.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_how_to_work_with_schedules_extern_op.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_how_to_work_with_schedules_extern_op.py:


External Tensor Functions
=========================
**Author**: `Tianqi Chen <https://tqchen.github.io>`_

While TVM supports transparent code generation, sometimes
it is also helpful to incorporate manual written code into
the pipeline. For example, we might want to use cuDNN for
some of the convolution kernels and define the rest of the stages.

TVM supports these black box function calls natively.
Specfically, TVM support all the tensor functions that are DLPack compatible.
Which means we can call any function with POD types(pointer, int, float)
or pointer to DLTensor as argument.


.. code-block:: default

    from __future__ import absolute_import, print_function

    import tvm
    from tvm import te
    import numpy as np
    from tvm.contrib import cblas
    import tvm.testing

    if not tvm.get_global_func("tvm.contrib.cblas.matmul", allow_missing=True):
        raise Exception("Not compiled with cblas support; can't build this tutorial")







Use Extern Tensor Function
--------------------------
In the example below, we use :any:`te.extern` to add an extern
array function call. In the extern call, we declare the shape
of output tensors. In the second argument we provide the list of inputs.

User will need to provide a function describing how to compute the result.
The compute function takes list of symbolic placeholder for the inputs,
list of symbolic placeholder for the outputs and returns the executing statement.

In this case we simply call a registered TVM function, which invokes a CBLAS call.
TVM does not control internal of the extern array function and treats it as blackbox.
We can further mix schedulable TVM calls that add a bias term to the result.



.. code-block:: default

    n = 1024
    l = 128
    m = 235
    bias = te.var("bias", dtype="float32")
    A = te.placeholder((n, l), name="A")
    B = te.placeholder((l, m), name="B")
    C = te.extern(
        (n, m),
        [A, B],
        lambda ins, outs: tvm.tir.call_packed(
            "tvm.contrib.cblas.matmul", ins[0], ins[1], outs[0], False, False
        ),
        name="C",
    )
    D = te.compute(C.shape, lambda i, j: C[i, j] + bias, name="D")
    s = te.create_schedule(D.op)







Verify the Result
-----------------
We can verify that the result matches what we expected.



.. code-block:: default

    dev = tvm.cpu(0)
    f = tvm.build(s, [A, B, D, bias], "llvm")
    a = tvm.nd.array(np.random.uniform(size=(n, l)).astype(A.dtype), dev)
    b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), dev)
    d = tvm.nd.array(np.zeros((n, m), dtype=D.dtype), dev)
    bb = 10.0
    f(a, b, d, bb)
    tvm.testing.assert_allclose(d.numpy(), np.dot(a.numpy(), b.numpy()) + 10, rtol=1e-5)







Extern Contrib Wrappers
-----------------------
TVM also provide extern contrib wrappers to useful extern calls,
the following line is equivalent to the previous example.



.. code-block:: default

    from tvm.contrib import cblas

    C = cblas.matmul(A, B)
    D = te.compute(C.shape, lambda i, j: C[i, j] + bias, name="D")
    s = te.create_schedule(D.op)







Hook Python Function as Extern
------------------------------
Since we can call into any PackedFunc in TVM. We can use the extern
function to callback into python.

The following example registers a python function into TVM runtime system
and use it to complete one stage of the computation.
This makes TVM much more flexible. For example, we can insert front-end
callbacks to inspect the intermediate results or mix customized code
with TVM.



.. code-block:: default

    @tvm.register_func("tvm.contrib.my_tvm_addone")
    def my_tvm_addone(x, y):
        print("my_tvm_addone signatures: %s, %s" % (type(x), type(y)))
        tvm.nd.array(x.numpy() + 1).copyto(y)


    A = te.placeholder((n,), name="A")
    B = te.extern(
        A.shape,
        [A],
        lambda ins, outs: tvm.tir.call_packed("tvm.contrib.my_tvm_addone", ins[0], outs[0]),
        name="C",
    )
    s = te.create_schedule(B.op)
    f = tvm.build(s, [A, B], "llvm")
    a = tvm.nd.array(np.random.uniform(size=(n,)).astype(A.dtype), dev)
    b = tvm.nd.array(np.random.uniform(size=(n,)).astype(B.dtype), dev)
    f(a, b)
    tvm.testing.assert_allclose(b.numpy(), a.numpy() + 1, rtol=1e-5)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    my_tvm_addone signatures: <class 'tvm.runtime.ndarray.NDArray'>, <class 'tvm.runtime.ndarray.NDArray'>



Summary
-------
- TVM calls extern tensor function via :any:`te.extern`
- Use contrib wrappers for short sugars of extern tensor calls.
- We can hook front-end function as extern tensor callbacks.



.. _sphx_glr_download_how_to_work_with_schedules_extern_op.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: extern_op.py <extern_op.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: extern_op.ipynb <extern_op.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_