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Diffstat (limited to 'tests/validation/fixtures/GEMMLowpFixture.h')
-rw-r--r--tests/validation/fixtures/GEMMLowpFixture.h59
1 files changed, 32 insertions, 27 deletions
diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h
index b93a6447d7..1154d6c8de 100644
--- a/tests/validation/fixtures/GEMMLowpFixture.h
+++ b/tests/validation/fixtures/GEMMLowpFixture.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -91,12 +91,15 @@ void fill(U &&tensor, int i)
template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d, bool reinterpret_output_as_3d, typename OutputType, bool is_fused = false>
TensorType compute_gemmlowp_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
- GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo(), DataType data_type_b = DataType::QASYMM8, QuantizationInfo b_qinfo = QuantizationInfo())
+ GEMMLowpOutputStageInfo output_stage = GEMMLowpOutputStageInfo(), DataType data_type_a = DataType::QASYMM8, DataType data_type_b = DataType::QASYMM8,
+ QuantizationInfo b_qinfo = QuantizationInfo())
{
// Create tensors
- TensorType a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1);
+ DataType data_type_output = output_stage.type == GEMMLowpOutputStageType::NONE ? DataType::S32 : data_type_a;
+
+ TensorType a = create_tensor<TensorType>(shape_a, data_type_a, 1);
TensorType b = create_tensor<TensorType>(shape_b, data_type_b, 1); // gemm output before output stage mismatch if i pass data_layout_output here. to be investigated
- TensorType output = create_tensor<TensorType>(shape_output, output_stage.type == GEMMLowpOutputStageType::NONE ? DataType::S32 : DataType::QASYMM8, 1);
+ TensorType output = create_tensor<TensorType>(shape_output, data_type_output, 1);
a.info()->set_quantization_info(QuantizationInfo(1.0f / 255, a_offset));
@@ -150,9 +153,9 @@ TensorType compute_gemmlowp_target(const TensorShape &shape_a, const TensorShape
return output;
}
-template <bool reinterpret_input_as_3d, typename TW = uint8_t>
+template <bool reinterpret_input_as_3d, typename TI = uint8_t, typename TW = uint8_t>
SimpleTensor<int32_t> compute_gemmlowp_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
- DataType data_type_b = DataType::QASYMM8, QuantizationInfo b_qinfo = QuantizationInfo())
+ DataType data_type_a = DataType::QASYMM8, DataType data_type_b = DataType::QASYMM8, QuantizationInfo b_qinfo = QuantizationInfo())
{
TensorShape shape_a_to_use = shape_a;
if(reinterpret_input_as_3d)
@@ -162,13 +165,13 @@ SimpleTensor<int32_t> compute_gemmlowp_reference(const TensorShape &shape_a, con
}
// Create reference
- SimpleTensor<uint8_t> a{ shape_a_to_use, DataType::QASYMM8, 1 };
- SimpleTensor<TW> b{ shape_b, data_type_b, 1, data_type_b == DataType::QSYMM8_PER_CHANNEL ? b_qinfo : QuantizationInfo(1.0f / 255, b_offset) };
+ SimpleTensor<TI> a{ shape_a_to_use, data_type_a, 1 };
+ SimpleTensor<TW> b{ shape_b, data_type_b, 1, data_type_b == DataType::QSYMM8_PER_CHANNEL ? b_qinfo : QuantizationInfo(1.0f / 255, b_offset) };
// Fill reference
fill(a, 0);
fill(b, 1);
- return reference::gemmlowp_matrix_multiply_core<int32_t, uint8_t, TW>(a, b, shape_output, a_offset, b_offset);
+ return reference::gemmlowp_matrix_multiply_core<int32_t, TI, TW>(a, b, shape_output, a_offset, b_offset);
}
}
@@ -198,7 +201,7 @@ protected:
SimpleTensor<int32_t> _reference{};
};
-template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, typename TW = uint8_t>
+template <typename TensorType, typename AccessorType, typename FunctionType, bool reinterpret_input_as_3d = false, bool reinterpret_output_as_3d = false, typename TI = uint8_t, typename TW = uint8_t>
class GEMMLowpMatrixMultiplyCoreFusedOffsetOutputValidationFixture : public framework::Fixture
{
public:
@@ -206,6 +209,8 @@ public:
void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage, DataType data_type_b)
{
ARM_COMPUTE_EXPECT(output_stage.type != GEMMLowpOutputStageType::NONE, framework::LogLevel::ERRORS);
+ DataType data_type_a = data_type_b == DataType::QASYMM8_SIGNED ? DataType::QASYMM8_SIGNED : DataType::QASYMM8;
+
if(data_type_b == DataType::QSYMM8_PER_CHANNEL)
