' +SAVE_CKPT_FOLDER = "local:llm_ckpts" +LOAD_CKPT_FOLDER = "local:llm_ckpts/49" + +# boto3 Ckpt folder format: +# import os +# BOTO3_IP = os.environ["BOTO3_IP"] # boto3 bucket endpoint +# SAVE_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm" +# LOAD_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm/snapshot/1/" +CHECKPOINT_EVERY = 50 +ckpt = dict( + enable_save_ckpt=False, # enable ckpt save. + save_ckpt_folder=SAVE_CKPT_FOLDER, # Path to save training ckpt. + # load_ckpt_folder= dict(path=MODEL_ONLY_FOLDER, content=["model"], ckpt_type="normal"), + load_ckpt_folder="local:llm_ckpts/", + # 'load_ckpt_info' setting guide: + # 1. the 'path' indicate ckpt path, + # 2. the 'content‘ means what states will be loaded, support: "model", "sampler", "optimizer", "scheduler", "all" + # 3. the ’ckpt_type‘ means the type of checkpoint to be loaded, now only 'normal' type is supported. + load_ckpt_info=dict(path=MODEL_ONLY_FOLDER, content=("model",), ckpt_type="internlm"), + checkpoint_every=CHECKPOINT_EVERY, + async_upload=True, # async ckpt upload. (only work for boto3 ckpt) + async_upload_tmp_folder="/dev/shm/internlm_tmp_ckpt/", # path for temporarily files during asynchronous upload. + oss_snapshot_freq=int(CHECKPOINT_EVERY / 2), # snapshot ckpt save frequency. +) + +TRAIN_FOLDER = "/path/to/dataset" +VALID_FOLDER = "/path/to/dataset" +data = dict( + seq_len=SEQ_LEN, + # micro_num means the number of micro_batch contained in one gradient update + micro_num=4, + # packed_length = micro_bsz * SEQ_LEN + micro_bsz=2, + # defaults to the value of micro_num + valid_micro_num=4, + # defaults to 0, means disable evaluate + valid_every=50, + pack_sample_into_one=False, + total_steps=50000, + skip_batches="", + rampup_batch_size="", + # Datasets with less than 50 rows will be discarded + min_length=50, + # train_folder=TRAIN_FOLDER, + # valid_folder=VALID_FOLDER, + empty_cache_and_diag_interval=10, + diag_outlier_ratio=1.1, +) + +grad_scaler = dict( + fp16=dict( + # the initial loss scale, defaults to 2**16 + initial_scale=2**16, + # the minimum loss scale, defaults to None + min_scale=1, + # the number of steps to increase loss scale when no overflow occurs + growth_interval=1000, + ), + # the multiplication factor for increasing loss scale, defaults to 2 + growth_factor=2, + # the multiplication factor for decreasing loss scale, defaults to 0.5 + backoff_factor=0.5, + # the maximum loss scale, defaults to None + max_scale=2**24, + # the number of overflows before decreasing loss scale, defaults to 2 + hysteresis=2, +) + +hybrid_zero_optimizer = dict( + # Enable low_level_optimzer overlap_communication + overlap_sync_grad=True, + overlap_sync_param=True, + # bucket size for nccl communication params + reduce_bucket_size=512 * 1024 * 1024, + # grad clipping + clip_grad_norm=1.0, +) + +loss = dict( + label_smoothing=0, +) + +adam = dict( + lr=1e-4, + adam_beta1=0.9, + adam_beta2=0.95, + adam_beta2_c=0, + adam_eps=1e-8, + weight_decay=0.01, +) + +lr_scheduler = dict( + total_steps=data["total_steps"], + init_steps=0, # optimizer_warmup_step + warmup_ratio=0.01, + eta_min=1e-5, + last_epoch=-1, +) + +beta2_scheduler = dict( + init_beta2=adam["adam_beta2"], + c=adam["adam_beta2_c"], + cur_iter=-1, +) + +model = dict( + checkpoint=False, # The proportion of layers for activation aheckpointing, the optional value are True/False/[0-1] + num_attention_heads=NUM_ATTENTION_HEAD, + embed_split_hidden=True, + vocab_size=VOCAB_SIZE, + embed_grad_scale=1, + parallel_output=True, + hidden_size=HIDDEN_SIZE, + num_layers=NUM_LAYER, + mlp_ratio=MLP_RATIO, + apply_post_layer_norm=False, + dtype="torch.float16", # Support: "torch.float16", "torch.half", "torch.bfloat16", "torch.float32", "torch.tf32" + norm_type="rmsnorm", + layer_norm_epsilon=1e-5, + use_flash_attn=True, + num_chunks=1, # if num_chunks > 1, interleaved pipeline scheduler is used. +) +""" +zero1 