COMPMID-410 Port BatchNormalization to use fixed point 16
Change-Id: I7d3e9ff70c717ef5e6de2bcfbfd277f39006702f
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/78956
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
index d0aec69..d1adfa7 100644
--- a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
@@ -47,7 +47,7 @@
// Only compute denominator and NEON vectors once per feature map.
int slice = -1;
- int fixed_point_position = in->info()->fixed_point_position();
+ const int fixed_point_position = in->info()->fixed_point_position();
const auto input_mean = reinterpret_cast<const qint8_t *>(mean->ptr_to_element(Coordinates(0, 0)));
const auto input_var = reinterpret_cast<const qint8_t *>(var->ptr_to_element(Coordinates(0, 0)));
const auto input_gamma = reinterpret_cast<const qint8_t *>(gamma->ptr_to_element(Coordinates(0, 0)));
@@ -82,6 +82,50 @@
input, output);
}
+void batch_normalization_q16(const ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
+{
+ Iterator input(in, window);
+ Iterator output(out, window);
+
+ // Hold information about the current feature map we are iterating.
+ // Only compute denominator and NEON vectors once per feature map.
+ int slice = -1;
+
+ const int fixed_point_position = in->info()->fixed_point_position();
+ const auto input_mean = reinterpret_cast<const qint16_t *>(mean->ptr_to_element(Coordinates(0, 0)));
+ const auto input_var = reinterpret_cast<const qint16_t *>(var->ptr_to_element(Coordinates(0, 0)));
+ const auto input_gamma = reinterpret_cast<const qint16_t *>(gamma->ptr_to_element(Coordinates(0, 0)));
+ const auto input_beta = reinterpret_cast<const qint16_t *>(beta->ptr_to_element(Coordinates(0, 0)));
+
+ qint16x8_t mean_vec = vdupq_n_qs16(0);
+ qint16x8_t var_vec = vdupq_n_qs16(0);
+ qint16x8_t gamma_vec = vdupq_n_qs16(0);
+ qint16x8_t beta_vec = vdupq_n_qs16(0);
+ qint16x8_t denominator = vdupq_n_qs16(0);
+ const qint16x8_t epsilon_vec = vdupq_n_qs16(sqcvt_qs16_f32(epsilon, fixed_point_position));
+ execute_window_loop(window, [&](const Coordinates & id)
+ {
+ if(slice != id.z())
+ {
+ // Conctruct vectors
+ mean_vec = vdupq_n_qs16(*(input_mean + id.z()));
+ var_vec = vdupq_n_qs16(*(input_var + id.z()));
+ gamma_vec = vdupq_n_qs16(*(input_gamma + id.z()));
+ beta_vec = vdupq_n_qs16(*(input_beta + id.z()));
+
+ // Calculate denominator
+ denominator = vqinvsqrtq_qs16(vqaddq_qs16(var_vec, epsilon_vec), fixed_point_position);
+ slice = id.z();
+ }
+
+ // Calculate x bar and store results
+ const qint16x8_t numerator = vqsubq_qs16(vld1q_qs16(reinterpret_cast<const qint16_t *>(input.ptr())), mean_vec);
+ const qint16x8_t x_bar = vqmulq_qs16(numerator, denominator, fixed_point_position);
+ vst1q_qs16(reinterpret_cast<qint16_t *>(output.ptr()), vqmlaq_qs16(beta_vec, x_bar, gamma_vec, fixed_point_position));
+ },
+ input, output);
+}
+
void batch_normalization_fp32(const ITensor *in, ITensor *out, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon, const Window &window)
{
Iterator input(in, window);
@@ -127,7 +171,7 @@
void NEBatchNormalizationLayerKernel::configure(const ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta, const ITensor *gamma, float epsilon)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F32);
ARM_COMPUTE_ERROR_ON_NULLPTR(output);
// Output tensor auto initialization if not yet initialized
@@ -155,6 +199,10 @@
_func = &batch_normalization_q8;
num_elems_processed_per_iteration = 16;
break;
+ case DataType::QS16:
+ _func = &batch_normalization_q16;
+ num_elems_processed_per_iteration = 8;
+ break;
case DataType::F32:
_func = &batch_normalization_fp32;
num_elems_processed_per_iteration = 4;