func._schema is a pybind, accessing the arguments/returns is expensive, and we have no reason to do it anyway.

ghstack-source-id: 062823f
Pull Request resolved: #161292

func._schema is a pybind, accessing the arguments/returns is expensive, and we have no reason to do it anyway.

ghstack-source-id: 43af3ef
Pull Request resolved: #161292

…61304)

`is` checks object identity and is more efficient. Google seems to confirm it is the correct way to do an exact type check.
Pull Request resolved: #161304
Approved by: https://github.com/Skylion007, https://github.com/malfet, https://github.com/bdhirsh
ghstack dependencies: #161301, #161292

…#161308)

- Empty containers are Falsey
- Hoist cheap checks first
- Microbenchmarked single-element set access method

Benchmark code:
```
import timeit

to_test = [
    ('list(x)', 'x = set([3])'),
    ('x[0]', 'x = [3]'),
    ('list(x)[0]', 'x = set([3])'),
    ('next(iter(x))', 'x = set([3])'),
]

for (stmt, setup) in to_test:
    res = timeit.timeit(stmt=stmt, setup=setup)
    print(f"Time for `{stmt}`: {res}")
```

Result with Python 3.13 on Mac (with excess digits manually trimmed; directionally matches result on Linux)
```
Time for `list(x)`: 0.03418
Time for `x[0]`: 0.00852
Time for `list(x)[0]`: 0.03561
Time for `next(iter(x))`: 0.02278
```

FWIW, I was surprised by this result, so I guess I'm glad I wrote the benchmark!
Pull Request resolved: #161308
Approved by: https://github.com/Skylion007, https://github.com/bdhirsh
ghstack dependencies: #161301, #161292, #161304

)

Scanning a list of pybind enums with `in` is slow. See NOTE in code for full explanation.

This is a significant optimization; will be updating the torchdispatch/return_and_correct_aliasing portion of this stack with benchmark and results soonish.
Pull Request resolved: #161315
Approved by: https://github.com/Skylion007, https://github.com/bdhirsh
ghstack dependencies: #161301, #161292, #161304, #161308

…61317)

This assertion was expensive because of is_traceable_wrapper_subclass. Finding a cheap check to run first that's likely to let us skip the rest seems to improve things significantly.
Pull Request resolved: #161317
Approved by: https://github.com/ezyang, https://github.com/XilunWu, https://github.com/bdhirsh
ghstack dependencies: #161301, #161292, #161304, #161308, #161315

Not a huge cost, but free win is free.
Pull Request resolved: #161328
Approved by: https://github.com/Skylion007
ghstack dependencies: #161301, #161292, #161304, #161308, #161315, #161317

`auto` forces a copy. Confirmed this did something noticable with perf.
Pull Request resolved: #161329
Approved by: https://github.com/zpcore, https://github.com/fduwjj, https://github.com/Skylion007, https://github.com/bdhirsh
ghstack dependencies: #161301, #161292, #161304, #161308, #161315, #161317, #161328

If we want them interned, we should intern at callsites. (The numpy reference has bit rotted; see numpy/numpy@b222eb6#diff-6bdb6105198083838f51c57b55b3a49472ed23043bb40018f1ea41138e687163)

Profiling a simple torchdispatch benchmark with perf before/after seems to show that time spent copying std::strings and interning Python strings is gone, though there is some noise and the improvement is very small.
Pull Request resolved: #161432
Approved by: https://github.com/ezyang
ghstack dependencies: #161301, #161292, #161304, #161308, #161315, #161317, #161328, #161329

)

symbools are not identical with Py_True or PyFalse, so we can do those cheap checks first and at least get plain old bools to go fast.
Pull Request resolved: #161455
Approved by: https://github.com/Skylion007
ghstack dependencies: #161301, #161292, #161304, #161308, #161315, #161317, #161328, #161329, #161432

