!12394 add raises description for Adam, Lamb, Momentum, etc. operators

From: @wangshuide2020
Reviewed-by: 
Signed-off-by:

mindspore/nn/layer/combined.py

@@ -76,6 +76,12 @@ class Conv2dBnAct(Cell):
     Outputs:
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
+    Raises:
+        TypeError: If `in_channels`, `out_channels`, `stride`, `padding` or `dilation` is not an int.
+        TypeError: If `has_bias` is not a bool.
+        ValueError: If `in_channels` or `out_channels` `stride`, `padding` or `dilation` is less than 1.
+        ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -170,6 +176,12 @@ class DenseBnAct(Cell):
     Outputs:
         Tensor of shape :math:`(N, out\_channels)`.
 
+    Raises:
+        TypeError: If `in_channels` or `out_channels` is not an int.
+        TypeError: If `has_bias`, `has_bn` or `after_fake` is not a bool.
+        TypeError: If `momentum` or `eps` is not a float.
+        ValueError: If `momentum` is not in range [0, 1.0].
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 

mindspore/nn/layer/conv.py

@@ -372,7 +372,7 @@ class Conv1d(_Conv):
         ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> net = nn.Conv1d(120, 240, 4, has_bias=False, weight_init='normal')

mindspore/nn/layer/image.py

@@ -29,6 +29,7 @@ from ..cell import Cell
 
 __all__ = ['ImageGradients', 'SSIM', 'MSSSIM', 'PSNR', 'CentralCrop']
 
+
 class ImageGradients(Cell):
     r"""
     Returns two tensors, the first is along the height dimension and the second is along the width dimension.
@@ -50,6 +51,9 @@ class ImageGradients(Cell):
         - **dy** (Tensor) - vertical image gradients, the same type and shape as input.
         - **dx** (Tensor) - horizontal image gradients, the same type and shape as input.
 
+    Raises:
+        ValueError: If length of shape of `images` is not equal to 4.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -89,7 +93,7 @@ class ImageGradients(Cell):
 
 def _convert_img_dtype_to_float32(img, max_val):
     """convert img dtype to float32"""
-    # Ususally max_val is 1.0 or 255, we will do the scaling if max_val > 1.
+    # Usually max_val is 1.0 or 255, we will do the scaling if max_val > 1.
     # We will scale img pixel value if max_val > 1. and just cast otherwise.
     ret = F.cast(img, mstype.float32)
     max_val = F.scalar_cast(max_val, mstype.float32)
@@ -214,6 +218,13 @@ class SSIM(Cell):
     Outputs:
         Tensor, has the same dtype as img1. It is a 1-D tensor with shape N, where N is the batch num of img1.
 
+    Raises:
+        TypeError: If `max_val` is neither int nor float.
+        TypeError: If `k1`, `k2` or `filter_sigma` is not a float.
+        TypeError: If `filter_size` is not an int.
+        ValueError: If `max_val` or `filter_sigma` is less than or equal to 0.
+        ValueError: If `filter_size` is less than 0.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -296,6 +307,15 @@ class MSSSIM(Cell):
     Outputs:
         Tensor, the value is in range [0, 1]. It is a 1-D tensor with shape N, where N is the batch num of img1.
 
+    Raises:
+        TypeError: If `max_val` is neither int nor float.
+        TypeError: If `power_factors` is neither tuple nor list.
+        TypeError: If `k1`, `k2` or `filter_sigma` is not a float.
+        TypeError: If `filter_size` is not an int.
+        ValueError: If `max_val` or `filter_sigma` is less than or equal to 0.
+        ValueError: If `filter_size` is less than 0.
+        ValueError: If length of shape of `img1` or `img2` is not equal to 4.
+
     Supported Platforms:
         ``Ascend``
 
@@ -391,6 +411,11 @@ class PSNR(Cell):
     Outputs:
         Tensor, with dtype mindspore.float32. It is a 1-D tensor with shape N, where N is the batch num of img1.
 
+    Raises:
+        TypeError: If `max_val` is neither int nor float.
+        ValueError: If `max_val` is less than or equal to 0.
+        ValueError: If length of shape of `img1` or `img2` is not equal to 4.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -451,6 +476,7 @@ def _get_bbox(rank, shape, central_fraction):
 
     return bbox_begin, bbox_size
 
+
 class CentralCrop(Cell):
     """
     Crop the centeral region of the images with the central_fraction.
@@ -464,6 +490,10 @@ class CentralCrop(Cell):
     Outputs:
         Tensor, 3-D or 4-D float tensor, according to the input.
 
+    Raises:
+        TypeError: If `central_fraction` is not a float.
+        ValueError: If `central_fraction` is not in range (0, 1.0].
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 

mindspore/nn/layer/math.py

@@ -75,6 +75,11 @@ class ReduceLogSumExp(Cell):
         - If axis is tuple(int), set as (2, 3), and keep_dims is False,
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.
 
+    Raises:
+        TypeError: If `axis` is not one of int, list, tuple.
+        TypeError: If `keep_dims` is not bool.
+        TypeError: If dtype of `x` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -205,7 +210,7 @@ class LGamma(Cell):
         Tensor, has the same shape and dtype as the `x`.
 
     Raises:
-        TypeError: If dtype of input x is not float16 nor float32.
+        TypeError: If dtype of `x` is neither float16 nor float32.
 
     Supported Platforms:
         ``Ascend`` ``GPU``
@@ -323,7 +328,7 @@ class DiGamma(Cell):
         Tensor, has the same shape and dtype as the `x`.
 
     Raises:
-        TypeError: If dtype of input x is not float16 nor float32.
+        TypeError: If dtype of `x` is neither float16 nor float32.
 
     Supported Platforms:
         ``Ascend`` ``GPU``
@@ -679,8 +684,8 @@ class LBeta(Cell):
         Tensor, has the same dtype as `x` and `y`.
 
     Raises:
-        TypeError: If dtype of input x and a is not float16 nor float32,
-                   or if x has different dtype with a.
+        TypeError: If dtype of `x` or `y` is neither float16 nor float32,
+                   or if `x` has different dtype with `y`.
 
     Supported Platforms:
         ``Ascend`` ``GPU``
@@ -857,6 +862,11 @@ class MatMul(Cell):
     Outputs:
         Tensor, the shape of the output tensor depends on the dimension of input tensors.
 
+    Raises:
+        TypeError: If `transpose_x1` or `transpose_x2` is not a bool.
+        ValueError: If the column of matrix dimensions of `input_x1` is not equal to
+                    the row of matrix dimensions of `input_x2`.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -933,6 +943,11 @@ class Moments(Cell):
         - **mean** (Tensor) - The mean of input x, with the same date type as input x.
         - **variance** (Tensor) - The variance of input x, with the same date type as input x.
 
+    Raises:
+        TypeError: If `axis` is not one of int, tuple, None.
+        TypeError: If `keep_dims` is neither bool nor None.
+        TypeError: If dtype of `input_x` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -993,7 +1008,7 @@ class MatInverse(Cell):
         Tensor, has the same dtype as the `a`.
 
     Raises:
-        TypeError: If dtype of input x is not float16 nor float32.
+        TypeError: If dtype of `a` is neither float16 nor float32.
 
     Supported Platforms:
         ``GPU``
@@ -1033,7 +1048,7 @@ class MatDet(Cell):
         Tensor, has the same dtype as the `a`.
 
     Raises:
-        TypeError: If dtype of input x is not float16 nor float32.
+        TypeError: If dtype of `a` is neither float16 nor float32.
 
     Supported Platforms:
         ``GPU``

mindspore/nn/layer/pooling.py

@@ -104,6 +104,13 @@ class MaxPool2d(_PoolNd):
     Outputs:
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
+    Raises:
+        TypeError: If `kernel_size` or `strides` is neither int nor tuple.
+        ValueError: If `pad_mode` is neither 'valid' nor 'same' with not case sensitive.
+        ValueError: If `data_format` is neither 'NCHW' nor 'NHWC'.
+        ValueError: If `kernel_size` or `strides` is less than 1.
+        ValueError: If length of shape of `input` is not equal to 4.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -164,8 +171,15 @@ class MaxPool1d(_PoolNd):
     Outputs:
         Tensor of shape :math:`(N, C, L_{out}))`.
 
