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/CPP/FullyConnectedLayer.cpp b/tests/validation/CPP/FullyConnectedLayer.cpp
index 2b32c4b..6b618a9 100644
--- a/tests/validation/CPP/FullyConnectedLayer.cpp
+++ b/tests/validation/CPP/FullyConnectedLayer.cpp
@@ -24,8 +24,11 @@
#include "FullyConnectedLayer.h"
#include "arm_compute/core/Types.h"
+#include "tests/validation/CPP/UtilsQuantizedAsymm.h"
#include "tests/validation/FixedPoint.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+
#include <numeric>
namespace arm_compute
@@ -39,22 +42,34 @@
namespace
{
// Vector matrix multiply for floating point
-template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type = 0>
-void vector_matrix_multiply(const T *src, const T *weights, const T *bias, T *dst, int cols_weights, int rows_weights, uint8_t fixed_point_position)
+template < typename T, typename TB, typename std::enable_if < is_floating_point<T>::value &&is_floating_point<TB>::value, int >::type = 0 >
+void vector_matrix_multiply(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, int offset_src, int offset_dst, int cols_weights,
+ int rows_weights, uint8_t fixed_point_position)
{
ARM_COMPUTE_UNUSED(fixed_point_position);
+ const T *src_ptr = src.data() + offset_src;
+ const T *weights_ptr = weights.data();
+ const TB *bias_ptr = bias.data();
+ T *dst_ptr = dst.data() + offset_dst;
+
for(int y = 0; y < rows_weights; ++y)
{
- dst[y] = std::inner_product(src, src + cols_weights, weights, static_cast<T>(0)) + bias[y];
- weights += cols_weights;
+ dst_ptr[y] = std::inner_product(src_ptr, src_ptr + cols_weights, weights_ptr, static_cast<T>(0)) + bias_ptr[y];
+ weights_ptr += cols_weights;
}
}
// Vector matrix multiply for fixed point type
-template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
-void vector_matrix_multiply(const T *src, const T *weights, const T *bias, T *dst, int cols_weights, int rows_weights, uint8_t fixed_point_position)
+template < typename T, typename TB, typename std::enable_if < std::is_integral<T>::value &&std::is_integral<TB>::value, int >::type = 0 >
+void vector_matrix_multiply(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, int offset_src, int offset_dst, int cols_weights,
+ int rows_weights, uint8_t fixed_point_position)
{
+ const T *src_ptr = src.data() + offset_src;
+ const T *weights_ptr = weights.data();
+ const TB *bias_ptr = bias.data();
+ T *dst_ptr = dst.data() + offset_dst;
+
using namespace fixed_point_arithmetic;
using promoted_type = fixed_point_arithmetic::traits::promote_t<T>;
@@ -65,31 +80,79 @@
for(int x = 0; x < cols_weights; ++x)
{
- const fixed_point<promoted_type> i_value(src[x], fixed_point_position, true);
- const fixed_point<promoted_type> w_value(weights[x], fixed_point_position, true);
+ const fixed_point<promoted_type> i_value(src_ptr[x], fixed_point_position, true);
+ const fixed_point<promoted_type> w_value(weights_ptr[x], fixed_point_position, true);
acc = acc + i_value * w_value;
}
// Get the bias
- const fixed_point<T> b(bias[y], fixed_point_position, true);
+ const fixed_point<T> b(bias_ptr[y], fixed_point_position, true);
// Convert back and accumulate the bias
fixed_point<T> res(acc);
res = res + b;
// Store the result
- dst[y] = res.raw();
+ dst_ptr[y] = res.raw();
- weights += cols_weights;
+ weights_ptr += cols_weights;
+ }
+}
+
+// Vector matrix multiply for quantized type
+template <>
+void vector_matrix_multiply(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, SimpleTensor<uint8_t> &dst, int offset_src, int offset_dst,
+ int cols_weights, int rows_weights, uint8_t fixed_point_position)
+{
