mirror of https://github.com/InternLM/InternLM
docs(doc/code-docs): add figure for training docs (#307)
* add training image for docs * docs(doc/code-docs): add training img for en doc * docs(doc/code-docs): fix en docs for initialize * docs(doc/code-docs): update conf file for readthedocs * docs(doc/code-docs): fix typos * docs(doc/code-docs): fix typos for reathedocs * docs(doc/code-docs): minor typo fix for reathedocs * docs(doc/code-docs): fix readthedocs conf file * docs(doc/code-docs): update training image * docs(doc/code-docs): fix typos * docs(doc/code-docs): update training image * docs(doc/code-docs): move training image to section initialize * docs(doc/code-docs): fix lint * add badge about reathedocs statuspull/312/head
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@ -16,6 +16,7 @@
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[](./LICENSE)
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[](https://github.com/internLM/OpenCompass/)
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[](https://internlm.readthedocs.io/zh_CN/latest/?badge=latest)
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[📘使用法](./doc/en/usage.md) |
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[🛠️インストール](./doc/en/install.md) |
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@ -16,6 +16,7 @@
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[](https://github.com/open-mmlab/mmdetection/blob/main/LICENSE)
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[](https://github.com/internLM/OpenCompass/)
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[](https://internlm.readthedocs.io/zh_CN/latest/?badge=latest)
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[📘使用文档](./doc/usage.md) |
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[🛠️安装教程](./doc/install.md) |
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@ -16,6 +16,7 @@
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[](./LICENSE)
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[](https://github.com/internLM/OpenCompass/)
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[](https://internlm.readthedocs.io/zh_CN/latest/?badge=latest)
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[📘Usage](./doc/en/usage.md) |
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[🛠️Installation](./doc/en/install.md) |
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@ -8,7 +8,7 @@ msgid ""
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msgstr ""
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"Project-Id-Version: InternLM \n"
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"Report-Msgid-Bugs-To: \n"
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"POT-Creation-Date: 2023-09-07 10:56+0800\n"
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"POT-Creation-Date: 2023-09-13 17:07+0800\n"
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"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
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"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
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"Language: en\n"
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@ -19,30 +19,33 @@ msgstr ""
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"Content-Transfer-Encoding: 8bit\n"
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"Generated-By: Babel 2.12.1\n"
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#: ../../source/checkpoint.rst:2 09c8645fba264cdf9a80c4b62c2bb4d1
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#: ../../source/checkpoint.rst:2
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msgid "模型保存"
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msgstr "Model Checkpointing"
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#: ../../source/checkpoint.rst:4 8b158d34631045b1afdb4fb0169b3c71
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#: ../../source/checkpoint.rst:4
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msgid ""
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"InternLM 使用 ``internlm.utils.model_checkpoint.CheckpointManager`` "
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"来管理模型保存。 其中，可以 使用 ``CheckpointManager.try_save_checkpoint(train_state)`` "
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"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。"
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"来管理模型保存。其中，可以使用 ``CheckpointManager.try_save_checkpoint(train_state)`` "
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"来保存指定 step 的模型状态。"
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msgstr ""
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"InternLM uses ``internlm.utils.model_checkpoint.CheckpointManager`` to manage model checkpointing. In the implementation, "
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"we use ``CheckpointManager.try_save_checkpoint(train_state)`` to checkpoint training states at specific steps. InternLM supports "
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"automatic loading of latest ckpt at startup and automatic model checkpointing at signal quit."
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"InternLM uses ``internlm.utils.model_checkpoint.CheckpointManager`` to "
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"manage model checkpointing. In the implementation, we use "
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"``CheckpointManager.try_save_checkpoint(train_state)`` to checkpoint "
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"training states at specific steps. "
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#: ../../source/checkpoint.rst:8 a023b5a6d15749bfaa51cf2da194bda1
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#: ../../source/checkpoint.rst:6
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msgid "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。"
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msgstr "InternLM supports automatic loading of latest ckpt at startup and automatic model checkpointing at signal quit. "
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#: ../../source/checkpoint.rst:9
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msgid "Checkpointing"
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msgstr ""
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#: 938575c699d1426c87e0b3f589a85d50
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#: internlm.utils.model_checkpoint.CheckpointManager:1 of
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msgid "StorageManagerContext"
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msgstr ""
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#: 754d6881cd034c5ebaab0f3362dd14c2
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#: internlm.utils.model_checkpoint.CheckpointManager.quit_signal_handler:1 of
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msgid ""
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"Exit signal detection function, if we write the exit step in the "
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@ -51,34 +54,27 @@ msgid ""
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"quit."