+    Raises:
+        TypeError: If `kernel_size` or `strides` is not an int.
+        ValueError: If `pad_mode` is neither 'valid' nor 'same' with not case sensitive.
+        ValueError: If `data_format` is neither 'NCHW' nor 'NHWC'.
+        ValueError: If `kernel_size` or `strides` is less than 1.
+        ValueError: If length of shape of `input` is not equal to 4.
+
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> max_pool = nn.MaxPool1d(kernel_size=3, stride=1)
@@ -246,6 +260,13 @@ class AvgPool2d(_PoolNd):
     Outputs:
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
+    Raises:
+        TypeError: If `kernel_size` or `strides` is neither int nor tuple.
+        ValueError: If `pad_mode` is neither 'valid' nor 'same' with not case sensitive.
+        ValueError: If `data_format` is neither 'NCHW' nor 'NHWC'.
+        ValueError: If `kernel_size` or `strides` is less than 1.
+        ValueError: If length of shape of `input` is not equal to 4.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -311,6 +332,12 @@ class AvgPool1d(_PoolNd):
     Outputs:
         Tensor of shape :math:`(N, C_{out}, L_{out})`.
 
+    Raises:
+        TypeError: If `kernel_size` or `stride` is not an int.
+        ValueError: If `pad_mode` is neither 'same' nor 'valid' with not case sensitive.
+        ValueError: If `kernel_size` or `strides` is less than 1.
+        ValueError: If length of shape of `input` is not equal to 3.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 

mindspore/nn/layer/quant.py

@@ -286,6 +286,15 @@ class FakeQuantWithMinMaxObserver(UniformQuantObserver):
     Outputs:
         Tensor, with the same type and shape as the `input`.
 
+    Raises:
+        TypeError: If `min_init` or `max_init` is neither int nor float.
+        TypeError: If `quant_delay` is not an int.
+        TypeError: If `min_init` is not less than `max_init`.
+        TypeError: If `quant_delay` is not greater than or equal to 0.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> fake_quant = nn.FakeQuantWithMinMaxObserver()
         >>> input = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
@@ -432,10 +441,19 @@ class Conv2dBnFoldQuantOneConv(Cell):
     Outputs:
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
+    Raises:
+        TypeError: If `in_channels`, `out_channels`, `stride`, `padding` or `dilation` is not an int.
+        TypeError: If `has_bias` is not a bool.
+        ValueError: If `in_channels` or `out_channels` `stride`, `padding` or `dilation` is less than 1.
+        ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU``
+
     Examples:
         >>> qconfig = compression.quant.create_quant_config()
-        >>> conv2d_bnfold = nn.Conv2dBnFoldQuant(1, 6, kernel_size=(2, 2), stride=(1, 1), pad_mode="valid",
-        ...                                      quant_config=qconfig)
+        >>> conv2d_bnfold = nn.Conv2dBnFoldQuantOneConv(1, 6, kernel_size=(2, 2), stride=(1, 1), pad_mode="valid",
+        ...                                             quant_config=qconfig)
         >>> input = Tensor(np.random.randint(-2, 2, (2, 1, 3, 3)), mindspore.float32)
         >>> result = conv2d_bnfold(input)
         >>> output = result.shape
@@ -634,6 +652,12 @@ class Conv2dBnFoldQuant(Cell):
     Outputs:
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
+    Raises:
+        TypeError: If `in_channels`, `out_channels`, `stride`, `padding` or `dilation` is not an int.
+        TypeError: If `has_bias` is not a bool.
+        ValueError: If `in_channels` or `out_channels` `stride`, `padding` or `dilation` is less than 1.
+        ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -926,6 +950,12 @@ class Conv2dQuant(Cell):
     Outputs:
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
+    Raises:
+        TypeError: If `in_channels`, `out_channels`, `stride`, `padding` or `dilation` is not an int.
+        TypeError: If `has_bias` is not a bool.
+        ValueError: If `in_channels` or `out_channels` `stride`, `padding` or `dilation` is less than 1.
+        ValueError: If `pad_mode` is not one of 'same', 'valid', 'pad'.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -1033,6 +1063,11 @@ class DenseQuant(Cell):
     Outputs:
         Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
+    Raises:
+        TypeError: If `in_channels`, `out_channels` is not an int.
+        TypeError: If `has_bias` is not a bool.
+        ValueError: If `in_channels` or `out_channels` is less than 1.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -1145,6 +1180,10 @@ class ActQuant(_QuantActivation):
     Outputs:
         Tensor, with the same type and shape as the `input`.
 
+    Raises:
+        TypeError: If `activation` is not an instance of Cell.
+        TypeError: If `fake_before` is not a bool.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -1212,6 +1251,9 @@ class TensorAddQuant(Cell):
     Outputs:
         Tensor, with the same type and shape as the `input_x1`.
 
+    Raises:
+        TypeError: If `ema_decay` is not a float.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -1265,6 +1307,9 @@ class MulQuant(Cell):
     Outputs:
         Tensor, with the same type and shape as the `input_x1`.
 
