[DOC] Capitalize TVM consistently (#3316)

tutorials/autotvm/tune_simple_template.py

@@ -19,19 +19,19 @@ Writing tunable template and Using auto-tuner
 =============================================
 **Author**: `Lianmin Zheng <https://github.com/merrymercy>`_
 
-This is an introduction tutorial to the auto-tuning module in tvm.
+This is an introduction tutorial to the auto-tuning module in TVM.
 
 There are two steps in auto-tuning.
 The first step is defining a search space.
 The second step is running a search algorithm to explore through this space.
-In this tutorial, you can learn how to perform these two steps in tvm.
+In this tutorial, you can learn how to perform these two steps in TVM.
 The whole workflow is illustrated by a matrix multiplication example.
 """
 
 ######################################################################
 # Install dependencies
 # --------------------
-# To use autotvm package in tvm, we need to install some extra dependencies.
+# To use autotvm package in TVM, we need to install some extra dependencies.
 # (change "3" to "2" if you use python2):
 #
 # .. code-block:: bash
@@ -65,7 +65,7 @@ from tvm import autotvm
 # tunable schedule template. You can regard the process of search space definition
 # as the parameterization of our existing 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):
@@ -236,7 +236,7 @@ def matmul(N, L, M, dtype):
 # In step 1, we build the search space by extending our old schedule code
 # into a template. The next step is to pick a tuner and explore in this space.
 #
-# Auto-tuners in tvm
+# Auto-tuners in TVM
 # ^^^^^^^^^^^^^^^^^^
 # The job for a tuner can be described by following pseudo code
 #

tutorials/language/schedule_primitives.py

@@ -144,7 +144,7 @@ print(tvm.lower(s, [A, B], simple_mode=True))
 ######################################################################
 # compute_at
 # ----------
-# For a schedule consists of multiple operators, tvm will compute
+# For a schedule consists of multiple operators, TVM will compute
 # tensors at the root separately by default.
 A = tvm.placeholder((m,), name='A')
 B = tvm.compute((m,), lambda i: A[i]+1, name='B')