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msgstr ""
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#: 2169f9fb4a8b40bc9bf6093894fc7a5e 6a55d2b2b24a44c8b78b40f19f4d950b
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#: internlm.utils.model_checkpoint.CheckpointManager.quit_signal_handler
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#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training of
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#: internlm.utils.model_checkpoint.CheckpointManager.quit_signal_handler of
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msgid "参数"
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msgstr ""
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#: 360a89b1591e4627ac432f4d75050354
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#: internlm.utils.model_checkpoint.CheckpointManager.quit_signal_handler of
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msgid "返回"
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msgstr ""
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#: 2426832f4a8a4c5481be1c940e0e7b50
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#: internlm.utils.model_checkpoint.CheckpointManager.quit_signal_handler:9 of
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msgid "whether to quit."
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msgstr ""
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#: 5f6842c261544a3c89f32d981b3ad755
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#: internlm.utils.model_checkpoint.CheckpointManager.quit_signal_handler of
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msgid "返回类型"
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msgstr ""
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#: 1392da84b6e645bcb8dab605e1231fdc
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#: internlm.utils.model_checkpoint.CheckpointManager.wait_async_upload_finish:1
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#: of
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msgid "wait for all checkpoint uploads to be completed"
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msgstr ""
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#: d1774593e9c94608b49b10504bfbc38b
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#: internlm.utils.model_checkpoint.CheckpointManager.query_latest_snapshot_step_boto3:1
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#: of
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msgid ""
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@ -86,38 +82,25 @@ msgid ""
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"found, None will return."
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msgstr ""
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#: a3abbbd2bd574872892d908ab248e804
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#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training:1 of
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msgid "Attempt to restore the training state of the last ckpt."
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msgstr ""
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#: de021d1eb6d54955a2850c11c0191710
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#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training:3 of
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msgid "lr_scheduler object."
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msgstr ""
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#: 20be15854f2e420a9d96c86b5869bfa6
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#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training:5 of
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msgid "optimizer object."
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msgstr ""
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#: 68f69086c5054acc8aca15c8a764acc5
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#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training:7 of
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msgid "learning rate."
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msgstr ""
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#: 5d34d34a972d4abeab4bda3e49ee157b
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#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training:9 of
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msgid "traing states."
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msgstr ""
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#: 82ebb67afaa748ecabc4cef598d7fc30
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#: internlm.utils.model_checkpoint.CheckpointManager.try_resume_training:11 of
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msgid "traning dataloader object"
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msgstr ""
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#: 0c95dfcd712749279daca78166bb4326
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#: internlm.utils.model_checkpoint.CheckpointManager.save_checkpoint:1 of
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msgid "Save checkpoint to the given folder path."
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msgstr ""
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#~ msgid "Attempt to restore the training state of the last ckpt."
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#~ msgstr ""
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#~ msgid "lr_scheduler object."
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#~ msgstr ""
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#~ msgid "optimizer object."
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#~ msgstr ""
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#~ msgid "learning rate."
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#~ msgstr ""
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#~ msgid "traing states."
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#~ msgstr ""
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#~ msgid "traning dataloader object"
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#~ msgstr ""
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@ -37,8 +37,8 @@ msgstr "Start Training"
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#: ../../source/example/30B_demo.rst:166 24974384d5ab42e68266aeb67ae222ce
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msgid "完成以上训练配置后，可启动模型训练，以在 ``slurm`` 平台上为例，启动两节点 16GPU 的训练命令如下所示："
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msgstr "After completing the data preparation and relevant training configurations, you can start the demo training.
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The following example shows how to start distributed training in ``slurm`` environments with 16 GPUs."
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msgstr "After completing the data preparation and relevant training configurations, you can start the demo training. "
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"The following example shows how to start distributed training in ``slurm`` environments with 16 GPUs."
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#: ../../source/example/30B_demo.rst:173 948ac71ed53848f9bad07f69d956c4bb
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msgid "训练结果"
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@ -37,8 +37,8 @@ msgstr "Start Training"
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#: ../../source/example/7B_demo.rst:164 9e7a864ae2e14d05b0681f16792e5278
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msgid "完成以上训练配置后，可启动模型训练，以在 ``slurm`` 平台上为例，启动单节点 8GPU 的训练命令如下所示："
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msgstr "After completing the data preparation and relevant training configurations, you can start the demo training.
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The following example shows how to start distributed training in ``slurm`` environments with 8 GPUs."
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msgstr "After completing the data preparation and relevant training configurations, you can start the demo training. "
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"The following example shows how to start distributed training in ``slurm`` environments with 8 GPUs."
