[functorch] Remove some xfails for transform coverage (pytorch/functorch#910)
diff --git a/functorch/functorch/csrc/BatchRulesLinearAlgebra.cpp b/functorch/functorch/csrc/BatchRulesLinearAlgebra.cpp
index 79ec97e..bf6f8d0 100644
--- a/functorch/functorch/csrc/BatchRulesLinearAlgebra.cpp
+++ b/functorch/functorch/csrc/BatchRulesLinearAlgebra.cpp
@@ -197,7 +197,7 @@
VARIADIC_BDIMS_BOXED(symeig);
VARIADIC_BDIMS_BOXED(triangular_solve);
- VARIADIC_BDIMS_BOXED(_det_lu_based_helper);
+ VARIADIC_BDIMS_BOXED(_linalg_det);
VARIADIC_BDIMS_BOXED(_lu_with_info);
}
}}
diff --git a/functorch/functorch/csrc/PyTorchOperatorHacks.cpp b/functorch/functorch/csrc/PyTorchOperatorHacks.cpp
index d72702d..8990206 100644
--- a/functorch/functorch/csrc/PyTorchOperatorHacks.cpp
+++ b/functorch/functorch/csrc/PyTorchOperatorHacks.cpp
@@ -212,7 +212,7 @@
const Tensor& pos_weight = c10::value_or_else(pos_weight_opt, [] {return Tensor();});
Tensor loss;
- auto max_val = (-input).clamp_min_(0);
+ auto max_val = (-input).clamp_min(0);
if (pos_weight.defined()) {
// pos_weight need to be broadcasted, thus mul(target) is not inplace.
auto log_weight = (pos_weight - 1).mul(target).add_(1);
diff --git a/functorch/test/test_ops.py b/functorch/test/test_ops.py
index 93b27e7..35192fa 100644
--- a/functorch/test/test_ops.py
+++ b/functorch/test/test_ops.py
@@ -379,11 +379,6 @@
skip('svd_lowrank', ''), # fails on cuda, runs okay on cpu
skip('nn.functional.dropout2d', ''), # fails on cuda, runs okay on cpu
- # The following don't have a forward-mode AD formula in PyTorch core
- # (check derivatives.yaml).
- xfail('var_mean'),
- xfail('std_mean'),
-
# =============================================
# NB: The above failures also fail using PyTorch core's
# forward-mode AD and vmap.
@@ -674,14 +669,11 @@
# skip because this is flaky depending on what the max_norm is!
skip('nn.functional.embedding', ''),
xfail('nn.functional.soft_margin_loss', ''),
- xfail('nn.functional.binary_cross_entropy_with_logits', ''),
xfail('linalg.householder_product'),
xfail('tensor_split'),
xfail('quantile'),
- xfail('var_mean'),
xfail('as_strided'),
xfail('nn.functional.gaussian_nll_loss'),
- xfail('std_mean'),
xfail('scatter'),
xfail('matrix_exp'),
xfail('nanquantile'),
@@ -765,6 +757,8 @@
xfail('nn.functional.max_pool3d'),
xfail('vdot'),
xfail('linalg.cross'),
+ xfail('nanmean'),
+ xfail('nansum'),
xfail('nn.functional.feature_alpha_dropout', 'without_train'),
xfail('linalg.lu_factor', ''),
xfail('nn.functional.dropout2d', ''),
@@ -782,7 +776,6 @@
xfail('nn.functional.smooth_l1_loss', ''),
xfail('nn.functional.max_unpool2d', 'grad'),
xfail('nn.functional.soft_margin_loss', ''),
- xfail('nn.functional.binary_cross_entropy_with_logits', ''),
xfail('nn.functional.max_unpool1d', 'grad'),
xfail('nn.functional.embedding', ''),
xfail('lu_unpack'),
@@ -1044,23 +1037,16 @@
# RuntimeError: Trying to set a forward gradient that has a different size than that of the original Tensor,
# this is not supported. Tensor is of size [5, 2, 3] while the given forward gradient is of size [1, 2, 3].
xfail('normal', ''),
- xfail('_masked.amax', ''),
- xfail('_masked.amin', ''),
xfail('_masked.log_softmax', ''),
xfail('_masked.softmax', ''),
xfail('_masked.softmin', ''),
- xfail('amax', ''),
- xfail('amin', ''),
xfail('cdist', ''),
xfail('cholesky', ''),
xfail('eig', ''),
xfail('linalg.det', ''),
- xfail('linalg.matrix_norm', ''),
xfail('linalg.slogdet', ''),
xfail('logcumsumexp', ''),
xfail('logdet', ''),
- xfail('nanmean', ''),
- xfail('nansum', ''),
xfail('nn.functional.embedding_bag', ''),
xfail('nn.functional.grid_sample', ''),
xfail('nn.functional.hardsigmoid', ''),
@@ -1070,9 +1056,7 @@
xfail('nn.functional.softmin', ''),
xfail('nn.functional.softmin', 'with_dtype'),
xfail('renorm', ''),
- xfail('std_mean', ''),
xfail('symeig', ''),
- xfail('var_mean', ''),
xfail('nn.functional.feature_alpha_dropout', 'with_train'),
xfail('nn.functional.kl_div', ''),
xfail('pca_lowrank', ''),
@@ -1090,7 +1074,6 @@
xfail('scatter_reduce', 'mean'),
xfail('scatter_reduce', 'prod'),
skip('linalg.householder_product', '', device_type='cuda'), # flaky, I'm not sure why
- xfail('nn.functional.binary_cross_entropy_with_logits'),
}))
def test_jvpvjp(self, device, dtype, op):
if not op.supports_autograd:
diff --git a/functorch/test/test_vmap.py b/functorch/test/test_vmap.py
index 7c9c381..36b2220 100644
--- a/functorch/test/test_vmap.py
+++ b/functorch/test/test_vmap.py
@@ -3177,6 +3177,7 @@
xfail('histogram'),
xfail('index_fill'),
xfail('nansum'),
+ xfail('nanmean'),
# `index_put` OpInfo in pytorch/pytorch has
# masked index as input which is not supported
xfail('index_put', ''),
