def __init__(self, options: str):
pipeline = (
+ f'builtin.func(linalg-generalize-named-ops,linalg-fuse-elementwise-ops),'
f'sparsification{{{options}}},'
f'sparse-tensor-conversion,'
- f'builtin.func(convert-linalg-to-loops,convert-vector-to-scf),'
+ f'builtin.func(linalg-bufferize,convert-linalg-to-loops,convert-vector-to-scf),'
f'convert-scf-to-std,'
f'func-bufferize,'
f'tensor-constant-bufferize,'
f'builtin.func(tensor-bufferize,std-bufferize,finalizing-bufferize),'
f'convert-vector-to-llvm{{reassociate-fp-reductions=1 enable-index-optimizations=1}},'
+ f'lower-affine,'
f'convert-memref-to-llvm,'
f'convert-std-to-llvm,'
f'reconcile-unrealized-casts')
# CHECK-LABEL: TEST: testSpMM
-# CHECK: Passed 72 tests
+# CHECK: Passed 8 tests
@run
def testSpMM():
# Obtain path to runtime support library.
with ir.Context() as ctx, ir.Location.unknown():
count = 0
- # Fixed compiler optimization strategy.
- # TODO: explore state space here too
+ # Loop over various ways to compile and annotate the SpMM kernel with
+ # a *single* sparse tensor. Note that we deliberate do not exhaustively
+ # search the full state space to reduce runtime of the test. It is
+ # straightforward to adapt the code below to explore more combinations.
par = 0
vec = 0
vl = 1
opt = (f'parallelization-strategy={par} '
f'vectorization-strategy={vec} '
f'vl={vl} enable-simd-index32={e}')
- # Exhaustive loop over various ways to annotate a kernel with
- # a *single* sparse tensor. Even this subset already gives
- # quite a large state space!
levels = [[st.DimLevelType.dense, st.DimLevelType.dense],
[st.DimLevelType.dense, st.DimLevelType.compressed],
[st.DimLevelType.compressed, st.DimLevelType.dense],
ir.AffineMap.get_permutation([0, 1]),
ir.AffineMap.get_permutation([1, 0])
]
- bitwidths = [0, 8, 32]
+ bitwidths = [0]
for level in levels:
for ordering in orderings:
for pwidth in bitwidths: