Typo fixes related to new memory profiling

doc/extending/type.txt

@@ -108,36 +108,36 @@ default values.
 
     .. method:: get_shape_info(obj)
 
-      Optional. Only needed to profile the memory of this Type of object
+      Optional. Only needed to profile the memory of this Type of object.
 
-      Return the information needed to compute the memory size of obj.
+      Return the information needed to compute the memory size of ``obj``.
 
-      The memory size is only the data, so this exclude the container.
+      The memory size is only the data, so this excludes the container.
       For an ndarray, this is the data, but not the ndarray object and
-      others data structures as shape and strides.
+      other data structures such as shape and strides.
 
-      get_shape_info() and get_size() work in tendem for the memory profiler.
+      ``get_shape_info()`` and ``get_size()`` work in tandem for the memory profiler.
 
-      get_shape_info() is called during the execution of the function.
+      ``get_shape_info()`` is called during the execution of the function.
       So it is better that it is not too slow.
 
-      get_size() will be called with the output of this function
+      ``get_size()`` will be called on the output of this function
       when printing the memory profile.
 
-      :param obj: The object that this Type represent during execution
-      :return: Python object that self.get_size() understand
+      :param obj: The object that this Type represents during execution
+      :return: Python object that ``self.get_size()`` understands
 
 
     .. method:: get_size(shape_info)
 
-        Number of bytes taken by the object represented by shape_info
+        Number of bytes taken by the object represented by shape_info.
 
-        Optional. Only needed to profile the memory of this Type of object
+        Optional. Only needed to profile the memory of this Type of object.
 
         :param shape_info: the output of the call to get_shape_info()
-        :return: the number of bytes taken by the object described in
-          shape_info.
-        """
+        :return: the number of bytes taken by the object described by
+            ``shape_info``.
+
 For each method, the *default* is what ``Type`` defines
 for you. So, if you create an instance of ``Type`` or an
 instance of a subclass of ``Type``, you

doc/library/config.txt

@@ -271,7 +271,7 @@ import theano and print the config variable, as in:
 
     Default False
 
-    Do the vm/cvm linkers profile the execution of Theano functions?
+    Do the vm/cvm linkers profile the execution time of Theano functions?
 
 .. attribute:: profile_memory
 
@@ -279,8 +279,8 @@ import theano and print the config variable, as in:
 
     Default False
 
-    Do the vm/cvm linkers profile the memory of Theano functions get printed?
-    It only work when profile=True.
+    Do the vm/cvm linkers profile the memory usage of Theano functions?
+    It only works when profile=True.
 
 .. attribute:: profile_optimizer
 
@@ -289,26 +289,26 @@ import theano and print the config variable, as in:
     Default False
 
     Do the vm/cvm linkers profile the optimization phase when compiling a Theano function?
-    It only work when profile=True.
+    It only works when profile=True.
 
 .. attribute:: profiling.n_apply
 
     Positive int value, default: 20.
 
-    The number of apply node to print in the profiler output
+    The number of Apply nodes to print in the profiler output
 
 .. attribute:: profiling.n_ops
 
     Positive int value, default: 20.
 
-    The number of ops to print in the profiler output
+    The number of Ops to print in the profiler output
 
 .. attribute:: profiling.min_memory_size
 
     Positive int value, default: 1024.
 
-    For the memory profile, do not print apply nodes if the size
-    of their outputs (in bytes) is lower then this.
+    For the memory profile, do not print Apply nodes if the size
+    of their outputs (in bytes) is lower than this.
 
 .. attribute:: config.lib.amdlibm
 

theano/compile/profilemode.py

@@ -49,12 +49,12 @@ class Profile_Maker(FunctionMaker):
             theano.sandbox.cuda.cuda_enabled):
             if os.environ.get('CUDA_LAUNCH_BLOCKING', '0') != '1':
                 raise Exception(
-                    "You are running Theano profiler with CUDA enabled."
-                    " Theano GPU ops execution are asynchron by default."
+                    "You are running the Theano profiler with CUDA enabled."
+                    " Theano GPU ops execution is asynchronous by default."
                     " So by default, the profile is useless."
-                    " You must use set the environment variable"
-                    " CUDA_LAUNCH_BLOCKING to 1 to tell the CUDA drvier to"
-                    " synchonize the execution to get meaning full profile.")
+                    " You must set the environment variable"
+                    " CUDA_LAUNCH_BLOCKING to 1 to tell the CUDA driver to"
+                    " synchronize the execution to get a meaningful profile.")
 
         # create a function-specific storage container for profiling info
         profile = ProfileStats(atexit_print=False)

theano/compile/profiling.py

@@ -37,18 +37,18 @@ AddConfigVar('profiling.time_thunks',
              BoolParam(True))
 
 AddConfigVar('profiling.n_apply',
-             "Number of apply instances to print by default",
+             "Number of Apply instances to print by default",
              IntParam(20, lambda i: i > 0),
              in_c_key=False)
 
 AddConfigVar('profiling.n_ops',
-             "Number of ops to print by default",
+             "Number of Ops to print by default",
              IntParam(20, lambda i: i > 0),
              in_c_key=False)
 
 AddConfigVar('profiling.min_memory_size',
-             """For the memory profile, do not print apply nodes if the size
-             of their outputs (in bytes) is lower then this threshold""",
+             """For the memory profile, do not print Apply nodes if the size
+             of their outputs (in bytes) is lower than this threshold""",
              IntParam(1024, lambda i: i >= 0),
              in_c_key=False)
 
@@ -185,12 +185,12 @@ class ProfileStats(object):
             theano.sandbox.cuda.cuda_enabled):
             if os.environ.get('CUDA_LAUNCH_BLOCKING', '0') != '1':
                 raise Exception(
-                    "You are running Theano profiler with CUDA enabled."
-                    " Theano GPU ops execution are asynchron by default."
+                    "You are running the Theano profiler with CUDA enabled."
+                    " Theano GPU ops execution is asynchronous by default."
                     " So by default, the profile is useless."
-                    " You must use set the environment variable"
-                    " CUDA_LAUNCH_BLOCKING to 1 to tell the CUDA drvier to"
-                    " synchonize the execution to get meaning full profile.")
+                    " You must set the environment variable"
+                    " CUDA_LAUNCH_BLOCKING to 1 to tell the CUDA driver to"
+                    " synchronize the execution to get a meaningful profile.")
 
         self.apply_callcount = {}
         self.output_size = {}
@@ -708,7 +708,7 @@ class ProfileStats(object):
 
         if len(fct_memory) > 1:
             print >> file,  ("Memory Profile "
-                             "(the max between all function in that profile)")
+                             "(the max between all functions in that profile)")
         else:
             print >> file,  "Memory Profile"
 
@@ -717,15 +717,15 @@ class ProfileStats(object):
         print >> file,  "---"
 #        print >> file,  "    Max if no gc, inplace and view: %dKB" % int(
 #            round(max_sum_size / 1024))
-        print >> file,  "    Max if linker=cvm (default): unknow"
+        print >> file,  "    Max if linker=cvm (default): unknown"
         print >> file,  "    Max if no gc (allow_gc=False): %dKB" % int(round(
                              max_node_memory_size / 1024.))
         print >> file,  "    Max if linker=c|py: %dKB" % int(round(
             max_running_max_memory_size / 1024.))
-#        print >> file,  "    Memory saved if view are used: %dKB" % int(round(
-#            max_node_memory_saved_by_view / 1024.))
-#        print >> file,  "    Memory saved if inplace op are used: %dKB" % int(
-#            round(max_node_memory_saved_by_inplace / 1024.))
+#        print >> file,  "    Memory saved if views are used: %dKB" % int(
+#            round(max_node_memory_saved_by_view / 1024.))
+#        print >> file,  "    Memory saved if inplace ops are used: %dKB" % \
+#            int(round(max_node_memory_saved_by_inplace / 1024.))
         print >> file,  "    Memory saved if gc is enabled (linker=c|py): %dKB" % int(
             round(max_node_memory_size - max_running_max_memory_size) / 1024.)
         if (hasattr(theano, 'sandbox') and
@@ -734,7 +734,7 @@ class ProfileStats(object):
             hasattr(theano.sandbox.cuda.cuda_ndarray.cuda_ndarray,
                     'theano_allocated')):
             _, gpu_max = theano.sandbox.cuda.cuda_ndarray.cuda_ndarray.theano_allocated()
-            print >> file,  ("    Max Memory allocated on the GPU"
+            print >> file,  ("    Max Memory allocated on the GPU "
                              "(for all functions): %dKB" %
                              int(round(gpu_max / 1024.)))
 
