Many of the iterator adaptors will perform faster folds if they forward
to their inner iterator's folds, especially for inner types like `Chain`
which are optimized too. The following types are newly specialized:
| Type | `fold` | `rfold` |
| ----------- | ------ | ------- |
| `Enumerate` | ✓ | ✓ |
| `Filter` | ✓ | ✓ |
| `FilterMap` | ✓ | ✓ |
| `FlatMap` | exists | ✓ |
| `Fuse` | ✓ | ✓ |
| `Inspect` | ✓ | ✓ |
| `Peekable` | ✓ | N/A¹ |
| `Skip` | ✓ | N/A² |
| `SkipWhile` | ✓ | N/A¹ |
¹ not a `DoubleEndedIterator`
² `Skip::next_back` doesn't pull skipped items at all, but this couldn't
be avoided if `Skip::rfold` were to call its inner iterator's `rfold`.
Benchmarks
----------
In the following results, plain `_sum` computes the sum of a million
integers -- note that `sum()` is implemented with `fold()`. The
`_ref_sum` variants do the same on a `by_ref()` iterator, which is
limited to calling `next()` one by one, without specialized `fold`.
The `chain` variants perform the same tests on two iterators chained
together, to show a greater benefit of forwarding `fold` internally.
test iter::bench_enumerate_chain_ref_sum ... bench: 2,216,264 ns/iter (+/- 29,228)
test iter::bench_enumerate_chain_sum ... bench: 922,380 ns/iter (+/- 2,676)
test iter::bench_enumerate_ref_sum ... bench: 476,094 ns/iter (+/- 7,110)
test iter::bench_enumerate_sum ... bench: 476,438 ns/iter (+/- 3,334)
test iter::bench_filter_chain_ref_sum ... bench: 2,266,095 ns/iter (+/- 6,051)
test iter::bench_filter_chain_sum ... bench: 745,594 ns/iter (+/- 2,013)
test iter::bench_filter_ref_sum ... bench: 889,696 ns/iter (+/- 1,188)
test iter::bench_filter_sum ... bench: 667,325 ns/iter (+/- 1,894)
test iter::bench_filter_map_chain_ref_sum ... bench: 2,259,195 ns/iter (+/- 353,440)
test iter::bench_filter_map_chain_sum ... bench: 1,223,280 ns/iter (+/- 1,972)
test iter::bench_filter_map_ref_sum ... bench: 611,607 ns/iter (+/- 2,507)
test iter::bench_filter_map_sum ... bench: 611,610 ns/iter (+/- 472)
test iter::bench_fuse_chain_ref_sum ... bench: 2,246,106 ns/iter (+/- 22,395)
test iter::bench_fuse_chain_sum ... bench: 634,887 ns/iter (+/- 1,341)
test iter::bench_fuse_ref_sum ... bench: 444,816 ns/iter (+/- 1,748)
test iter::bench_fuse_sum ... bench: 316,954 ns/iter (+/- 2,616)
test iter::bench_inspect_chain_ref_sum ... bench: 2,245,431 ns/iter (+/- 21,371)
test iter::bench_inspect_chain_sum ... bench: 631,645 ns/iter (+/- 4,928)
test iter::bench_inspect_ref_sum ... bench: 317,437 ns/iter (+/- 702)
test iter::bench_inspect_sum ... bench: 315,942 ns/iter (+/- 4,320)
test iter::bench_peekable_chain_ref_sum ... bench: 2,243,585 ns/iter (+/- 12,186)
test iter::bench_peekable_chain_sum ... bench: 634,848 ns/iter (+/- 1,712)
test iter::bench_peekable_ref_sum ... bench: 444,808 ns/iter (+/- 480)
test iter::bench_peekable_sum ... bench: 317,133 ns/iter (+/- 3,309)
test iter::bench_skip_chain_ref_sum ... bench: 1,778,734 ns/iter (+/- 2,198)
test iter::bench_skip_chain_sum ... bench: 761,850 ns/iter (+/- 1,645)
test iter::bench_skip_ref_sum ... bench: 478,207 ns/iter (+/- 119,252)
test iter::bench_skip_sum ... bench: 315,614 ns/iter (+/- 3,054)
test iter::bench_skip_while_chain_ref_sum ... bench: 2,486,370 ns/iter (+/- 4,845)
test iter::bench_skip_while_chain_sum ... bench: 633,915 ns/iter (+/- 5,892)
test iter::bench_skip_while_ref_sum ... bench: 666,926 ns/iter (+/- 804)
test iter::bench_skip_while_sum ... bench: 444,405 ns/iter (+/- 571)
`FlatMap` can use internal iteration for its `fold`, which shows a
performance advantage in the new benchmarks:
test iter::bench_flat_map_chain_ref_sum ... bench: 4,354,111 ns/iter (+/- 108,871)
test iter::bench_flat_map_chain_sum ... bench: 468,167 ns/iter (+/- 2,274)
test iter::bench_flat_map_ref_sum ... bench: 449,616 ns/iter (+/- 6,257)
test iter::bench_flat_map_sum ... bench: 348,010 ns/iter (+/- 1,227)
... where the "ref" benches are using `by_ref()` that isn't optimized.
So this change shows a decent advantage on its own, but much more when
combined with a `chain` iterator that also optimizes `fold`.
The benefit of using internal iteration is shown in new benchmarks:
test iter::bench_for_each_chain_fold ... bench: 635,110 ns/iter (+/- 5,135)
test iter::bench_for_each_chain_loop ... bench: 2,249,983 ns/iter (+/- 42,001)
test iter::bench_for_each_chain_ref_fold ... bench: 2,248,061 ns/iter (+/- 51,940)