+    Raises:
+        TypeError: If `ema_decay` is not a float.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 

mindspore/nn/learning_rate_schedule.py

@@ -82,6 +82,15 @@ class ExponentialDecayLR(LearningRateSchedule):
     Outputs:
         Tensor. The learning rate value for the current step.
 
+    Raises:
+        TypeError: If `learning_rate` or `decay_rate` is not a float.
+        TypeError: If `decay_steps` is not an int or `is_stair` is not a bool.
+        ValueError: If `decay_steps` is less than 1.
+        ValueError: If `learning_rate` or `decay_rate` is less than or equal to 0.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> learning_rate = 0.1
         >>> decay_rate = 0.9
@@ -140,6 +149,15 @@ class NaturalExpDecayLR(LearningRateSchedule):
     Outputs:
         Tensor. The learning rate value for the current step.
 
+    Raises:
+        TypeError: If `learning_rate` or `decay_rate` is not a float.
+        TypeError: If `decay_steps` is not an int or `is_stair` is not a bool.
+        ValueError: If `decay_steps` is less than 1.
+        ValueError: If `learning_rate` or `decay_rate` is less than or equal to 0.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> learning_rate = 0.1
         >>> decay_rate = 0.9
@@ -199,6 +217,15 @@ class InverseDecayLR(LearningRateSchedule):
     Outputs:
         Tensor. The learning rate value for the current step.
 
+    Raises:
+        TypeError: If `learning_rate` or `decay_rate` is not a float.
+        TypeError: If `decay_steps` is not an int or `is_stair` is not a bool.
+        ValueError: If `decay_steps` is less than 1.
+        ValueError: If `learning_rate` or `decay_rate` is less than or equal to 0.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> learning_rate = 0.1
         >>> decay_rate = 0.9
@@ -247,6 +274,15 @@ class CosineDecayLR(LearningRateSchedule):
     Outputs:
         Tensor. The learning rate value for the current step.
 
+    Raises:
+        TypeError: If `min_lr` or `max_lr` is not a float.
+        TypeError: If `decay_steps` is not an int.
+        ValueError: If `min_lr` is less than 0 or `decay_steps` is less than 1.
+        ValueError: If `max_lr` is less than or equal to 0.
+
+        Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> min_lr = 0.01
         >>> max_lr = 0.1
@@ -314,6 +350,15 @@ class PolynomialDecayLR(LearningRateSchedule):
     Outputs:
         Tensor. The learning rate value for the current step.
 
+    Raises:
+        TypeError: If `learning_rate`, `end_learning_rate` or `power` is not a float.
+        TypeError: If `decay_steps` is not an int or `update_decay_steps` is not a bool.
+        ValueError: If `end_learning_rate` is less than 0 or `decay_steps` is less than 1.
+        ValueError: If `learning_rate` or `power` is less than or equal to 0.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> learning_rate = 0.1
         >>> end_learning_rate = 0.01
@@ -384,6 +429,15 @@ class WarmUpLR(LearningRateSchedule):
     Outputs:
         Tensor. The learning rate value for the current step.
 
+    Raises:
+        TypeError: If `learning_rate` is not a float.
+        TypeError: If `warmup_steps` is not an int.
+        ValueError: If `warmup_steps` is less than 1.
+        ValueError: If `learning_rate` is less than or equal to 0.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> learning_rate = 0.1
         >>> warmup_steps = 2

mindspore/nn/optim/ada_grad.py

@@ -81,7 +81,8 @@ class Adagrad(Optimizer):
             Default: 0.001.
         update_slots (bool): If true, update accumulation. Default: True.
         loss_scale (float): Value for the loss scale. It must be greater than 0.0. Default: 1.0.
-        weight_decay (float): Weight decay value to multiply weight, must be zero or positive value. Default: 0.0.
+        weight_decay (Union[float, int]): Weight decay value to multiply weight, must be zero or positive value.
+            Default: 0.0.
 
     Inputs:
         - **grads** (tuple[Tensor]) - The gradients of `params` in the optimizer, the shape is the same as the `params`
@@ -90,6 +91,15 @@ class Adagrad(Optimizer):
     Outputs:
         Tensor[bool], the value is True.
 
