[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

1 files changed, 4 insertions, 3 deletions

diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h
index cca134e8b4..55d592ff94 100644
--- a/tensorflow/compiler/xla/service/hlo_instruction.h
+++ b/tensorflow/compiler/xla/service/hlo_instruction.h
@@ -405,9 +405,9 @@ class HloInstruction {
   // and window describes how the filter is applied to lhs.
   static std::unique_ptr<HloInstruction> CreateConvolve(
       const Shape& shape, HloInstruction* lhs, HloInstruction* rhs,
-      const Window& window,
+      int64 feature_group_count, const Window& window,
       const ConvolutionDimensionNumbers& dimension_numbers,
-      int64 feature_group_count = 1);
+      const PrecisionConfigProto& precision_config);
 
   // Creates an FFT op, of the type indicated by fft_type.
   static std::unique_ptr<HloInstruction> CreateFft(
@@ -418,7 +418,8 @@ class HloInstruction {
   // dimensions specified in 'dimension_numbers'.
   static std::unique_ptr<HloInstruction> CreateDot(
       const Shape& shape, HloInstruction* lhs, HloInstruction* rhs,
-      const DotDimensionNumbers& dimension_numbers);
+      const DotDimensionNumbers& dimension_numbers,
+      const PrecisionConfigProto& precision_config);
 
   // Creates a dot op with operands 'lhs' and 'rhs' that contracts dimension 1
   // of the LHS with dimension 0 of the RHS with no batch dimensions.  Both LHS