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#: ../../source/example/7B_demo.rst:171 fdd053efb1854d46aabf6c0f279fe7fc
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msgid "训练结果"
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@ -8,7 +8,7 @@ msgid ""
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msgstr ""
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"Project-Id-Version: InternLM \n"
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"Report-Msgid-Bugs-To: \n"
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"POT-Creation-Date: 2023-09-08 15:32+0800\n"
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"POT-Creation-Date: 2023-09-14 12:23+0800\n"
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"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
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"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
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"Language: zh_CN\n"
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msgid "训练构建"
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msgstr "Training Setup"
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#: ../../source/initialize.rst:7
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#: ../../source/initialize.rst:4
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msgid "InternLM 的训练流程可以归纳为两个步骤："
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msgstr "The training process of InternLM can be summarized into two steps: "
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#: ../../source/initialize.rst:6
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msgid "初始化"
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msgstr "Initialization"
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#: ../../source/initialize.rst:8
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msgid "初始化模型、优化器、数据加载器、Trainer，生成不同种类的进程组，为混合并行的迭代训练做准备。"
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msgstr ""
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"Initialize model, optimizer, dataloader, trainer, and create different "
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"types of process groups to prepare for iterative steps of hybrid parallel training. "
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#: ../../source/initialize.rst:9
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msgid "初始化Logger、Checkpoint管理器、Monitor管理器、Profiler，对迭代训练的过程观察、预警、记录。"
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msgstr ""
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"Initialize logger, checkpoint manager, monitor manager, and profiler to "
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"watch, alert, and record the iterative training steps. "
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#: ../../source/initialize.rst:11
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msgid "迭代训练"
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msgstr "Iterative training steps"
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#: ../../source/initialize.rst:13
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msgid "根据配置文件定义的张量并行、流水线并行、数据并行的大小，加载训练引擎和调度器进行混合并行训练。"
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msgstr ""
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"Load the training engine and scheduler for hybrid parallel training "
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"according to the configuration such as tensor parallel size, pipeline "
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"parallel size, and data parallel size. "
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#: ../../source/initialize.rst:14
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msgid "在迭代训练中，调用 Trainer API 进行梯度置零，前向传播计算损失并反向传播，参数更新。"
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msgstr ""
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"In iterative training steps, the Trainer API is called to perform zero "
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"gradients, forward-loss-backward, and parameter update."
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#: ../../source/initialize.rst:20
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msgid "InternLM训练流程图"
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msgstr "InternLM training process"
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#: ../../source/initialize.rst:25
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msgid "命令行参数解析"
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msgstr "Argument Parsing"
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#: ../../source/initialize.rst:9
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#, fuzzy
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#: ../../source/initialize.rst:27
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msgid ""
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"InternLM 使用 `argparse <https://docs.python.org/3/library/argparse.html>`_"
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" 库来向InternLM运行时提供命令行参数配置。用户可使用 "
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"``internlm.initialize.get_default_parser()`` 来获取 InternLM "
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"的默认解析器，其中包含一些内置参数，用户可以向此解析器添加自定义参数。"
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" 库来向InternLM运行时提供命令行参数配置。"
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msgstr ""
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"InternLM uses the `argparse "
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"<https://docs.python.org/3/library/argparse.html>`_ library to supply "
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"commandline configuration to the InternLM runtime. Use "
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"``internlm.initialize.get_default_parser()`` to get InternLM's default "
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"parser with some builtin arguments, users can add custom parameters to "
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"this parser."
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"commandline configuration to the InternLM runtime. "
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#: ../../source/initialize.rst:29
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msgid ""
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"用户可使用 ``internlm.initialize.get_default_parser()`` 来获取 InternLM "
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"的默认解析器，其中包含一些内置参数，用户可以向此解析器添加自定义参数。"
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msgstr ""
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"Use ``internlm.initialize.get_default_parser()`` to get InternLM's "
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"default parser with some builtin arguments, users can add custom "
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"parameters to this parser."
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#: internlm.initialize.launch.get_default_parser:1 of
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msgid ""
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msgid "返回类型"
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msgstr ""
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#: ../../source/initialize.rst:25
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#: ../../source/initialize.rst:45
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msgid "模型初始化"
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msgstr "Model Initialization"
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msgid "The neural network model to be trained or evaluated."
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msgstr ""
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#: ../../source/initialize.rst:29
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#: ../../source/initialize.rst:49
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msgid "InternLM 在配置文件中使用字段 ``model_type`` 和 ``model`` 来控制模型初始化过程。示例模型初始化配置定义如下："
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msgstr ""
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"InternLM uses the field ``model_type`` and ``model`` in the config file "
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"to control model initialization process. An example model initialization "
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"configuratio"
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#: ../../source/initialize.rst:57
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#: ../../source/initialize.rst:77
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msgid "字段 ``model_type`` 指明了要初始化的模型类型"
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msgstr ""
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"The field ``model_type`` specifics the model type has been registered and"
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" to be initialized."