+    Raises:
+        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
+        TypeError: If element of `parameters` is neither Parameter nor dict.
+        TypeError: If `accum` or `loss_scale` is not a float.
+        TypeError: If `update_slots` is not a bool.
+        TypeError: If `weight_decay` is neither float nor int.
+        ValueError: If `loss_scale` is less than or equal to 0.
+        ValueError: If `accum` or `weight_decay` is less than 0.
+
     Supported Platforms:
         ``Ascend`` ``CPU`` ``GPU``
 

mindspore/nn/optim/adam.py

@@ -267,6 +267,16 @@ class Adam(Optimizer):
     Outputs:
         Tensor[bool], the value is True.
 
+    Raises:
+        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
+        TypeError: If element of `parameters` is neither Parameter nor dict.
+        TypeError: If `beta1`, `beta2`, `eps` or `loss_scale` is not a float.
+        TypeError: If `weight_decay` is neither float nor int.
+        TypeError: If `use_locking` or `use_nesterov` is not a bool.
+        ValueError: If `loss_scale` or `eps` is less than or equal to 0.
+        ValueError: If `beta1`, `beta2` is not in range (0.0, 1.0).
+        ValueError: If `weight_decay` is less than 0.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -414,6 +424,15 @@ class AdamWeightDecay(Optimizer):
     Outputs:
         tuple[bool], all elements are True.
 
+    Raises:
+        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
+        TypeError: If element of `parameters` is neither Parameter nor dict.
+        TypeError: If `beta1`, `beta2` or `eps` is not a float.
+        TypeError: If `weight_decay` is neither float nor int.
+        ValueError: If `eps` is less than or equal to 0.
+        ValueError: If `beta1`, `beta2` is not in range (0.0, 1.0).
+        ValueError: If `weight_decay` is less than 0.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -545,6 +564,16 @@ class AdamOffload(Optimizer):
     Outputs:
         Tensor[bool], the value is True.
 
+    Raises:
+        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
+        TypeError: If element of `parameters` is neither Parameter nor dict.
+        TypeError: If `beta1`, `beta2`, `eps` or `loss_scale` is not a float.
+        TypeError: If `weight_decay` is neither float nor int.
+        TypeError: If `use_locking` or `use_nesterov` is not a bool.
+        ValueError: If `loss_scale` or `eps` is less than or equal to 0.
+        ValueError: If `beta1`, `beta2` is not in range (0.0, 1.0).
+        ValueError: If `weight_decay` is less than 0.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 

mindspore/nn/optim/ftrl.py

@@ -140,7 +140,8 @@ class FTRL(Optimizer):
         l2 (float): l2 regularization strength, must be greater than or equal to zero. Default: 0.0.
         use_locking (bool): If true, use locks for updating operation. Default: False.
         loss_scale (float): Value for the loss scale. It must be equal to or greater than 1.0. Default: 1.0.
-        weight_decay (float): Weight decay value to multiply weight, must be zero or positive value. Default: 0.0.
+        weight_decay (Union[float, int]): Weight decay value to multiply weight, must be zero or positive value.
+            Default: 0.0.
 
     Inputs:
         - **grads** (tuple[Tensor]) - The gradients of `params` in the optimizer, the shape is the same as the `params`
@@ -149,6 +150,15 @@ class FTRL(Optimizer):
     Outputs:
         tuple[Parameter], the updated parameters, the shape is the same as `params`.
 
+    Raises:
+        TypeError: If `initial_accum`, `learning_rate`, `lr_power`, `l1`, `l2` or `loss_scale` is not a float.
+        TypeError: If element of `parameters` is neither Parameter nor dict.
+        TypeError: If `weight_decay` is neither float nor int.
+        TypeError: If `use_nesterov` is not a bool.
+        ValueError: If `lr_power` is greater than 0.
+        ValueError: If `loss_scale` is less than or equal to 0.
+        ValueError: If `initial_accum`, `l1` or `l2` is less than 0.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 

mindspore/nn/optim/lamb.py

@@ -224,6 +224,15 @@ class Lamb(Optimizer):
     Outputs:
         tuple[bool], all elements are True.
 
+    Raises:
+        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
+        TypeError: If element of `parameters` is neither Parameter nor dict.
+        TypeError: If `beta1`, `beta2` or `eps` is not a float.
+        TypeError: If `weight_decay` is neither float nor int.
+        ValueError: If `eps` is less than or equal to 0.
+        ValueError: If `beta1`, `beta2` is not in range (0.0, 1.0).
+        ValueError: If `weight_decay` is less than 0.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 

mindspore/nn/optim/lazyadam.py

@@ -177,7 +177,7 @@ class LazyAdam(Optimizer):
         use_nesterov (bool): Whether to use Nesterov Accelerated Gradient (NAG) algorithm to update the gradients.
             If true, update the gradients using NAG.
             If false, update the gradients without using NAG. Default: False.
-        weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
+        weight_decay (Union[float, int]): Weight decay (L2 penalty). Default: 0.0.
         loss_scale (float): A floating point value for the loss scale. Should be equal to or greater than 1. Default:
                             1.0.
 
