COMPMID-2805: Add QASYMM8_SIGNED support in NEGEMMLowpOutputStage
Add support from requantizing down from S32 to Int8 with fixed point
requantization. This involves the following:
- Compute fixed point multiplication between each entry of input by
result_fixedpoint_multiplier
- Add bias to final result if bias tensor is not a nullptr
- Round to nearest division by a power-of-two using result_shift
- Add offset to each result
- Clamp the value between the specified min and max bounds
- Cast to int8 data type
Change-Id: I641b3fac0833c568d8565ccb859bbc561a24c17d
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2340
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/NEON/GEMMLowp.cpp b/tests/validation/NEON/GEMMLowp.cpp
index b79523d..78fbc58 100644
--- a/tests/validation/NEON/GEMMLowp.cpp
+++ b/tests/validation/NEON/GEMMLowp.cpp
@@ -410,6 +410,119 @@
TEST_SUITE_END() // QuantizeDownInt32ToUint8ScaleByFixedPoint
+TEST_SUITE(QuantizeDownInt32ToInt8ScaleByFixedPoint)
+
+const auto quantize_down_int32_to_int8_scale_by_fixedpoint_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1,
+ 2)
+ * framework::dataset::make("result_offset_after_shift", 2, 3) * framework::dataset::make("min", 0) * framework::dataset::make("max", 0) * framework::dataset::make("addBias", { false, true });
+
+const auto quantize_down_int32_to_int8_scale_by_fixedpoint_relu_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1,
+ 2)
+ * framework::dataset::make("result_offset_after_shift", 2, 3) * framework::dataset::make("min", -2, 0) * framework::dataset::make("max", 1, 3) * framework::dataset::make("addBias", { false, true });
+
+using NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointFixture =
+ GEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointValidationFixture<Tensor, Accessor, NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint>;
+
+// *INDENT-OFF*
+// clang-format off
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
+ framework::dataset::make("InputAInfo", { TensorInfo(TensorShape(21U, 13U), 1, DataType::F32), // Invalid input data type
+ TensorInfo(TensorShape(21U, 13U), 1, DataType::S32), // Invalid min and max
+ TensorInfo(TensorShape(20U, 13U), 1, DataType::S32), // Wrong output data type
+ TensorInfo(TensorShape(21U, 13U), 1, DataType::S32),
+ }),
+ framework::dataset::make("InputBInfo",{ TensorInfo(TensorShape(21U), 1, DataType::S32),
+ TensorInfo(TensorShape(21U), 1, DataType::S32),
+ TensorInfo(TensorShape(20U), 1, DataType::S32),
+ TensorInfo(TensorShape(21U), 1, DataType::S32),
+ })),
+ framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8_SIGNED),
+ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8_SIGNED),
+ TensorInfo(TensorShape(20U, 13U), 1, DataType::S32),
+ TensorInfo(TensorShape(21U, 13U), 1, DataType::QASYMM8_SIGNED),
+ })),
+ framework::dataset::make("Min",{ -110,
+ -130,
+ -113,
+ -113,
+ })),
+ framework::dataset::make("Max",{ 87,
+ 140,
+ 97,
+ 97,
+ })),
+ framework::dataset::make("Expected", { false, false, false, true })),
+ a_info, b_info, output_info, min, max, expected)
+{
+ // Lock tensors
+ Status status = NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint::validate(&a_info.clone()->set_is_resizable(false),
+ &b_info.clone()->set_is_resizable(false),
+ &output_info.clone()->set_is_resizable(false),
+ min,
+ max);
+ ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
+}
+// clang-format on
+// *INDENT-ON*
+
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::SmallShapes(),
+ quantize_down_int32_to_int8_scale_by_fixedpoint_cases),
+ shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias)
+{
+ TensorShape shape_bias(shape[0]);
+
+ // Create tensors
+ Tensor in = create_tensor<Tensor>(shape, DataType::S32);
+ Tensor bias = create_tensor<Tensor>(shape_bias, DataType::S32);
+ Tensor out = create_tensor<Tensor>(shape, DataType::QASYMM8_SIGNED);
+
+ ARM_COMPUTE_EXPECT(in.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(out.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Create and configure function
+ NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPoint output_stage;
+ output_stage.configure(&in, add_bias ? &bias : nullptr, &out, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
