Graph Rewriting#
In this document we will explain how graph rewriting works and how graph rewrites can be constructed in PyTensor.
Todo
The old “optimization” nomenclature is still in use throughout some of these documents and the codebase; however, this is being changed to more accurately distinguish between general graph rewriting for any purpose and the kind that is explicitly intended to “optimize” a graph in some way.
Graph and Node Rewriters#
There are two types of basic rewriters: graph rewriters and node rewriters.
A graph rewriter takes a FunctionGraph
object (see its
documentation for more details) and navigates through it
in a suitable way, replacing some Variable
s by others in the process.
A node rewriter, on the other hand, is defined as a function on a
single Apply node and must return either False
(to mean that
nothing is to be done) or a list of new Variable
s that we would like to
substitute for the node’s current outputs.
Some graph rewriters navigate the computation graph in a particular fashion
(e.g. in topological order, reverse-topological order, random order, etc.) and
apply one or more node rewriters at each step. WalkingGraphRewriter
is
one such example.
Rewriters that are holistic, meaning that they must take into account dependencies that might be all over the graph, should usually be graph rewriters. Rewrites that only need a narrow view of sub-graphs are better defined as node rewrites.
Graph Rewriting#
- class GraphRewriter#
- apply(fgraph)#
This method takes a
FunctionGraph
object which contains the computation graph and does modifications in line with what the rewriter is meant to do. This is one of the main methods of the rewriter.
- add_requirements(fgraph)#
This method takes a
FunctionGraph
object and adds features to it. These features are “plugins” that are needed for theGraphRewriter.apply()
method to do its job properly.
- rewrite(fgraph)#
This is the interface function called by PyTensor. It calls
GraphRewriter.apply()
by default.
Node Rewriting#
A node rewriter is an object which defines the following methods:
- class NodeRewriter#
- transform(fgraph, node)#
This method takes a
FunctionGraph
and anApply
node and returns eitherFalse
to signify that no changes are to be done or a list ofVariable
s which matches the length of the node’soutputs
list. When theNodeRewriter
is applied by aNodeProcessingGraphRewriter
, the outputs of the node passed as argument to theNodeRewriter
will be replaced by the list returned.
A Simplification Rule#
For starters, let’s define the following simplification:
We will implement it in three ways: using a graph rewriter, a node rewriter with
a NodeProcessingGraphRewriter
, and then using the
PatternNodeRewriter
.
Graph Rewriter Implementation#
Here is the code for a graph rewriter implementing the simplification described above:
import pytensor
from pytensor.graph.rewriting.basic import GraphRewriter
from pytensor.graph.features import ReplaceValidate
class Simplify(GraphRewriter):
def add_requirements(self, fgraph):
fgraph.attach_feature(ReplaceValidate())
def apply(self, fgraph):
for node in fgraph.toposort():
if node.op == true_div:
x, y = node.inputs
z = node.outputs[0]
if x.owner and x.owner.op == mul:
a, b = x.owner.inputs
if y == a:
fgraph.replace_validate(z, b)
elif y == b:
fgraph.replace_validate(z, a)
simplify = Simplify()
Here’s how it works: first, in add_requirements()
, we add the
ReplaceValidate
Feature
located in
features – [doc TODO]. This feature adds the replace_validate()
method to fgraph
, which is an enhanced version of FunctionGraph.replace()
that
does additional checks to ensure that we are not messing up the
computation graph.
In a nutshell, ReplaceValidate
grants access to fgraph.replace_validate()
,
and fgraph.replace_validate()
allows us to replace a Variable
with
another while respecting certain validation constraints. As an
exercise, try to rewrite Simplify
using NodeFinder
. (Hint: you
want to use the method it publishes instead of the call to toposort)
Then, in GraphRewriter.apply()
we do the actual job of simplification. We start by
iterating through the graph in topological order. For each node
encountered, we check if it’s a div
node. If not, we have nothing
to do here. If so, we put in x
, y
and z
the numerator,
denominator and quotient (output) of the division.
The simplification only occurs when the numerator is a multiplication,
so we check for that. If the numerator is a multiplication we put the
two operands in a
and b
, so
we can now say that z == (a*b)/y
. If y==a
then z==b
and if
y==b
then z==a
. When either case happens then we can replace
z
by either a
or b
using FunctionGraph.replace_validate()
; otherwise, we do
nothing.
Now, we test the rewriter:
>>> from pytensor.scalar import float64, add, mul, true_div
>>> x = float64('x')
>>> y = float64('y')
>>> z = float64('z')
>>> a = add(z, mul(true_div(mul(y, x), y), true_div(z, x)))
>>> e = pytensor.graph.fg.FunctionGraph([x, y, z], [a])
>>> e
FunctionGraph(add(z, mul(true_div(mul(y, x), y), true_div(z, x))))
>>> simplify.rewrite(e)
>>> e
FunctionGraph(add(z, mul(x, true_div(z, x))))
You can check what happens if you put many instances of \(\frac{xy}{y}\) in the graph. Note that it sometimes won’t work for reasons that have nothing to do with the quality of the rewrite you wrote. For example, consider the following:
>>> x = float64('x')
>>> y = float64('y')
>>> z = float64('z')
>>> a = true_div(mul(add(y, z), x), add(y, z))
>>> e = pytensor.graph.fg.FunctionGraph([x, y, z], [a])
>>> e
FunctionGraph(true_div(mul(add(y, z), x), add(y, z)))
>>> simplify.rewrite(e)
>>> e
FunctionGraph(true_div(mul(add(y, z), x), add(y, z)))
Nothing happened here. The reason is: add(y, z) != add(y,
z)
. That is the case for efficiency reasons. To fix this problem we
first need to merge the parts of the graph that represent the same
computation, using the MergeOptimizer
defined in
pytensor.graph.rewriting.basic
.
>>> from pytensor.graph.rewriting.basic import MergeOptimizer
>>> MergeOptimizer().rewrite(e)
(0, ..., None, None, {}, 1, 0)
>>> e
FunctionGraph(true_div(mul(*1 -> add(y, z), x), *1))
>>> simplify.rewrite(e)
>>> e
FunctionGraph(x)
Once the merge is done, both occurrences of add(y, z)
are
collapsed into a single one and is used as an input in two
places. Note that add(x, y)
and add(y, x)
are still considered
to be different because PyTensor has no clue that add
is
commutative. You may write your own graph rewrite to identify
computations that are identical with full knowledge of the rules of
arithmetic that your Ops implement. PyTensor might provide facilities
for this somewhere in the future.
