Ports logdet from method_tests() to op_db (#55743)
Summary:
Per title. Also updates some tensor construction helpers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55743
Reviewed By: ngimel
Differential Revision: D27702060
Pulled By: mruberry
fbshipit-source-id: f64b7bee855733ad1f4fd182819ceec5831d9878
diff --git a/test/test_torch.py b/test/test_torch.py
index 9ae8a13..a171772 100644
--- a/test/test_torch.py
+++ b/test/test_torch.py
@@ -4490,7 +4490,7 @@
def make_arg(batch_sizes, n, dim, contig):
size_arg = batch_sizes[:dim] + (n,) + batch_sizes[dim:]
- return make_tensor(size_arg, device, dtype, low=None, high=None, discontiguous=not contig)
+ return make_tensor(size_arg, device, dtype, low=None, high=None, noncontiguous=not contig)
def ref_index_copy(tgt, dim, idx, src):
for i in range(idx.size(0)):
@@ -4598,7 +4598,7 @@
def make_arg(batch_sizes, n, dim, contig):
size_arg = batch_sizes[:dim] + (n,) + batch_sizes[dim:]
- return make_tensor(size_arg, device, dtype, low=None, high=None, discontiguous=not contig)
+ return make_tensor(size_arg, device, dtype, low=None, high=None, noncontiguous=not contig)
def ref_index_select(src, dim, idx):
# bfloat16 is just used on GPU, so it's not supported on numpy
@@ -4613,7 +4613,7 @@
for other_sizes in ((), (4, 5)):
for dim in range(len(other_sizes)):
src = make_arg(other_sizes, num_src, dim, src_contig)
- idx = make_tensor((num_out,), device, dtype=torch.int64, low=0, high=num_src, discontiguous=not idx_contig)
+ idx = make_tensor((num_out,), device, dtype=torch.int64, low=0, high=num_src, noncontiguous=not idx_contig)
out = torch.index_select(src, dim, idx)
out2 = ref_index_select(src, dim, idx)
self.assertEqual(out, out2)
@@ -4622,7 +4622,7 @@
other_sizes = (3, 2)
dim = 1
src = make_arg(other_sizes, num_src, dim, True)
- idx = make_tensor((num_out,), device, dtype=idx_type, low=0, high=num_src, discontiguous=False)
+ idx = make_tensor((num_out,), device, dtype=idx_type, low=0, high=num_src, noncontiguous=False)
out = torch.index_select(src, dim, idx)
out2 = ref_index_select(src, dim, idx)
self.assertEqual(out, out2)
@@ -4652,8 +4652,8 @@
for src_contig, idx_contig, idx_reshape in product([True, False], repeat=3):
for src_size in ((5,), (4, 5)):
- src = make_arg(src_size, discontiguous=not src_contig)
- idx = make_idx(idx_size, high=src.numel(), discontiguous=not idx_contig)
+ src = make_arg(src_size, noncontiguous=not src_contig)
+ idx = make_idx(idx_size, high=src.numel(), noncontiguous=not idx_contig)
if idx_reshape:
idx = idx.reshape(2, 2)
out = torch.take(src, idx)
@@ -4685,8 +4685,8 @@
for dst_contig, src_contig, idx_contig, idx_reshape, accumulate in product([True, False], repeat=5):
for dst_size in ((5,), (4, 5)):
- dst = make_arg(dst_size, discontiguous=not dst_contig)
- src = make_arg(src_size, discontiguous=not src_contig)
+ dst = make_arg(dst_size, noncontiguous=not dst_contig)
+ src = make_arg(src_size, noncontiguous=not src_contig)
# If accumulate=True, `put_` should be deterministic regardless of the inputs on CPU
# On CUDA it may not be, but the test has enough tolerance to account for this
