Fix some typos in api docs and comments

include/tvm/relay/error.h

@@ -83,7 +83,7 @@ struct Error : public dmlc::Error {
  *
  * The final mode represents the old mode, if we report an error that has no span or
  * expression, we will default to throwing an exception with a textual representation
- * of the error and no indication of where it occured in the original program.
+ * of the error and no indication of where it occurred in the original program.
  *
  * The latter mode is not ideal, and the goal of the new error reporting machinery is
  * to avoid ever reporting errors in this style.

include/tvm/runtime/c_runtime_api.h

@@ -187,7 +187,7 @@ TVM_DLL void TVMAPISetLastError(const char* msg);
 /*!
  * \brief return str message of the last error
  *  all function in this file will return 0 when success
- *  and -1 when an error occured,
+ *  and -1 when an error occurred,
  *  TVMGetLastError can be called to retrieve the error
  *
  *  this function is threadsafe and can be called by different thread

nnvm/include/nnvm/c_api.h

@@ -60,7 +60,7 @@ NNVM_DLL void NNAPISetLastError(const char* msg);
 /*!
  * \brief return str message of the last error
  *  all function in this file will return 0 when success
- *  and -1 when an error occured,
+ *  and -1 when an error occurred,
  *  NNGetLastError can be called to retrieve the error
  *
  *  this function is threadsafe and can be called by different thread

nnvm/python/nnvm/frontend/common.py

@@ -58,7 +58,7 @@ def __call__(self, inputs, attrs, *args):
 
 
 class AttrConverter(object):
-    """Common attribute conveter. An AttrConverter instance is a callable:
+    """Common attribute converter. An AttrConverter instance is a callable:
     ```
     attr_converter = AttrConverter(op_name, transforms={'a':'b', 'c':('d', 1)})
     new_op_name, new_attr = attr_converter(attrs)
@@ -72,12 +72,12 @@ class AttrConverter(object):
         `op_name = func(attr)`
     transforms : dict of `new_name, or (new_name, default_value, transform function)`
         If only a new_name is provided, it's like renaming the attribute name.
-        If default_value if provded, then the attribute is considered as optional.
+        If default_value if provided, then the attribute is considered as optional.
         If transform function is provided, the original attribute value is handled
         by transform function.
     excludes : list
         A list of excluded attributes that should `NOT` appear.
-        Raise NotImplementedError if occured.
+        Raise NotImplementedError if occurred.
     disables : list
         A list of attributes that is disabled in nnvm. Log warnings.
     ignores : list

nnvm/python/nnvm/frontend/tensorflow.py

@@ -1177,7 +1177,7 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
             -> All Const nodes are params.
             -> Last node is assumed as graph output.
             -> _output_shapes : Graph should be frozen with add_shapes=True.
-                                Or user can pass input shape dictionaly optionally.
+                                Or user can pass input shape dictionary optionally.
             -> DecodeJpeg, ResizeBilinear: These are dummy operators.
                                            Hence user should handle preprocessing outside.
             -> CheckNumerics: No implementation as of now for this.

python/tvm/relay/frontend/common.py

@@ -286,7 +286,7 @@ def clear_padding(self):
 
 
 class AttrCvt(object):
-    """Common attribute conveter. An AttrConverter instance is a callable:
+    """Common attribute converter. An AttrConverter instance is a callable:
     ```
     attr_converter = AttrConverter(op_name, transforms={'a':'b', 'c':('d', 1)})
     new_op_name, new_attr = attr_converter(attrs)
@@ -300,12 +300,12 @@ class AttrCvt(object):
         `op_name = func(attr)`
     transforms : dict of `new_name, or (new_name, default_value, transform function)`
         If only a new_name is provided, it's like renaming the attribute name.
-        If default_value if provded, then the attribute is considered as optional.
+        If default_value if provided, then the attribute is considered as optional.
         If transform function is provided, the original attribute value is handled
         by transform function.
     excludes : list
         A list of excluded attributes that should `NOT` appear.
-        Raise NotImplementedError if occured.
+        Raise NotImplementedError if occurred.
     disables : list
         A list of attributes that is disabled in relay. Log warnings.
     ignores : list

python/tvm/relay/frontend/tensorflow.py

@@ -77,12 +77,12 @@ class AttrCvt(object):
         `op_name = func(attr)`
     transforms : dict of `new_name, or (new_name, default_value, transform function)`
         If only a new_name is provided, it's like renaming the attribute name.
-        If default_value if provded, then the attribute is considered as optional.
+        If default_value if provided, then the attribute is considered as optional.
         If transform function is provided, the original attribute value is handled
         by transform function.
     excludes : list
         A list of excluded attributes that should `NOT` appear.
-        Raise NotImplementedError if occured.
+        Raise NotImplementedError if occurred.
     disables : list
         A list of attributes that is disabled in relay. Log warnings.
     ignores : list
@@ -1567,7 +1567,7 @@ def _in_while_loop(control_flow_node_map, op_name):
     Parameters
     ----------
     control_flow_node_map : Dict[str, Set[str]]
-        A dictionay contains the unqiue control flow execution frame name to
+        A dictionay contains the unique control flow execution frame name to
         a set of primitive operators mapping.
 