Note
FunctionGraph
is an PyTensor structure intended for the rewrite
phase. It is used internally by pytensor.function()
and is rarely
exposed to the end user.
Node Rewriter Implementation#
The local version of the above code would be the following:
from pytensor.graph.rewriting.basic import NodeRewriter
class LocalSimplify(NodeRewriter):
def transform(self, fgraph, node):
if node.op == true_div:
x, y = node.inputs
if x.owner and x.owner.op == mul:
a, b = x.owner.inputs
if y == a:
return [b]
elif y == b:
return [a]
return False
def tracks(self):
# This tells certain navigators to only apply this `NodeRewriter`
# on these kinds of `Op`s
return [true_div]
local_simplify = LocalSimplify()
In this case, the transformation is defined in the
NodeRewriter.transform()
method, which is given an explicit
Apply
node on which to work. The entire graph–as a fgraph
–is
also provided, in case global information is needed.
If no changes are to be made, False
must be returned; otherwise, a list of replacements for the node’s
outputs are returned. This list must have the same length as
node.outputs
. If one of node.outputs
doesn’t have clients
(e.g. available via fgraph.clients
), then it is not used elsewhere in the graph and
you can put None
in the returned list to remove it.
In order to apply the node rewriter throughout a graph, we use it in conjunction
with a NodeProcessingGraphRewriter
. A NodeProcessingGraphRewriter
is
a graph rewriter that loops through all nodes in the graph (or a well-defined
subset of them) and applies one or several node rewriters.
>>> x = float64('x')
>>> y = float64('y')
>>> z = float64('z')
>>> a = add(z, mul(true_div(mul(y, x), y), true_div(z, x)))
>>> e = pytensor.graph.fg.FunctionGraph([x, y, z], [a])
>>> e
FunctionGraph(add(z, mul(true_div(mul(y, x), y), true_div(z, x))))
>>> simplify = pytensor.graph.rewriting.basic.WalkingGraphRewriter(local_simplify)
>>> simplify.rewrite(e)
(<pytensor.graph.rewriting.basic.WalkingGraphRewriter object at 0x...>, 1, 5, 3, ..., ..., ...)
>>> e
FunctionGraph(add(z, mul(x, true_div(z, x))))
SubstitutionNodeRewriter
, RemovalNodeRewriter
, PatternNodeRewriter
#
PyTensor defines some shortcuts to make NodeRewriter
s:
- SubstitutionNodeRewriter(op1, op2)#
Replaces all uses of
op1
byop2
. In other words, the outputs of allApply
nodes usingop1
by the outputs ofApply
nodes involvingop2
, where their inputs are the same.
- RemovalNodeRewriter(op)#
Removes all uses of
op
in the following way: ify = op(x)
theny
is replaced byx
.op
must have as many outputs as it has inputs. The first output becomes the first input, the second output becomes the second input, and so on.
- PatternNodeRewriter(pattern1, pattern2)#
Replaces all occurrences of the first pattern by the second pattern. See
PatternNodeRewriter
.
from pytensor.scalar import identity
from pytensor.graph.rewriting.basic import SubstitutionNodeRewriter, RemovalNodeRewriter, PatternNodeRewriter
# Replacing `add` by `mul` (this is not recommended for primarily
# mathematical reasons):
add_to_mul = SubstitutionNodeRewriter(add, mul)
# Removing `identity`
remove_identity = RemovalNodeRewriter(identity)
# The "simplify" operation we've been defining in the past few
# sections. Note that we need two patterns to account for the
# permutations of the arguments to `mul`.
local_simplify_1 = PatternNodeRewriter((true_div, (mul, 'x', 'y'), 'y'), 'x')
local_simplify_2 = PatternNodeRewriter((true_div, (mul, 'x', 'y'), 'x'), 'y')
Note
SubstitutionNodeRewriter
, RemovalNodeRewriter
and PatternNodeRewriter
produce node rewriters, which
means that everything we said previously about node rewriters
apply (e.g. they need to be wrapped in a NodeProcessingGraphRewriter
, etc.)
When a rewriter can be naturally expressed using SubstitutionNodeRewriter
, RemovalNodeRewriter
or PatternNodeRewriter
, it is highly recommended to use them.
Unification and reification#
The PatternNodeRewriter
class uses unification and reification to implement a
more succinct and reusable form of “pattern matching and replacement”.
In general, use of the unification and reification tools is preferable when
a rewrite’s matching and replacement are non-trivial, so we will briefly explain
them in the following.
PyTensor’s unification and reification tools are provided by the
logical-unification package.
The basic tools are unify()
, reify()
, and var
. The class var
construct logic variables, which represent the elements to be unified/matched, unify()
performs the “matching”, and reify()
performs the “replacements”.
See unification
’s documentation for an introduction to unification and reification.
In order to use unify()
and reify()
with PyTensor graphs, we need an intermediate
structure that will allow us to represent PyTensor graphs that contain var
s, because
PyTensor Op
s and Apply
nodes will not accept these foreign objects as inputs.
PatternNodeRewriter
uses Python tuple
s to effectively represent Apply
nodes and
str
s to represent logic variables (i.e. var
s in the unification
library).
Behind the scenes, these tuple
s are converted to a tuple
subclass called ExpressionTuple
s,
which behave just like normal tuple
s except for some special caching features that allow for easy
evaluation and caching. These ExpressionTuple
s are provided by the
etuples library.
Here is an illustration of all the above components used together:
>>> from unification import unify, reify, var
>>> from etuples import etuple
>>> y_lv = var() # Create a logic variable
>>> y_lv
~_1
>>> s = unify(add(x, y), etuple(add, x, y_lv))
>>> s
{~_1: y}
In this example, unify()
matched the PyTensor graph in the first argument with the “pattern”
given by the etuple()
in the second. The result is a dict
mapping logic variables to
the objects to which they were successfully unified. When a unify()
doesn’t succeed, it will
return False
.
reify()
uses dict
s like the kind produced by unify()
to replace
logic variables within structures:
>>> res = reify(etuple(add, y_lv, y_lv), s)
>>> res
e(<pytensor.scalar.basic.Add at 0x7f54dfa5a350>, y, y)
Since ExpressionTuple
s can be evaluated, we can produce a complete PyTensor graph from these
results as follows:
>>> res.evaled_obj
add.0
>>> pytensor.dprint(res.evaled_obj)
add [id A] ''
|y [id B]
|y [id B]
Because ExpressionTuple
s effectively model S-expressions, they can be used with the cons package to unify and reify
graphs structurally.