+ ARM_COMPUTE_UNUSED(fixed_point_position);
+
+ const uint8_t *src_ptr = src.data() + offset_src;
+ const uint8_t *weights_ptr = weights.data();
+ const int32_t *bias_ptr = bias.data();
+ uint8_t *dst_ptr = dst.data() + offset_dst;
+
+ const int input_offset = -src.quantization_info().offset;
+ const float input_scale = src.quantization_info().scale;
+ const int weights_offset = -weights.quantization_info().offset;
+ const float weights_scale = weights.quantization_info().scale;
+ const int output_offset = dst.quantization_info().offset;
+ const float output_scale = dst.quantization_info().scale;
+
+ int output_multiplier = 0;
+ int output_shift = 0;
+ const float multiplier = input_scale * weights_scale / output_scale;
+ arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+
+ for(int y = 0; y < rows_weights; ++y)
+ {
+ // Reset accumulator
+ int32_t acc = 0;
+
+ for(int x = 0; x < cols_weights; ++x)
+ {
+ acc += (src_ptr[x] + input_offset) * (weights_ptr[x] + weights_offset);
+ }
+
+ // Accumulate the bias
+ acc += bias_ptr[y];
+
+ acc = asymm_rounding_divide_by_pow2(asymm_int_mult(acc, output_multiplier), output_shift);
+ acc += output_offset;
+ acc = clamp<int32_t>(acc, 0, 255);
+
+ // Store the result
+ dst_ptr[y] = static_cast<uint8_t>(acc);
+
+ weights_ptr += cols_weights;
}
}
} // namespace
-template <typename T>
-SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &dst_shape)
+template <typename T, typename TB>
+SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &dst_shape)
{
// Create reference
- SimpleTensor<T> dst{ TensorShape{ dst_shape }, src.data_type(), 1, src.fixed_point_position() };
+ SimpleTensor<T> dst{ TensorShape{ dst_shape }, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() };
// Sanity checks
const int num_batch_dimensions = std::max(0, static_cast<int>(dst_shape.num_dimensions()) - 1);
@@ -110,10 +173,15 @@
for(int k = 0; k < num_batches; ++k)
{
- vector_matrix_multiply<T>(src.data() + k * cols_weights,
- weights.data(),
- bias.data(),
- dst.data() + k * rows_weights,
+ const int offset_in = k * cols_weights;
+ const int offset_out = k * rows_weights;
+
+ vector_matrix_multiply<T>(src,
+ weights,
+ bias,
+ dst,
+ offset_in,
+ offset_out,
cols_weights,
rows_weights,
src.fixed_point_position());
@@ -126,6 +194,7 @@
template SimpleTensor<half> fully_connected_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &weights, const SimpleTensor<half> &bias, const TensorShape &dst_shape);
template SimpleTensor<qint8_t> fully_connected_layer(const SimpleTensor<qint8_t> &src, const SimpleTensor<qint8_t> &weights, const SimpleTensor<qint8_t> &bias, const TensorShape &dst_shape);
template SimpleTensor<qint16_t> fully_connected_layer(const SimpleTensor<qint16_t> &src, const SimpleTensor<qint16_t> &weights, const SimpleTensor<qint16_t> &bias, const TensorShape &dst_shape);
+template SimpleTensor<uint8_t> fully_connected_layer(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &bias, const TensorShape &dst_shape);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/tests/validation/CPP/FullyConnectedLayer.h b/tests/validation/CPP/FullyConnectedLayer.h
index 05c570a..1dfb496 100644
--- a/tests/validation/CPP/FullyConnectedLayer.h
+++ b/tests/validation/CPP/FullyConnectedLayer.h
@@ -35,8 +35,8 @@
{
namespace reference
{
-template <typename T>
-SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<T> &bias, const TensorShape &dst_shape);
+template <typename T, typename TB>
+SimpleTensor<T> fully_connected_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &dst_shape);
} // namespace reference
} // namespace validation
} // namespace test