+
+ // Validate valid region input and output
+ const ValidRegion valid_region = shape_to_valid_region(shape);
+ validate(in.info()->valid_region(), valid_region);
+ validate(out.info()->valid_region(), valid_region);
+
+ // Validate valid region bias
+ if(add_bias)
+ {
+ const ValidRegion valid_region_bias = shape_to_valid_region(shape_bias);
+ validate(bias.info()->valid_region(), valid_region_bias);
+ }
+
+ // Validate padding
+ const PaddingSize padding(0);
+ validate(in.info()->padding(), padding);
+ validate(out.info()->padding(), padding);
+
+ if(add_bias)
+ {
+ validate(bias.info()->padding(), padding);
+ }
+}
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(),
+ quantize_down_int32_to_int8_scale_by_fixedpoint_cases))
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
+
+TEST_SUITE(BoundedReLu)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointFixture, framework::DatasetMode::ALL, combine(datasets::SmallShapes(),
+ quantize_down_int32_to_int8_scale_by_fixedpoint_relu_cases))
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
+TEST_SUITE_END() // BoundedReLu
+TEST_SUITE_END() // QuantizeDownInt32ToInt8ScaleByFixedPoint
+
TEST_SUITE(QuantizeDownInt32ToInt16ScaleByFixedPoint)
const auto quantize_down_int32_to_int16_scale_by_fixedpoint_cases = framework::dataset::make("result_fixedpoint_multiplier", 254601600, 254601602) * framework::dataset::make("result_shift", 1,
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index 5d092ec..c17105e 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -254,8 +254,8 @@
output_stage.gemmlowp_offset, output_stage.gemmlowp_multipliers, output_stage.gemmlowp_shifts, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
break;
case GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT:
- return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(output, bias,
- output_stage.gemmlowp_multipliers, output_stage.gemmlowp_shifts, output_stage.gemmlowp_offset, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
+ return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, uint8_t>(output, bias,
+ output_stage.gemmlowp_multipliers, output_stage.gemmlowp_shifts, output_stage.gemmlowp_offset, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
break;
default:
ARM_COMPUTE_ERROR("Not Supported!");
@@ -361,6 +361,101 @@
};
template <typename TensorType, typename AccessorType, typename FunctionType>
+class GEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(TensorShape shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias)
+ {
+ _target = compute_target(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias);
+ _reference = compute_reference(shape, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, add_bias);
+ }
+
+protected:
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ std::uniform_int_distribution<> distribution(-6000, 6000);
+ library->fill(tensor, distribution, i);
+ }
+
+ TensorType compute_target(const TensorShape &shape, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max, bool add_bias)
+ {
+ TensorShape shape_bias(shape[0]);
+
+ // Create tensors
+ TensorType a = create_tensor<TensorType>(shape, DataType::S32, 1);
+ TensorType b = create_tensor<TensorType>(shape_bias, DataType::S32, 1);
+ TensorType c = create_tensor<TensorType>(shape, DataType::QASYMM8_SIGNED, 1);
+
+ // Create and configure function
+ FunctionType output_stage;
+ output_stage.configure(&a, add_bias ? &b : nullptr, &c, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
+
+ ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(c.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ a.allocator()->allocate();
+ c.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!a.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!c.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensor
+ fill(AccessorType(a), 0);
+
+ if(add_bias)
+ {
+ ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate bias tensor
+ b.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!b.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensor
+ fill(AccessorType(b), 1);
+ }
+
+ // Compute GEMM function
+ output_stage.run();
+ return c;
+ }
+
+ SimpleTensor<int8_t> compute_reference(const TensorShape &shape, int32_t result_fixed_point_multiplier, int32_t result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max,
+ bool add_bias)
+ {
+ // Create reference
+ TensorShape shape_bias(shape[0]);
+
+ SimpleTensor<int32_t> a{ shape, DataType::S32, 1 };
+ SimpleTensor<int32_t> b{ shape_bias, DataType::S32, 1 };
+
+ // Fill reference
+ fill(a, 0);
+
+ const std::vector<int32_t> result_fixed_point_multiplier_vec = { result_fixed_point_multiplier };
+ const std::vector<int32_t> result_shift_vec = { result_shift };
+
+ if(add_bias)
+ {
+ // Fill bias
+ fill(b, 1);
+
+ return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, int8_t>(a, b, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max);
+ }
+ else
+ {
+ return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, int8_t>(a, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max);
+ }
+ }
+
+ TensorType _target{};
+ SimpleTensor<int8_t> _reference{};
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType>
class GEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointValidationFixture : public framework::Fixture
{
public:
@@ -443,11 +538,11 @@
// Fill bias
fill(b, 1);
- return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(a, b, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max);
+ return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, uint8_t>(a, b, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max);
}
else
{
- return reference::gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint<int32_t>(a, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max);
+ return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, uint8_t>(a, result_fixed_point_multiplier_vec, result_shift_vec, result_offset_after_shift, min, max);
}
}
@@ -530,16 +625,19 @@
// Fill reference
fill(a, 0);
+ const std::vector<int32_t> result_fixed_point_multiplier_vec = { result_fixed_point_multiplier };
+ const std::vector<int32_t> result_shift_vec = { result_shift };
+
if(add_bias)
{
// Fill bias
fill(b, 1);
- return reference::gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint<int32_t>(a, b, result_fixed_point_multiplier, result_shift, min, max);
+ return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, int16_t>(a, b, result_fixed_point_multiplier_vec, result_shift_vec, 0, min, max);
}
else
{
- return reference::gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint<int32_t>(a, result_fixed_point_multiplier, result_shift, min, max);
+ return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, int16_t>(a, result_fixed_point_multiplier_vec, result_shift_vec, 0, min, max);
}
}
diff --git a/tests/validation/reference/GEMMLowp.cpp b/tests/validation/reference/GEMMLowp.cpp
index 08be4a5..4529b91 100644
--- a/tests/validation/reference/GEMMLowp.cpp
+++ b/tests/validation/reference/GEMMLowp.cpp
@@ -39,6 +39,28 @@
namespace
{
template <typename T>
+struct DataTypeExtractor
+{
+ static DataType data_type()
+ {
+ DataType data_type = DataType::UNKNOWN;
+ if(std::is_same<T, int8_t>::value)
+ {
+ data_type = DataType::QASYMM8_SIGNED;
+ }
+ else if(std::is_same<T, uint8_t>::value)
+ {
+ data_type = DataType::QASYMM8;
+ }
+ else if(std::is_same<T, int16_t>::value)
+ {
+ data_type = DataType::QSYMM16;
+ }
+ return data_type;
+ }
+};
+
+template <typename T>
void quantize_down_int32_to_uint8_scale(const SimpleTensor<T> *in, const SimpleTensor<T> *bias, SimpleTensor<uint8_t> *dst, int32_t result_offset, std::vector<int32_t> result_mult_int,
std::vector<int32_t> result_shift, int32_t min, int32_t max)
{
@@ -68,16 +90,16 @@
}
}
-template <typename T>
-void quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<T> *in, const SimpleTensor<T> *bias, SimpleTensor<uint8_t> *dst, std::vector<int32_t> result_fixedpoint_multiplier,
- std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max)
+template <typename TIn, typename TOut>
+void quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> *in, const SimpleTensor<TIn> *bias, SimpleTensor<TOut> *dst, std::vector<int32_t> result_fixedpoint_multiplier,
+ std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max)
{
const int cols_in = in->shape().x();