Let’s say we want to match graphs that use the add
Op
but could have a
varying number of arguments:
>>> from cons import cons
>>> op_lv = var()
>>> args_lv = var()
>>> s = unify(cons(op_lv, args_lv), add(x, y))
>>> s
{~_2: <pytensor.scalar.basic.Add at 0x7f54dfa5a350>, ~_3: e(x, y)}
>>> s = unify(cons(op_lv, args_lv), add(x, y, z))
>>> s
{~_2: <pytensor.scalar.basic.Add at 0x7f54dfa5a350>, ~_3: e(x, y, z)}
From here, we can check s[op_lv] == add
to confirm that we have the correct Op
and
proceed with our rewrite.
>>> res = reify(cons(mul, args_lv), s)
>>> res
e(<pytensor.scalar.basic.Mul at 0x7f54dfa5ae10>, x, y, z)
>>> pytensor.dprint(res.evaled_obj)
mul [id A] ''
|x [id B]
|y [id C]
|z [id D]
miniKanren#
Given that unification and reification are fully implemented for PyTensor objects via the unificiation
package,
the kanren package can be used with PyTensor graphs, as well.
kanren
implements the miniKanren domain-specific language for relational programming.
Refer to the links above for a proper introduction to miniKanren, but suffice it to say that miniKanren orchestrates the unification and reification operations described in Unification and reification, and it does so in the context of relational operators (e.g. equations like \(x + x = 2 x\)). This means that a relation that–say–represents \(x + x = 2 x\) can be utilized in both directions.
Currently, the node rewriter KanrenRelationSub
provides a means of
turning kanren
relations into NodeRewriter
s; however,
kanren
can always be used directly from within a custom Rewriter
, so
KanrenRelationSub
is not necessary.
The following is an example that distributes dot products across additions.
import pytensor
import pytensor.tensor as at
from pytensor.graph.rewriting.kanren import KanrenRelationSub
from pytensor.graph.rewriting.basic import EquilibriumGraphRewriter
from pytensor.graph.rewriting.utils import rewrite_graph
from pytensor.tensor.math import _dot
from etuples import etuple
from kanren import conso, eq, fact, heado, tailo
from kanren.assoccomm import assoc_flatten, associative
from kanren.core import lall
from kanren.graph import mapo
from unification import vars as lvars
# Make the graph pretty printing results a little more readable
pytensor.pprint.assign(
_dot, pytensor.printing.OperatorPrinter("@", -1, "left")
)
# Tell `kanren` that `add` is associative
fact(associative, at.add)
def dot_distributeo(in_lv, out_lv):
"""A `kanren` goal constructor relation for the relation ``A.dot(a + b ...) == A.dot(a) + A.dot(b) ...``."""
A_lv, add_term_lv, add_cdr_lv, dot_cdr_lv, add_flat_lv = lvars(5)
return lall(
# Make sure the input is a `_dot`
eq(in_lv, etuple(_dot, A_lv, add_term_lv)),
# Make sure the term being `_dot`ed is an `add`
heado(at.add, add_term_lv),
# Flatten the associative pairings of `add` operations
assoc_flatten(add_term_lv, add_flat_lv),
# Get the flattened `add` arguments
tailo(add_cdr_lv, add_flat_lv),
# Add all the `_dot`ed arguments and set the output
conso(at.add, dot_cdr_lv, out_lv),
# Apply the `_dot` to all the flattened `add` arguments
mapo(lambda x, y: conso(_dot, etuple(A_lv, x), y), add_cdr_lv, dot_cdr_lv),
)
dot_distribute_rewrite = EquilibriumGraphRewriter([KanrenRelationSub(dot_distributeo)], max_use_ratio=10)
Below, we apply dot_distribute_rewrite
to a few example graphs. First we create simple test graph:
>>> x_at = at.vector("x")
>>> y_at = at.vector("y")
>>> A_at = at.matrix("A")
>>> test_at = A_at.dot(x_at + y_at)
>>> print(pytensor.pprint(test_at))
(A @ (x + y))
Next we apply the rewrite to the graph:
>>> res = rewrite_graph(test_at, include=[], custom_rewrite=dot_distribute_rewrite, clone=False)
>>> print(pytensor.pprint(res))
((A @ x) + (A @ y))
We see that the dot product has been distributed, as desired. Now, let’s try a few more test cases:
>>> z_at = at.vector("z")
>>> w_at = at.vector("w")
>>> test_at = A_at.dot((x_at + y_at) + (z_at + w_at))
>>> print(pytensor.pprint(test_at))
(A @ ((x + y) + (z + w)))
>>> res = rewrite_graph(test_at, include=[], custom_rewrite=dot_distribute_rewrite, clone=False)
>>> print(pytensor.pprint(res))
(((A @ x) + (A @ y)) + ((A @ z) + (A @ w)))
>>> B_at = at.matrix("B")
>>> w_at = at.vector("w")
>>> test_at = A_at.dot(x_at + (y_at + B_at.dot(z_at + w_at)))
>>> print(pytensor.pprint(test_at))
(A @ (x + (y + ((B @ z) + (B @ w)))))
>>> res = rewrite_graph(test_at, include=[], custom_rewrite=dot_distribute_rewrite, clone=False)
>>> print(pytensor.pprint(res))
((A @ x) + ((A @ y) + ((A @ (B @ z)) + (A @ (B @ w)))))
This example demonstrates how non-trivial matching and replacement logic can be neatly expressed in miniKanren’s DSL, but it doesn’t quite demonstrate miniKanren’s relational properties.
To do that, we will create another Rewriter
that simply reverses the arguments
to the relation dot_distributeo()
and apply it to the distributed result in res
:
>>> dot_gather_rewrite = EquilibriumGraphRewriter([KanrenRelationSub(lambda x, y: dot_distributeo(y, x))], max_use_ratio=10)
>>> rev_res = rewrite_graph(res, include=[], custom_rewrite=dot_gather_rewrite, clone=False)
>>> print(pytensor.pprint(rev_res))
(A @ (x + (y + (B @ (z + w)))))
As we can see, the kanren
relation works both ways, just like the underlying
mathematical relation does.
miniKanren relations can be used to explore rewrites of graphs in sophisticated
ways. It also provides a framework that more directly maps to the mathematical
identities that drive graph rewrites. For some simple examples of relational graph rewriting
in kanren
see here. For a
high-level overview of miniKanren’s use as a tool for symbolic computation see
“miniKanren as a Tool for Symbolic Computation in Python”.