@@ -187,6 +187,16 @@ class LazyAdam(Optimizer):
     Outputs:
         Tensor[bool], the value is True.
 
+    Raises:
+        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
+        TypeError: If element of `parameters` is neither Parameter nor dict.
+        TypeError: If `beta1`, `beta2`, `eps` or `loss_scale` is not a float.
+        TypeError: If `weight_decay` is neither float nor int.
+        TypeError: If `use_locking` or `use_nesterov` is not a bool.
+        ValueError: If `loss_scale` or `eps` is less than or equal to 0.
+        ValueError: If `beta1`, `beta2` is not in range (0.0, 1.0).
+        ValueError: If `weight_decay` is less than 0.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 

mindspore/nn/optim/momentum.py

@@ -96,7 +96,7 @@ class Momentum(Optimizer):
         momentum (float): Hyperparameter of type float, means momentum for the moving average.
             It must be at least 0.0.
         weight_decay (int, float): Weight decay (L2 penalty). It must be equal to or greater than 0.0. Default: 0.0.
-        loss_scale (int, float): A floating point value for the loss scale. It must be greater than 0.0. Default: 1.0.
+        loss_scale (float): A floating point value for the loss scale. It must be greater than 0.0. Default: 1.0.
         use_nesterov (bool): Enable Nesterov momentum. Default: False.
 
     Inputs:
@@ -106,8 +106,13 @@ class Momentum(Optimizer):
         tuple[bool], all elements are True.
 
     Raises:
-        ValueError: If the momentum is less than 0.0.
-        TypeError: If the momentum is not a float or use_nesterov is not a bool.
+        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
+        TypeError: If element of `parameters` is neither Parameter nor dict.
+        TypeError: If `loss_scale` or `momentum` is not a float.
+        TypeError: If `weight_decay` is neither float nor int.
+        TypeError: If `use_nesterov` is not a bool.
+        ValueError: If `loss_scale` is less than or equal to 0.
+        ValueError: If `weight_decay` or `momentum` is less than 0.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``

mindspore/nn/optim/optimizer.py

@@ -83,14 +83,20 @@ class Optimizer(Cell):
             - grad_centralization: Optional. If "grad_centralization" is in the keys, the set value will be used.
               If not, the `grad_centralization` is False by default.
 
-        weight_decay (float): A floating point value for the weight decay. It must be equal to or greater than 0.
+        weight_decay (Union[float, int]): An int or a floating point value for the weight decay.
+            It must be equal to or greater than 0.
             If the type of `weight_decay` input is int, it will be converted to float. Default: 0.0.
         loss_scale (float): A floating point value for the loss scale. It must be greater than 0. If the
             type of `loss_scale` input is int, it will be converted to float. Default: 1.0.
 
     Raises:
-        ValueError: If the learning_rate is a Tensor, but the dimension of tensor is greater than 1.
-        TypeError: If the learning_rate is not any of the three types: float, Tensor, nor Iterable.
+        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
+        TypeError: If element of `parameters` is neither Parameter nor dict.
+        TypeError: If `loss_scale` is not a float.
+        TypeError: If `weight_decay` is neither float nor int.
+        ValueError: If `loss_scale` is less than or equal to 0.
+        ValueError: If `weight_decay` is less than 0.
+        ValueError: If `learning_rate` is a Tensor, but the dimension of tensor is greater than 1.
 
     Supported Platforms:
         ``Ascend`` ``GPU``

mindspore/nn/optim/proximal_ada_grad.py

@@ -101,7 +101,8 @@ class ProximalAdagrad(Optimizer):
         l2 (float): l2 regularization strength, must be greater than or equal to zero. Default: 0.0.
         use_locking (bool): If true, use locks for updating operation. Default: False.
         loss_scale (float): Value for the loss scale. It must be greater than 0.0. Default: 1.0.
-        weight_decay (float): Weight decay value to multiply weight, must be zero or positive value. Default: 0.0.
+        weight_decay (Union[float, int]): Weight decay value to multiply weight, must be zero or positive value.
+            Default: 0.0.
 
