.. _optimizations:

==============
Optimizations
==============

PyTensor applies many kinds of graph rewrites, some of which can be considered "optimizations":
 * simplifying and standardizing the form of the expression graph (e.g.  :term:`merge`, :term:`add canonicalization` ),
 * reducing the maximum memory footprint (e.g. :term:`inplace_elemwise`),
 * increasing execution speed (e.g. :term:`constant folding`).

The optimizations are listed in roughly chronological order.  The table below
gives a quick summary of the optimizations included in the default modes.
The descriptions are brief and point to further reading.

If you would like to add an additional optimization, see :ref:`graph_rewriting`.

When compiling, we can make a tradeoff between compile-time and run-time.
Faster compile times will result in fewer optimizations being applied, hence generally slower run-times.
For making this tradeoff when compiling, we provide a set of 4 optimization
modes, 'o1' to 'o4', where 'o1' leads to fastest compile-time and 'o4' leads to
fastest run-time in general.
For an even faster run-time, we could disable assertions (which could be time
consuming) for valid user inputs, using the optimization mode 'unsafe', but this
is, as the name suggests, unsafe.  See :ref:`unsafe_rewrites`.

..  note::

    This list is partial.

    The print_summary method allows several OpDBs and optimizers to list the
    executed optimizations.  This makes it possible to have an up-to-date list.

    .. code-block:: bash

        python -c "import pytensor; pytensor.compile.optdb.query(pytensor.compile.predefined_optimizers['<OPT_ID>']).print_summary()"

    where <OPT_ID> can be one of o1 (:ref:`† <o1=>`), o2, o3, o4 (:ref:`* <o4=>`),
    Stabilization or unsafe.


========================================================= ============== === === ================= ============= ======
Optimization                                              o4             o3  o2  o1                Stabilization unsafe
                                                          :ref:`* <o4=>`         :ref:`† <o1=>`
========================================================= ============== === === ================= ============= ======
:term:`merge`                                             x              x   x    x                              x
:term:`constant folding<constant folding>`                x              x   x    x                              x
:term:`shape promotion<shape promotion>`                  x              x                                       x
:term:`fill cut<fill cut>`                                x              x                                       x
:term:`inc_subtensor srlz.<inc_subtensor serialization>`  x              x                                       x
:term:`reshape_chain`                                     x              x                                       x
:term:`const. elimination<constant elimination>`          x              x                                       x
:term:`add canonical. <add canonicalization>`             x              x                                       x
:term:`mul canonical. <mul canonicalization>`             x              x                                       x
:term:`dot22`                                             x              x                                       x
:term:`sparse_dot`                                        x              x                                       x
:term:`sum_scalar_mul`                                    x              x                                       x
:term:`neg_neg`                                           x              x                                       x
:term:`neg_div_neg`                                       x              x                                       x
:term:`add specialize <add specialization>`               x              x                                       x
:term:`mul specialize <mul specialization>`               x              x                                       x
:term:`pow specialize <pow specialization>`               x              x                                       x
:term:`inplace_setsubtensor`                              x
:term:`gemm`                                              x              x                                       x
:term:`inplace_elemwise`                                  x
:term:`inplace_random`                                    x
:term:`elemwise fusion`                                   x              x   x                                   x
:term:`local_log_softmax`                                 x              x                         x             x
:term:`local_remove_all_assert`                                                                                  x
========================================================= ============== === === ================= ============= ======

..  note::

    .. _o4=:

    \*) o4 is equivalent to fast_run

    .. _o1=:

    †) o1 is equivalent to fast_compile

.. glossary::

    merge
        A simple optimization in which redundant :term:`Apply` nodes are
        combined.  For example, in ``function([x,y], [(x+y)*2, (x+y)*3])`` the merge
        optimization will ensure that ``x`` and ``y`` are only added once.

        This optimization is very useful because it frees users to write
        highly redundant mathematical code.  PyTensor will make sure to compute
        just what is necessary.

        See :class:`MergeOptimizer`.

    constant folding
        When all the inputs to an expression are constant, then the expression
        can be pre-computed at compile-time.

        See :func:`opt.constant_folding`

    shape promotion
        PyTensor often knows how to infer the shape of an output from the shape
        of its inputs.  Without this optimization, it would otherwise have to
        compute things (e.g. ``log(x)``) just to find out the shape of it!

        See :func:`opt.local_shape_lift_*`

    fill cut
        `Fill(a,b)` means to make a tensor of the shape of `a` full of the value `b`.
        Often when fills are used with elementwise operations (e.g. f) they are
        un-necessary:
        * ``f(fill(a,b), c) -> f(b, c)``
        * ``f(fill(a, b), fill(c, d), e) -> fill(a, fill(c, f(b, d, e)))``

        See :func:`opt.local_fill_sink`

    inc_subtensor serialization
        Incrementing a small subregion of a large tensor can be done quickly
        using an inplace operation, but if two increments are being done on
        the same large tensor, then only one of them can be done inplace.
        This optimization reorders such graphs so that all increments can be
        done inplace.