The Optimization Database (optdb
)#
PyTensor exports a symbol called optdb
which acts as a sort of ordered
database of rewrites. When a new rewrite is constructed, it must be inserted at
the proper place in the database in order for it to be deployed during function
compilation.
Each rewrite in a database can be assigned a set of tags that serve as a basis for filtering/querying.
The point of optdb
is that one might want to apply many rewrites
to a graph in many unique patterns.
For example, one might want to perform rewrite X, then rewrite Y, then
rewrite Z. Perhaps rewrite Y is an EquilibriumGraphRewriter
containing
NodeRewriter
s A, B and C, which are applied on every node of until
they all fail to change it. If some rewrites fail, we may want an easy way to
turn them off. Similarly, if some rewrites are very CPU-intensive and we don’t
want to take the time to apply them, then we should be able to disable them.
The optdb
system allows us to tag each rewrite with a unique name,
as well as informative descriptions such as ‘stable’, ‘buggy’ or
‘cpu_intensive’.
For instance, the rewrite tag cxx_only
is used for rewrites that
insert Op
s that have no Python implementation (i.e. they only have C
implementations). Rewrites with this tag can be skipped when the C backend
is not being used.
Definition of optdb
#
optdb
is an object which is an instance of
SequenceDB
,
itself a subclass of RewriteDatabase
.
There exist (for now) two types of RewriteDatabase
, SequenceDB
and EquilibriumDB
.
When given an appropriate RewriteDatabaseQuery
, RewriteDatabase
objects build an Rewriter
matching
the query.
A SequenceDB
contains Rewriter
or RewriteDatabase
objects. Each of them
has a name, an arbitrary number of tags and an integer representing their order
in the sequence. When a RewriteDatabaseQuery
is applied to a SequenceDB
, all Rewriter
s whose
tags match the query are inserted in proper order in a SequenceRewriter
, which
is returned. If the SequenceDB
contains RewriteDatabase
instances, the RewriteDatabaseQuery
will be passed to them as well and the
rewriters they return will be put in their places.
An EquilibriumDB
contains NodeRewriter
or RewriteDatabase
objects. Each of them
has a name and an arbitrary number of tags. When a RewriteDatabaseQuery
is applied to
an EquilibriumDB
, all NodeRewriter
s that match the query are
inserted into an EquilibriumGraphRewriter
, which is returned. If the
EquilibriumDB
contains RewriteDatabase
instances, the
RewriteDatabaseQuery
will be passed to them as well and the
NodeRewriter
s they return will be put in their places
(note that as of yet no RewriteDatabase
can produce NodeRewriter
objects, so this
is a moot point).
PyTensor contains one principal RewriteDatabase
object, optdb
, which
contains all of PyTensor’s rewriters with proper tags. It is
recommended to insert new Rewriter
s in it. As mentioned previously,
optdb
is a SequenceDB
, so, at the top level, PyTensor applies a sequence
of graph rewrites to the graphs it compiles.
RewriteDatabaseQuery
#
A RewriteDatabaseQuery
is built by the following call:
pytensor.graph.rewriting.db.RewriteDatabaseQuery(include, require=None, exclude=None, subquery=None)
- class RewriteDatabaseQuery#
- include#
A set of tags (a tag being a string) such that every rewrite obtained through this
RewriteDatabaseQuery
must have one of the tags listed. This field is required and basically acts as a starting point for the search.
- require#
A set of tags such that every rewrite obtained through this
RewriteDatabaseQuery
must have all of these tags.
- exclude#
A set of tags such that every rewrite obtained through this
RewriteDatabaseQuery
must have none of these tags.
- subquery#
optdb
can contain sub-databases; subquery is a dictionary mapping the name of a sub-database to a specialRewriteDatabaseQuery
. If no subquery is given for a sub-database, the originalRewriteDatabaseQuery
will be used again.
Furthermore, a RewriteDatabaseQuery
object includes three methods, including()
,
requiring()
and excluding()
, which each produce a new RewriteDatabaseQuery
object
with the include, require, and exclude sets refined to contain the new entries.
Examples#
Here are a few examples of how to use a RewriteDatabaseQuery
on optdb
to produce an
Rewriter
:
from pytensor.graph.rewriting.db import RewriteDatabaseQuery
from pytensor.compile import optdb
# This is how the rewrites for the fast_run mode are defined
fast_run = optdb.query(RewriteDatabaseQuery(include=['fast_run']))
# This is how the rewrites for the fast_compile mode are defined
fast_compile = optdb.query(RewriteDatabaseQuery(include=['fast_compile']))
# This is the same as fast_run but no rewrites will replace
# any operation by an inplace version. This assumes, of course,
# that all inplace operations are tagged as 'inplace' (as they
# should!)
fast_run_no_inplace = optdb.query(RewriteDatabaseQuery(include=['fast_run'],
exclude=['inplace']))
Registering a Rewriter
#
Let’s say we have a graph rewriter called simplify
. We can add
it to optdb
as follows:
optdb.register('simplify', simplify, 'fast_run', position=0.5)
Once this is done, the FAST_RUN
mode will automatically include the
rewrite, since it was given the 'fast_run'
tag. Of course,
already-compiled functions will see no change. The position
parameter
is specific to the type of rewrite database that obtdb
is, and
is explained in optdb structure.
Registering a NodeRewriter
#
NodeRewriter
s may be registered in two ways:
Wrap them in a
NodeProcessingGraphRewriter
and insert them like a graph rewriter (see previous section).Put them in an
EquilibriumDB
.
PyTensor defines two EquilibriumDB
s in which one can put node
rewrites:
- canonicalize()#
This contains rewrites that aim to put graphs in a standard “canonical” form:
Replace rare or esoterical operations with their equivalents using elementary operations.
Order operations in a canonical way. For example, any sequence of multiplications and divisions can be rewritten to contain at most one division (e.g.
x * x
can be rewritten tox**2
, etc.)Fold constants (e.g.
Constant(2) * Constant(2)
becomesConstant(4)
).