     Inputs:
         - **grads** (tuple[Tensor]) - The gradients of `params` in the optimizer, the shape is the same as the `params`
@@ -110,6 +111,14 @@ class ProximalAdagrad(Optimizer):
     Outputs:
         Tensor[bool], the value is True.
 
+    Raises:
+        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
+        TypeError: If element of `parameters` is neither Parameter nor dict.
+        TypeError: If `accum`, `l1`, `l2` or `loss_scale` is not a float.
+        TypeError: If `weight_decay` is neither float nor int.
+        ValueError: If `loss_scale` is less than or equal to 0.
+        ValueError: If `accum`, `l1`, `l2` or `weight_decay` is less than 0.
+
     Supported Platforms:
         ``Ascend``
 

mindspore/nn/optim/rmsprop.py

@@ -125,7 +125,7 @@ class RMSProp(Optimizer):
                              updated. Default: False.
         centered (bool): If true, gradients are normalized by the estimated variance of the gradient. Default: False.
         loss_scale (float): A floating point value for the loss scale. Should be greater than 0. Default: 1.0.
-        weight_decay (float): Weight decay (L2 penalty). Should be equal to or greater than 0. Default: 0.0.
+        weight_decay (Union[float, int]): Weight decay (L2 penalty). Should be equal to or greater than 0. Default: 0.0.
 
     Inputs:
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
@@ -133,6 +133,15 @@ class RMSProp(Optimizer):
     Outputs:
         Tensor[bool], the value is True.
 
+    Raises:
+        TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
+        TypeError: If `decay`, `momentum`, `epsilon` or `loss_scale` is not a float.
+        TypeError: If element of `parameters` is neither Parameter nor dict.
+        TypeError: If `weight_decay` is neither float nor int.
+        TypeError: If `use_locking` or `centered` is not a bool.
+        ValueError: If `epsilon` is less than or equal to 0.
+        ValueError: If `decay` or `momentum` is less than 0.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 

mindspore/nn/wrap/cell_wrapper.py

@@ -18,6 +18,7 @@ from types import FunctionType, MethodType
 from mindspore.parallel._utils import (_get_device_num, _get_gradients_mean,
                                        _get_parallel_mode)
 from mindspore.context import ParallelMode
+from mindspore._checkparam import Validator as validator
 from ...common import dtype as mstype
 from ...common.parameter import Parameter, ParameterTuple
 from ...ops import composite as C
@@ -80,6 +81,9 @@ class WithLossCell(Cell):
     Outputs:
         Tensor, a scalar tensor with shape :math:`()`.
 
+    Raises:
+        TypeError: If dtype of `data` or `label` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -139,6 +143,9 @@ class WithGradCell(Cell):
     Outputs:
         list, a list of Tensors with identical shapes as trainable weights.
 
+    Raises:
+        TypeError: If `sens` is not one of None, Tensor, Scalar or Tuple.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -294,6 +301,9 @@ class TrainOneStepCell(Cell):
     Outputs:
         Tensor, a scalar Tensor with shape :math:`()`.
 
+    Raises:
+        TypeError: If `sens` is not a number.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -468,6 +478,9 @@ class WithEvalCell(Cell):
         Tuple, containing a scalar loss Tensor, a network output Tensor of shape :math:`(N, \ldots)`
         and a label Tensor of shape :math:`(N, \ldots)`.
 
+    Raises:
+        TypeError: If `add_cast_fp32` is not a bool.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -482,7 +495,7 @@ class WithEvalCell(Cell):
         super(WithEvalCell, self).__init__(auto_prefix=False)
         self._network = network
         self._loss_fn = loss_fn
-        self.add_cast_fp32 = add_cast_fp32
+        self.add_cast_fp32 = validator.check_value_type("add_cast_fp32", add_cast_fp32, [bool], self.cls_name)
 
     def construct(self, data, label):
         outputs = self._network(data)

mindspore/nn/wrap/loss_scale.py

@@ -50,6 +50,7 @@ def _tensor_grad_overflow(grad):
 def _tensor_grad_overflow_row_tensor(grad):
     return grad_overflow(grad.values)
 
+
 class DynamicLossScaleUpdateCell(Cell):
     r"""
     Dynamic Loss scale update cell.
@@ -73,6 +74,9 @@ class DynamicLossScaleUpdateCell(Cell):
     Outputs:
         Tensor, a scalar Tensor with shape :math:`()`.
 
