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This PR adds --stride and --offset parameters to the compare.py script (for now only used in the instcombine benchmark), which can be used to control which subset of the input files are to be run. We then use these parameters in a matrix strategy to distribute the LLVM evaluation CI job over 3 runners (but this could be scaled arbitrarily).
Only after implementing this I noticed that the collect.py script seems to be broken, it reports the following error:
Traceback (most recent call last):
File "/home/runner/work/lean-mlir/lean-mlir/bv-evaluation/./collect.py", line 1149, in <module>
main()
File "/home/runner/work/lean-mlir/lean-mlir/bv-evaluation/./collect.py", line 1145, in main
collect(b, reps=reps)
File "/home/runner/work/lean-mlir/lean-mlir/bv-evaluation/./collect.py", line 941, in collect
print("max of percentage stddev/av: "+str(np.max(ratios))+ "%")
^^^^^^^^^^^^^^
File "/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py", line 2810, in max
return _wrapreduction(a, np.maximum, 'max', axis, None, out,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py", line 88, in _wrapreduction
return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: zero-size array to reduction operation maximum which has no identity
So I can't really confirm that I didn't broke anything.
bv_decide solved 0 theorems.
bitwuzla solved 0 theorems.
bv_decide found 0 counterexamples.
bitwuzla found 0 counterexamples.
bv_decide only failed on 0 problems.
bitwuzla only failed on 0 problems.
both bitwuzla and bv_decide failed on 0 problems.
In total, bitwuzla saw 0 problems.
In total, bv_decide saw 0 problems.
ran rg 'LeanSAT provided a counter' | wc -l, this file found 0, rg found 0, SUCCESS
ran rg 'Bitwuzla provided a counter' | wc -l, this file found 0, rg found 0, SUCCESS
ran rg 'LeanSAT proved' | wc -l, this file found 0, rg found 0, SUCCESS
ran rg 'Bitwuzla proved' | wc -l, this file found 0, rg found 0, SUCCESS
The InstCombine benchmark contains 4520 theorems in total.
Saved dataframe at: /code/lean-mlir/bv-evaluation/raw-data/InstCombine/instcombine_ceg_data.csv
all_files_solved_bitwuzla_times_stddev avg: nan | stddev: nan
all_files_solved_bv_decide_times_stddev avg: nan | stddev: nan
all_files_solved_bv_decide_rw_times_stddev avg: nan | stddev: nan
all_files_solved_bv_decide_bb_times_stddev avg: nan | stddev: nan
all_files_solved_bv_decide_sat_times_stddev avg: nan | stddev: nan
all_files_solved_bv_decide_lratt_times_stddev avg: nan | stddev: nan
all_files_solved_bv_decide_lratc_times_stddev avg: nan | stddev: nan
mean of percentage stddev/av: nan%
bv_decide solved 0 theorems.
bitwuzla solved 0 theorems.
bv_decide found 0 counterexamples.
bitwuzla found 0 counterexamples.
bv_decide only failed on 0 problems.
bitwuzla only failed on 0 problems.
both bitwuzla and bv_decide failed on 0 problems.
In total, bitwuzla saw 0 problems.
In total, bv_decide saw 0 problems.
ran rg 'LeanSAT provided a counter' | wc -l, this file found 0, rg found 0, SUCCESS
ran rg 'Bitwuzla provided a counter' | wc -l, this file found 0, rg found 0, SUCCESS
ran rg 'LeanSAT proved' | wc -l, this file found 0, rg found 0, SUCCESS
ran rg 'Bitwuzla proved' | wc -l, this file found 0, rg found 0, SUCCESS
The InstCombine benchmark contains 4520 theorems in total.
Saved dataframe at: /home/runner/work/lean-mlir/lean-mlir/bv-evaluation/raw-data/InstCombine/instcombine_ceg_data.csv
all_files_solved_bitwuzla_times_stddev avg: nan | stddev: nan
all_files_solved_bv_decide_times_stddev avg: nan | stddev: nan
all_files_solved_bv_decide_rw_times_stddev avg: nan | stddev: nan
all_files_solved_bv_decide_bb_times_stddev avg: nan | stddev: nan
all_files_solved_bv_decide_sat_times_stddev avg: nan | stddev: nan
all_files_solved_bv_decide_lratt_times_stddev avg: nan | stddev: nan
all_files_solved_bv_decide_lratc_times_stddev avg: nan | stddev: nan
mean of percentage stddev/av: nan%
alexkeizer
changed the title
feat: use matrix strategy to distribute LLVM evaluation
WIP: feat: use matrix strategy to distribute LLVM evaluation
Aug 29, 2025
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This PR adds
--strideand--offsetparameters to thecompare.pyscript (for now only used in the instcombine benchmark), which can be used to control which subset of the input files are to be run. We then use these parameters in a matrix strategy to distribute the LLVM evaluation CI job over 3 runners (but this could be scaled arbitrarily).Only after implementing this I noticed that the collect.py script seems to be broken, it reports the following error:
So I can't really confirm that I didn't broke anything.