- specialize()#
This contains rewrites that aim to specialize the graph:
Replace a combination of operations with a special operation that does the same thing (but better).
For each group, all rewrites of the group that are selected by
the RewriteDatabaseQuery
will be applied on the graph over and over
again until no changes are made.
When using EquilibriumDB
, be sure to check carefully that your rewrite
leads to a fixed-point (i.e. a graph for which the rewrite cannot be applied
anymore), at which point it returns False
to indicate its job is done. Also
be careful not to undo the work of another rewrites in the group, because the
graph will oscillate between two or more states and nothing will get done.
optdb
structure#
optdb
contains the following Rewriters
s and sub-DBs, with the given
priorities and tags:
Order |
Name |
Description |
---|---|---|
0 |
merge1 |
First merge operation |
1 |
canonicalize |
Simplify the graph |
2 |
specialize |
Add specialized operations |
49 |
merge2 |
Second merge operation |
49.5 |
add_destroy_handler |
Enable inplace rewrites |
100 |
merge3 |
Third merge operation |
The merge operations are meant to put together parts of the graph that represent the same computation. Since rewrites can modify the graph in such a way that two previously different-looking parts of the graph become similar, we merge at the beginning, in the middle and at the very end. Technically, we only really need to do it at the end, but doing it in previous steps reduces the size of the graph and therefore increases the efficiency of the process.
See previous section for more information about the canonicalize and specialize steps.
The add_destroy_handler
step is not really an rewrite. It is
a marker. Basically:
Warning
Any rewrite which inserts inplace operations in the
computation graph must appear after the add_destroy_handler
“rewriter”. In other words, the priority of any such rewrites
must be >= 50. Failure to comply by this restriction can lead
to the creation of incorrect computation graphs.
The reason the destroy handler is not inserted at the beginning is that it is costly to run. It is cheaper to run most rewrites under the assumption there are no inplace operations.
NodeProcessingGraphRewriter
#
- class pytensor.graph.rewriting.basic.NodeProcessingGraphRewriter(node_rewriter: Optional[NodeRewriter], ignore_newtrees: Literal[True, False, 'auto'], failure_callback: Optional[Callable[[Exception, NodeProcessingGraphRewriter, list[tuple[pytensor.graph.basic.Variable, None]], NodeRewriter, Apply], None]] = None)[source]
A class providing a base implementation for applying
NodeRewriter.transform
results to a graph.This rewriter accepts the output of
NodeRewriter.transform
implementations and applies them to aFunctionGraph
.It accepts a sequence of new output nodes or
dict``s. Entries in these ``dict
s can beVariable
s and their new values. It also accepts a special"remove"
key. A sequence ofVariable
s mapped to the key"remove"
are removed from theFunctionGraph
.It also adds some interface elements for simple reentrant/recursive application of rewrites. The parameter
NodeRewriter.ignore_newtrees
is intended to be used by subclasses, alongside theNodeRewriter.attach_updater
andNodeRewriter.detach_updater
methods, to determine whether or not sub-graphs created by rewrites are to have the same rewrites applied to them.
Profiling PyTensor Function Compilation#
If one finds that compiling an PyTensor function is taking too much time, profiling information about each PyTensor rewrite can be obtained. The normal PyTensor profiler provides some high-level performance information. The indentation shows the included in/subset relationship between sections. The top of its output look like this:
Function profiling
==================
Message: PATH_TO_A_FILE:23
Time in 0 calls to Function.__call__: 0.000000e+00s
Total compile time: 1.131874e+01s
Number of Apply nodes: 50
PyTensor rewriter time: 1.152431e+00s
PyTensor validate time: 2.790451e-02s
PyTensor Linker time (includes C, CUDA code generation/compiling): 7.893991e-02s
Import time 1.153541e-02s
Time in all call to pytensor.grad() 4.732513e-02s
Explanations:
Total compile time: 1.131874e+01s
gives the total time spent insidepytensor.function
.Number of Apply nodes: 50
means that after rewriting, there are 50 apply node in the graph.PyTensor rewrite time: 1.152431e+00s
means that we spend 1.15s in the rewriting phase ofpytensor.function
.PyTensor validate time: 2.790451e-02s
means that we spent 2.8e-2s in the validation phase of rewriting.PyTensor Linker time (includes C code generation/compiling): 7.893991e-02s
means that we spent 7.9e-2s in linker phase ofpytensor.function
.Import time 1.153541e-02s
is a subset of the linker time where we import the compiled module.Time in all call to pytensor.grad() 4.732513e-02s
tells that we spent a total of 4.7e-2s in all calls topytensor.grad
. This is outside of the calls topytensor.function
.
The linker phase includes the generation of the C code, the time spent by g++ to compile and the time needed by PyTensor to build the object we return. The C code generation and compilation is cached, so the first time you compile a function and the following ones could take different amount of execution time.
Detailed Profiling of PyTensor Rewrites#
You can get more detailed profiling information about the PyTensor
rewriting phase by setting to True
the PyTensor flags
config.profile_optimizer
(this requires config.profile
to be True
as well).
This will output something like this:
Rewriter Profile
----------------
SequentialGraphRewriter OPT_FAST_RUN time 1.152s for 123/50 nodes before/after rewriting
0.028s for fgraph.validate()
0.131s for callback
time - (name, class, index) - validate time
0.751816s - ('canonicalize', 'EquilibriumGraphRewriter', 4) - 0.004s
EquilibriumGraphRewriter canonicalize
time 0.751s for 14 passes
nb nodes (start, end, max) 108 81 117
time io_toposort 0.029s
time in node rewriters 0.687s
time in graph rewriters 0.010s
0 - 0.050s 27 (0.000s in global rewrites, 0.002s io_toposort) - 108 nodes - ('local_dimshuffle_lift', 9) ('local_upcast_elemwise_constant_inputs', 5) ('local_shape_to_shape_i', 3) ('local_fill_sink', 3) ('local_fill_to_alloc', 2) ...
1 - 0.288s 26 (0.002s in global rewrites, 0.002s io_toposort) - 117 nodes - ('local_dimshuffle_lift', 8) ('local_fill_sink', 4) ('constant_folding', 4) ('local_useless_elemwise', 3) ('local_subtensor_make_vector', 3) ...
