Replace node level check ASSERT_MSG_NODE()/FATAL_ERROR_NODE() with REQUIRE() or ERROR_IF()
- Adding return code enum class: {VALID, UNPREDICTABLE, ERROR}
- Runtime errors (e.g. memory allocation failure) will abort immediately, or will return one of the three return codes
Part of the codes are re-written to pass REQUIRE() to the top-level (e.g. apply_scale_32/16())
- Update setExpectedFailure() to setExpectedReturnCode() on test generation script
- Update test regression script to interface with reference model change
Signed-off-by: Kevin Cheng <kevin.cheng@arm.com>
Change-Id: Ia063c936bcb2a54d6e379a5bb6801aa72d1186f1
diff --git a/reference_model/src/ops/type_conversion.cc b/reference_model/src/ops/type_conversion.cc
index d988c57..657eebf 100644
--- a/reference_model/src/ops/type_conversion.cc
+++ b/reference_model/src/ops/type_conversion.cc
@@ -23,8 +23,11 @@
using namespace tosa;
template <int Rank, DType InDtype, DType OutDtype>
-OpRescale<Rank, InDtype, OutDtype>::OpRescale(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_)
- : GraphNode(Op_RESCALE, id_)
+OpRescale<Rank, InDtype, OutDtype>::OpRescale(SubgraphTraverser* sgt_,
+ TosaAttributeBase* attribute_,
+ TosaQuantInfoBase* qinfo_,
+ uint64_t id_)
+ : GraphNode(sgt_, Op_RESCALE, id_)
{
setRequiredOperands(1, 1);
setRequiredRank(0, 6);
@@ -101,53 +104,68 @@
int32_t channel_multiplier, channel_shift;
Eigen::array<Eigen::Index, 2> begin, size;
size = Eigen::array<Eigen::Index, 2>({ shape_2d[0], 1 });
- for (int32_t i = 0; i < shape_2d[1]; i++)
+ try
{
- begin = Eigen::array<Eigen::Index, 2>({ 0, i });
- curr_channel_slice_prescaled = input_reshaped.slice(begin, size);
- channel_multiplier = multiplier[i];
- channel_shift = shift[i];
- curr_channel_slice_postscaled =
- curr_channel_slice_prescaled.unaryExpr([input_zp, output_zp, channel_multiplier, channel_shift,
- double_round, scale32](InEigenType in_val) -> OutEigenType {
- InEigenType input_zp_shifted = in_val - (InEigenType)input_zp;
- int32_t scaled;
- if (scale32)
- scaled = TosaReference::QuantUtil::apply_scale_32(input_zp_shifted, channel_multiplier,
- channel_shift, double_round);
- else
- scaled = TosaReference::QuantUtil::apply_scale_16(input_zp_shifted, channel_multiplier,
- channel_shift);
- OutEigenType out_val = (OutEigenType)(scaled + output_zp);
- out_val = std::max<OutEigenType>(out_val, QMin);
- out_val = std::min<OutEigenType>(out_val, QMax);
- return out_val;
- });
-
- for (int32_t j = 0; j < shape_2d[0]; j++)
+ for (int32_t i = 0; i < shape_2d[1]; i++)
{
- output_2d(j, i) = curr_channel_slice_postscaled(j, 0);
+ begin = Eigen::array<Eigen::Index, 2>({ 0, i });
+ curr_channel_slice_prescaled = input_reshaped.slice(begin, size);
+ channel_multiplier = multiplier[i];
+ channel_shift = shift[i];
+ curr_channel_slice_postscaled =
+ curr_channel_slice_prescaled.unaryExpr([input_zp, output_zp, channel_multiplier, channel_shift,
+ double_round, scale32](InEigenType in_val) -> OutEigenType {
+ InEigenType input_zp_shifted = in_val - (InEigenType)input_zp;
+ int32_t scaled;
+ if (scale32)
+ scaled = TosaReference::QuantUtil::apply_scale_32(input_zp_shifted, channel_multiplier,
+ channel_shift, double_round);
+ else
+ scaled = TosaReference::QuantUtil::apply_scale_16(input_zp_shifted, channel_multiplier,
+ channel_shift);
+ OutEigenType out_val = (OutEigenType)(scaled + output_zp);
+ out_val = std::max<OutEigenType>(out_val, QMin);
+ out_val = std::min<OutEigenType>(out_val, QMax);
+ return out_val;
+ });
+
+ for (int32_t j = 0; j < shape_2d[0]; j++)
+ {
+ output_2d(j, i) = curr_channel_slice_postscaled(j, 0);
+ }
}
}
+ catch (std::string desc)
+ {
+ REQUIRE(false, "OpRescale apply_scale_32/16() fails: %s.", desc.c_str());
+ }
}
else
{
int32_t tensor_multiplier = multiplier[0];
int32_t tensor_shift = shift[0];
- output_2d = input_reshaped.unaryExpr([input_zp, output_zp, tensor_multiplier, tensor_shift, double_round,
- scale32](InEigenType in_val) -> OutEigenType {
- InEigenType input_zp_shifted = in_val - (InEigenType)input_zp;
- int32_t scaled;
- if (scale32)
- scaled = TosaReference::QuantUtil::apply_scale_32(input_zp_shifted, tensor_multiplier, tensor_shift,
- double_round);
- else
- scaled = TosaReference::QuantUtil::apply_scale_16(input_zp_shifted, tensor_multiplier, tensor_shift);
- OutEigenType out_val = (OutEigenType)(scaled + output_zp);
- out_val = std::max<OutEigenType>(out_val, QMin);
- out_val = std::min<OutEigenType>(out_val, QMax);
- return out_val;
- });
+ try
+ {
+ output_2d = input_reshaped.unaryExpr([input_zp, output_zp, tensor_multiplier, tensor_shift, double_round,
+ scale32](InEigenType in_val) -> OutEigenType {
+ InEigenType input_zp_shifted = in_val - (InEigenType)input_zp;
+ int32_t scaled;
+ if (scale32)
+ scaled = TosaReference::QuantUtil::apply_scale_32(input_zp_shifted, tensor_multiplier, tensor_shift,
+ double_round);
+ else
+ scaled =
+ TosaReference::QuantUtil::apply_scale_16(input_zp_shifted, tensor_multiplier, tensor_shift);
+ OutEigenType out_val = (OutEigenType)(scaled + output_zp);
+ out_val = std::max<OutEigenType>(out_val, QMin);
+ out_val = std::min<OutEigenType>(out_val, QMax);
+ return out_val;
+ });
+ }
+ catch (std::string desc)
+ {
+ REQUIRE(false, "OpRescale apply_scale_32/16() fails: %s.", desc.c_str());
+ }
}
// reshape [d0 * d1 ..., dn] back to [d0, d1, ..., dn]
@@ -162,8 +180,11 @@
}
template <int Rank, DType InDtype, DType OutDtype>
-OpCast<Rank, InDtype, OutDtype>::OpCast(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_)
- : GraphNode(Op_CAST, id_)
+OpCast<Rank, InDtype, OutDtype>::OpCast(SubgraphTraverser* sgt_,
+ TosaAttributeBase* attribute_,
+ TosaQuantInfoBase* qinfo_,
+ uint64_t id_)
+ : GraphNode(sgt_, Op_CAST, id_)
{
setRequiredOperands(1, 1);
setRequiredRank(0, 6);