        ``inc_subtensor(a,b,idx) + inc_subtensor(a,c,idx) -> inc_subtensor(inc_subtensor(a,b,idx),c,idx)``

        See :func:`local_IncSubtensor_serialize`

    reshape_chain
        This optimizes graphs like ``reshape(reshape(x, shape1), shape2)`` -> ``reshape(x, shape2)``

        See :func:`local_reshape_chain`

    constant elimination
        Many constants indicate special cases, such as ``pow(x,1) -> x``.
        PyTensor recognizes many of these special cases.

        See :func:`local_mul_specialize`, :func:`local_mul_specialize`,:func:`local_mul_specialize`

    add canonicalization
        Rearrange expressions of additions and subtractions to a canonical
        form:

        .. math::

            (a+b+c+...) - (z + x + y + ....)

        See :class:`AlgebraicCanonizer`, :attr:`local_add_canonizer`

    mul canonicalization
        Rearrange expressions of multiplication and division to a canonical
        form:

        .. math::

            \frac{a * b * c * ...}{z * x * y * ....}

        See :class:`AlgebraicCanonizer`, :attr:`local_mul_canonizer`

    dot22
        This simple optimization replaces dot(matrix, matrix) with a special
        `dot22` op that only works for matrix multiplication.  This op is
        implemented with a call to GEMM, and sometimes replaced entirely by
        the :term:`gemm` optimization.

        See :func:`local_dot_to_dot22`

    sparse_dot
        PyTensor has a sparse matrix multiplication algorithm that is faster in
        many cases than scipy's (for dense matrix output).  This optimization
        swaps scipy's algorithm for ours.

        See :func:`local_structured_dot`

    sum_scalar_mul
        This optimizes graphs like ``sum(scalar * tensor)`` -> ``scalar * sum(tensor)``

        See :func:`local_sum_mul_by_scalar`

    neg_neg
        Composition of two negatives can be cancelled out.

        See :func:`local_neg_neg`

    neg_div_neg
        Matching negatives in both the numerator and denominator can both be removed.

        See :func:`local_neg_div_neg`

    add specialization
        This optimization simplifies expressions involving the addition of
        zero.

        See :func:`local_add_specialize`

    mul specialization
        Several special cases of mul() exist, and this optimization tries to
        recognize them. Some examples include:
        * ``mul(x,x)`` -> ``x**2``
        * ``mul(x,0)`` -> ``zeros_like(x)``
        * ``mul(x, -1)`` -> ``neg(x)``

        See :func:`local_mul_specialize`

    pow specialization
        Several special cases of pow() exist, and this optimization tries to
        recognize them. Some examples include:
        * ``pow(x,2)`` -> ``x**2``
        * ``pow(x,0)`` -> ``ones_like(x)``
        * ``pow(x, -0.5)`` -> ``reciprocal(sqrt(x))``

        See :func:`local_pow_specialize`


    inplace_setsubtensor
        In order to be a pure Op, setsubtensor must copy its entire input, and
        modify just the subtensor in question (possibly a single element).  It
        is much more efficient to modify that element inplace.

        See :func:`local_inplace_setsubtensor`

    gemm
        Numerical libraries such as MKL and ATLAS implement the BLAS-level-3
        interface, and provide a function `GEMM` that implements
        :math:`Z \leftarrow \alpha A \cdot B + \beta Z`, for matrices `A`, `B`
        and `Z`, and scalars :math:`\alpha, \beta`.

        This optimization tries to rearrange a variety of linear algebra
        expressions into one or more instances of this motif, and replace them
        each with a single `Gemm` Op.

        See :class:`GemmOptimizer`

    inplace_elemwise
        When one of the inputs to an elementwise expression has the same type
        and shape as the output, and is no longer needed for computation after
        the elemwise expression is evaluated, then we can reuse the storage of
        the input to store the output.

        See :func:`insert_inplace_optimizer`

    inplace_random
        Typically when a graph uses random numbers, the RandomState is stored
        in a shared variable, used once per call and, updated after each function
        call.  In this common case, it makes sense to update the random number generator in-place.

        See :func:`random_make_inplace`

    elemwise fusion
        This optimization compresses subgraphs of computationally cheap
        elementwise operations into a single Op that does the whole job in a
        single pass over the inputs (like loop fusion).  This is a win when
        transfer from main memory to the CPU is a bottleneck.

        See :class:`FusionOptimizer`

    local_log_softmax
        This is a stabilization optimization.
        It can happen due to rounding errors that the softmax probability of one value gets to 0.
        Taking the log of 0 would generate -inf that will probably generate NaN later.
        We return a closer answer.

    local_remove_all_assert
        This is an unsafe optimization.
        For the fastest possible PyTensor, this optimization can be enabled by
	setting ``optimizer_including=local_remove_all_assert`` which will
	remove all assertions in the graph for checking user inputs are valid.
        Use this optimization if you are sure everything is valid in your graph.

	See :ref:`unsafe_rewrites`
