New attribute parsing infrastructure

[jdonszelmann]

Another step in the plan outlined in #131229

introduces infrastructure for structured parsers for attributes, as well as converting a couple of complex attributes to have such structured parsers.

This PR may prove too large to review. I left some of my own comments to guide it a little. Some general notes:

• The first commit is basically standalone. It just preps some mostly unrelated sources for the rest of the PR to work. It might not have enormous merit on its own, but not negative merit either. Could be merged alone, but also doesn't make the review a whole lot easier. (but it's only +274 -209)
• The second commit is the one that introduces new infrastructure. It's the important one to review.
• The 3rd commit uses the new infrastructure showing how some of the more complex attributes can be parsed using it. Theoretically can be split up, though the parsers in this commit are the ones that really test the new infrastructure and show that it all works.
• The 4th commit fixes up rustdoc and clippy. In the previous 2 they didn't compile yet while the compiler does. Separated them out to separate concerns and make the rest more palatable.
• The 5th commit blesses some test outputs. Sometimes that's just because a diagnostic happens slightly earlier than before, which I'd say is acceptable. Sometimes a diagnostic is now only emitted once where it would've been twice before (yay! fixed some bugs). One test I actually moved from crashes to fixed, because it simply doesn't crash anymore. That's why this PR Closes ICE: malformed malformed repr(align(N)) #132391. I think most choices I made here are generally reasonable, but let me know if you disagree anywhere.
• The 6th commit adds a derive to pretty print attributes
• The 7th removes smir apis for attributes, for the time being. The api will at some point be replaced by one based on rustc_ast_data_structures::AttributeKind

In general, a lot of the additions here are comments. I've found it very important to document new things in the 2nd commit well so other people can start using it.

Closes #132391
Closes #136717

[rustbot]

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[Noratrieb]

r? oli-obk

[bors]

⌛ Testing commit 4daa35c with merge 7d8c6e781d347e087c7d30ea393d7dcd725ed623...

[jdonszelmann]

omg :D

[BoxyUwU]

@bors rollup=never p=1 bitrotty

[bors]

☀️ Test successful - checks-actions
Approved by: oli-obk
Pushing 7d8c6e7 to master...

[rust-timer]

Finished benchmarking commit (7d8c6e7): comparison URL.

Overall result: ❌✅ regressions and improvements - please read the text below

Our benchmarks found a performance regression caused by this PR.
This might be an actual regression, but it can also be just noise.

Next Steps:

• If the regression was expected or you think it can be justified,
  please write a comment with sufficient written justification, and add
  @rustbot label: +perf-regression-triaged to it, to mark the regression as triaged.
• If you think that you know of a way to resolve the regression, try to create
  a new PR with a fix for the regression.
• If you do not understand the regression or you think that it is just noise,
  you can ask the @rust-lang/wg-compiler-performance working group for help (members of this group
  were already notified of this PR).

@rustbot label: +perf-regression
cc @rust-lang/wg-compiler-performance

Instruction count

This is the most reliable metric that we have; it was used to determine the overall result at the top of this comment. However, even this metric can sometimes exhibit noise.

	mean	range	count
Regressions ❌ (primary)	0.4%	[0.2%, 0.8%]	38
Regressions ❌ (secondary)	0.6%	[0.3%, 1.3%]	43
Improvements ✅ (primary)	-4.1%	[-4.1%, -4.1%]	1
Improvements ✅ (secondary)	-0.7%	[-2.4%, -0.3%]	8
All ❌✅ (primary)	0.3%	[-4.1%, 0.8%]	39

Max RSS (memory usage)

Results (primary -0.1%, secondary 0.2%)

This is a less reliable metric that may be of interest but was not used to determine the overall result at the top of this comment.

	mean	range	count
Regressions ❌ (primary)	1.2%	[0.4%, 4.5%]	44
Regressions ❌ (secondary)	1.0%	[0.4%, 3.4%]	76
Improvements ✅ (primary)	-0.9%	[-2.7%, -0.4%]	68
Improvements ✅ (secondary)	-0.9%	[-4.4%, -0.4%]	53
All ❌✅ (primary)	-0.1%	[-2.7%, 4.5%]	112

Cycles

Results (primary 0.4%, secondary 0.7%)

This is a less reliable metric that may be of interest but was not used to determine the overall result at the top of this comment.

	mean	range	count
Regressions ❌ (primary)	0.9%	[0.4%, 5.1%]	97
Regressions ❌ (secondary)	1.1%	[0.4%, 2.7%]	109
Improvements ✅ (primary)	-1.6%	[-4.2%, -0.4%]	20
Improvements ✅ (secondary)	-1.4%	[-3.8%, -0.5%]	18
All ❌✅ (primary)	0.4%	[-4.2%, 5.1%]	117

Binary size

Results (primary -0.1%)

This is a less reliable metric that may be of interest but was not used to determine the overall result at the top of this comment.

	mean	range	count
Regressions ❌ (primary)	-	-	0
Regressions ❌ (secondary)	-	-	0
Improvements ✅ (primary)	-0.1%	[-0.7%, -0.0%]	9
Improvements ✅ (secondary)	-	-	0
All ❌✅ (primary)	-0.1%	[-0.7%, -0.0%]	9

Bootstrap: 770.447s -> 772.781s (0.30%)
Artifact size: 359.63 MiB -> 362.01 MiB (0.66%)

[klensy]

Why? item == items[0], as destructured in let [item] = &items[..] else