+    Raises:
+        TypeError: If dtype of `inputs` or `label` is neither float16 nor float32.
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 
@@ -227,6 +231,10 @@ class TrainOneStepWithLossScaleCell(TrainOneStepCell):
         - **overflow** (Tensor) -  Tensor with shape :math:`()`, type is bool.
         - **loss scaling value** (Tensor) -  Tensor with shape :math:`()`
 
+    Raises:
+        TypeError: If `scale_sense` is neither Cell nor Tensor.
+        ValueError: If shape of `scale_sense` is neither (1,) nor ().
+
     Supported Platforms:
         ``Ascend`` ``GPU``
 

mindspore/ops/operations/array_ops.py

@@ -821,7 +821,7 @@ class Gather(PrimitiveWithCheck):
         TypeError: If `axis` is not an int.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_params = Tensor(np.array([[1, 2, 7, 42], [3, 4, 54, 22], [2, 2, 55, 3]]), mindspore.float32)

mindspore/ops/operations/comm_ops.py

@@ -428,7 +428,7 @@ class Broadcast(PrimitiveWithInfer):
         TypeError: If root_rank is not a integer or group is not a string.
 
     Supported Platforms:
-        ``Ascend``, ``GPU``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> # This example should be run with multiple processes.

mindspore/ops/operations/math_ops.py

@@ -747,6 +747,12 @@ class MatMul(PrimitiveWithCheck):
     Outputs:
         Tensor, the shape of the output tensor is :math:`(N, M)`.
 
+    Raises:
+        TypeError: If `transpose_a` or `transpose_b` is not a bool.
+        ValueError: If the column of matrix dimensions of `input_x` is not equal to
+                    the row of matrix dimensions of `input_y`.
+        ValueError: If length of shape of `input_x` or `input_y` is not equal to 2.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -827,7 +833,8 @@ class BatchMatMul(MatMul):
 
     Raises:
         TypeError: If `transpose_a` or `transpose_b` is not a bool.
-        ValueError: If length of shape of `input_x` is less than 3 or not equal to length of shape of `input_y`.
+        ValueError: If length of shape of `input_x` is not equal to length of shape of `input_y` or
+                    length of shape of `input_x` is less than 3.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``

mindspore/ops/operations/nn_ops.py

@@ -1722,7 +1722,7 @@ class MaxPool(_Pool):
             represent height and width of movement respectively. Default: 1.
         pad_mode (str): The optional value for pad mode, is "same" or "valid", not case sensitive.
             Default: "valid".
-        format (str) : The optional value for data format, is 'NHWC' or 'NCHW'.
+        data_format (str) : The optional value for data format, is 'NHWC' or 'NCHW'.
             Default: 'NCHW'.
 
             - same: Adopts the way of completion. The height and width of the output will be the same as
@@ -1739,6 +1739,13 @@ class MaxPool(_Pool):
     Outputs:
         Tensor, with shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
+    Raises:
+        TypeError: If `kernel_size` or `strides` is neither int nor tuple.
+        ValueError: If `pad_mode` is neither 'valid' nor 'same' with not case sensitive.
+        ValueError: If `data_format` is neither 'NCHW' nor 'NHWC'.
+        ValueError: If `kernel_size` or `strides` is less than 1.
+        ValueError: If length of shape of `input` is not equal to 4.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -1973,6 +1980,13 @@ class AvgPool(_Pool):
     Outputs:
         Tensor, with shape :math:`(N, C_{out}, H_{out}, W_{out})`.
 
+    Raises:
+        TypeError: If `kernel_size` or `strides` is neither int nor tuple.
+        ValueError: If `pad_mode` is neither 'valid' nor 'same' with not case sensitive.
+        ValueError: If `data_format` is neither 'NCHW' nor 'NHWC'.
+        ValueError: If `kernel_size` or `strides` is less than 1.
+        ValueError: If length of shape of `input` is not equal to 4.
+
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
 
@@ -3096,7 +3110,7 @@ class L2Normalize(PrimitiveWithInfer):
         Tensor, with the same type and shape as the input.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> l2_normalize = ops.L2Normalize()
@@ -4237,6 +4251,10 @@ class Adam(PrimitiveWithInfer):
         - **m** (Tensor) - The same shape and data type as `m`.
         - **v** (Tensor) - The same shape and data type as `v`.
 
+    Raises:
+        TypeError: If `use_locking` or `use_nesterov` is not a bool.
+        ValueError: If shape of `var`, `m` and `v` is not the same.
+
     Supported Platforms:
         ``Ascend`` ``GPU``