Upgrade `finch-tensor` - Githubissues

pydata / sparse

Sparse multi-dimensional arrays for the PyData ecosystem

https://sparse.pydata.org

BSD 3-Clause "New" or "Revised" License

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Upgrade `finch-tensor` #702

Closed mtsokol closed 1 month ago

mtsokol commented 1 month ago

Hi @hameerabbasi,

Once we have new Finch.jl and finch-tensor releases in some time we can merge it, as we solved:

floor_divide return dtype mismatch
in matmul_example.py we can replace lazy indexing with actual @ operator as for the benchmarked case (on my machine) Finch is x40 times faster than Numba (and x7 times slower than SciPy)

mtsokol commented 1 month ago

So I think matmul_example.py is written exactly as the user would write it and it shows speedup compared to Numba.

hameerabbasi commented 1 month ago

Thanks, @mtsokol. Waiting on the release and CI before I review.

hameerabbasi commented 1 month ago

Ping me when the release is up, I'll approve.

mtsokol commented 1 month ago

@hameerabbasi The PR is ready (except for hanging Finch Array API job at 95%).

willow-ahrens commented 1 month ago

Is the benchmark any faster with lazy indexing?

mtsokol commented 1 month ago

Is the benchmark any faster with lazy indexing?

@willow-ahrens I would say matmul with the lazy notation is slightly faster but the notation a @ b is much closer to what user would write compared to lazy indexing form:

SIZE = 100000 x 100000
DENSITY = 0.00001
FORMAT = csr
ITERS = 3

######
# Finch a @ b
Finch
Took 0.040337721506754555 s.

Numba
Took 2.880397001902262 s.

SciPy
Took 0.0067259470621744795 s.
######
# Finch lazy indexing
Finch
Took 0.05138166745503744 s.

Numba
Took 2.861244281133016 s.

SciPy
Took 0.006536006927490234 s.
######