2 - 0.044s 13 (0.002s in global rewrites, 0.003s io_toposort) - 96 nodes - ('constant_folding', 4) ('local_dimshuffle_lift', 3) ('local_fill_sink', 3) ('local_useless_elemwise', 1) ('local_fill_to_alloc', 1) ...
3 - 0.045s 11 (0.000s in global rewrites, 0.002s io_toposort) - 91 nodes - ('constant_folding', 3) ('local_fill_to_alloc', 2) ('local_dimshuffle_lift', 2) ('local_mul_canonizer', 2) ('MergeOptimizer', 1) ...
4 - 0.035s 8 (0.002s in global rewrites, 0.002s io_toposort) - 93 nodes - ('local_fill_sink', 3) ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('constant_folding', 1)
5 - 0.035s 6 (0.000s in global rewrites, 0.002s io_toposort) - 88 nodes - ('local_fill_sink', 2) ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('local_mul_canonizer', 1)
6 - 0.038s 10 (0.001s in global rewrites, 0.002s io_toposort) - 95 nodes - ('local_fill_sink', 3) ('local_dimshuffle_lift', 3) ('constant_folding', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1)
7 - 0.032s 5 (0.001s in global rewrites, 0.002s io_toposort) - 91 nodes - ('local_fill_sink', 3) ('MergeOptimizer', 1) ('local_dimshuffle_lift', 1)
8 - 0.034s 5 (0.000s in global rewrites, 0.002s io_toposort) - 92 nodes - ('local_fill_sink', 3) ('MergeOptimizer', 1) ('local_greedy_distributor', 1)
9 - 0.031s 6 (0.001s in global rewrites, 0.002s io_toposort) - 90 nodes - ('local_fill_sink', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('local_dimshuffle_lift', 1) ('local_greedy_distributor', 1)
10 - 0.032s 5 (0.000s in global rewrites, 0.002s io_toposort) - 89 nodes - ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('local_fill_sink', 1)
11 - 0.030s 5 (0.000s in global rewrites, 0.002s io_toposort) - 88 nodes - ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('constant_folding', 1)
12 - 0.026s 1 (0.000s in global rewrites, 0.003s io_toposort) - 81 nodes - ('MergeOptimizer', 1)
13 - 0.031s 0 (0.000s in global rewrites, 0.003s io_toposort) - 81 nodes -
times - times applied - nb node created - name:
0.263s - 15 - 0 - constant_folding
0.096s - 2 - 14 - local_greedy_distributor
0.066s - 4 - 19 - local_mul_canonizer
0.046s - 28 - 57 - local_fill_sink
0.042s - 35 - 78 - local_dimshuffle_lift
0.018s - 5 - 15 - local_upcast_elemwise_constant_inputs
0.010s - 11 - 4 - MergeOptimizer
0.009s - 4 - 0 - local_useless_elemwise
0.005s - 11 - 2 - local_fill_to_alloc
0.004s - 3 - 6 - local_neg_to_mul
0.002s - 1 - 3 - local_lift_transpose_through_dot
0.002s - 3 - 4 - local_shape_to_shape_i
0.002s - 2 - 4 - local_subtensor_lift
0.001s - 3 - 0 - local_subtensor_make_vector
0.001s - 1 - 1 - local_sum_all_to_none
0.131s - in 62 rewrite(s) that where not used (display only those with a runtime > 0)
0.050s - local_add_canonizer
0.018s - local_mul_zero
0.016s - local_one_minus_erf
0.010s - local_func_inv
0.006s - local_0_dot_x
0.005s - local_track_shape_i
0.004s - local_mul_switch_sink
0.004s - local_fill_cut
0.004s - local_one_minus_erf2
0.003s - local_remove_switch_const_cond
0.003s - local_cast_cast
0.002s - local_IncSubtensor_serialize
0.001s - local_sum_div_dimshuffle
0.001s - local_div_switch_sink
0.001s - local_dimshuffle_no_inplace_at_canonicalize
0.001s - local_cut_useless_reduce
0.001s - local_reduce_join
0.000s - local_sum_sum
0.000s - local_useless_alloc
0.000s - local_reshape_chain
0.000s - local_useless_subtensor
0.000s - local_reshape_lift
0.000s - local_flatten_lift
0.000s - local_useless_slice
0.000s - local_subtensor_of_alloc
0.000s - local_subtensor_of_dot
0.000s - local_subtensor_merge
0.101733s - ('elemwise_fusion', 'SequentialGraphRewriter', 13) - 0.000s
SequentialGraphRewriter elemwise_fusion time 0.102s for 78/50 nodes before/after rewriting
0.000s for fgraph.validate()
0.004s for callback
0.095307s - ('composite_elemwise_fusion', 'FusionOptimizer', 1) - 0.000s
FusionOptimizer
nb_iter 3
nb_replacement 10
nb_inconsistency_replace 0
validate_time 0.000249624252319
callback_time 0.00316381454468
time_toposort 0.00375390052795
0.006412s - ('local_add_mul_fusion', 'FusionOptimizer', 0) - 0.000s
FusionOptimizer
nb_iter 2
nb_replacement 3
nb_inconsistency_replace 0
validate_time 6.43730163574e-05
callback_time 0.000783205032349
time_toposort 0.0035240650177
0.090089s - ('inplace_elemwise_optimizer', 'FromFunctionGraphRewriter', 30) - 0.019s
0.048993s - ('BlasOpt', 'SequentialGraphRewriter', 8) - 0.000s
SequentialGraphRewriter BlasOpt time 0.049s for 81/80 nodes before/after rewriting
0.000s for fgraph.validate()
0.003s for callback
0.035997s - ('gemm_optimizer', 'GemmOptimizer', 1) - 0.000s
GemmOptimizer
nb_iter 2
nb_replacement 2
nb_replacement_didn_t_remove 0
nb_inconsistency_make 0
nb_inconsistency_replace 0
time_canonicalize 0.00720071792603
time_factor_can 9.05990600586e-06
time_factor_list 0.00128507614136
