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import pytest
try:
import cotengra as ctg
ctg_missing = False
except ImportError:
ctg_missing = True
ctg = None
import cotengrust as ctgr
requires_cotengra = pytest.mark.skipif(ctg_missing, reason="requires cotengra")
@pytest.mark.parametrize("which", ["greedy", "optimal"])
def test_basic_call(which):
inputs = [('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', 'a')]
output = ('b', 'd')
size_dict = {'a': 2, 'b': 3, 'c': 4, 'd': 5}
path = {
"greedy": ctgr.optimize_greedy,
"optimal": ctgr.optimize_optimal,
}[
which
](inputs, output, size_dict)
assert all(len(con) <= 2 for con in path)
def find_output_str(lhs):
tmp_lhs = lhs.replace(",", "")
return "".join(s for s in sorted(set(tmp_lhs)) if tmp_lhs.count(s) == 1)
def eq_to_inputs_output(eq):
if "->" not in eq:
eq += "->" + find_output_str(eq)
inputs, output = eq.split("->")
inputs = inputs.split(",")
inputs = [list(s) for s in inputs]
output = list(output)
return inputs, output
def get_rand_size_dict(inputs, d_min=2, d_max=3):
import random
size_dict = {}
for term in inputs:
for ix in term:
if ix not in size_dict:
size_dict[ix] = random.randint(d_min, d_max)
return size_dict
# these are taken from opt_einsum
test_case_eqs = [
# Test single-term equations
"->",
"a->a",
"ab->ab",
"ab->ba",
"abc->bca",
"abc->b",
"baa->ba",
"aba->b",
# Test scalar-like operations
"a,->a",
"ab,->ab",
",ab,->ab",
",,->",
# Test hadamard-like products
"a,ab,abc->abc",
"a,b,ab->ab",
# Test index-transformations
"ea,fb,gc,hd,abcd->efgh",
"ea,fb,abcd,gc,hd->efgh",
"abcd,ea,fb,gc,hd->efgh",
# Test complex contractions
"acdf,jbje,gihb,hfac,gfac,gifabc,hfac",
"cd,bdhe,aidb,hgca,gc,hgibcd,hgac",
"abhe,hidj,jgba,hiab,gab",
"bde,cdh,agdb,hica,ibd,hgicd,hiac",
"chd,bde,agbc,hiad,hgc,hgi,hiad",
"chd,bde,agbc,hiad,bdi,cgh,agdb",
"bdhe,acad,hiab,agac,hibd",
# Test collapse
"ab,ab,c->",
"ab,ab,c->c",
"ab,ab,cd,cd->",
"ab,ab,cd,cd->ac",
"ab,ab,cd,cd->cd",
"ab,ab,cd,cd,ef,ef->",
# Test outer prodcuts
"ab,cd,ef->abcdef",
"ab,cd,ef->acdf",
"ab,cd,de->abcde",
"ab,cd,de->be",
"ab,bcd,cd->abcd",
"ab,bcd,cd->abd",
# Random test cases that have previously failed
"eb,cb,fb->cef",
"dd,fb,be,cdb->cef",
"bca,cdb,dbf,afc->",
"dcc,fce,ea,dbf->ab",
"fdf,cdd,ccd,afe->ae",
"abcd,ad",
"ed,fcd,ff,bcf->be",
"baa,dcf,af,cde->be",
"bd,db,eac->ace",
"fff,fae,bef,def->abd",
"efc,dbc,acf,fd->abe",
# Inner products
"ab,ab",
"ab,ba",
"abc,abc",
"abc,bac",
"abc,cba",
# GEMM test cases
"ab,bc",
"ab,cb",
"ba,bc",
"ba,cb",
"abcd,cd",
"abcd,ab",
"abcd,cdef",
"abcd,cdef->feba",
"abcd,efdc",
# Inner than dot
"aab,bc->ac",
"ab,bcc->ac",
"aab,bcc->ac",
"baa,bcc->ac",
"aab,ccb->ac",
# Randomly built test caes
"aab,fa,df,ecc->bde",
"ecb,fef,bad,ed->ac",
"bcf,bbb,fbf,fc->",
"bb,ff,be->e",
"bcb,bb,fc,fff->",
"fbb,dfd,fc,fc->",
"afd,ba,cc,dc->bf",
"adb,bc,fa,cfc->d",
"bbd,bda,fc,db->acf",
"dba,ead,cad->bce",
"aef,fbc,dca->bde",
]
@requires_cotengra
@pytest.mark.parametrize("eq", test_case_eqs)
@pytest.mark.parametrize("which", ["greedy", "optimal"])
def test_manual_cases(eq, which):
inputs, output = eq_to_inputs_output(eq)
size_dict = get_rand_size_dict(inputs)
path = {
"greedy": ctgr.optimize_greedy,
"optimal": ctgr.optimize_optimal,
}[
which
](inputs, output, size_dict)
assert all(len(con) <= 2 for con in path)
tree = ctg.ContractionTree.from_path(
inputs, output, size_dict, path=path, check=True
)
assert tree.is_complete()
@requires_cotengra
@pytest.mark.parametrize("seed", range(10))
@pytest.mark.parametrize("which", ["greedy", "optimal"])
def test_basic_rand(seed, which):
inputs, output, shapes, size_dict = ctg.utils.rand_equation(
n=10,
reg=4,
n_out=2,
n_hyper_in=1,
n_hyper_out=1,
d_min=2,
d_max=3,
seed=seed,
)
path = {
"greedy": ctgr.optimize_greedy,
"optimal": ctgr.optimize_optimal,
}[
which
](inputs, output, size_dict)
assert all(len(con) <= 2 for con in path)
tree = ctg.ContractionTree.from_path(
inputs, output, size_dict, path=path, check=True
)
assert tree.is_complete()
@requires_cotengra
def test_optimal_lattice_eq():
inputs, output, _, size_dict = ctg.utils.lattice_equation(
[4, 5], d_max=2, seed=42
)
path = ctgr.optimize_optimal(inputs, output, size_dict, minimize='flops')
tree = ctg.ContractionTree.from_path(
inputs, output, size_dict, path=path
)
assert tree.is_complete()
assert tree.contraction_cost() == 964
path = ctgr.optimize_optimal(inputs, output, size_dict, minimize='size')
assert all(len(con) <= 2 for con in path)
tree = ctg.ContractionTree.from_path(
inputs, output, size_dict, path=path
)
assert tree.contraction_width() == pytest.approx(5)
@requires_cotengra
def test_optimize_random_greedy_log_flops():
inputs, output, _, size_dict = ctg.utils.lattice_equation(
[10, 10], d_max=3, seed=42
)
path, cost1 = ctgr.optimize_random_greedy_track_flops(
inputs, output, size_dict, ntrials=4, seed=42
)
_, cost2 = ctgr.optimize_random_greedy_track_flops(
inputs, output, size_dict, ntrials=4, seed=42
)
assert cost1 == cost2
tree = ctg.ContractionTree.from_path(
inputs, output, size_dict, path=path
)
assert tree.is_complete()
assert tree.contraction_cost(log=10) == pytest.approx(cost1)
|
