I've been thinking about Python performance improvements, and I played around with an inline cache enhancement that supports slots. The results on a very simple benchmark look promising (30% speedup) but I'm terrible with our benchmarking tools, and this should be considered *very* carefully before we go ahead with it, since it could potentially pessimize the inline cache for standard attributes.

I'll attach a PR in a minute.

Slot means so many different things in Python. Here it's about data descriptors created when you set __slots__ in the class definition.

It is amazing that so large speed up can be achieved for such base operation.

I will try to run today the pyperformance test suite to see if there is any impact on standard attribute access

These are the results:

venv ❯ python -m pyperf compare_to json_old/* -G --min-speed=2 --table
+---------------------+--------------------------------------+------------------------------------------+
| Benchmark           | 2021-01-14_09-40-master-14cfa325c298 | 2021-01-14_10-39-slotscache-9123a84ad8d4 |
+=====================+======================================+==========================================+
| float               | 148 ms                               | 133 ms: 1.11x faster                     |
+---------------------+--------------------------------------+------------------------------------------+
| pidigits            | 265 ms                               | 252 ms: 1.05x faster                     |
+---------------------+--------------------------------------+------------------------------------------+
| scimark_monte_carlo | 130 ms                               | 126 ms: 1.03x faster                     |
+---------------------+--------------------------------------+------------------------------------------+
| spectral_norm       | 168 ms                               | 164 ms: 1.03x faster                     |
+---------------------+--------------------------------------+------------------------------------------+
| logging_silent      | 221 ns                               | 216 ns: 1.02x faster                     |
+---------------------+--------------------------------------+------------------------------------------+
| sympy_expand        | 598 ms                               | 585 ms: 1.02x faster                     |
+---------------------+--------------------------------------+------------------------------------------+
| regex_v8            | 31.9 ms                              | 32.6 ms: 1.02x slower                    |
+---------------------+--------------------------------------+------------------------------------------+
| deltablue           | 8.93 ms                              | 9.16 ms: 1.03x slower                    |
+---------------------+--------------------------------------+------------------------------------------+
| unpickle            | 17.0 us                              | 17.7 us: 1.04x slower                    |
+---------------------+--------------------------------------+------------------------------------------+
| Geometric mean      | (ref)                                | 1.00x faster                             |
+---------------------+--------------------------------------+------------------------------------------+

Benchmark hidden because not significant (50): sympy_sum, sqlalchemy_imperative, sympy_str, sympy_integrate, dulwich_log, scimark_fft, genshi_text, tornado_http, regex_dna, sqlalchemy_declarative, mako, meteor_contest, unpickle_pure_python, xml_etree_parse, genshi_xml, scimark_sor, sqlite_synth, pickle_pure_python, nbody, pickle_dict, pyflate, regex_effbot, xml_etree_process, logging_simple, python_startup, 2to3, fannkuch, python_startup_no_site, raytrace, go, hexiom, scimark_lu, json_dumps, richards, logging_format, xml_etree_generate, chaos, telco, pickle_list, unpack_sequence, regex_compile, django_template, json_loads, crypto_pyaes, xml_etree_iterparse, unpickle_list, nqueens, chameleon, scimark_sparse_mat_mult, pickle
Ignored benchmarks (1) of json_old/2021-01-14_09-40-master-14cfa325c298.json.gz: pathlib


I uploaded the pyperf result data to bpo as well

So it seems that everything is in the noise range except the "float" benchmark that is 1.11x faster

Some microbenchmarks:

CURRENT MASTER:

Variable and attribute read access:
   4.5 ns       read_local
   5.8 ns       read_nonlocal
   7.8 ns       read_global
   7.8 ns       read_builtin
  21.2 ns       read_classvar_from_class
  19.1 ns       read_classvar_from_instance
  16.4 ns       read_instancevar
  23.6 ns       read_instancevar_slots
  20.2 ns       read_namedtuple
  40.5 ns       read_boundmethod

PR 24216

Variable and attribute read access:
   4.5 ns       read_local
   5.8 ns       read_nonlocal
   7.8 ns       read_global
   8.2 ns       read_builtin
  21.4 ns       read_classvar_from_class
  19.0 ns       read_classvar_from_instance
  16.6 ns       read_instancevar
  10.2 ns       read_instancevar_slots
  20.5 ns       read_namedtuple
  42.9 ns       read_boundmethod

> So it seems that everything is in the noise range except the "float" benchmark that is 1.11x faster

Yeah, this is why. https://github.com/python/pyperformance/blob/master/pyperformance/benchmarks/bm_float.py#L12

This is a great result, IMO. I'm +1 to merge this.

> Some microbenchmarks:


Can you add a new one `read_slots`?

> This is a great result, IMO. I'm +1 to merge this.

Same here. It would be good if Inada-san could confirm the benchmarks.

> Can you add a new one `read_slots`?

That should be "read_instancevar_slots". Or you refer to some other check?

+1 Thanks for this.

Thanks for all the positive feedback! What is the next step?

> What is the next step?

I would say just finishing the PR (making sure that we do not miss some arcane edge case) and updating the what's new for 3.10 :)

I created a benchmark for this, see https://github.com/python/pyperformance/pull/86.

Next I'll add a blurb and then it's off to reviewers.

New changeset 5c5a938573ce665f00e362c7766912d9b3f3b44e by Guido van Rossum in branch 'master':
bpo-42927: Inline cache for attributes defined with '__slots__' (#24216)
https://github.com/python/cpython/commit/5c5a938573ce665f00e362c7766912d9b3f3b44e

New changeset a776da90b8f2a1342f4f9bfd23a62cea9a0497c6 by Pablo Galindo in branch 'master':
bpo-42927: Update the What's new entry for LOAD_ATTR optimizations (GH-24383)
https://github.com/python/cpython/commit/a776da90b8f2a1342f4f9bfd23a62cea9a0497c6