const bool is_per_channel = result_fixedpoint_multiplier.size() > 1;
for(int i = 0; i < in->num_elements(); ++i)
{
- int32_t result = (*in)[i];
+ TIn result = (*in)[i];
if(bias != nullptr)
{
@@ -88,7 +110,14 @@
const int32_t multiplier = (is_per_channel) ? result_fixedpoint_multiplier[i % cols_in] : result_fixedpoint_multiplier[0];
const int32_t shift = (is_per_channel) ? result_shift[i % cols_in] : result_shift[0];
- result = asymm_rounding_divide_by_pow2(asymm_int_mult(result, multiplier), shift);
+ if(shift < 0)
+ {
+ result = asymm_int_mult(result * (1 << (-shift)), multiplier);
+ }
+ else
+ {
+ result = asymm_rounding_divide_by_pow2(asymm_int_mult(result, multiplier), shift);
+ }
result += result_offset_after_shift;
// Bounded ReLu
@@ -97,42 +126,8 @@
result = std::max(min, std::min(max, result));
}
- (*dst)[i] = static_cast<uint8_t>(std::max(0, std::min(255, result)));
- }
-}
-
-template <typename T>
-void quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<T> *in, const SimpleTensor<T> *bias, SimpleTensor<int16_t> *dst, int32_t result_fixedpoint_multiplier, int32_t result_shift,
- int32_t min, int32_t max)
-{
- const int cols_in = in->shape().x();
-
- for(int i = 0; i < in->num_elements(); ++i)
- {
- int32_t result = (*in)[i];
-
- if(bias != nullptr)
- {
- result += (*bias)[i % cols_in];
- }
-
- // Fixed point multiplication
- if(result_shift < 0)
- {
- result = asymm_int_mult(result * (1 << (-result_shift)), result_fixedpoint_multiplier);
- }
- else
- {
- result = asymm_rounding_divide_by_pow2(asymm_int_mult(result, result_fixedpoint_multiplier), result_shift);
- }
-
- // Bounded ReLu
- if(min != max)
- {
- result = std::max(min, std::min(max, result));
- }
-
- (*dst)[i] = static_cast<int16_t>(std::max(-32768, std::min(32767, result)));
+ (*dst)[i] = static_cast<TOut>(std::max<TIn>(std::numeric_limits<TOut>::lowest(),
+ std::min<TIn>(std::numeric_limits<TOut>::max(), result)));
}
}
} // namespace
@@ -219,59 +214,43 @@
return dst;
}
-template <typename T>
-SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<T> &in, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift,
- int32_t result_offset_after_shift, int32_t min, int32_t max)
+template <typename TIn, typename TOut>
+SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift,
+ int32_t result_offset_after_shift, int32_t min, int32_t max)
{
- SimpleTensor<uint8_t> dst(in.shape(), DataType::QASYMM8);
+ SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());
- quantize_down_int32_to_uint8_scale_by_fixedpoint<T>(&in, nullptr, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
+ quantize_down_scale_by_fixedpoint<TIn, TOut>(&in, nullptr, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
return dst;
}
-template <typename T>
-SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, std::vector<int32_t> result_fixedpoint_multiplier,
- std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max)
+template <typename TIn, typename TOut>
+SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, std::vector<int32_t> result_fixedpoint_multiplier,
+ std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max)
{
- SimpleTensor<uint8_t> dst(in.shape(), DataType::QASYMM8);
+ SimpleTensor<TOut> dst(in.shape(), DataTypeExtractor<TOut>::data_type());
- quantize_down_int32_to_uint8_scale_by_fixedpoint<T>(&in, &bias, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
+ quantize_down_scale_by_fixedpoint<TIn, TOut>(&in, &bias, &dst, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
return dst;
}
-template <typename T>
-SimpleTensor<int16_t> gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<T> &in, int32_t result_fixedpoint_multiplier, int32_t result_shift, int32_t min,
- int32_t max)
-{
- SimpleTensor<int16_t> dst(in.shape(), DataType::QSYMM16);
-
- quantize_down_int32_to_int16_scale_by_fixedpoint<T>(&in, nullptr, &dst, result_fixedpoint_multiplier, result_shift, min, max);
-
- return dst;
-}
-
-template <typename T>
-SimpleTensor<int16_t> gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, int32_t result_fixedpoint_multiplier, int32_t result_shift,
- int32_t min, int32_t max)
-{
- SimpleTensor<int16_t> dst(in.shape(), DataType::QSYMM16);
-
