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update mixed_precision.po

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Wenwen Qu 2023-09-26 17:09:38 +08:00
parent 4f40b43b09
commit 7df4643c89
1 changed files with 18 additions and 6 deletions
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doc/code-docs/locales/en/LC_MESSAGES/mixed_precision.po
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@ -8,7 +8,7 @@ msgid ""
msgstr ""
"Project-Id-Version: InternLM \n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2023-09-26 15:24+0800\n"
"POT-Creation-Date: 2023-09-26 17:04+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language: en\n"
@ -25,11 +25,16 @@ msgstr "Mixed Precision"
#: ../../source/mixed_precision.rst:3
msgid ""
"混合精度是指在模型训练的过程中同时使用16位和32位浮点数类型，是一种在最小化精度损失的前提下加速模型训练的方法。"
"混合精度是指在模型训练的过程中同时使用16位和32位浮点数类型，是一种在最小化精度损失的前提下加速模型训练的方法。 "
"混合精度通过让模型的某些部分使用32位浮点数以保持数值稳定性，并在其余部分利用半精度浮点数加速训练并可以减少内存使用，在评估指标（如准确率）方面仍可以获得同等的训练效果。"
msgstr ""
"Mixed precision refers to using both 16-bit and 32-bit floating-point types to train model, which can accelerate the model training while minimizing the accuracy loss. "
"Mixed precision training uses 32-bit floating-point types in certain parts of the model to maintain numerical stability, and accelerate training and reduce memory usage by using 16-bit floating-point types in other parts. Mixed precision can achieve the same training effect in evaluating indicators such as accuracy."
"Mixed precision refers to using both 16-bit and 32-bit floating-point "
"types to train model, which can accelerate the model training while "
"minimizing the accuracy loss. Mixed precision training uses 32-bit "
"floating-point types in certain parts of the model to maintain numerical "
"stability, and accelerate training and reduce memory usage by using "
"16-bit floating-point types in other parts. Mixed precision can achieve "
"the same training effect in evaluating indicators such as accuracy."
#: internlm.core.naive_amp.NaiveAMPModel:1 of
msgid ""
@ -62,11 +67,18 @@ msgstr ""
#: ../../source/mixed_precision.rst:8
msgid "InternLM默认将模型转换为16位浮点数类型进行训练（在配置文件中可以设置默认类型为其他数据类型）。在使用混合精度时，需要在构建模型时使用"
msgstr "InternLM converts the model to 16-bit floating-point types for model training by default (the default type can be set to other data types in the configuration file). When using mixed precision, it is necessary to use "
msgstr ""
"InternLM converts the model to 16-bit floating-point types for model "
"training by default (the default type can be set to other data types in "
"the configuration file). When using mixed precision, it is necessary to "
"use "
#: ../../source/mixed_precision.rst:14
msgid "将模型的某个子模块设置为32位浮点数类型进行训练，InternLM会在模型训练时自动将数据类型转换成需要的精度。"
msgstr "to set a sub-module of the model to 16-bit floating-point types for training, and InternLM will automatically convert the data type to the required precision during model training."
msgstr ""
"to set a sub-module of the model to 16-bit floating-point types for "
"training, and InternLM will automatically convert the data type to the "
"required precision during model training."
#: ../../source/mixed_precision.rst:16
msgid "例如："