Add NEWS and docs for the new inlining algorithm

[ci skip]

NEWS.md

@@ -103,6 +103,11 @@ Library improvements
 Compiler/Runtime improvements
 -----------------------------
 
+  * The inlining heuristic now models the approximate runtime cost of
+    a method (using some strongly-simplifying assumptions). Functions
+    are inlined unless their estimated runtime cost substantially
+    exceeds the cost of setting up and issuing a subroutine
+    call. ([#22210], [#22732])
 
 Deprecated or removed
 ---------------------
@@ -962,8 +967,10 @@ Command-line option changes
 [#22182]: https://github.com/JuliaLang/julia/issues/22182
 [#22187]: https://github.com/JuliaLang/julia/issues/22187
 [#22188]: https://github.com/JuliaLang/julia/issues/22188
+[#22210]: https://github.com/JuliaLang/julia/issues/22210
 [#22224]: https://github.com/JuliaLang/julia/issues/22224
 [#22228]: https://github.com/JuliaLang/julia/issues/22228
 [#22245]: https://github.com/JuliaLang/julia/issues/22245
 [#22310]: https://github.com/JuliaLang/julia/issues/22310
 [#22523]: https://github.com/JuliaLang/julia/issues/22523
+[#22732]: https://github.com/JuliaLang/julia/issues/22732

doc/src/devdocs/inference.md

@@ -0,0 +1,98 @@
+
+# Inference
+
+## Debugging inference.jl
+
+You can start a Julia session, edit `inference.jl` (for example to
+insert `print` statements), and then replace `Core.Inference` in your
+running session by navigating to `base/` and executing
+`include("coreimg.jl")`. This trick typically leads to much faster
+development than if you rebuild Julia for each change.
+
+A convenient entry point into inference is `typeinf_code`. Here's a
+demo running inference on `convert(Int, UInt(1))`:
+
+```julia
+# Get the method
+atypes = Tuple{Type{Int}, UInt}  # argument types
+mths = methods(convert, atypes)  # worth checking that there is only one
+m = first(mths)
+
+# Create variables needed to call `typeinf_code`
+params = Core.Inference.InferenceParams(typemax(UInt))  # parameter is the world age,
+                                                        #   typemax(UInt) -> most recent
+sparams = Core.svec()      # this particular method doesn't have type-parameters
+optimize = true            # run all inference optimizations
+cached = false             # force inference to happen (do not use cached results)
+Core.Inference.typeinf_code(m, atypes, sparams, optimize, cached, params)
+```
+
+If your debugging adventures require a `MethodInstance`, you can look it up by
+calling `Core.Inference.code_for_method` using many of the variables above.
+A `CodeInfo` object may be obtained with
+```julia
+# Returns the CodeInfo object for `convert(Int, ::UInt)`:
+ci = (@code_typed convert(Int, UInt(1)))[1]
+```
+
+## The inlining algorithm (inline_worthy)
+
+Much of the hardest work for inlining runs in
+`inlining_pass`. However, if your question is "why didn't my function
+inline?" then you will most likely be interested in `isinlineable` and
+its primary callee, `inline_worthy`. `isinlineable` handles a number
+of special cases (e.g., critical functions like `next` and `done`,
+incorporating a bonus for functions that return tuples, etc.). The
+main decision-making happens in `inline_worthy`, which returns `true`
+if the function should be inlined.
+
+`inline_worthy` implements a cost-model, where "cheap" functions get
+inlined; more specifically, we inline functions if their anticipated
+run-time is not large compared to the time it would take to
+[issue a call](https://en.wikipedia.org/wiki/Calling_convention) to
+them if they were not inlined. The cost-model is extremely simple and
+ignores many important details: for example, all `for` loops are
+analyzed as if they will be executed once, and the cost of an
+`if...else...end` includes the summed cost of all branches. It's also
+worth acknowledging that we currently lack a suite of functions
+suitable for testing how well the cost model predicts the actual
+run-time cost, although
+[BaseBenchmarks](https://github.com/JuliaCI/BaseBenchmarks.jl)
+provides a great deal of indirect information about the successes and
+failures of any modification to the inlining algorithm.
+
+The foundation of the cost-model is a lookup table, implemented in
+`add_tfunc` and its callers, that assigns an estimated cost (measured
+in CPU cycles) to each of Julia's intrinsic functions. These costs are
+based on
+[standard ranges for common architectures](http://ithare.com/wp-content/uploads/part101_infographics_v08.png)
+(see
+[Agner Fog's analysis](http://www.agner.org/optimize/instruction_tables.pdf)
+for more detail).
+
+We supplement this low-level lookup table with a number of special
+cases. For example, an `:invoke` expression (a call for which all
+input and output types were inferred in advance) is assigned a fixed
+cost (currently 20 cycles). In contrast, a `:call` expression, for
+other than intrinsics/builtins, indicates that the call will require
+dynamic dispatch, in which case we assign a cost set by
+`InferenceParams.inline_nonleaf_penalty` (currently set at 1000). Note
+that this is not a "first-principles" estimate of the raw cost of
+dynamic dispatch, but a mere heuristic indicating that dynamic
+dispatch is extremely expensive.
+
+Each statement gets analyzed for its total cost in a function called
+`statement_cost`. You can run this yourself by following this example:
+
+```julia
+params = Core.Inference.InferenceParams(typemax(UInt))
+# Get the CodeInfo object
+ci = (@code_typed fill(3, (5, 5)))[1]  # we'll try this on the code for `fill(3, (5, 5))`
+# Calculate cost of each statement
+cost(stmt) = Core.Inference.statement_cost(stmt, ci, Base, params)
+cst = map(cost, ci.code)
+```
+
+The output is a `Vector{Int}` holding the estimated cost of each
+statement in `ci.code`.  Note that `ci` includes the consequences of
+inlining callees, and consequently the costs do too.

doc/src/index.md

@@ -90,6 +90,7 @@
       * [Arrays with custom indices](@ref)
       * [Base.LibGit2](@ref)
       * [Module loading](@ref)
+      * [Inference](@ref)
   * Developing/debugging Julia's C code
       * [Reporting and analyzing crashes (segfaults)](@ref)
       * [gdb debugging tips](@ref)