[DOC] Codebase walkthrough with Vector add example #2206
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vinx13
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Dec 1, 2018
docs/dev/codebase_walkthrough.rst
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| - ``topi`` - Compute definitions and backend schedules for standard neural network operators. | ||
| - ``nnvm`` - C++ code and Python frontend for graph optimization and compilation. Depends on three directories above. | ||
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| Using standard Deep Learning terminologies, ``nnvm`` is the component that manages a computational graph, and nodes in a graph are compiled and executed using infrastructures implemented in ``src`` and ``python``. Operators corresponding to each node are registered in ``nnvm``. Registration can be done via C++ or Python. Implemenations for operators are in ``topi``, and they are also coded in either C++ or Python. |
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Implemenations -> Implementations
docs/dev/codebase_walkthrough.rst
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| Using standard Deep Learning terminologies, ``nnvm`` is the component that manages a computational graph, and nodes in a graph are compiled and executed using infrastructures implemented in ``src`` and ``python``. Operators corresponding to each node are registered in ``nnvm``. Registration can be done via C++ or Python. Implemenations for operators are in ``topi``, and they are also coded in either C++ or Python. | ||
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| When an user invokes graph compilation by ``nnvm.compiler.build(...)``, the following sequence of actions happens for each node in the graph: |
docs/dev/codebase_walkthrough.rst
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| The process of ``tvm.build()`` can be divided into two steps: | ||
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| - Lowering, where an high level, initial loop nest structures are transformed into a final, low level IR |
docs/dev/codebase_walkthrough.rst
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| One of the interesting aspects of TVM codebase is that interop between C++ and Python is not unidirectional. Typically, all code that do heavy liftings are implemented in C++, and Python bindings are provided for user interface. This is also true in TVM, but in TVM codebase, C++ code also call into functions defined in a Python module. For example, the convolution operator is implemented in Python, and its implementation is invoked from C++ code in nnvm. | ||
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| At the time of writing (Nov. 30, 2018), there is an going effort to reimplement functionality offered by ``nnvm`` in a new intermidiate representation called Relay. New Relay code resides in ``src/relay`` and ``python/tvm/relay``. |
docs/dev/codebase_walkthrough.rst
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| B = tvm.placeholder((n,), name='B') | ||
| C = tvm.compute(A.shape, lambda i: A[i] + B[i], name="C") | ||
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| Here, types of ``A``, ``B``, ``C`` are ``tvm.tensor.Tensor``, defined in ``python/tvm/tensor.py``. The Python ``Tensor`` is backed by C++ ``Tensor``, implemented in ``include/tvm/tensor.h`` and ``src/lang/tensor.cc``. All Python types in TVM can be thought of as a handle to the underlining C++ type with the same name. If you look at the definition of Python ``Tensor`` type below, you can see it is an subclass of ``NodeBase``. |
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@vinx13 Thanks. Fixed. |
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Would probably be good to port this to Relay soonish too. One of the Relay developers can probably manage that though, I will add a note to the tracking issue. |
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merrymercy
reviewed
Dec 11, 2018
docs/dev/codebase_walkthrough.rst
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| B = tvm.placeholder((n,), name='B') | ||
| C = tvm.compute(A.shape, lambda i: A[i] + B[i], name="C") | ||
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| Here, types of ``A``, ``B``, ``C`` are ``tvm.tensor.Tensor``, defined in ``python/tvm/tensor.py``. The Python ``Tensor`` is backed by C++ ``Tensor``, implemented in ``include/tvm/tensor.h`` and ``src/lang/tensor.cc``. All Python types in TVM can be thought of as a handle to the underlining C++ type with the same name. If you look at the definition of Python ``Tensor`` type below, you can see it is a subclass of ``NodeBase``. |
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The first stab at #2160.
@tqchen @yzhliu @merrymercy @Ravenwater please review.