parallel: + 1. if zero1 <= 0, The size of the zero process group is equal to the size of the dp process group, + so parameters will be divided within the range of dp. + 2. if zero1 == 1, zero is not used, and all dp groups retain the full amount of model parameters. + 3. zero1 > 1 and zero1 <= dp world size, the world size of zero is a subset of dp world size. + For smaller models, it is usually a better choice to split the parameters within nodes with a setting <= 8. +pipeline parallel (dict): + 1. size: int, the size of pipeline parallel. + 2. interleaved_overlap: bool, enable/disable communication overlap when using interleaved pipeline scheduler. +tensor parallel: tensor parallel size, usually the number of GPUs per node. +""" +parallel = dict( + zero1=8, + pipeline=dict(size=1, interleaved_overlap=True), + sequence_parallel=False, +) + +cudnn_deterministic = False +cudnn_benchmark = False + +monitor = dict( + # feishu alert configs + alert=dict( + enable_feishu_alert=DO_ALERT, + feishu_alert_address=None, # feishu webhook to send alert message + light_monitor_address=None, # light_monitor address to send heartbeat + ), +) +``` #### Data Configuration Here are the key parameters and their explanations for data configuration: diff --git a/doc/usage.md b/doc/usage.md index 1b98c10..82c20e0 100644 --- a/doc/usage.md +++ b/doc/usage.md @@ -66,7 +66,174 @@ python tools/alpaca_tokenizer.py /path/to/alpaca_dataset /path/to/output_dataset ### 训练配置 -以 7B Demo 的配置文件`configs/7B_sft.py`为例，介绍启动一个模型训练所需要进行的数据、模型和并行等相关的配置。 +以 7B Demo 的配置文件`configs/7B_sft.py`为例： +```python +JOB_NAME = "7b_train" +DO_ALERT = False + +SEQ_LEN = 2048 +HIDDEN_SIZE = 4096 +NUM_ATTENTION_HEAD = 32 +MLP_RATIO = 8 / 3 +NUM_LAYER = 32 +VOCAB_SIZE = 103168 + +MODEL_ONLY_FOLDER = "local:llm_ckpts/xxxx" +# Ckpt folder format: +# fs: 'local:/mnt/nfs/XXX' +SAVE_CKPT_FOLDER = "local:llm_ckpts" +LOAD_CKPT_FOLDER = "local:llm_ckpts/49" + +# boto3 Ckpt folder format: +# import os +# BOTO3_IP = os.environ["BOTO3_IP"] # boto3 bucket endpoint +# SAVE_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm" +# LOAD_CKPT_FOLDER = f"boto3:s3://model_weights.{BOTO3_IP}/internlm/snapshot/1/" +CHECKPOINT_EVERY = 50 +ckpt = dict( + enable_save_ckpt=False, # enable ckpt save. + save_ckpt_folder=SAVE_CKPT_FOLDER, # Path to save training ckpt. + # load_ckpt_folder= dict(path=MODEL_ONLY_FOLDER, content=["model"], ckpt_type="normal"), + load_ckpt_folder="local:llm_ckpts/", + # 'load_ckpt_info' setting guide: + # 1. the 'path' indicate ckpt path, + # 2. the 'content‘ means what states will be loaded, support: "model", "sampler", "optimizer", "scheduler", "all" + # 3. the ’ckpt_type‘ means the type of checkpoint to be loaded, now only 'normal' type is supported. + load_ckpt_info=dict(path=MODEL_ONLY_FOLDER, content=("model",), ckpt_type="internlm"), + checkpoint_every=CHECKPOINT_EVERY, + async_upload=True, # async ckpt upload. (only work for boto3 ckpt) + async_upload_tmp_folder="/dev/shm/internlm_tmp_ckpt/", # path for temporarily files during asynchronous upload. + oss_snapshot_freq=int(CHECKPOINT_EVERY / 2), # snapshot ckpt save frequency. +) + +TRAIN_FOLDER = "/path/to/dataset" +VALID_FOLDER = "/path/to/dataset" +data = dict( + seq_len=SEQ_LEN, + # micro_num means the number of micro_batch contained in one gradient update + micro_num=4, + # packed_length = micro_bsz * SEQ_LEN + micro_bsz=2, + # defaults to the value of micro_num + valid_micro_num=4, + # defaults to 0, means disable evaluate + valid_every=50, + pack_sample_into_one=False, + total_steps=50000, + skip_batches="", + rampup_batch_size="", + # Datasets with less than 50 rows will be discarded + min_length=50, + # train_folder=TRAIN_FOLDER, + # valid_folder=VALID_FOLDER, + empty_cache_and_diag_interval=10, + diag_outlier_ratio=1.1, +) + +grad_scaler = dict( + fp16=dict( + # the initial loss scale, defaults to 2**16 + initial_scale=2**16, + # the