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#: ../../source/initialize.rst:58
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#: ../../source/initialize.rst:78
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msgid "字段 ``model`` 中的参数指定了在模型初始化过程中的参数设置"
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msgstr ""
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"The parameters in field ``model`` specific the configuration settings "
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"during model initialization."
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#: ../../source/initialize.rst:60
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#: ../../source/initialize.rst:80
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msgid ""
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"值得注意的是，用户可以定义新的模型类型，并使用装饰器 ``@MODEL_INITIALIZER.register_module`` "
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"注册模型的初始化函数，其中 ``MODEL_INITIALIZER`` 是类 "
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@ -112,7 +156,7 @@ msgstr ""
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" instantiated object of class ``internlm.util.registry.Registry``, the "
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"example is shown as follows."
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#: ../../source/initialize.rst:72
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#: ../../source/initialize.rst:92
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msgid "优化器初始化"
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msgstr "Optimizer Initialization"
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@ -134,7 +178,7 @@ msgstr ""
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msgid "A tuple of (optimizer, beta2_scheduler, lr_scheduler)."
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msgstr ""
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#: ../../source/initialize.rst:79
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#: ../../source/initialize.rst:99
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msgid "数据加载器初始化"
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msgstr "Dataloader Initialization"
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@ -162,7 +206,7 @@ msgstr ""
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msgid "A tuple of (train_dl, dataset_types)."
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msgstr ""
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#: ../../source/initialize.rst:86
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#: ../../source/initialize.rst:106
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msgid "Trainer 初始化"
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msgstr "Trainer Initialization"
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@ -8,7 +8,7 @@ msgid ""
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msgstr ""
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"Project-Id-Version: InternLM \n"
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"Report-Msgid-Bugs-To: \n"
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"POT-Creation-Date: 2023-09-08 15:32+0800\n"
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"POT-Creation-Date: 2023-09-14 11:05+0800\n"
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"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
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"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
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"Language: en\n"
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@ -32,13 +32,13 @@ msgid ""
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"InternLM 使用 ``internlm.train.initialize_llm_profile()`` "
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"来收集和分析模型训练或推理期间的性能数据，如 CPU/CUDA/memory 等性能数据。这个实现基于 `torch.profiler "
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"<https://pytorch.org/docs/stable/profiler.html>`_ ，输出的性能分析 trace 文件可以使用 "
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"`tensorboard <https://www.tensorflow.org>`_ 进行可视化。"
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"`tensorboard <https://www.tensorflow.org/tensorboard?hl=en>`_ 进行可视化。"
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msgstr ""
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"InternLM uses ``internlm.train.initialize_llm_profile()`` to profile "
|
||||
"performance data, execution time duration and breakdown analysis of step "
|
||||
"time. The implementation is based on `torch.profiler "
|
||||
"<https://pytorch.org/docs/stable/profiler.html>`_ and output tracing "
|
||||
"files can be visualized with `tensorboard <https://www.tensorflow.org>`_."
|
||||
"files can be visualized with `tensorboard <https://www.tensorflow.org/tensorboard?hl=en>`_."
|
||||
|
||||
#: ../../source/profiler.rst:11
|
||||
msgid ""
|
||||
|
|
@ -53,11 +53,15 @@ msgstr ""
|
|||
|
||||
#: ../../source/profiler.rst:13
|
||||
msgid "实际运行生成的 ``Torch Profiler`` 目录结构如下："
|
||||
msgstr "The directory structure of ``Torch Profiler`` generated files is as follows:"
|
||||
msgstr ""
|
||||
"The directory structure of ``Torch Profiler`` generated files is as "
|
||||
"follows:"
|
||||
|
||||
#: ../../source/profiler.rst:22
|
||||
msgid "其中， ``traces`` 可以通过 ``TensorBoard`` 可视化，运行命令"
|
||||
msgstr "Among them, ``traces`` can be visualized through ``TensorBoard`` and run with the command"
|
||||
msgstr ""
|
||||
"Among them, ``traces`` can be visualized through ``TensorBoard`` and run "
|
||||
"with the command"
|
||||
|
||||
#: ../../source/profiler.rst:29
|
||||
msgid ""
|
||||
|
|
@ -66,7 +70,12 @@ msgid ""
|
|||
"tensorboard "
|
||||
"<https://pytorch.org/tutorials/intermediate/tensorboard_profiler_tutorial.html"
|
||||
"#pytorch-profiler-with-tensorboard>`_"
|
||||
msgstr "In the opened ``TensorBoard -> PyTorch Profiler -> Views -> Trace`` page, you can see the timeline of profiled operators and GPU kernels. For more usage, please refer to `torch profiler with tensorboard <https://pytorch.org/tutorials/intermediate/tensorboard_profiler_tutorial.html#pytorch-profiler-with-tensorboard>`_"
|
||||
msgstr ""
|
||||
"In the opened ``TensorBoard -> PyTorch Profiler -> Views -> Trace`` page,"
|
||||
" you can see the timeline of profiled operators and GPU kernels. For more"
|
||||
" usage, please refer to `torch profiler with tensorboard "
|
||||
"<https://pytorch.org/tutorials/intermediate/tensorboard_profiler_tutorial.html"
|
||||
"#pytorch-profiler-with-tensorboard>`_"
|
||||
|
||||
#: internlm.train.training_internlm.initialize_llm_profile:1 of
|
||||
msgid "Initialize and return the profiler context manager instance."
