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[torch-mlir][sparse] enable test on ReLu (#3336) 

Downstream MLIR sparsifier has some (rudimentary) support for ReLU now,
and this test can now be enabled with correct end-to-end behavior.

Also see discussion at:

https://discourse.llvm.org/t/min-max-abs-relu-recognition-starter-project/78918
pull/3337/head
Aart Bik 2024-05-13 15:34:26 -07:00 committed by GitHub
parent 08355be5d0
commit 667dfcbc5a
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1 changed files with 11 additions and 2 deletions
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13
test/python/fx_importer/sparse_test.py
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@ -459,6 +459,11 @@ def test_sparse_eltwise():
# CHECK: values=tensor([ 0., 0., 1., 2., 3., 1000.]),
# CHECK: size=(10, 20, 30), nnz=6, dtype=torch.float64, layout=torch.sparse_coo)
# CHECK: torch.mlir
# CHECK: [0 6]
# CHECK: [0 1 1 4 9 9]
# CHECK: [ 0 1 1 5 19 19]
# CHECK: [ 0 1 3 6 28 29]
# CHECK: [ 0. 0. 1. 2. 3. 1000.]
#
def test_sparse_coo3():
class COO3Net(torch.nn.Module):
@ -481,11 +486,15 @@ def test_sparse_coo3():
# Run it with PyTorch torch.sparse and with TORCH-MLIR sparse_jit.
res1 = net(sparse_input)
# TODO: make coo3 work
# res2 = sparse_jit(net, sparse_input)
res2 = sparse_jit(net, sparse_input)
print("torch.sparse")
print(res1)
print("torch.mlir")
print(res2[0])
print(res2[1])
print(res2[2])
print(res2[3])
print(res2[4])
@run