…ing (pytorch#161292)

func._schema is a pybind, accessing the arguments/returns is expensive, we have no reason to do it anyway, and even though pytorch#161301 makes accessing the arguments/returns less expensive, this still seems to improve performance.
Pull Request resolved: pytorch#161292
Approved by: https://github.com/wconstab, https://github.com/malfet, https://github.com/bdhirsh
ghstack dependencies: pytorch#161301

…torch#161304)

`is` checks object identity and is more efficient. Google seems to confirm it is the correct way to do an exact type check.
Pull Request resolved: pytorch#161304
Approved by: https://github.com/Skylion007, https://github.com/malfet, https://github.com/bdhirsh
ghstack dependencies: pytorch#161301, pytorch#161292

…pytorch#161308)

- Empty containers are Falsey
- Hoist cheap checks first
- Microbenchmarked single-element set access method

Benchmark code:
```
import timeit

to_test = [
    ('list(x)', 'x = set([3])'),
    ('x[0]', 'x = [3]'),
    ('list(x)[0]', 'x = set([3])'),
    ('next(iter(x))', 'x = set([3])'),
]

for (stmt, setup) in to_test:
    res = timeit.timeit(stmt=stmt, setup=setup)
    print(f"Time for `{stmt}`: {res}")
```

Result with Python 3.13 on Mac (with excess digits manually trimmed; directionally matches result on Linux)
```
Time for `list(x)`: 0.03418
Time for `x[0]`: 0.00852
Time for `list(x)[0]`: 0.03561
Time for `next(iter(x))`: 0.02278
```

FWIW, I was surprised by this result, so I guess I'm glad I wrote the benchmark!
Pull Request resolved: pytorch#161308
Approved by: https://github.com/Skylion007, https://github.com/bdhirsh
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304

…rch#161315)

Scanning a list of pybind enums with `in` is slow. See NOTE in code for full explanation.

This is a significant optimization; will be updating the torchdispatch/return_and_correct_aliasing portion of this stack with benchmark and results soonish.
Pull Request resolved: pytorch#161315
Approved by: https://github.com/Skylion007, https://github.com/bdhirsh
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304, pytorch#161308

…torch#161317)

This assertion was expensive because of is_traceable_wrapper_subclass. Finding a cheap check to run first that's likely to let us skip the rest seems to improve things significantly.
Pull Request resolved: pytorch#161317
Approved by: https://github.com/ezyang, https://github.com/XilunWu, https://github.com/bdhirsh
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304, pytorch#161308, pytorch#161315

…61328)

Not a huge cost, but free win is free.
Pull Request resolved: pytorch#161328
Approved by: https://github.com/Skylion007
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304, pytorch#161308, pytorch#161315, pytorch#161317

`auto` forces a copy. Confirmed this did something noticable with perf.
Pull Request resolved: pytorch#161329
Approved by: https://github.com/zpcore, https://github.com/fduwjj, https://github.com/Skylion007, https://github.com/bdhirsh
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304, pytorch#161308, pytorch#161315, pytorch#161317, pytorch#161328

…h#161432)

If we want them interned, we should intern at callsites. (The numpy reference has bit rotted; see numpy/numpy@b222eb6#diff-6bdb6105198083838f51c57b55b3a49472ed23043bb40018f1ea41138e687163)

Profiling a simple torchdispatch benchmark with perf before/after seems to show that time spent copying std::strings and interning Python strings is gone, though there is some noise and the improvement is very small.
Pull Request resolved: pytorch#161432
Approved by: https://github.com/ezyang
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304, pytorch#161308, pytorch#161315, pytorch#161317, pytorch#161328, pytorch#161329

…rch#161455)

symbools are not identical with Py_True or PyFalse, so we can do those cheap checks first and at least get plain old bools to go fast.
Pull Request resolved: pytorch#161455
Approved by: https://github.com/Skylion007
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304, pytorch#161308, pytorch#161315, pytorch#161317, pytorch#161328, pytorch#161329, pytorch#161432

