Merge pull request #242 from brettkoonce/more_minor_sp

docs: minor spelling tweaks

Author: prigoyal

docs/source/framework/pytorch_integration/autograd_with_tc.rst

@@ -1,7 +1,7 @@
 Autograd with TC
 ================
 
-We provide the TC intergation with PyTorch `autograd` so that it is easy to write
+We provide the TC integration with PyTorch `autograd` so that it is easy to write
 a training layer with TC and be able to run backwards as well if the layer is part
 of a network. We do not support double backwards right now. In order to write a
 training layer with TC, you need to follow the steps below:

docs/source/framework/pytorch_integration/note_about_performance.rst

@@ -5,8 +5,8 @@ Reuse outputs
 -------------
 
 TC depends on a tensor library to do the allocations for temporary variables or output tensors.
-So everytime TC is run on given input sizes, the output tensor shapes inferred by
-TC backend is passed back to the tensor library and the output variables are allocated
+So every time TC is run on given input sizes, the output tensor shapes inferred by
+TC backend are passed back to the tensor library and the output variables are allocated
 by making a :code:`malloc` call. However, this can be expensive and effect performance
 significantly. Rather, if your input tensor sizes do not change every time TC is run,
 you can keep reusing the output tensor already allocated in previous call. This helps

docs/source/integrating_any_ml_framework.rst

@@ -46,7 +46,7 @@ Concretely, following functions need to be defined:
 
 * :code:`compile`: This takes the dlpack tensors converted in previous step and dispatches compilation call to TC backend on those input dlpack tensors.
 
-* :code:`prepareOutputs`: TC backend send back the output tensors infor (strides, shapes, type etc.) and framework should allocate the outputs storage.
+* :code:`prepareOutputs`: TC backend send back the output tensors info (strides, shapes, type etc.) and framework should allocate the outputs storage.
 
 * :code:`run`: This simply dispatches the output tensor pointers to the TC backend and returns the outputs received.