Minor doc fixes (#1458)

tutorials/autotvm/tune_simple_template.py

@@ -28,7 +28,7 @@ from tvm import autotvm
 # tunable schedule template. You can regard the process of search space definition
 # as the parametrization of our exiting schedule code.
 #
-# To begin with, here is how we implement a blocked matrix multiplication in tvm
+# To begin with, here is how we implement a blocked matrix multiplication in tvm.
 
 # Matmul V0: Constant tiling factor
 def matmul_v0(N, L, M, dtype):

tutorials/cross_compilation_and_rpc.py

@@ -60,7 +60,7 @@ for example.
 #
 #     make runtime
 #
-# After success of buildind runtime, we need set environment varibles
+# After building runtime successfully, we need to set environment varibles
 # in :code:`~/.bashrc` file of yourself account or :code:`/etc/profile`
 # of system enviroment variables. Assuming your TVM directory is in
 # :code:`~/tvm` and set environment variables below your account.
@@ -69,7 +69,7 @@ for example.
 #
 #    vi ~/.bashrc
 #
-# We need edit :code:`~/.bashrc` using :code:`vi ~/.bashrc` and add
+# We need to edit :code:`~/.bashrc` using :code:`vi ~/.bashrc` and add
 # lines below (Assuming your TVM directory is in :code:`~/tvm`):
 #
 #   .. code-block:: bash
@@ -92,8 +92,8 @@ for example.
 #
 #     python -m tvm.exec.rpc_server --host 0.0.0.0 --port=9090
 #
-# After executing command above, if you see these lines below, it's
-# successful to start RPC server on your device.
+# After executing the command above, if you see these lines below, it means
+# the RPC server started successfully on your device.
 #
 #    .. code-block:: bash
 #
@@ -215,7 +215,7 @@ np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
 
 ######################################################################
 # When you want to evaluate the performance of the kernel on the remote
-# device, it is important to avoid overhead of remote function call.
+# device, it is important to avoid overhead of remote function calls.
 # :code:`time_evaluator` will returns a remote function that runs the
 # function over number times, measures the cost per run on the remote
 # device and returns the measured cost.

tutorials/language/tuple_inputs.py

@@ -18,7 +18,7 @@ import numpy as np
 # Describe Batchwise Computation
 # ------------------------------
 # For operators which have the same shape, we can put them together as
-# the inputs of :any:`tvm.compute`, if we wish they can be scheduled
+# the inputs of :any:`tvm.compute`, if we want them to be scheduled
 # together in the next schedule procedure.
 #
 n = tvm.var("n")