…ing (pytorch#161292)

func._schema is a pybind, accessing the arguments/returns is expensive, we have no reason to do it anyway, and even though pytorch#161301 makes accessing the arguments/returns less expensive, this still seems to improve performance.
Pull Request resolved: pytorch#161292
Approved by: https://github.com/wconstab, https://github.com/malfet, https://github.com/bdhirsh
ghstack dependencies: pytorch#161301

…torch#161304)

`is` checks object identity and is more efficient. Google seems to confirm it is the correct way to do an exact type check.
Pull Request resolved: pytorch#161304
Approved by: https://github.com/Skylion007, https://github.com/malfet, https://github.com/bdhirsh
ghstack dependencies: pytorch#161301, pytorch#161292

…pytorch#161308)

- Empty containers are Falsey
- Hoist cheap checks first
- Microbenchmarked single-element set access method

Benchmark code:
```
import timeit

to_test = [
    ('list(x)', 'x = set([3])'),
    ('x[0]', 'x = [3]'),
    ('list(x)[0]', 'x = set([3])'),
    ('next(iter(x))', 'x = set([3])'),
]

for (stmt, setup) in to_test:
    res = timeit.timeit(stmt=stmt, setup=setup)
    print(f"Time for `{stmt}`: {res}")
```

Result with Python 3.13 on Mac (with excess digits manually trimmed; directionally matches result on Linux)
```
Time for `list(x)`: 0.03418
Time for `x[0]`: 0.00852
Time for `list(x)[0]`: 0.03561
Time for `next(iter(x))`: 0.02278
```

FWIW, I was surprised by this result, so I guess I'm glad I wrote the benchmark!
Pull Request resolved: pytorch#161308
Approved by: https://github.com/Skylion007, https://github.com/bdhirsh
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304

…rch#161315)

Scanning a list of pybind enums with `in` is slow. See NOTE in code for full explanation.

This is a significant optimization; will be updating the torchdispatch/return_and_correct_aliasing portion of this stack with benchmark and results soonish.
Pull Request resolved: pytorch#161315
Approved by: https://github.com/Skylion007, https://github.com/bdhirsh
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304, pytorch#161308

…torch#161317)

This assertion was expensive because of is_traceable_wrapper_subclass. Finding a cheap check to run first that's likely to let us skip the rest seems to improve things significantly.
Pull Request resolved: pytorch#161317
Approved by: https://github.com/ezyang, https://github.com/XilunWu, https://github.com/bdhirsh
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304, pytorch#161308, pytorch#161315

…61328)

Not a huge cost, but free win is free.
Pull Request resolved: pytorch#161328
Approved by: https://github.com/Skylion007
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304, pytorch#161308, pytorch#161315, pytorch#161317

`auto` forces a copy. Confirmed this did something noticable with perf.
Pull Request resolved: pytorch#161329
Approved by: https://github.com/zpcore, https://github.com/fduwjj, https://github.com/Skylion007, https://github.com/bdhirsh
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304, pytorch#161308, pytorch#161315, pytorch#161317, pytorch#161328

…h#161432)

If we want them interned, we should intern at callsites. (The numpy reference has bit rotted; see numpy/numpy@b222eb6#diff-6bdb6105198083838f51c57b55b3a49472ed23043bb40018f1ea41138e687163)

Profiling a simple torchdispatch benchmark with perf before/after seems to show that time spent copying std::strings and interning Python strings is gone, though there is some noise and the improvement is very small.
Pull Request resolved: pytorch#161432
Approved by: https://github.com/ezyang
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304, pytorch#161308, pytorch#161315, pytorch#161317, pytorch#161328, pytorch#161329

…rch#161455)

symbools are not identical with Py_True or PyFalse, so we can do those cheap checks first and at least get plain old bools to go fast.
Pull Request resolved: pytorch#161455
Approved by: https://github.com/Skylion007
ghstack dependencies: pytorch#161301, pytorch#161292, pytorch#161304, pytorch#161308, pytorch#161315, pytorch#161317, pytorch#161328, pytorch#161329, pytorch#161432