COMPMID-661: QASYMM8 support for fully connected layer.
Change-Id: I70e04d3a175ba366432ada98e9ca893c9f81b260
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/111094
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/tests/validation/fixtures/FullyConnectedLayerFixture.h b/tests/validation/fixtures/FullyConnectedLayerFixture.h
index b19c40d..dba20bb 100644
--- a/tests/validation/fixtures/FullyConnectedLayerFixture.h
+++ b/tests/validation/fixtures/FullyConnectedLayerFixture.h
@@ -46,27 +46,43 @@
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool run_interleave>
-class FullyConnectedLayerValidationFixedPointFixture : public framework::Fixture
+class FullyConnectedLayerValidationGenericFixture : public framework::Fixture
{
public:
+ using TBias = typename std::conditional<std::is_same<typename std::decay<T>::type, uint8_t>::value, int32_t, T>::type;
+
+public:
template <typename...>
- void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type, int fractional_bits)
+ void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights,
+ DataType data_type, int fractional_bits, QuantizationInfo quantization_info)
{
ARM_COMPUTE_UNUSED(weights_shape);
ARM_COMPUTE_UNUSED(bias_shape);
- _fractional_bits = fractional_bits;
- _data_type = data_type;
+ _data_type = data_type;
+ _bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
+ _fractional_bits = fractional_bits;
+ _quantization_info = quantization_info;
- _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights, data_type, fractional_bits);
- _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights, data_type, fractional_bits);
+ _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights);
+ _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, transpose_weights, reshape_weights);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
- if(is_data_type_float(_data_type))
+ if(is_data_type_quantized_asymmetric(_data_type))
+ {
+ std::uniform_int_distribution<uint8_t> distribution(0, 30);
+ library->fill(tensor, distribution, i);
+ }
+ else if(_data_type == DataType::S32)
+ {
+ std::uniform_int_distribution<int32_t> distribution(-50, 50);
+ library->fill(tensor, distribution, i);
+ }
+ else if(is_data_type_float(_data_type))
{
std::uniform_real_distribution<> distribution(0.5f, 1.f);
library->fill(tensor, distribution, i);
@@ -78,7 +94,7 @@
}
TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, bool transpose_weights,
- bool reshape_weights, DataType data_type, int fixed_point_position)
+ bool reshape_weights)
{
TensorShape reshaped_weights_shape(weights_shape);
@@ -102,7 +118,7 @@
// Transpose 1xW for batched version
if(!reshape_weights && output_shape.y() > 1 && run_interleave)
{
- const int transpose_width = 16 / data_size_from_type(data_type);
+ const int transpose_width = 16 / data_size_from_type(_data_type);
const float shape_x = reshaped_weights_shape.x();
reshaped_weights_shape.set(0, reshaped_weights_shape.y() * transpose_width);
reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(shape_x / transpose_width)));
@@ -110,10 +126,10 @@
}
// Create tensors
- TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position);
- TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, data_type, 1, fixed_point_position);
- TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position);
- TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position);
+ TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _fractional_bits, _quantization_info);
+ TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _data_type, 1, _fractional_bits, _quantization_info);
+ TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _fractional_bits, _quantization_info);
+ TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _fractional_bits, _quantization_info);
// Create and configure function.
FunctionType fc;
@@ -142,7 +158,7 @@
if(!reshape_weights || !transpose_weights)
{
TensorShape tmp_shape(weights_shape);
- RawTensor tmp(tmp_shape, data_type, 1, fixed_point_position);
+ RawTensor tmp(tmp_shape, _data_type, 1, _fractional_bits);
// Fill with original shape
fill(tmp, 1);
@@ -180,12 +196,12 @@
}
SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, bool transpose_weights,
- bool reshape_weights, DataType data_type, int fixed_point_position = 0)
+ bool reshape_weights)
{
// Create reference
- SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position };
- SimpleTensor<T> weights{ weights_shape, data_type, 1, fixed_point_position };
- SimpleTensor<T> bias{ bias_shape, data_type, 1, fixed_point_position };
+ SimpleTensor<T> src{ input_shape, _data_type, 1, _fractional_bits, _quantization_info };
+ SimpleTensor<T> weights{ weights_shape, _data_type, 1, _fractional_bits, _quantization_info };
+ SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _fractional_bits, _quantization_info };
// Fill reference
fill(src, 0);
@@ -195,22 +211,51 @@
return reference::fully_connected_layer<T>(src, weights, bias, output_shape);
}
- TensorType _target{};
- SimpleTensor<T> _reference{};
- int _fractional_bits{};
- DataType _data_type{};
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+ DataType _data_type{};
+ DataType _bias_data_type{};
+ int _fractional_bits{};
+ QuantizationInfo _quantization_info{};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool run_interleave>
-class FullyConnectedLayerValidationFixture : public FullyConnectedLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T, run_interleave>
+class FullyConnectedLayerValidationFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, run_interleave>
{
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type)
{
- FullyConnectedLayerValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T, run_interleave>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights,
- reshape_weights, data_type,
- 0);
+ FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, run_interleave>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights,
+ reshape_weights, data_type,
+ 0, QuantizationInfo());
+ }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool run_interleave>
+class FullyConnectedLayerValidationFixedPointFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, run_interleave>
+{
+public:
+ template <typename...>
+ void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type, int fractional_bits)
+ {
+ FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, run_interleave>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights,
+ reshape_weights, data_type,
+ fractional_bits, QuantizationInfo());
+ }
+};
+
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool run_interleave>
+class FullyConnectedLayerValidationQuantizedFixture : public FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, run_interleave>
+{
+public:
+ template <typename...>
+ void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, bool transpose_weights, bool reshape_weights, DataType data_type,
+ QuantizationInfo quantization_info)
+ {
+ FullyConnectedLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T, run_interleave>::setup(input_shape, weights_shape, bias_shape, output_shape, transpose_weights,
+ reshape_weights, data_type,
+ 0, quantization_info);
}
};
} // namespace validation