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delete the dtype argument at decorate api #5815

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3 changes: 1 addition & 2 deletions docs/api/paddle/amp/decorate_cn.rst
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Expand Up @@ -3,7 +3,7 @@
decorate
-------------------------------

.. py:function:: paddle.amp.decorate(models, optimizers=None, level='O1', dtype='float16', master_weight=None, save_dtype=None, master_grad=False, excluded_layers=None)
.. py:function:: paddle.amp.decorate(models, optimizers=None, level='O1', master_weight=None, save_dtype=None, master_grad=False, excluded_layers=None)


装饰神经网络参数，来支持动态图模式下执行的算子的自动混合精度策略（AMP）。
Expand All @@ -17,7 +17,6 @@ decorate
- **models** (Layer|list of Layer) - 网络模型。在 ``O2`` 模式下，输入的模型参数将由 float32 转为 float16 或 bfloat16。
- **optimizers** (Optimizer|list of Optimizer，可选) - 优化器，默认值为 None，若传入优化器或由优化器组成的 list 列表，将依据 master_weight 对优化器的 master_weight 属性进行设置。
- **level** (str，可选) - 混合精度训练模式，默认 ``O1`` 模式。
- **dtype** (str，可选) - 混合精度训练数据类型使用 float16 还是 bfloat16，默认为 float16 类型。
- **master_weight** (bool|None，可选) - 是否使用 master weight 策略。支持 master weight 策略的优化器包括 ``adam``、``adamW``、``momentum``，默认值为 None，在 ``O2`` 模式下使用 master weight 策略。
- **save_dtype** (str|None，可选) - 网络存储类型，可为 float16、bfloat16、float32、float64。通过 ``save_dtype`` 可指定通过 ``paddle.save`` 和 ``paddle.jit.save`` 存储的网络参数数据类型。默认为 None，采用现有网络参数类型进行存储。
- **master_grad** (bool, 可选) - 在 ``O2`` 模式下是否使用 float32 类型的权重梯度进行梯度裁剪、权重衰减、权重更新等计算。如果被启用，在反向传播结束后权重的梯度将会是 float32 类型。默认值：False，模型仅保存一份 float16 类型的权重梯度。
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