fix(mge/module): add kwargs param for all modules

GitOrigin-RevId: 7245e669a7

imperative/python/megengine/module/activation.py

@@ -48,8 +48,8 @@ class Softmax(Module):
 
     """
 
-    def __init__(self, axis=None):
-        super().__init__()
+    def __init__(self, axis=None, **kwargs):
+        super().__init__(**kwargs)
         self.axis = axis
 
     def forward(self, inputs):
@@ -167,8 +167,8 @@ class PReLU(Module):
 
     """
 
-    def __init__(self, num_parameters: int = 1, init: float = 0.25):
-        super().__init__()
+    def __init__(self, num_parameters: int = 1, init: float = 0.25, **kwargs):
+        super().__init__(**kwargs)
         self.num_parameters = num_parameters
         if num_parameters > 1:
             # Assume format is NCHW
@@ -225,8 +225,8 @@ class LeakyReLU(Module):
 
     """
 
-    def __init__(self, negative_slope: float = 0.01):
-        super().__init__()
+    def __init__(self, negative_slope: float = 0.01, **kwargs):
+        super().__init__(**kwargs)
         self.negative_slope = negative_slope
 
     def forward(self, inputs):

imperative/python/megengine/module/adaptive_pooling.py

@@ -15,10 +15,8 @@ from .module import Module
 
 
 class _AdaptivePoolNd(Module):
-    def __init__(
-        self, oshp: Union[Tuple[int, int], int, Tensor],
-    ):
-        super(_AdaptivePoolNd, self).__init__()
+    def __init__(self, oshp: Union[Tuple[int, int], int, Tensor], **kwargs):
+        super(_AdaptivePoolNd, self).__init__(**kwargs)
         self.oshp = oshp
 
     @abstractmethod

imperative/python/megengine/module/batchnorm.py

@@ -26,8 +26,9 @@ class _BatchNorm(Module):
         affine=True,
         track_running_stats=True,
         freeze=False,
+        **kwargs
     ):
-        super(_BatchNorm, self).__init__()
+        super(_BatchNorm, self).__init__(**kwargs)
         self.num_features = num_features
         self.eps = eps
         self.momentum = momentum
@@ -151,9 +152,10 @@ class SyncBatchNorm(_BatchNorm):
         track_running_stats=True,
         freeze=False,
         group: Optional[Group] = WORLD,
+        **kwargs
     ) -> None:
         super().__init__(
-            num_features, eps, momentum, affine, track_running_stats, freeze
+            num_features, eps, momentum, affine, track_running_stats, freeze, **kwargs
         )
         self.group = group
 

imperative/python/megengine/module/conv.py

@@ -37,8 +37,9 @@ class _ConvNd(Module):
         dilation: Union[int, Tuple[int, int]],
         groups: int,
         bias: bool = True,
+        **kwargs
     ):
-        super().__init__()
+        super().__init__(**kwargs)
         if in_channels % groups != 0:
             raise ValueError("in_channels must be divisible by groups")
         if out_channels % groups != 0:
@@ -176,6 +177,7 @@ class Conv1d(_ConvNd):
         bias: bool = True,
         conv_mode: str = "CROSS_CORRELATION",
         compute_mode: str = "DEFAULT",
+        **kwargs
     ):
         kernel_size = kernel_size
         stride = stride
@@ -192,6 +194,7 @@ class Conv1d(_ConvNd):
             dilation,
             groups,
             bias,
+            **kwargs,
         )
 
     def _get_fanin(self):
@@ -334,6 +337,7 @@ class Conv2d(_ConvNd):
         bias: bool = True,
         conv_mode: str = "CROSS_CORRELATION",
         compute_mode: str = "DEFAULT",
+        **kwargs
     ):
         kernel_size = _pair_nonzero(kernel_size)
         stride = _pair_nonzero(stride)
@@ -350,6 +354,7 @@ class Conv2d(_ConvNd):
             dilation,
             groups,
             bias,
+            **kwargs,
         )
 
     def _get_fanin(self):
@@ -444,6 +449,7 @@ class ConvTranspose2d(_ConvNd):
         bias: bool = True,
         conv_mode: str = "CROSS_CORRELATION",
         compute_mode: str = "DEFAULT",
+        **kwargs
     ):
         kernel_size = _pair_nonzero(kernel_size)
         stride = _pair_nonzero(stride)
@@ -460,6 +466,7 @@ class ConvTranspose2d(_ConvNd):
             dilation,
             groups,
             bias,
+            **kwargs,
         )
 
     def _get_fanin(self):
@@ -536,6 +543,7 @@ class LocalConv2d(Conv2d):
         dilation: Union[int, Tuple[int, int]] = 1,
         groups: int = 1,
         conv_mode: str = "CROSS_CORRELATION",
+        **kwargs
     ):
         self.input_height = input_height
         self.input_width = input_width
@@ -548,6 +556,7 @@ class LocalConv2d(Conv2d):
             dilation,
             groups,
             bias=False,
+            **kwargs,
         )
 
     def _infer_weight_shape(self):

imperative/python/megengine/module/conv_bn.py

@@ -30,6 +30,7 @@ class _ConvBnActivation2d(Module):
         momentum=0.9,
         affine=True,
         track_running_stats=True,
+        **kwargs
     ):
         super().__init__()
         self.conv = Conv2d(
@@ -43,6 +44,7 @@ class _ConvBnActivation2d(Module):
             bias,
             conv_mode,
             compute_mode,
+            **kwargs,
         )
         self.bn = BatchNorm2d(out_channels, eps, momentum, affine, track_running_stats)
 

imperative/python/megengine/module/dropout.py

@@ -20,8 +20,8 @@ class Dropout(Module):
     :param drop_prob: The probability to drop (set to zero) each single element
     """
 
-    def __init__(self, drop_prob=0.0):
-        super().__init__()
+    def __init__(self, drop_prob=0.0, **kwargs):
+        super().__init__(**kwargs)
         self.drop_prob = drop_prob
 
     def forward(self, inputs):

imperative/python/megengine/module/elemwise.py

@@ -72,8 +72,8 @@ class Elemwise(Module):
         * "NOT": bool unary: ~x
     """
 
-    def __init__(self, method):
-        super().__init__()
+    def __init__(self, method, **kwargs):
+        super().__init__(**kwargs)
         self.method = method
 
     def forward(self, *inps):

imperative/python/megengine/module/embedding.py

@@ -64,8 +64,9 @@ class Embedding(Module):
         norm_type: Optional[float] = None,
         initial_weight: Parameter = None,
         freeze: bool = False,
+        **kwargs
     ):
-        super().__init__()
+        super().__init__(**kwargs)
         if padding_idx is not None:
             raise ValueError("Not support padding index now.")
         if max_norm is not None or norm_type is not None:

imperative/python/megengine/module/external.py

@@ -19,10 +19,8 @@ class TensorrtRuntimeSubgraph(Module):
     See :func:`~.tensorrt_runtime_opr` for more details.
     """
 
-    def __init__(
-        self, data,
-    ):
-        super(TensorrtRuntimeSubgraph, self).__init__()
+    def __init__(self, data, **kwargs):
+        super(TensorrtRuntimeSubgraph, self).__init__(**kwargs)
         self._data = data
 
     @property

imperative/python/megengine/module/normalization.py

@@ -20,8 +20,8 @@ class GroupNorm(Module):
     Reference: https://arxiv.org/pdf/1803.08494.pdf.
     """
 
-    def __init__(self, num_groups, num_channels, eps=1e-5, affine=True):
-        super().__init__()
+    def __init__(self, num_groups, num_channels, eps=1e-5, affine=True, **kwargs):
+        super().__init__(**kwargs)
         assert num_channels % num_groups == 0
         self.num_groups = num_groups
         self.num_channels = num_channels
@@ -70,8 +70,8 @@ class InstanceNorm(Module):
     Note that InstanceNorm equals using GroupNome with num_groups=num_channels.
     """
 
-    def __init__(self, num_channels, eps=1e-05, affine=True):
-        super().__init__()
+    def __init__(self, num_channels, eps=1e-05, affine=True, **kwargs):
+        super().__init__(**kwargs)
         self.num_channels = num_channels
         self.eps = eps
         self.affine = affine
@@ -114,8 +114,8 @@ class LayerNorm(Module):
     Note that LayerNorm equals using GroupNorm with num_groups=1.
     """
 
-    def __init__(self, num_channels, eps=1e-05, affine=True):
-        super().__init__()
+    def __init__(self, num_channels, eps=1e-05, affine=True, **kwargs):
+        super().__init__(**kwargs)
         self.num_channels = num_channels
         self.eps = eps
         self.affine = affine

imperative/python/megengine/module/pooling.py

@@ -19,8 +19,9 @@ class _PoolNd(Module):
         kernel_size: Union[int, Tuple[int, int]],
         stride: Union[int, Tuple[int, int]] = None,
         padding: Union[int, Tuple[int, int]] = 0,
+        **kwargs
     ):
-        super(_PoolNd, self).__init__()
+        super(_PoolNd, self).__init__(**kwargs)
         self.kernel_size = kernel_size
         self.stride = stride or kernel_size
         self.padding = padding

imperative/python/megengine/module/sequential.py

@@ -46,8 +46,8 @@ class Sequential(Module):
         pred1 = net1(data)
     """
 
-    def __init__(self, *args):
-        super().__init__()
+    def __init__(self, *args, **kwargs):
+        super().__init__(**kwargs)
         self.layer_keys = []
         if len(args) == 1 and isinstance(args[0], OrderedDict):
             for key, module in args[0].items():