From 4e577ce104b4d427244e811fed7eabe5729d3b20 Mon Sep 17 00:00:00 2001
From: lvyufeng <lvyufeng@cqu.edu.cn>
Date: Wed, 4 Jan 2023 17:52:29 +0800
Subject: [PATCH] fix amp docs

---
 docs/api/api_python/amp/mindspore.amp.all_finite.rst   |  1 +
 mindspore/python/mindspore/amp.py                      |  8 +++++---
 mindspore/python/mindspore/train/loss_scale_manager.py | 10 +++++-----
 3 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/docs/api/api_python/amp/mindspore.amp.all_finite.rst b/docs/api/api_python/amp/mindspore.amp.all_finite.rst
index ce7e25820bc..38c000b9af9 100644
--- a/docs/api/api_python/amp/mindspore.amp.all_finite.rst
+++ b/docs/api/api_python/amp/mindspore.amp.all_finite.rst
@@ -12,6 +12,7 @@ mindspore.amp.all_finite
 
     参数：
         - **inputs** (Union(tuple(Tensor), list(Tensor))) - 可迭代的Tensor。
+        - **status** (Tensor) - 溢出检测时所需要的初始状态，仅在Ascend需要。默认值：None。
 
     返回：
         Tensor，布尔类型的标量Tensor。
diff --git a/mindspore/python/mindspore/amp.py b/mindspore/python/mindspore/amp.py
index 2a571a01147..ba9f73ed88a 100644
--- a/mindspore/python/mindspore/amp.py
+++ b/mindspore/python/mindspore/amp.py
@@ -63,7 +63,7 @@ def init_status():
     Returns a Tensor indicating initialized status for overflow detection.
 
     Note:
-        Only Ascend need status to capture overflow status, you can alse call
+        Only Ascend need status to capture overflow status, you can also call
         this function on GPU or CPU, but the return value is useless.
 
     Returns:
@@ -73,7 +73,7 @@ def init_status():
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
-        >>> status = init_status()
+        >>> status = amp.init_status()
     """
     if _ascend_target():
         status = ops.NPUAllocFloatStatus()()
@@ -98,6 +98,8 @@ def all_finite(inputs, status=None):
 
     Args:
         inputs (Union(tuple(Tensor), list(Tensor))): a iterable Tensor.
+        status (Tensor): the status Tensor for overflow detection, only required on
+            Ascend. Default: None.
 
     Returns:
         Tensor, a scalar Tensor and the dtype is bool.
@@ -107,7 +109,7 @@ def all_finite(inputs, status=None):
 
     Examples:
         >>> x = (Tensor(np.array([np.log(-1), 1, np.log(0)])), Tensor(np.array([1.0]))
-        >>> output = all_finite(x)
+        >>> output = amp.all_finite(x)
     """
     if _ascend_target():
         if status is None:
diff --git a/mindspore/python/mindspore/train/loss_scale_manager.py b/mindspore/python/mindspore/train/loss_scale_manager.py
index d0330298b10..4021f7d443a 100644
--- a/mindspore/python/mindspore/train/loss_scale_manager.py
+++ b/mindspore/python/mindspore/train/loss_scale_manager.py
@@ -58,17 +58,17 @@ class FixedLossScaleManager(LossScaleManager):
 
     Examples:
         >>> import mindspore as ms
-        >>> from mindspore import nn
+        >>> from mindspore import amp, nn
         >>>
         >>> net = Net()
         >>> #1) Drop the parameter update if there is an overflow
-        >>> loss_scale_manager = ms.FixedLossScaleManager()
+        >>> loss_scale_manager = amp.FixedLossScaleManager()
         >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
         >>> model = ms.Model(net, loss_scale_manager=loss_scale_manager, optimizer=optim)
         >>>
         >>> #2) Execute parameter update even if overflow occurs
         >>> loss_scale = 1024.0
-        >>> loss_scale_manager = ms.FixedLossScaleManager(loss_scale, False)
+        >>> loss_scale_manager = amp.FixedLossScaleManager(loss_scale, False)
         >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9, loss_scale=loss_scale)
         >>> model = ms.Model(net, loss_scale_manager=loss_scale_manager, optimizer=optim)
     """
@@ -133,10 +133,10 @@ class DynamicLossScaleManager(LossScaleManager):
 
     Examples:
         >>> import mindspore as ms
-        >>> from mindspore import nn
+        >>> from mindspore import amp, nn
         >>>
         >>> net = Net()
-        >>> loss_scale_manager = ms.DynamicLossScaleManager()
+        >>> loss_scale_manager = amp.DynamicLossScaleManager()
         >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
         >>> model = ms.Model(net, loss_scale_manager=loss_scale_manager, optimizer=optim)
     """