@@ -785,11 +785,11 @@ class ProfileStats(object):
                                                    )
         print >> file, ''
         if N == 0:
-            print >> file, ('    All Apply node have outputs size that take'
-                            ' less then %dB.' %
+            print >> file, ('    All Apply nodes have output sizes that take'
+                            ' less than %dB.' %
                             config.profiling.min_memory_size)
         print >> file,  (
-            "    <created/inplace/view> is taked from the op declaration.")
+            "    <created/inplace/view> is taken from the Op's declaration.")
         print >> file,  ("    Apply nodes marked 'inplace' or 'view' may"
                          " actually allocate memory, this is not reported"
                          " here. If you use DebugMode, warnings will be"

theano/gof/vm.py

@@ -582,12 +582,12 @@ class VM_Linker(link.LocalLinker):
             theano.sandbox.cuda.cuda_enabled):
             if os.environ.get('CUDA_LAUNCH_BLOCKING', '0') != '1':
                 raise Exception(
-                    "You are running Theano profiler with CUDA enabled."
-                    " Theano GPU ops execution are asynchron by default."
+                    "You are running the Theano profiler with CUDA enabled."
+                    " Theano GPU ops execution is asynchronous by default."
                     " So by default, the profile is useless."
-                    " You must use set the environment variable"
-                    " CUDA_LAUNCH_BLOCKING to 1 to tell the CUDA drvier to"
-                    " synchonize the execution to get meaning full profile.")
+                    " You must set the environment variable"
+                    " CUDA_LAUNCH_BLOCKING to 1 to tell the CUDA driver to"
+                    " synchronize the execution to get a meaningful profile.")
 
         if no_recycling is None:
             no_recycling = []

theano/tensor/basic.py

@@ -1199,32 +1199,33 @@ class TensorType(Type):
         return numpy.zeros(shape, dtype=self.dtype)
 
     def get_shape_info(self, obj):
-        """Return the information needed to compute the memory size of obj.
-
+        """
+        Return the information needed to compute the memory size of ``obj``.
 
-        The memory size is only the data, so this exclude the container.
+        The memory size is only the data, so this excludes the container.
         For an ndarray, this is the data, but not the ndarray object and
-        others data structures as shape and strides.
+        other data structures such as shape and strides.
 
-        get_shape_info() and get_size() work in tendem for the memory profiler.
+        ``get_shape_info()`` and ``get_size()`` work in tandem for the memory
+        profiler.
 
-        get_shape_info() is called during the execution of the function.
+        ``get_shape_info()`` is called during the execution of the function.
         So it is better that it is not too slow.
 
-        get_size() will be called with the output of this function
+        ``get_size()`` will be called on the output of this function
         when printing the memory profile.
 
-        :param obj: The object that this Type represent during execution
-        :return: Python object that self.get_size() understand
+        :param obj: The object that this Type represents during execution
+        :return: Python object that ``self.get_size()`` understands
         """
         return obj.shape
 
     def get_size(self, shape_info):
-        """ Number of bytes taken by the object represented by shape_info
+        """ Number of bytes taken by the object represented by shape_info.
 
         :param shape_info: the output of the call to get_shape_info()
-        :return: the number of bytes taken by the object described in
-          shape_info.
+        :return: the number of bytes taken by the object described by
+            ``shape_info``.
         """
         if shape_info:
             return numpy.prod(shape_info) * numpy.dtype(self.dtype).itemsize