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Using the Numba Rewrite Pass for Fun and Optimization

Overview

This section introduces intermediate representation (IR) rewrites, and how they can be used to implement optimizations.

As discussed earlier in “Stage 5a: Rewrite typed IR”, rewriting the Numba IR allows us to perform optimizations that would be much more difficult to perform at the lower LLVM level. Similar to the Numba type and lowering subsystems, the rewrite subsystem is user extensible. This extensibility affords Numba the possibility of supporting a wide variety of domain-specific optimizations (DSO’s).

The remaining subsections detail the mechanics of implementing a rewrite, registering a rewrite with the rewrite registry, and provide examples of adding new rewrites, as well as internals of the array expression optimization pass. We conclude by reviewing some use cases exposed in the examples, as well as reviewing any points where developers should take care.

Rewriting Passes

Rewriting passes have a simple match() and apply() interface. The division between matching and rewriting follows how one would define a term rewrite in a declarative domain-specific languages (DSL’s). In such DSL’s, one may write a rewrite as follows:

<match> => <replacement>

The <match> and <replacement> symbols represent IR term expressions, where the left-hand side presents a pattern to match, and the right-hand side an IR term constructor to build upon matching. Whenever the rewrite matches an IR pattern, any free variables in the left-hand side are bound within a custom environment. When applied, the rewrite uses the pattern matching environment to bind any free variables in the right-hand side.

As Python is not commonly used in a declarative capacity, Numba uses object state to handle the transfer of information between the matching and application steps.

The Rewrite Base Class

class Rewrite

The Rewrite class simply defines an abstract base class for Numba rewrites. Developers should define rewrites as subclasses of this base type, overloading the match() and apply() methods.

pipeline

The pipeline attribute contains the numba.compiler.Pipeline instance that is currently compiling the function under consideration for rewriting.

__init__(self, pipeline, *args, **kws)

The base constructor for rewrites simply stashes its arguments into attributes of the same name. Unless being used in debugging or testing, rewrites should only be constructed by the RewriteRegistry in the RewriteRegistry.apply() method, and the construction interface should remain stable (though the pipeline will commonly contain just about everything there is to know).

match(self, block, typemap, callmap)

The match() method takes four arguments other than self:

• func_ir: This is an instance of numba.ir.FunctionIR for the function being rewritten.
• block: This is an instance of numba.ir.Block. The matching method should iterate over the instructions contained in the numba.ir.Block.body member.
• typemap: This is a Python dict instance mapping from symbol names in the IR, represented as strings, to Numba types.
• callmap: This is another dict instance mapping from calls, represented as numba.ir.Expr instances, to their corresponding call site type signatures, represented as a numba.typing.templates.Signature instance.

The match() method should return a bool result. A True result should indicate that one or more matches were found, and the apply() method will return a new replacement numba.ir.Block instance. A False result should indicate that no matches were found, and subsequent calls to apply() will return undefined or invalid results.

apply(self)

The apply() method should only be invoked following a successful call to match(). This method takes no additional parameters other than self, and should return a replacement numba.ir.Block instance.

As mentioned above, the behavior of calling apply() is undefined unless match() has already been called and returned True.

Subclassing Rewrite

Before going into the expectations for the overloaded methods any Rewrite subclass must have, let’s step back a minute to review what is taking place here. By providing an extensible compiler, Numba opens itself to user-defined code generators which may be incomplete, or worse, incorrect. When a code generator goes awry, it can cause abnormal program behavior or early termination. User-defined rewrites add a new level of complexity because they must not only generate correct code, but the code they generate should ensure that the compiler does not get stuck in a match/apply loop. Non-termination by the compiler will directly lead to non-termination of user function calls.

There are several ways to help ensure that a rewrite terminates:

• Typing: A rewrite should generally attempt to decompose composite types, and avoid composing new types. If the rewrite is matching a specific type, changing expression types to a lower-level type will ensure they will no long match after the rewrite is applied.
• Special instructions: A rewrite may synthesize custom operators or use special functions in the target IR. This technique again generates code that is no longer within the domain of the original match, and the rewrite will terminate.

In the “Case study: Array Expressions” subsection, below, we’ll see how the array expression rewriter uses both of these techniques.

Overloading Rewrite.match()

Every rewrite developer should seek to have their implementation of match() return a False value as quickly as possible. Numba is a just-in-time compiler, and adding compilation time ultimately adds to the user’s run time. When a rewrite returns False for a given block, the registry will no longer process that block with that rewrite, and the compiler is that much closer to proceeding to lowering.

This need for timeliness has to be balanced against collecting the necessary information to make a match for a rewrite. Rewrite developers should be comfortable adding dynamic attributes to their subclasses, and then having these new attributes guide construction of the replacement basic block.

Overloading Rewrite.apply()

The apply() method should return a replacement numba.ir.Block instance to replace the basic block that contained a match for the rewrite. As mentioned above, the IR built by apply() methods should preserve the semantics of the user’s code, but also seek to avoid generating another match for the same rewrite or set of rewrites.

The Rewrite Registry

When you want to include a rewrite in the rewrite pass, you should register it with the rewrite registry. The numba.rewrites module provides both the abstract base class and a class decorator for hooking into the Numba rewrite subsystem. The following illustrates a stub definition of a new rewrite:

from numba import rewrites

@rewrites.register_rewrite
class MyRewrite(rewrites.Rewrite):

    def match(self, block, typemap, calltypes):
        raise NotImplementedError("FIXME")

    def apply(self):
        raise NotImplementedError("FIXME")

Developers should note that using the class decorator as shown above will register a rewrite at import time. It is the developer’s responsibility to ensure their extensions are loaded before compilation starts.

Case study: Array Expressions

This subsection looks at the array expression rewriter in more depth. The array expression rewriter, and most of its support functionality, are found in the numba.npyufunc.array_exprs module. The rewriting pass itself is implemented in the RewriteArrayExprs class. In addition to the rewriter, the array_exprs module includes a function for lowering array expressions, _lower_array_expr(). The overall optimization process is as follows:

• RewriteArrayExprs.match(): The rewrite pass looks for two or more array operations that form an array expression.
• RewriteArrayExprs.apply(): Once an array expression is found, the rewriter replaces the individual array operations with a new kind of IR expression, the arrayexpr.
• numba.npyufunc.array_exprs._lower_array_expr(): During lowering, the code generator calls _lower_array_expr() whenever it finds an arrayexpr IR expression.

More details on each step of the optimization are given below.

The RewriteArrayExprs.match() method

The array expression optimization pass starts by looking for array operations, including calls to supported ufunc’s and user-defined DUFunc’s. Numba IR follows the conventions of a static single assignment (SSA) language, meaning that the search for array operators begins with looking for assignment instructions.

When the rewriting pass calls the RewriteArrayExprs.match() method, it first checks to see if it can trivially reject the basic block. If the method determines the block to be a candidate for matching, it sets up the following state variables in the rewrite object:

• crnt_block: The current basic block being matched.
• typemap: The typemap for the function being matched.
• matches: A list of variable names that reference array expressions.
• array_assigns: A map from assignment variable names to the actual assignment instructions that define the given variable.
• const_assigns: A map from assignment variable names to the constant valued expression that defines the constant variable.

At this point, the match method iterates iterates over the assignment instructions in the input basic block. For each assignment instruction, the matcher looks for one of two things:

• Array operations: If the right-hand side of the assignment instruction is an expression, and the result of that expression is an array type, the matcher checks to see if the expression is either a known array operation, or a call to a universal function. If an array operator is found, the matcher stores the left-hand variable name and the whole instruction in the array_assigns member. Finally, the matcher tests to see if any operands of the array operation have also been identified as targets of other array operations. If one or more operands are also targets of array operations, then the matcher will also append the left-hand side variable name to the matches member.
• Constants: Constants (even scalars) can be operands to array operations. Without worrying about the constant being apart of an array expression, the matcher stores constant names and values in the const_assigns member.