- quantize_down_int32_to_int16_scale_by_fixedpoint<T>(&in, &bias, &dst, result_fixedpoint_multiplier, result_shift, min, max);
-
- return dst;
-}
-
-template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
- std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
-template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
- std::vector<int32_t> result_fixedpoint_multiplier,
- std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
-template SimpleTensor<int16_t> gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, int32_t result_fixedpoint_multiplier, int32_t result_shift,
- int32_t min, int32_t max);
-template SimpleTensor<int16_t> gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_fixedpoint_multiplier,
- int32_t result_shift, int32_t min, int32_t max);
+template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
+ std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
+template SimpleTensor<uint8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
+ std::vector<int32_t> result_fixedpoint_multiplier,
+ std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
+template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
+ std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
+template SimpleTensor<int8_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
+ std::vector<int32_t> result_fixedpoint_multiplier,
+ std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
+template SimpleTensor<int16_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, std::vector<int32_t> result_fixedpoint_multiplier,
+ std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
+template SimpleTensor<int16_t> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b,
+ std::vector<int32_t> result_fixedpoint_multiplier,
+ std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min, int32_t max);
template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, int32_t result_offset, std::vector<int32_t> result_mult_int,
std::vector<int32_t> result_shift, int32_t min, int32_t max);
template SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<int32_t> &a, const SimpleTensor<int32_t> &b, int32_t result_offset, std::vector<int32_t> result_mult_int,
diff --git a/tests/validation/reference/GEMMLowp.h b/tests/validation/reference/GEMMLowp.h
index 815527e..7ff01ef 100644
--- a/tests/validation/reference/GEMMLowp.h
+++ b/tests/validation/reference/GEMMLowp.h
@@ -52,20 +52,13 @@
SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, int32_t result_offset, std::vector<int32_t> result_mult_int,
std::vector<int32_t> result_shift, int32_t min = 0, int32_t max = 0);
-template <typename T>
-SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<T> &in, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift,
- int32_t result_offset_after_shift, int32_t min = 0, int32_t max = 0);
+template <typename TIn, typename TOut>
+SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, std::vector<int32_t> result_fixedpoint_multiplier, std::vector<int32_t> result_shift,
+ int32_t result_offset_after_shift, int32_t min = 0, int32_t max = 0);
-template <typename T>
-SimpleTensor<uint8_t> gemmlowp_quantize_down_int32_to_uint8_scale_by_fixedpoint(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, std::vector<int32_t> result_fixedpoint_multiplier,
- std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min = 0, int32_t max = 0);
-
-template <typename T>
-SimpleTensor<int16_t> gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<T> &in, int32_t result_fixedpoint_multiplier, int32_t result_shift,
- int32_t min, int32_t max);
-template <typename T>
-SimpleTensor<int16_t> gemmlowp_quantize_down_int32_to_int16_scale_by_fixedpoint(const SimpleTensor<T> &in, const SimpleTensor<T> &bias, int32_t result_fixedpoint_multiplier,
- int32_t result_shift, int32_t min, int32_t max);
+template <typename TIn, typename TOut>
+SimpleTensor<TOut> gemmlowp_quantize_down_scale_by_fixedpoint(const SimpleTensor<TIn> &in, const SimpleTensor<TIn> &bias, std::vector<int32_t> result_fixedpoint_multiplier,
+ std::vector<int32_t> result_shift, int32_t result_offset_after_shift, int32_t min = 0, int32_t max = 0);
} // namespace reference
} // namespace validation
} // namespace test