     op_name : str
@@ -1619,7 +1619,7 @@ def f2():
                 return tf.add(4, 23)
             r = tf.cond(tf.less(i, j), f1, f2)
 
-    This condition statement should be coverted into Relay in the following
+    This condition statement should be converted into Relay in the following
     form:
 
     .. code-block:: python
@@ -1727,7 +1727,7 @@ def __init__(self):
         self._loop = None
 
     def _while_loop(self):
-        """An internal API to create a Relay recurisve call for a matched TF
+        """An internal API to create a Relay recursive call for a matched TF
         `while_loop` construct.
         """
         wl = tvm.relay.var('while_loop')
@@ -1796,7 +1796,7 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
             -> All Const nodes are params.
             -> Last node is assumed as graph output.
             -> _output_shapes : Graph should be frozen with add_shapes=True.
-                                Or user can pass input shape dictionaly optionally.
+                                Or user can pass input shape dictionary optionally.
             -> DecodeJpeg, ResizeBilinear: These are dummy operators.
                                            Hence user should handle preprocessing outside.
             -> CheckNumerics: No implementation as of now for this.

python/tvm/relay/op/nn/nn.py

@@ -67,7 +67,7 @@ def conv2d(data,
         The weight expressions.
 
     strides : tuple of int, optional
-        The strides of convoltution.
+        The strides of convolution.
 
     padding : tuple of int, optional
         The padding of convolution on both sides of inputs before convolution.
@@ -129,7 +129,7 @@ def conv2d_transpose(data,
         The weight expressions.
 
     strides : Tuple[int], optional
-        The strides of convoltution.
+        The strides of convolution.
 
     padding : Tuple[int], optional
         The padding of convolution on both sides of inputs.
@@ -842,7 +842,7 @@ def contrib_conv2d_winograd_without_weight_transform(data,
         The Tile size of winograd. E.g. 2 for F(2x2, 3x3) and 4 for F(4x4, 3x3)
 
     strides : tuple of int, optional
-        The strides of convoltution.
+        The strides of convolution.
 
     padding : tuple of int, optional
         The padding of convolution on both sides of inputs before convolution.
@@ -908,7 +908,7 @@ def contrib_conv2d_winograd_nnpack_without_weight_transform(data,
         The weight expressions.
 
     strides : tuple of int, optional
-        The strides of convoltution.
+        The strides of convolution.
 
     padding : tuple of int, optional
         The padding of convolution on both sides of inputs before convolution.
@@ -975,7 +975,7 @@ def contrib_conv2d_nchwc(data,
         The kernel expressions.
 
     strides : tuple of int, optional
-        The strides of convoltution.
+        The strides of convolution.
 
     padding : tuple of int, optional
         The padding of convolution on both sides of inputs before convolution.
@@ -1040,7 +1040,7 @@ def contrib_depthwise_conv2d_nchwc(data,
         The kernel expressions.
 
     strides : tuple of int, optional
-        The strides of convoltution.
+        The strides of convolution.
 
     padding : tuple of int, optional
         The padding of convolution on both sides of inputs before convolution.
@@ -1156,7 +1156,7 @@ def deformable_conv2d(data,
         The weight expressions.
 
     strides : tuple of int, optional
-        The strides of convoltution.
+        The strides of convolution.
 
     padding : tuple of int, optional
         The padding of convolution on both sides of inputs before convolution.

src/common/socket.h

@@ -373,7 +373,7 @@ class TCPSocket : public Socket {
   }
   /*!
    * \brief decide whether the socket is at OOB mark
-   * \return 1 if at mark, 0 if not, -1 if an error occured
+   * \return 1 if at mark, 0 if not, -1 if an error occurred
    */
   int AtMark() const {
 #ifdef _WIN32

src/pass/arg_binder.h

@@ -50,7 +50,7 @@ namespace ir {
  *  - assert bufferB.shape[1] == n + 3
  *
  *  In general, this is a constraint solving problem. We have simplified assumption
- *  over the binding declaration, such that we require the variable occured in
+ *  over the binding declaration, such that we require the variable occurred in
  *  constraint must be declared in argument list. So it is illegal to have signature
  *  f(tA(shape=(n+3))) without any argument variable corresponds to n, even though
  *  it is already enough to derive n from the input argument.

topi/python/topi/cuda/reduction.py

@@ -37,7 +37,7 @@ def _schedule_reduce(op, sch, is_idx_reduce=False):
         num_thread = 32
         target = tvm.target.current_target()
         if target and target.target_name == "opencl":
-            # without it, CL_INVALID_WORK_GROUP_SIZE occured when running test_topi_reduce.py
+            # without it, CL_INVALID_WORK_GROUP_SIZE occurred when running test_topi_reduce.py
             # don't know why
             num_thread = 16
         block_x = tvm.thread_axis("blockIdx.x")