time_toposort 0.00311398506165
validate_time 4.60147857666e-05
callback_time 0.00174236297607
0.004569s - ('local_dot_to_dot22', 'WalkingGraphRewriter', 0) - 0.000s
WalkingGraphRewriter
nb_node (start, end, changed) (81, 81, 5)
init io_toposort 0.00139284133911
loop time 0.00312399864197
callback_time 0.00172805786133
0.002283s - ('local_dot22_to_dot22scalar', 'WalkingGraphRewriter', 2) - 0.000s
WalkingGraphRewriter
nb_node (start, end, changed) (80, 80, 0)
init io_toposort 0.00171804428101
loop time 0.000502109527588
callback_time 0.0
0.002257s - ('local_gemm_to_gemv', 'EquilibriumGraphRewriter', 3) - 0.000s
EquilibriumGraphRewriter local_gemm_to_gemv
time 0.002s for 1 passes
nb nodes (start, end, max) 80 80 80
time io_toposort 0.001s
time in node rewriters 0.000s
time in graph rewriters 0.000s
0 - 0.002s 0 (0.000s in global rewrites, 0.001s io_toposort) - 80 nodes -
0.002227s - ('use_c_blas', 'WalkingGraphRewriter', 4) - 0.000s
WalkingGraphRewriter
nb_node (start, end, changed) (80, 80, 0)
init io_toposort 0.0014750957489
loop time 0.00068998336792
callback_time 0.0
0.001632s - ('use_scipy_ger', 'WalkingGraphRewriter', 5) - 0.000s
WalkingGraphRewriter
nb_node (start, end, changed) (80, 80, 0)
init io_toposort 0.00138401985168
loop time 0.000202178955078
callback_time 0.0
0.031740s - ('specialize', 'EquilibriumGraphRewriter', 9) - 0.000s
EquilibriumGraphRewriter specialize
time 0.031s for 2 passes
nb nodes (start, end, max) 80 78 80
time io_toposort 0.003s
time in node rewriters 0.022s
time in graph rewriters 0.004s
0 - 0.017s 6 (0.002s in global rewrites, 0.001s io_toposort) - 80 nodes - ('constant_folding', 2) ('local_mul_to_sqr', 1) ('local_elemwise_alloc', 1) ('local_div_to_inv', 1) ('local_mul_specialize', 1)
1 - 0.014s 0 (0.002s in global rewrites, 0.001s io_toposort) - 78 nodes -
times - times applied - nb node created - name:
0.003s - 1 - 1 - local_mul_specialize
0.002s - 1 - 2 - local_elemwise_alloc
0.002s - 2 - 0 - constant_folding
0.001s - 1 - 1 - local_div_to_inv
0.001s - 1 - 1 - local_mul_to_sqr
0.016s - in 69 rewrite(s) that where not used (display only those with a runtime > 0)
0.004s - crossentropy_to_crossentropy_with_softmax_with_bias
0.002s - local_one_minus_erf
0.002s - Elemwise{sub,no_inplace}(z, Elemwise{mul,no_inplace}(alpha subject to <function <lambda> at 0x7f475e4da050>, SparseDot(x, y))) -> Usmm{no_inplace}(Elemwise{neg,no_inplace}(alpha), x, y, z)
0.002s - local_add_specialize
0.001s - local_func_inv
0.001s - local_useless_elemwise
0.001s - local_abs_merge
0.001s - local_track_shape_i
0.000s - local_one_minus_erf2
0.000s - local_sum_mul_by_scalar
0.000s - local_elemwise_sub_zeros
0.000s - local_cast_cast
0.000s - local_alloc_unary
0.000s - Elemwise{log,no_inplace}(Softmax(x)) -> <function make_out_pattern at 0x7f47619a8410>(x)
0.000s - local_sum_div_dimshuffle
0.000s - local_sum_alloc
0.000s - local_dimshuffle_lift
0.000s - local_reduce_broadcastable
0.000s - local_grad_log_erfc_neg
0.000s - local_advanced_indexing_crossentropy_onehot
0.000s - local_log_erfc
0.000s - local_log1p
0.000s - local_log_add
0.000s - local_useless_alloc
0.000s - local_neg_neg
0.000s - local_neg_div_neg
...
To understand this profile here is some explanation of how rewrites work:
Rewrites are organized in a hierarchy. At the top level, there is a
SequentialGraphRewriter
. It contains other rewriters, and applies them in the order they were specified. Those sub-rewriters can be of other types, but are all graph rewriters.Each
Rewriter
in the hierarchy will print some stats about itself. The information that it prints depends of the type of the rewriter.The
SequentialGraphRewriter
will print some stats at the start:Rewriter Profile ---------------- SequentialGraphRewriter OPT_FAST_RUN time 1.152s for 123/50 nodes before/after rewriting 0.028s for fgraph.validate() 0.131s for callback time - (name, class, index) - validate time
Then it will print, with some additional indentation, each sub-rewriter’s profile information. These sub-profiles are ordered by the time they took to execute, not by their execution order.
OPT_FAST_RUN
is the name of the rewriter1.152s is the total time spent in that rewriter
123/50 means that before this rewriter, there were 123 apply node in the function graph, and after only 50.
0.028s means it spent that time calls to
fgraph.validate()
0.131s means it spent that time for callbacks. This is a mechanism that can trigger other execution when there is a change to the FunctionGraph.
time - (name, class, index) - validate time
tells how the information for each sub-rewriter get printed.All other instances of
SequentialGraphRewriter
are described like this. In particular, some sub-rewriter fromOPT_FAST_RUN
that are alsoSequentialGraphRewriter
.