minimum loss scale, defaults to None + min_scale=1, + # the number of steps to increase loss scale when no overflow occurs + growth_interval=1000, + ), + # the multiplication factor for increasing loss scale, defaults to 2 + growth_factor=2, + # the multiplication factor for decreasing loss scale, defaults to 0.5 + backoff_factor=0.5, + # the maximum loss scale, defaults to None + max_scale=2**24, + # the number of overflows before decreasing loss scale, defaults to 2 + hysteresis=2, +) + +hybrid_zero_optimizer = dict( + # Enable low_level_optimzer overlap_communication + overlap_sync_grad=True, + overlap_sync_param=True, + # bucket size for nccl communication params + reduce_bucket_size=512 * 1024 * 1024, + # grad clipping + clip_grad_norm=1.0, +) + +loss = dict( + label_smoothing=0, +) + +adam = dict( + lr=1e-4, + adam_beta1=0.9, + adam_beta2=0.95, + adam_beta2_c=0, + adam_eps=1e-8, + weight_decay=0.01, +) + +lr_scheduler = dict( + total_steps=data["total_steps"], + init_steps=0, # optimizer_warmup_step + warmup_ratio=0.01, + eta_min=1e-5, + last_epoch=-1, +) + +beta2_scheduler = dict( + init_beta2=adam["adam_beta2"], + c=adam["adam_beta2_c"], + cur_iter=-1, +) + +model = dict( + checkpoint=False, # The proportion of layers for activation aheckpointing, the optional value are True/False/[0-1] + num_attention_heads=NUM_ATTENTION_HEAD, + embed_split_hidden=True, + vocab_size=VOCAB_SIZE, + embed_grad_scale=1, + parallel_output=True, + hidden_size=HIDDEN_SIZE, + num_layers=NUM_LAYER, + mlp_ratio=MLP_RATIO, + apply_post_layer_norm=False, + dtype="torch.float16", # Support: "torch.float16", "torch.half", "torch.bfloat16", "torch.float32", "torch.tf32" + norm_type="rmsnorm", + layer_norm_epsilon=1e-5, + use_flash_attn=True, + num_chunks=1, # if num_chunks > 1, interleaved pipeline scheduler is used. +) +""" +zero1 parallel: + 1. if zero1 <= 0, The size of the zero process group is equal to the size of the dp process group, + so parameters will be divided within the range of dp. + 2. if zero1 == 1, zero is not used, and all dp groups retain the full amount of model parameters. + 3. zero1 > 1 and zero1 <= dp world size, the world size of zero is a subset of dp world size. + For smaller models, it is usually a better choice to split the parameters within nodes with a setting <= 8. +pipeline parallel (dict): + 1. size: int, the size of pipeline parallel. + 2. interleaved_overlap: bool, enable/disable communication overlap when using interleaved pipeline scheduler. +tensor parallel: tensor parallel size, usually the number of GPUs per node. +""" +parallel = dict( + zero1=8, + pipeline=dict(size=1, interleaved_overlap=True), + sequence_parallel=False, +) + +cudnn_deterministic = False +cudnn_benchmark = False + +monitor = dict( + # feishu alert configs + alert=dict( + enable_feishu_alert=DO_ALERT, + feishu_alert_address=None, # feishu webhook to send alert message + light_monitor_address=None, # light_monitor address to send heartbeat + ), +) +``` +接下来将详细介绍启动一个模型训练所需要进行的数据、模型、并行和监控等相关的配置。 #### 数据配置 数据相关的关键参数配置及释义如下所示： diff --git a/internlm/utils/model_checkpoint.py b/internlm/utils/model_checkpoint.py index b6aab02..dad2fc6 100644 --- a/internlm/utils/model_checkpoint.py +++ b/internlm/utils/model_checkpoint.py @@ -447,8 +447,8 @@ class CheckpointManager: Args: ckpt_config (dict): model checkpoint config. - model (nn.module): model obj - optimizer (object): optimzier obj. + model (nn.module): model obj. + optimizer (object): optimizer obj. lr_scheduler (object): lr_scheduler obj. model_config (dict): model config. """ @@ -712,7 +712,6 @@ now step_count is {train_state.step_count}", return dict(path=latest_ckpt, content=("all",), ckpt_type="internlm") def try_resume_training(self, train_state: TrainState, current_time=""): - if self.load_ckpt_info is None or self.load_ckpt_info["path"] is None: if gpc.is_rank_for_log(): logger.info(