|
||||
|
|
|
|||
|
|
@ -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: 2023-09-14 12:23+0800\n"
|
||||
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
|
||||
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
|
||||
"Language: en\n"
|
||||
|
|
@ -19,109 +19,144 @@ msgstr ""
|
|||
"Content-Transfer-Encoding: 8bit\n"
|
||||
"Generated-By: Babel 2.12.1\n"
|
||||
|
||||
#: ../../source/training.rst:2 6eafa5eb08e040039309a39cdb0f1bfe
|
||||
#: ../../source/training.rst:2
|
||||
msgid "训练 API"
|
||||
msgstr "Training API"
|
||||
|
||||
#: ../../source/training.rst:4 74d81f3d0ca54c839d4e80bd589aedb2
|
||||
#: ../../source/training.rst:4
|
||||
msgid ""
|
||||
"InternLM 的训练 API 由 ``internlm.core.trainer.Trainer`` "
|
||||
"管理。在定义了训练引擎和调度器之后，我们可以调用 Trainer API 来执行模型训练、评估、梯度清零和参数更新等。"
|
||||
msgstr ""
|
||||
"InternLM training API is managed in ``internlm.core.trainer.Trainer``. After defining the "
|
||||
"training engine and runtime scheduler, we can call training API to perform training, evaluation, "
|
||||
"zero gradients and parameter update steps."
|
||||
"InternLM training API is managed in ``internlm.core.trainer.Trainer``. "
|
||||
"After defining the training engine and runtime scheduler, we can call "
|
||||
"training API to perform training, evaluation, zero gradients and "
|
||||
"parameter update steps."
|
||||
|
||||
#: ../../source/training.rst:6 0e0cfddbb2334d3da99d3289edf4161d
|
||||
#: ../../source/training.rst:6
|
||||
msgid "有关详细用法，请参阅 Trainer API 文档和示例。"
|
||||
msgstr "For detailed usage, please refer to Trainer API documentation and examples."
|
||||
msgstr ""
|
||||
"For detailed usage, please refer to Trainer API documentation and "
|
||||
"examples."
|
||||
|
||||
#: 7ea10280a8f1489984cb9994aa08976b internlm.core.trainer.Trainer:1 of
|
||||
#: internlm.core.trainer.Trainer:1 of
|
||||
msgid ""
|
||||
"This is a class tending for easy deployments of users' training and "
|
||||
"evaluation instead of writing their own scripts."
|
||||
msgstr ""
|
||||
|
||||
#: 7969dca55840451193bffd3b071ab3b3 aff576168b59460491bb5da0ce41ea74
|
||||
#: internlm.core.trainer.Trainer internlm.core.trainer.Trainer.execute_schedule
|
||||
#: of
|
||||
msgid "参数"
|
||||
msgstr ""
|
||||
|
||||
#: 59754d3e9ee8452a872bf397c01e0d8c internlm.core.trainer.Trainer:4 of
|
||||
#: internlm.core.trainer.Trainer:4 of
|
||||
msgid "Engine responsible for the process function."
|
||||
msgstr ""
|
||||
|
||||
#: 2d18ff15256e48f98901c7a7e0cbbe35 internlm.core.trainer.Trainer:6 of
|
||||
#: internlm.core.trainer.Trainer:6 of
|
||||
msgid "Runtime schedule. Defaults to None."
|
||||
msgstr ""
|
||||
|
||||
#: 76f4b3c7feba40eca3ee2b32559c53f5 internlm.core.trainer.Trainer.engine:1 of
|
||||
#: internlm.core.trainer.Trainer.engine:1 of
|
||||
msgid ""
|
||||
"Returns the engine that responsible for managing the training and "
|
||||
"evaluation process."
|
||||
msgstr ""
|
||||
|
||||
#: c7eae2d4d06c4ef891e314902d80b7f3 internlm.core.trainer.Trainer.schedule:1 of
|
||||
#: internlm.core.trainer.Trainer.schedule:1 of
|
||||
msgid "Returns the runtime scheduler."
