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wenyuanbo
tic
Commits
7d6ca1b3
Commit
7d6ca1b3
authored
Sep 10, 2018
by
Jian Weng
Committed by
Tianqi Chen
Sep 10, 2018
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[Tutorial] fix the link thing for the pass tutorial (#1700)
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tutorials/dev/low_level_custom_pass.py
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tutorials/optimize/opt_gemm.py
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tutorials/dev/low_level_custom_pass.py
View file @
7d6ca1b3
...
...
@@ -7,18 +7,22 @@ TVM is a framework that abstracts away the heterogenity of machine learning acce
Sometimes users may want customize some analysis and IR transformations
to adapt TVM to their own specialized hardware. This tutorial helps users write
a customized pass in TVM.
Prerequisites
-------------
--------------
Before reading this tutorial, we assume readers have already known these topics well:
- Writing an algorithm in TVM and schedule it. Otherwise, see example tutorials like
`Optimize GeMM on CPU <https://docs.tvm.ai/tutorials/optimize/opt_gemm.html>_
`.
:ref:`opt-gemm
`.
- The basic structure of HalideIR. Otherwise, see ``HalideIR/src/ir/IR.h`` to learn what
attributes of IR nodes are defined.
- Visitor design pattern. Otherwise, check the
`Python AST module <https://docs.python.org/3/library/ast.html>
_`
to see how an AST
`Python AST module <https://docs.python.org/3/library/ast.html>
`_
to see how an AST
visitor is implemented.
- How a HalideIR/Schedule is lowered to either a LoweredFunc class or a LLVM module. Otherwise,
take a look at ``python/tvm/build_module.py`` to get some basics.
"""
from
__future__
import
absolute_import
,
print_function
...
...
tutorials/optimize/opt_gemm.py
View file @
7d6ca1b3
"""
.. _opt-gemm:
How to optimize GEMM on CPU
===========================
**Author**: `Jian Weng <https://github.com/were>`_,
\
...
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