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This was a large commit, and most of it is a follow-up to commit 3309,
undoing what is probably the final ill-considered optimization I added
to s-expressions in Mu: I was always representing (a b c) as (a b . c),
etc. That is now gone.
Why did I need to take it out? The key problem was the error silently
ignored in layer 30. That was causing size_of("(type)") to silently
return garbage rather than loudly complain (assuming 'type' was a simple
type).
But to take it out I had to modify types_strictly_match (layer 21) to
actually strictly match and not just do a prefix match.
In the process of removing the prefix match, I had to make extracting
recipe types from recipe headers more robust. So far it only matched the
first element of each ingredient's type; these matched:
(recipe address:number -> address:number)
(recipe address -> address)
I didn't notice because the dotted notation optimization was actually
representing this as:
(recipe address:number -> address number)
---
One final little thing in this commit: I added an alias for 'assert'
called 'assert_for_now', to indicate that I'm not sure something's
really an invariant, that it might be triggered by (invalid) user
programs, and so require more thought on error handling down the road.
But this may well be an ill-posed distinction. It may be overwhelmingly
uneconomic to continually distinguish between model invariants and error
states for input. I'm starting to grow sympathetic to Google Analytics's
recent approach of just banning assertions altogether. We'll see..
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Rip out everything to fix one failing unit test (commit 3290; type
abbreviations).
This commit does several things at once that I couldn't come up with a
clean way to unpack:
A. It moves to a new representation for type trees without changing
the actual definition of the `type_tree` struct.
B. It adds unit tests for our type metadata precomputation, so that
errors there show up early and in a simpler setting rather than dying
when we try to load Mu code.
C. It fixes a bug, guarding against infinite loops when precomputing
metadata for recursive shape-shifting containers. To do this it uses a
dumb way of comparing type_trees, comparing their string
representations instead. That is likely incredibly inefficient.
Perhaps due to C, this commit has made Mu incredibly slow. Running all
tests for the core and the edit/ app now takes 6.5 minutes rather than
3.5 minutes.
== more notes and details
I've been struggling for the past week now to back out of a bad design
decision, a premature optimization from the early days: storing atoms
directly in the 'value' slot of a cons cell rather than creating a
special 'atom' cons cell and storing it on the 'left' slot. In other
words, if a cons cell looks like this:
o
/ | \
left val right
..then the type_tree (a b c) used to look like this (before this
commit):
o
| \
a o
| \
b o
| \
c null
..rather than like this 'classic' approach to s-expressions which never
mixes val and right (which is what we now have):
o
/ \
o o
| / \
a o o
| / \
b o null
|
c
The old approach made several operations more complicated, most recently
the act of replacing a (possibly atom/leaf) sub-tree with another. That
was the final straw that got me to realize the contortions I was going
through to save a few type_tree nodes (cons cells).
Switching to the new approach was hard partly because I've been using
the old approach for so long and type_tree manipulations had pervaded
everything. Another issue I ran into was the realization that my layers
were not cleanly separated. Key parts of early layers (precomputing type
metadata) existed purely for far later ones (shape-shifting types).
Layers I got repeatedly stuck at:
1. the transform for precomputing type sizes (layer 30)
2. type-checks on merge instructions (layer 31)
3. the transform for precomputing address offsets in types (layer 36)
4. replace operations in supporting shape-shifting recipes (layer 55)
After much thrashing I finally noticed that it wasn't the entirety of
these layers that was giving me trouble, but just the type metadata
precomputation, which had bugs that weren't manifesting until 30 layers
later. Or, worse, when loading .mu files before any tests had had a
chance to run. A common failure mode was running into types at run time
that I hadn't precomputed metadata for at transform time.
Digging into these bugs got me to realize that what I had before wasn't
really very good, but a half-assed heuristic approach that did a whole
lot of extra work precomputing metadata for utterly meaningless types
like `((address number) 3)` which just happened to be part of a larger
type like `(array (address number) 3)`.
So, I redid it all. I switched the representation of types (because the
old representation made unit tests difficult to retrofit) and added unit
tests to the metadata precomputation. I also made layer 30 only do the
minimal metadata precomputation it needs for the concepts introduced
until then. In the process, I also made the precomputation more correct
than before, and added hooks in the right place so that I could augment
the logic when I introduced shape-shifting containers.
== lessons learned
There's several levels of hygiene when it comes to layers:
1. Every layer introduces precisely what it needs and in the simplest
way possible. If I was building an app until just that layer, nothing
would seem over-engineered.
2. Some layers are fore-shadowing features in future layers. Sometimes
this is ok. For example, layer 10 foreshadows containers and arrays and
so on without actually supporting them. That is a net win because it
lets me lay out the core of Mu's data structures out in one place. But
if the fore-shadowing gets too complex things get nasty. Not least
because it can be hard to write unit tests for features before you
provide the plumbing to visualize and manipulate them.
3. A layer is introducing features that are tested only in later layers.
4. A layer is introducing features with tests that are invalidated in
later layers. (This I knew from early on to be an obviously horrendous
idea.)
Summary: avoid Level 2 (foreshadowing layers) as much as possible.
Tolerate it indefinitely for small things where the code stays simple
over time, but become strict again when things start to get more
complex.
Level 3 is mostly a net lose, but sometimes it can be expedient (a real
case of the usually grossly over-applied term "technical debt"), and
it's better than the conventional baseline of no layers and no
scenarios. Just clean it up as soon as possible.
Definitely avoid layer 4 at any time.
== minor lessons
Avoid unit tests for trivial things, write scenarios in context as much as
possible. But within those margins unit tests are fine. Just introduce them
before any scenarios (commit 3297).
Reorganizing layers can be easy. Just merge layers for starters! Punt on
resplitting them in some new way until you've gotten them to work. This is the
wisdom of Refactoring: small steps.
What made it hard was not wanting to merge *everything* between layer 30
and 55. The eventual insight was realizing I just need to move those two
full-strength transforms and nothing else.
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Never mind, just close your nose and replace that function parameter
with a global variable.
This may not always be the solution for the problem of layers being
unable to add parameters and arguments, but here it works well and it's
unclear what problems the global might cause.
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Replace an integer with a boolean across two layers of function calls.
It has long been one of the ugliest consequences of my approach with
layers that functions might need to be introduced with unnecessary
arguments simply because we have no clean way to add parameters to a
function definition after the fact -- or to add the default argument
corresponding to that parameter in calls. This problem is exacerbated by
the redundant argument having to be passed in through multiple layers of
functions. In this instance:
In layer 20 we define write_memory with an argument called
'saving_instruction_products' which isn't used yet.
In layer 36 we reveal that we use this argument in a call to
should_update_refcounts_in_write_memory() -- where it is again not used
yet.
Layer 43 finally clarifies what we're shooting for:
a) In general when we need to update some memory, we always want to
update refcounts.
b) The only exception is when we're reclaiming locals in a function
that set up its stack frame using 'local-scope' (signalling that it
wants immediate reclamation). At that point we avoid decrementing
refcounts of 'escaping' addresses that are being returned, and we also
avoid incrementing refcounts of products in the caller instruction.
The latter case is basically why we need this boolean and its dance
across 3 layers.
In summary, write_memory() needs to update refcounts except if:
we're writing products for an instruction,
the instruction is not a primitive, and
the (callee) recipe for the instruction starts with 'local-scope'.
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