|
||||
msgstr ""
|
||||
|
||||
#: cb495b21b3444881aec83803e92386d9
|
||||
#: internlm.core.trainer.Trainer.uses_pipeline:1 of
|
||||
msgid "Returns whether the pipeline parallel is used or not."
|
||||
msgstr ""
|
||||
|
||||
#: 86b0b631189e46468281a397c5e97350 internlm.core.trainer.Trainer.train:1 of
|
||||
#: internlm.core.trainer.Trainer.train:1 of
|
||||
msgid "Sets the model to training mode."
|
||||
msgstr ""
|
||||
|
||||
#: f997e13120ee4d8b9e45ea6698b3e2a6 internlm.core.trainer.Trainer.eval:1 of
|
||||
#: internlm.core.trainer.Trainer.eval:1 of
|
||||
msgid "Sets the model to evaluation mode."
|
||||
msgstr ""
|
||||
|
||||
#: a8179e50312d47dcbe9de0433a65c2f7 internlm.core.trainer.Trainer.zero_grad:1
|
||||
#: of
|
||||
#: internlm.core.trainer.Trainer.zero_grad:1 of
|
||||
msgid "Sets the gradient of all parameters in the model to zero."
|
||||
msgstr ""
|
||||
|
||||
#: f936136ef9e0452ca439b7c66dc8884b internlm.core.trainer.Trainer.step:1 of
|
||||
#: internlm.core.trainer.Trainer.step:1 of
|
||||
msgid "Executes the parameter update step."
|
||||
msgstr ""
|
||||
|
||||
#: 250e2af89cfd432c84d228f9e03c174c
|
||||
#: internlm.core.trainer.Trainer.execute_schedule:1 of
|
||||
msgid ""
|
||||
"Runs the forward, loss computation, and backward for the model. Returns a"
|
||||
" tuple of (output, label, loss)."
|
||||
msgstr ""
|
||||
|
||||
#: 6ca7de83033b432792eb0d7935ea04da
|
||||
#: internlm.core.trainer.Trainer.execute_schedule:4 of
|
||||
msgid "The data iterator."
|
||||
msgstr ""
|
||||
|
||||
#: 6d3044e75b3149beba3c659e15607b79
|
||||
#: internlm.core.trainer.Trainer.execute_schedule:6 of
|
||||
msgid "Additional keyword arguments."
|
||||
msgstr ""
|
||||
|
||||
#: 99d5a297d6414c30b432acf2566f0d3c
|
||||
#: internlm.core.trainer.Trainer.execute_schedule of
|
||||
msgid "返回"
|
||||
msgstr ""
|
||||
|
||||
#: b625ebf0cf874edba384456d33e740b4
|
||||
#: internlm.core.trainer.Trainer.execute_schedule:8 of
|
||||
msgid "A tuple of (output, label, loss)."
|
||||
msgstr ""
|
||||
|
||||
#: 391cde57d2e2478d8f83a7ad270c2a65
|
||||
#: internlm.core.trainer.Trainer.execute_schedule of
|
||||
msgid "返回类型"
|
||||
msgstr ""
|
||||
|
||||
#: d4c4fb0fbddb499786970509cf0c9e13
|
||||
#: internlm.core.trainer.Trainer.execute_schedule:9 of
|
||||
msgid "Tuple[:class:`torch.Tensor`]"
|
||||
msgstr ""
|
||||
|
||||
#~ msgid "InternLM 的训练流程可以归纳为两个步骤："
|
||||
#~ msgstr "The training process of InternLM can be summarized into two steps: "
|
||||
|
||||
#~ msgid "初始化"
|
||||
#~ msgstr "Initialization"
|
||||
|
||||
#~ msgid "初始化模型、优化器、数据加载器、Trainer，生成不同种类的进程组，为混合并行的迭代训练做准备。"
|
||||
#~ msgstr ""
|
||||
#~ "Initialize model, optimizer, dataloader, "
|
||||
#~ "trainer, and create different types of"
|
||||
#~ " process groups to prepare for "
|
||||
#~ "iterative steps of hybrid parallel "
|
||||
#~ "training. "
|
||||
|
||||
#~ msgid "初始化Logger、Checkpoint管理器、Monitor管理器、Profiler，对迭代训练的过程观察、预警、记录。"
|
||||
#~ msgstr ""
|
||||
#~ "Initialize logger, checkpoint manager, monitor"
|
||||
#~ " manager, and profiler to watch, "
|
||||
#~ "alert, and record the iterative training"
|
||||
#~ " steps. "
|
||||
|
||||
#~ msgid "迭代训练"
|
||||
#~ msgstr "Iterative training steps"
|
||||
|
||||
#~ msgid "根据配置文件定义的张量并行、流水线并行、数据并行的大小，加载训练引擎和调度器进行混合并行训练。"
|
||||
#~ msgstr ""
|
||||
#~ "Load the training engine and scheduler"
|
||||
#~ " for hybrid parallel training according "
|
||||
#~ "to the configuration such as tensor "
|
||||
#~ "parallel size, pipeline parallel size, "
|
||||
#~ "and data parallel size. "
|
||||
|
||||
#~ msgid "在迭代训练中，调用 Trainer API 进行梯度置零，前向传播计算损失并反向传播，参数更新。"
|
||||
#~ msgstr ""
|
||||
#~ "In iterative training steps, the Trainer"
|
||||
#~ " API is called to perform zero "
|
||||
#~ "gradients, forward-loss-backward, and "
|
||||
#~ "parameter update."