The
SequentialGraphRewriter
will print some stats at the start:0.751816s - ('canonicalize', 'EquilibriumGraphRewriter', 4) - 0.004s EquilibriumGraphRewriter canonicalize time 0.751s for 14 passes nb nodes (start, end, max) 108 81 117 time io_toposort 0.029s time in node rewriters 0.687s time in graph rewriters 0.010s 0 - 0.050s 27 (0.000s in global rewrites, 0.002s io_toposort) - 108 nodes - ('local_dimshuffle_lift', 9) ('local_upcast_elemwise_constant_inputs', 5) ('local_shape_to_shape_i', 3) ('local_fill_sink', 3) ('local_fill_to_alloc', 2) ... 1 - 0.288s 26 (0.002s in global rewrites, 0.002s io_toposort) - 117 nodes - ('local_dimshuffle_lift', 8) ('local_fill_sink', 4) ('constant_folding', 4) ('local_useless_elemwise', 3) ('local_subtensor_make_vector', 3) ... 2 - 0.044s 13 (0.002s in global rewrites, 0.003s io_toposort) - 96 nodes - ('constant_folding', 4) ('local_dimshuffle_lift', 3) ('local_fill_sink', 3) ('local_useless_elemwise', 1) ('local_fill_to_alloc', 1) ... 3 - 0.045s 11 (0.000s in global rewrites, 0.002s io_toposort) - 91 nodes - ('constant_folding', 3) ('local_fill_to_alloc', 2) ('local_dimshuffle_lift', 2) ('local_mul_canonizer', 2) ('MergeOptimizer', 1) ... 4 - 0.035s 8 (0.002s in global rewrites, 0.002s io_toposort) - 93 nodes - ('local_fill_sink', 3) ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('constant_folding', 1) 5 - 0.035s 6 (0.000s in global rewrites, 0.002s io_toposort) - 88 nodes - ('local_fill_sink', 2) ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('local_mul_canonizer', 1) 6 - 0.038s 10 (0.001s in global rewrites, 0.002s io_toposort) - 95 nodes - ('local_fill_sink', 3) ('local_dimshuffle_lift', 3) ('constant_folding', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) 7 - 0.032s 5 (0.001s in global rewrites, 0.002s io_toposort) - 91 nodes - ('local_fill_sink', 3) ('MergeOptimizer', 1) ('local_dimshuffle_lift', 1) 8 - 0.034s 5 (0.000s in global rewrites, 0.002s io_toposort) - 92 nodes - ('local_fill_sink', 3) ('MergeOptimizer', 1) ('local_greedy_distributor', 1) 9 - 0.031s 6 (0.001s in global rewrites, 0.002s io_toposort) - 90 nodes - ('local_fill_sink', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('local_dimshuffle_lift', 1) ('local_greedy_distributor', 1) 10 - 0.032s 5 (0.000s in global rewrites, 0.002s io_toposort) - 89 nodes - ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('local_fill_sink', 1) 11 - 0.030s 5 (0.000s in global rewrites, 0.002s io_toposort) - 88 nodes - ('local_dimshuffle_lift', 2) ('local_fill_to_alloc', 1) ('MergeOptimizer', 1) ('constant_folding', 1) 12 - 0.026s 1 (0.000s in global rewrites, 0.003s io_toposort) - 81 nodes - ('MergeOptimizer', 1) 13 - 0.031s 0 (0.000s in global rewrites, 0.003s io_toposort) - 81 nodes - times - times applied - nb node created - name: 0.263s - 15 - 0 - constant_folding 0.096s - 2 - 14 - local_greedy_distributor 0.066s - 4 - 19 - local_mul_canonizer 0.046s - 28 - 57 - local_fill_sink 0.042s - 35 - 78 - local_dimshuffle_lift 0.018s - 5 - 15 - local_upcast_elemwise_constant_inputs 0.010s - 11 - 4 - MergeOptimizer 0.009s - 4 - 0 - local_useless_elemwise 0.005s - 11 - 2 - local_fill_to_alloc 0.004s - 3 - 6 - local_neg_to_mul 0.002s - 1 - 3 - local_lift_transpose_through_dot 0.002s - 3 - 4 - local_shape_to_shape_i 0.002s - 2 - 4 - local_subtensor_lift 0.001s - 3 - 0 - local_subtensor_make_vector 0.001s - 1 - 1 - local_sum_all_to_none 0.131s - in 62 rewrite(s) that where not used (display only those with a runtime > 0) 0.050s - local_add_canonizer 0.018s - local_mul_zero 0.016s - local_one_minus_erf 0.010s - local_func_inv 0.006s - local_0_dot_x 0.005s - local_track_shape_i 0.004s - local_mul_switch_sink 0.004s - local_fill_cut 0.004s - local_one_minus_erf2 0.003s - local_remove_switch_const_cond 0.003s - local_cast_cast 0.002s - local_IncSubtensor_serialize 0.001s - local_sum_div_dimshuffle 0.001s - local_div_switch_sink 0.001s - local_dimshuffle_no_inplace_at_canonicalize 0.001s - local_cut_useless_reduce 0.001s - local_reduce_join 0.000s - local_sum_sum 0.000s - local_useless_alloc 0.000s - local_reshape_chain 0.000s - local_useless_subtensor 0.000s - local_reshape_lift 0.000s - local_flatten_lift 0.000s - local_useless_slice 0.000s - local_subtensor_of_alloc 0.000s - local_subtensor_of_dot 0.000s - local_subtensor_merge
0.751816s - ('canonicalize', 'EquilibriumGraphRewriter', 4) - 0.004s
This line is fromSequentialGraphRewriter
, and indicates information related to a sub-rewriter. It means that this sub-rewriter took a total of .7s. Its name is'canonicalize'
. It is anEquilibriumGraphRewriter
. It was executed at index 4 by theSequentialGraphRewriter
. It spent 0.004s in the validate phase.All other lines are from the profiler of the
EquilibriumGraphRewriter
.An
EquilibriumGraphRewriter
does multiple passes on the Apply nodes from the graph, trying to apply local and graph rewriters. Conceptually, it tries to execute all graph rewriters, and to apply all node rewriters on all nodes in the graph. If no rewrites got applied during a pass, it stops. So it tries to find an equilibrium state where no further rewrites can be applied. This is useful when we do not know a fixed order for the execution of rewrites.time 0.751s for 14 passes
means that it took .7s and did 14 passes over the graph.nb nodes (start, end, max) 108 81 117
means that at the start, the graph had 108 node, at the end, it had 81 and the maximum size was 117.Then it prints some global timing information: it spent 0.029s in
io_toposort()
, all node rewriters took 0.687s together for all passes, and graph rewriters took a total of 0.010s.Then we print the timing for each pass, the rewrite that got applied, and the number of time they got applied. For example, in pass zero, the
local_dimshuffle_lift()
rewrite changed the graph nine time.Then we print the time spent in each rewriter, the number of times they changed the graph and the number of nodes they introduced in the graph.
Rewrites with that pattern
local_op_lift()
indicate that a node with thatOp
will be replaced by another node with the sameOp
, but will do computation closer to the inputs of the graph: i.e. a “lift” of theOp
. For instance, inlocal_op(f(x))
,local_op
is lifted throughf
to producef(local_op(x))
.Rewrites with that pattern
local_op_sink()
is the opposite of lifting. For instance, inf(local_op(x))
,local_op
is sunk throughf
to producelocal_op(f(x))
.Local rewriters can replace any arbitrary node in the graph, not only the nodes they receive as input. In this case, the local rewrite returns a
dict
, where the keys areVariable
s to be replaced and the values are the corresponding replacements.