diff options
| author |  David Majnemer <majnemer@google.com> | 2018-09-04 11:17:30 -0700 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-09-04 11:41:30 -0700 |
| commit | 5d183ab7fc7b82f1dea0b9fa9c6412c39ade15a1 (patch) | |
| tree | 79a4f6fcf270617fc56082702b0209240425ae8c /tensorflow/compiler/xla/service/shape_inference_test.cc | |
| parent | 9ae8214229960c634c9f82c00f2c0df287c27a9d (diff) | |
[XLA] Make kConvolution, kDot HLO attributes mandatory
HLO transformations would forget to propagate the feature depth attribute.
Making these attributes mandatory, while slightly less convenient for tests,
makes HLO transformations more robust.
PiperOrigin-RevId: 211490160
Diffstat (limited to 'tensorflow/compiler/xla/service/shape_inference_test.cc')
| -rw-r--r-- | tensorflow/compiler/xla/service/shape_inference_test.cc | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/tensorflow/compiler/xla/service/shape_inference_test.cc b/tensorflow/compiler/xla/service/shape_inference_test.cc index cc92e58ef8..864ed43118 100644 --- a/tensorflow/compiler/xla/service/shape_inference_test.cc +++ b/tensorflow/compiler/xla/service/shape_inference_test.cc @@ -419,8 +419,8 @@ TEST_F(ShapeInferenceTest, Convolve) { dim1->set_padding_high(0); dim1->set_window_dilation(1); dim1->set_base_dilation(1); - auto inferred_status = - ShapeInference::InferConvolveShape(lhs_shape, rhs_shape, window, dnums); + auto inferred_status = ShapeInference::InferConvolveShape( + lhs_shape, rhs_shape, /*feature_group_count=*/1, window, dnums); ASSERT_IS_OK(inferred_status.status()); Shape inferred_shape = inferred_status.ValueOrDie(); ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {10, 12, 2, 3}), @@ -464,8 +464,8 @@ TEST_F(ShapeInferenceTest, ConvolveWithWindowDilation) { dim1->set_padding_high(1); dim1->set_window_dilation(2); dim1->set_base_dilation(1); - auto inferred_status = - ShapeInference::InferConvolveShape(lhs_shape, rhs_shape, window, dnums); + auto inferred_status = ShapeInference::InferConvolveShape( + lhs_shape, rhs_shape, /*feature_group_count=*/1, window, dnums); ASSERT_IS_OK(inferred_status.status()); Shape inferred_shape = inferred_status.ValueOrDie(); ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {10, 12, 31, 5}), @@ -509,8 +509,8 @@ TEST_F(ShapeInferenceTest, ConvolveWithBaseDilation) { dim1->set_padding_high(1); dim1->set_window_dilation(1); dim1->set_base_dilation(2); - auto inferred_status = - ShapeInference::InferConvolveShape(lhs_shape, rhs_shape, window, dnums); + auto inferred_status = ShapeInference::InferConvolveShape( + lhs_shape, rhs_shape, /*feature_group_count=*/1, window, dnums); ASSERT_IS_OK(inferred_status.status()); Shape inferred_shape = inferred_status.ValueOrDie(); ASSERT_TRUE(ShapeUtil::Equal(ShapeUtil::MakeShape(F32, {10, 12, 4, 9}), @@ -547,8 +547,8 @@ TEST_F(ShapeInferenceTest, ConvolveDimensionNumbersOverlapError) { dim1->set_stride(2); dim1->set_padding_low(1); dim1->set_padding_high(1); - auto inferred_status = - ShapeInference::InferConvolveShape(lhs_shape, rhs_shape, window, dnums); + auto inferred_status = ShapeInference::InferConvolveShape( + lhs_shape, rhs_shape, /*feature_group_count=*/1, window, dnums); ASSERT_FALSE(inferred_status.ok()); ASSERT_THAT(inferred_status.status().error_message(), HasSubstr("each dimension exactly once")); |