|
||||
|
||||
#~ msgid "InternLM训练流程图"
|
||||
#~ msgstr "InternLM training process"
|
||||
|
||||
|
|
|
|||
|
|
@ -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: 2023-09-13 17:07+0800\n"
|
||||
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
|
||||
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\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 +160,81 @@ 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
|
||||
#: ../../../usage.md:70
|
||||
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。"
|
||||
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 "
|
||||
"required to start a model training."
|
||||
|
||||
#: ../../../usage.md:72 247cfe98a7f44c2293aa2e2351f1ea69
|
||||
#: ../../../usage.md:72
|
||||
msgid "数据配置"
|
||||
msgstr "Data Configuration"
|
||||
|
||||
#: ../../../usage.md:73 31327e7dce5848778db5361b3fbded1c
|
||||
#: ../../../usage.md:73
|
||||
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:88
|
||||
msgid ""
|
||||
msgstr ""
|
||||
|
||||
#: ../../../usage.md:88 c5acb028f2694712b2af788a864d5927
|
||||
#: ../../../usage.md:88
|
||||
msgid "pack_into_one"
|
||||
msgstr ""
|
||||
|
||||
#: ../../../usage.md:91 db6b9ce8e8294952845893dd7aad098f
|
||||
#: ../../../usage.md:91
|
||||
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:98
|
||||
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:100
|
||||
msgid "模型配置"
|
||||
msgstr "Model Configuration"
|
||||
|
||||
#: ../../../usage.md:102 ecf278a0a851496fae2e49c436e59368
|
||||
#: ../../../usage.md:102
|
||||
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:115
|
||||
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:116
|
||||
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:118
|
||||
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:142
|
||||
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 +245,7 @@ msgstr ""
|
|||
"interface function can be obtained through the `model_type` "
|
||||
"configuration."
|
||||
|
||||
#: ../../../usage.md:145 34823bcbe7754190bc9747758c1aad0c
|
||||
#: ../../../usage.md:144
|
||||
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 "
|
||||
|
|
@ -261,79 +255,76 @@ msgstr ""
|
|||
"OpenXLab [ModelZoo](https://github.com/InternLM/InternLM/tree/main#model-"
|
||||
"zoo) to download weights*."
|
||||
|
||||
#: ../../../usage.md:147 4cabc928f8884cd38a6bb683b3bfade3
|
||||
#: ../../../usage.md:146
|
||||
msgid "并行配置"
|
||||
msgstr "Parallel Configuration"
|
||||
|
||||
#: ../../../usage.md:149 f97ade07340340959345e73567bae793
|
||||
#: ../../../usage.md:148
|
||||
msgid "训练并行配置样例如下："
|
||||
msgstr "Training parallel configuration example:"
|
||||
|
||||
#: ../../../usage.md:158 87fb5a4e4a4047ee8a9b8bb43915636d
|
||||
#: ../../../usage.md:157
|
||||
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
|
||||
#, fuzzy
|
||||
#: ../../../usage.md:158
|
||||
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 ""
|
||||
"When `zero1 <= 0`, the size of the zero1 process group is equal to the "
|
||||
"size of the data parallel process group, so the optimizer state "
|
||||
"parameters will be split within the data parallel range."
|
||||
|
||||
#: ../../../usage.md:160 67e2ebd795d840b29fd1d684a068e90d
|
||||
#, fuzzy
|
||||
#: ../../../usage.md:159
|
||||
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
|
||||
#, fuzzy
|
||||
#: ../../../usage.md:160
|
||||
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:161
|
||||
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:162
|
||||
msgid "pipeline：流水线并行策略"
|
||||
msgstr "pipeline: pipeline parallel strategy"
|
||||
|
||||
#: ../../../usage.md:164 c8c38f6ab2ea432eb9ebbb62618ca33e
|
||||
#: ../../../usage.md:163
|
||||
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:164
|
||||
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:165
|
||||
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:167
|
||||
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:169
|
||||
msgid "启动训练"
|
||||
msgstr "Start Training"
|
||||
|
||||
#: ../../../usage.md:172 795e51542ed84cea83b63c5233bb88bc
|
||||
#: ../../../usage.md:171
|
||||
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 +332,30 @@ msgstr ""
|
|||
"following examples demonstrate how to start the training in both slurm "
|
||||
"and torch environments."