{
output_stage.is_quantized_per_channel = true;
@@ -220,28 +225,28 @@ public:
quantization::calculate_quantized_multiplier(scales[i], &output_stage.gemmlowp_multipliers[i], &output_stage.gemmlowp_shifts[i]);
}
- _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, 0, output_stage, data_type_b, QuantizationInfo(scales));
- _target = compute_target(shape_a, shape_b, shape_output, a_offset, 0, output_stage, data_type_b, QuantizationInfo(scales));
+ _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, 0, output_stage, data_type_a, data_type_b, QuantizationInfo(scales));
+ _target = compute_target(shape_a, shape_b, shape_output, a_offset, 0, output_stage, data_type_a, data_type_b, QuantizationInfo(scales));
}
else
{
- _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage, data_type_b, QuantizationInfo());
- _target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage, data_type_b, QuantizationInfo());
+ _reference = compute_reference(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage, data_type_a, data_type_b, QuantizationInfo());
+ _target = compute_target(shape_a, shape_b, shape_output, a_offset, b_offset, output_stage, data_type_a, data_type_b, QuantizationInfo());
}
}
protected:
TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset, GEMMLowpOutputStageInfo output_stage,
- DataType data_type_b, QuantizationInfo b_qinfo)
+ DataType data_type_a, DataType data_type_b, QuantizationInfo b_qinfo)
{
return compute_gemmlowp_target<TensorType, AccessorType, FunctionType, reinterpret_input_as_3d, reinterpret_output_as_3d, qasymm8_t, true>(shape_a, shape_b, shape_output, a_offset, b_offset,
- output_stage, data_type_b, b_qinfo);
+ output_stage, data_type_a, data_type_b, b_qinfo);
}
- SimpleTensor<qasymm8_t> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
- GEMMLowpOutputStageInfo output_stage, DataType data_type_b, QuantizationInfo b_qinfo)
+ SimpleTensor<TI> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_output, int32_t a_offset, int32_t b_offset,
+ GEMMLowpOutputStageInfo output_stage, DataType data_type_a, DataType data_type_b, QuantizationInfo b_qinfo)
{
- SimpleTensor<int32_t> output = compute_gemmlowp_reference<reinterpret_input_as_3d, TW>(shape_a, shape_b, shape_output, a_offset, b_offset, data_type_b, b_qinfo);
+ SimpleTensor<int32_t> output = compute_gemmlowp_reference<reinterpret_input_as_3d, TI, TW>(shape_a, shape_b, shape_output, a_offset, b_offset, data_type_a, data_type_b, b_qinfo);
TensorShape bias_shape(shape_b[0]);
SimpleTensor<int32_t> bias{ bias_shape, DataType::S32, 1 };
@@ -250,20 +255,20 @@ protected:
switch(output_stage.type)
{
case GEMMLowpOutputStageType::QUANTIZE_DOWN:
- return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(output, bias,
- output_stage.gemmlowp_offset, output_stage.gemmlowp_multipliers, output_stage.gemmlowp_shifts, output_stage.gemmlowp_min_bound, output_stage.gemmlowp_max_bound);
+ return reference::gemmlowp_quantize_down_scale<int32_t, TW>(output, bias,
+ 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_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);
+ return reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, TW>(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!");
}
}
- TensorType _target{};
- SimpleTensor<qasymm8_t> _reference{};
+ TensorType _target{};
+ SimpleTensor<TI> _reference{};
};
template <typename TensorType, typename AccessorType, typename FunctionType>
@@ -348,11 +353,11 @@ protected:
// Fill bias
fill(b, 1);
- return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, b, result_offset, result_mult_int_vec, result_shift_vec, min, max);
+ return reference::gemmlowp_quantize_down_scale<int32_t, uint8_t>(a, b, result_offset, result_mult_int_vec, result_shift_vec, min, max);
}
else
{
- return reference::gemmlowp_quantize_down_int32_to_uint8_scale<int32_t>(a, result_offset, result_mult_int_vec, result_shift_vec, min, max);
+ return reference::gemmlowp_quantize_down_scale<int32_t, uint8_t>(a, result_offset, result_mult_int_vec, result_shift_vec, min, max);
}
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-04-29 21:52:47 +0000