The end of the matching method simply checks for a non-empty matches list, returning True if there were one or more matches, and False when matches is empty.

The RewriteArrayExprs.apply() method

When one or matching array expressions are found by RewriteArrayExprs.match(), the rewriting pass will call RewriteArrayExprs.apply(). The apply method works in two passes. The first pass iterates over the matches found, and builds a map from instructions in the old basic block to new instructions in the new basic block. The second pass iterates over the instructions in the old basic block, copying instructions that are not changed by the rewrite, and replacing or deleting instructions that were identified by the first pass.

The RewriteArrayExprs._handle_matches() implements the first pass of the code generation portion of the rewrite. For each match, this method builds a special IR expression that contains an expression tree for the array expression. To compute the leaves of the expression tree, the _handle_matches() method iterates over the operands of the identified root operation. If the operand is another array operation, it is translated into an expression sub-tree. If the operand is a constant, _handle_matches() copies the constant value. Otherwise, the operand is marked as being used by an array expression. As the method builds array expression nodes, it builds a map from old instructions to new instructions (replace_map), as well as sets of variables that may have moved (used_vars), and variables that should be removed altogether (dead_vars). These three data structures are returned back to the calling RewriteArrayExprs.apply() method.

The remaining part of the RewriteArrayExprs.apply() method iterates over the instructions in the old basic block. For each instruction, this method either replaces, deletes, or duplicates that instruction based on the results of RewriteArrayExprs._handle_matches(). The following list describes how the optimization handles individual instructions:

• When an instruction is an assignment, apply() checks to see if it is in the replacement instruction map. When an assignment instruction is found in the instruction map, apply() must then check to see if the replacement instruction is also in the replacement map. The optimizer continues this check until it either arrives at a None value or an instruction that isn’t in the replacement map. Instructions that have a replacement that is None are deleted. Instructions that have a non-None replacement are replaced. Assignment instructions not in the replacement map are appended to the new basic block with no changes made.
• When the instruction is a delete instruction, the rewrite checks to see if it deletes a variable that may still be used by a later array expression, or if it deletes a dead variable. Delete instructions for used variables are added to a map of deferred delete instructions that apply() uses to move them past any uses of that variable. The loop copies delete instructions for non-dead variables, and ignores delete instructions for dead variables (effectively removing them from the basic block).
• All other instructions are appended to the new basic block.

Finally, the apply() method returns the new basic block for lowering.

The _lower_array_expr() function

If we left things at just the rewrite, then the lowering stage of the compiler would fail, complaining it doesn’t know how to lower arrayexpr operations. We start by hooking a lowering function into the target context whenever the RewriteArrayExprs class is instantiated by the compiler. This hook causes the lowering pass to call _lower_array_expr() whenever it encounters an arrayexr operator.

This function has two steps:

• Synthesize a Python function that implements the array expression: This new Python function essentially behaves like a Numpy ufunc, returning the result of the expression on scalar values in the broadcasted array arguments. The lowering function accomplishes this by translating from the array expression tree into a Python AST.
• Compile the synthetic Python function into a kernel: At this point, the lowering function relies on existing code for lowering ufunc and DUFunc kernels, calling numba.targets.numpyimpl.numpy_ufunc_kernel() after defining how to lower calls to the synthetic function.

The end result is similar to loop lifting in Numba’s object mode.

Conclusions and Caveats

We have seen how to implement rewrites in Numba, starting with the interface, and ending with an actual optimization. The key points of this section are:

• When writing a good plug-in, the matcher should try to get a go/no-go result as soon as possible.
• The rewrite application portion can be more computationally expensive, but should still generate code that won’t cause infinite loops in the compiler.
• We use object state to communicate any results of matching to the rewrite application pass.