|
||||
|
||||
#: ../../../usage.md:174 96402cbe443044c0a0a1695c9847140b
|
||||
#: ../../../usage.md:173
|
||||
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:178
|
||||
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:183
|
||||
msgid "运行结果"
|
||||
msgstr "Training Results"
|
||||
|
||||
#: ../../../usage.md:186 c68e8dfa259647c7a6e6e0c0446b0b18
|
||||
#: ../../../usage.md:185
|
||||
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."
|
||||
|
||||
|
|
|
|||
|
|
@ -1,8 +1,9 @@
|
|||
模型保存
|
||||
===================
|
||||
|
||||
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。
|
||||
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。
|
||||
|
||||
Checkpointing
|
||||
-------------
|
||||
|
|
|
|||
|
|
@ -72,14 +72,14 @@ exclude_patterns = []
|
|||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
|
||||
|
||||
html_theme = "sphinx_rtd_theme"
|
||||
html_static_path = ["_static"]
|
||||
html_static_path = []
|
||||
|
||||
# GitHub integration
|
||||
html_context = {
|
||||
"display_github": True,
|
||||
"github_user": "InternLM",
|
||||
"github_repo": "InternLM",
|
||||
"github_version": "master",
|
||||
"github_version": "main",
|
||||
"conf_py_path": "/doc/code-docs/source/",
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,32 @@
|
|||
训练构建
|
||||
==============
|
||||
|
||||
InternLM 的训练流程可以归纳为两个步骤：
|
||||
|
||||
1. 初始化
|
||||
|
||||
* 初始化模型、优化器、数据加载器、Trainer，生成不同种类的进程组，为混合并行的迭代训练做准备。
|
||||
* 初始化Logger、Checkpoint管理器、Monitor管理器、Profiler，对迭代训练的过程观察、预警、记录。
|
||||
|
||||
2. 迭代训练
|
||||
|
||||
* 根据配置文件定义的张量并行、流水线并行、数据并行的大小，加载训练引擎和调度器进行混合并行训练。
|
||||
* 在迭代训练中，调用 Trainer API 进行梯度置零，前向传播计算损失并反向传播，参数更新。
|
||||
|
||||
.. figure:: ../../imgs/hybrid_parallel_training.png
|
||||
:scale: 45%
|
||||
:class: with-border
|
||||
|
||||
InternLM训练流程图
|
||||
|
||||
.. _InternLM-args:
|
||||
|
||||
命令行参数解析
|
||||
----------------
|
||||
|
||||
InternLM 使用 `argparse <https://docs.python.org/3/library/argparse.html>`_ 库来向InternLM运行时提供命令行参数配置。用户可使用 ``internlm.initialize.get_default_parser()`` 来获取 InternLM 的默认解析器，其中包含一些内置参数，用户可以向此解析器添加自定义参数。
|
||||
InternLM 使用 `argparse <https://docs.python.org/3/library/argparse.html>`_ 库来向InternLM运行时提供命令行参数配置。
|
||||
|
||||
用户可使用 ``internlm.initialize.get_default_parser()`` 来获取 InternLM 的默认解析器，其中包含一些内置参数，用户可以向此解析器添加自定义参数。
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
Torch Profiler
|
||||
-----------------
|
||||
|
||||
InternLM 使用 ``internlm.train.initialize_llm_profile()`` 来收集和分析模型训练或推理期间的性能数据，如 CPU/CUDA/memory 等性能数据。这个实现基于 `torch.profiler <https://pytorch.org/docs/stable/profiler.html>`_ ，输出的性能分析 trace 文件可以使用 `tensorboard <https://www.tensorflow.org>`_ 进行可视化。
|
||||
InternLM 使用 ``internlm.train.initialize_llm_profile()`` 来收集和分析模型训练或推理期间的性能数据，如 CPU/CUDA/memory 等性能数据。这个实现基于 `torch.profiler <https://pytorch.org/docs/stable/profiler.html>`_ ，输出的性能分析 trace 文件可以使用 `tensorboard <https://www.tensorflow.org/tensorboard?hl=en>`_ 进行可视化。
|
||||
|
||||
用户如果想使用这个 torch 性能分析工具，需要在启动训练时传递 ``--profiling`` 参数以启用性能分析。完成 torch 性能分析后，用户可以在 ``{JOB_NAME}/{start_time}/traces/rank{}_dp{}_tp{}_pp{}`` 文件夹中看到性能分析结果。
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问&答
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====
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