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update thresholds for various ops in functorch/test_ops.py #102016

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update thresholds for various ops in functorch/test_ops.py #102016

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7a71ec5
update thresholds for various ops in functorch/test_ops.py
Fuzzkatt May 22, 2023
bb7a11a
lint
Fuzzkatt May 22, 2023
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21 changes: 16 additions & 5 deletions test/functorch/test_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -253,7 +253,6 @@ def wrapped(*args):
return wrapped, args



def get_jvp_variant(f, sample):
# We want this higher-order variant of jvp, so that it can
# be used to wrap vmap
Expand Down Expand Up @@ -403,9 +402,14 @@ class TestOperators(TestCase):
tol1('masked.cumprod',
{torch.float32: tol(atol=1e-05, rtol=1e-05)}),
tol1('svd_lowrank',
{torch.float32: tol(atol=3e-05, rtol=3e-04)}, device_type='cuda'),
{torch.float32: tol(atol=3e-04, rtol=3e-04)}, device_type='cuda'),
tol1('linalg.tensorsolve',
{torch.float32: tol(atol=3e-04, rtol=3e-04)}, device_type='cuda'),
tol1('__rmatmul__',
{torch.float32: tol(atol=3e-04, rtol=3e-04)}, device_type='cuda'),
tol1('matmul',
{torch.float32: tol(atol=3e-04, rtol=3e-04)}, device_type='cuda'),

))
def test_grad(self, device, dtype, op):
if op.name in vjp_fail:
Expand Down Expand Up @@ -541,7 +545,6 @@ def test_jvp(self, device, dtype, op):
clone_inputs=True,
fixme_ref_jvp_local=fixme_ref_jvp_local)


def jvp_opinfo_test(self, fn, sample, output_process_fn,
clone_inputs, fixme_ref_jvp_local):
# NB: we used requires_grad=True to determine where the primals are,
Expand Down Expand Up @@ -618,8 +621,12 @@ def maybe_clone_inputs():
{torch.float32: tol(atol=1e-05, rtol=1e-05)}),
tol1('linalg.tensorsolve',
{torch.float32: tol(atol=1e-05, rtol=1e-05)}),
tol1('linalg.multi_dot',
{torch.float32: tol(atol=1e-04, rtol=1e-04)}),
tol1('svd_lowrank',
{torch.float32: tol(atol=1e-04, rtol=1e-04)}),
tol1('pca_lowrank',
{torch.float32: tol(atol=1e-04, rtol=1e-04)}),
))
def test_vjp(self, device, dtype, op):
if not op.supports_autograd:
Expand Down Expand Up @@ -815,6 +822,8 @@ def fn(inp, *args, **kwargs):
{torch.float32: tol(atol=2e-03, rtol=2e-02)}),
tol1('svd',
{torch.float32: tol(atol=1e-03, rtol=5e-04)}),
tol1('matrix_exp',
{torch.float32: tol(atol=1e-03, rtol=5e-04)}),
))
@skipOps('TestOperators', 'test_vmapvjpvjp', {
xfail('as_strided', 'partial_views'),
Expand Down Expand Up @@ -935,6 +944,8 @@ def vjp_of_vjp(*args_and_cotangents):
{torch.float32: tol(atol=5e-04, rtol=1e-04)}, device_type="cuda"),
tol1('linalg.householder_product',
{torch.float32: tol(atol=1e-04, rtol=1e-04)}),
tol1('matrix_exp',
{torch.float32: tol(atol=5e-04, rtol=1e-04)}, device_type="cuda"),
))
@skipOps('TestOperators', 'test_vmapvjp', vmapvjp_fail.union({
xfail('as_strided', 'partial_views'),
Expand Down Expand Up @@ -1553,6 +1564,8 @@ def reference(primals, cotangents, primals_tangents, cotangents_tangents):
{torch.float32: tol(atol=5e-04, rtol=5e-04)}),
tol1('svd',
{torch.float32: tol(atol=5e-04, rtol=5e-04)}),
tol1('matrix_exp',
{torch.float32: tol(atol=5e-04, rtol=5e-04)}),
))
def test_vmapjvpvjp(self, device, dtype, op):
# Since we test `jvpvjp` seperately,
Expand Down Expand Up @@ -1597,7 +1610,6 @@ def jvp_of_vjp(*args):
for loop_out, batched_out in generator:
self.assertEqual(loop_out, batched_out)


def _make_extremal_inputs(self, shape, device):
if shape is None:
return (None,)
Expand Down Expand Up @@ -1670,7 +1682,6 @@ def test_extremal_numerics_softmax(self, device):
cotangents = torch.randn_like(result, device=device)
self._compare_jacobians_of_vjp(torch.nn.functional.softmax, (cotangents, input))


def test_extremal_numerics_log_softmax(self, device):
N, C, H, W = 3, 4, 5, 6
shapes = ((N, C), (N, C, H), (N, C, H, W))
Expand Down