docs(mge/core): refine the docstring of several apis

GitOrigin-RevId: c03fecfa6a

python_module/megengine/core/device.py

@@ -27,7 +27,7 @@ def get_device_count(device_type: str) -> int:
 
 
 def is_cuda_available() -> bool:
-    """ Returns whether cuda is avaiable.
+    """Returns whether cuda device is available on this system.
 
     """
     return mgb.config.get_device_count("gpu", warn=False) > 0

python_module/megengine/core/function.py

@@ -100,10 +100,11 @@ class Function(metaclass=ABCMeta):
         Users can call :meth:`~.function.Function.save_for_backward` in this method to save tensors.
 
         :param input: Input tensors.
+        :return: A tuple of Tensor or a single Tensor.
 
         .. note::
 
-            This method should return a tuple of Tensor representing the output
+            This method should return a tuple of Tensor or a single Tensor representing the output
             of the function.
         """
         raise NotImplementedError
@@ -113,7 +114,7 @@ class Function(metaclass=ABCMeta):
         self, *output_grads: Iterable[Union[Tensor, None]]
     ) -> Union[Tuple[Tensor], Tensor]:
         """
-        Compute the gradient of the function. It must be overriden by all subclasses.
+        Compute the gradient of the forward function. It must be overriden by all subclasses.
 
         :param output_grads: gradients of outputs that are returned by :meth:`~.function.Function.forward`
 
@@ -124,17 +125,17 @@ class Function(metaclass=ABCMeta):
 
         .. note::
 
-            This method should return a tuple containing gradients of all
-            inputs, in the same order as the ``inputs`` argument of :meth:`~.function.Function.forward` . A
-            ``Tensor`` could be returned instead if there is only one input.
-            If users want to stop the propagation of some gradients, the corresponding returned values should be ``None`` .
+            This method should return a tuple which containing the gradients of all inputs, in the same order
+            as the ``inputs`` argument of :meth:`~.function.Function.forward` . A ``Tensor`` could be returned
+            instead if there is only one input. If users want to stop the propagation of some gradients,
+            the corresponding returned values should be set ``None`` .
 
         """
         raise NotImplementedError
 
     def save_for_backward(self, *tensors: Iterable[Tensor]):
         """
-        Saves tensors for gradient computation. This method should be called only
+        Saves tensors needed for gradient computation. This method should be called only
         once in :meth:`~.function.Function.forward`, additional calls will replace values saved previously.
 
         The saved tensors can be accessed through the ``saved_tensors`` attribute.

python_module/megengine/core/graph.py

@@ -36,7 +36,7 @@ _default_graph = _DefaultGraph()
 
 class Graph(mgb.CompGraph):
     r"""
-    A ``comp_graph`` class supporting context management.
+    A computing graph that supporting context management.
 
     :param check_env_var: whether to check environment vars including ``MGB_COMP_GRAPH_OPT``.
     :param eager_evaluation: use dynamic graph(``True``) or static graph(``False``).
@@ -97,7 +97,7 @@ def _use_default_if_none(device, comp_graph):
 
 def dump(outputs, fpath, optimize_options=None, **kwargs):
     r"""
-    Serializes this computing graph and writes result to a file.
+    Serializes this computing graph and writes it to a file.
 
     :type outputs: ``Tensor`` or a collection of ``Tensor``
     :param outputs: output variables that need to be retrieved when
@@ -105,7 +105,7 @@ def dump(outputs, fpath, optimize_options=None, **kwargs):
     :type fpath: ``str``
     :param fpath: path for the output file
     :type optimize_options: ``list``
-    :param optimize_options: ``['f16_io_f32_comp', 'f16_io_comp', 'use_nhwcd4', 'fuse_conv_bias_nonlinearity']`` , four elements are optional, it can be an empty list, None or a list containing any of them. 
+    :param optimize_options: ``['f16_io_f32_comp', 'f16_io_comp', 'use_nhwcd4', 'fuse_conv_bias_nonlinearity']`` , four elements are optional, it can be an empty list, None or a list containing any of them.
 
         .. note::
 
@@ -115,7 +115,7 @@ def dump(outputs, fpath, optimize_options=None, **kwargs):
 
             ``use_nhwcd4`` – whether to use NHWCD4 data format. This is faster on some OpenCL devices;
 
-            ``fuse_conv_bias_nonlinearity`` – whether to fuse conv+bias+nonlinearty into one opr. This is supported only in NHWCD4 format.
+            ``fuse_conv_bias_nonlinearity`` – whether to fuse conv+bias+nonlinearty into one opr. This is supported only when ``use_nhwcd4`` is set.
 
     """
     from .tensor import Tensor

python_module/megengine/core/tensor.py

@@ -139,7 +139,7 @@ class Tensor:
         return tensor(data=obj, device=self.device)
 
     def numpy(self):
-        r"""Return the tensor value in ndarray format.
+        r"""Return the tensor value in numpy.ndarray format.
         """
         if self.__val is not None:
             assert self.__sym is None
@@ -235,6 +235,8 @@ class Tensor:
         self.__val.reset_zero()
 
     def to(self, device):
+        r"""Performs Tensor device conversion, returns Tensor with the specified device.
+        """
         return wrap_io_tensor(mgb.opr.copy)(self, comp_node=device)
 
     # https://docs.python.org/3/reference/datamodel.html#object.__hash__

python_module/megengine/core/tensor_factory.py

@@ -22,6 +22,9 @@ def scalar(
     device: Optional[mgb.CompNode] = None,
     comp_graph: Optional[mgb.CompGraph] = None,
 ) -> Tensor:
+    """
+    convert ``value`` to the type of :class:`~.Tensor`.
+    """
     device, comp_graph = _use_default_if_none(device, comp_graph)
     return Tensor(mgb.make_immutable(device, comp_graph, value, dtype=dtype, name=None))