Add more FOR_ITER specialization stats

[sweeneyde]

No description provided.

[markshannon]

How did you choose the categories? What do the stats look like?

They look quite tailored to the benchmark suite. Would it possible to generalize them a bit?
Certain builtin classes, like zip and enumerate are worth checking for. But beyond that it is probably only worth categorizing into broader categories like: "iterator for underlying sequence", "computed iterator", "implemented in C", "implemented in Python".
That sort of thing.

[sweeneyde]

I chose the categories by running the test suite and pyperformance with a printf at the end of _PySpecialization_ClassifyIterator until there stopped being so many prints. I can pare down the number of cases to ones we care about, but here are some of the results with all of those details:

• pyperformance FOR_ITER stats by benchmarks

• pyperformance FOR_ITER stats in total

• python -m test FOR_ITER stats

[markshannon]

Ok, we can always change the categories later, if we need to.
In the meantime, this is useful information.

[markshannon]

The values below 8 are common, in the section marked /* Common */ above.
You can always raise SPECIALIZATION_FAILURE_KINDS if you need.

[sweeneyde]

Results from python -m test, now with the new ascii string iterator:

Failure kind	Count	Ratio
list	40313806	30.7%
range	37450044	28.5%
itertools	10394906	7.9%
map	9850377	7.5%
tuple	7643483	5.8%
generator	7477618	5.7%
enumerate	3882317	3.0%
ascii string	3266112	2.5%
dict items	3198088	2.4%
callable	1707168	1.3%
dict keys	1115718	0.8%
bytes	1105824	0.8%
zip	984525	0.7%
seq iter	974430	0.7%
other	801038	0.6%
set	678627	0.5%
dict values	222521	0.2%
reversed list	184092	0.1%
string	72383	0.1%

I think this is useful information, so I'll go ahead and merge. As was said, we can always adjust things more later.