diff options
Diffstat (limited to 'src/cpu/kernels/fuse_batch_normalization/generic/impl.h')
| -rw-r--r-- | src/cpu/kernels/fuse_batch_normalization/generic/impl.h | 118 |
1 files changed, 118 insertions, 0 deletions
diff --git a/src/cpu/kernels/fuse_batch_normalization/generic/impl.h b/src/cpu/kernels/fuse_batch_normalization/generic/impl.h index d807148e37..0c90abccb1 100644 --- a/src/cpu/kernels/fuse_batch_normalization/generic/impl.h +++ b/src/cpu/kernels/fuse_batch_normalization/generic/impl.h @@ -32,6 +32,124 @@ namespace arm_compute { namespace cpu { +template <typename T, bool fused_activation, typename F> +void batch_normalization_nchw(const Window &window, + ITensor *in, + ITensor *out, + const ITensor *in_mean, + const ITensor *in_var, + const ITensor *in_beta, + const ITensor *in_gamma, + float epsilon, + ActivationLayerInfo act_info) +{ + /** SIMD vector tag type. */ + using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>; + + const int window_step_x = 16 / sizeof(T); + const auto window_start_x = static_cast<int>(window.x().start()); + const auto window_end_x = static_cast<int>(window.x().end()); + + Window win_to_use = window; + win_to_use.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator input(in, win_to_use); + Iterator output(out, win_to_use); + + F activation_functor(act_info); + + // Hold information about the current feature map we are iterating. + // Only compute denominator and constants once per feature map. + int slice = -1; + + const auto input_mean = reinterpret_cast<const T *>(in_mean->ptr_to_element(Coordinates(0, 0))); + const auto input_var = reinterpret_cast<const T *>(in_var->ptr_to_element(Coordinates(0, 0))); + const auto input_gamma = + (in_gamma != nullptr) ? reinterpret_cast<const T *>(in_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr; + const auto input_beta = + (in_beta != nullptr) ? reinterpret_cast<const T *>(in_beta->ptr_to_element(Coordinates(0, 0))) : nullptr; + + T mean = static_cast<T>(0); + T var = static_cast<T>(0); + T gamma = static_cast<T>(1); + T beta = static_cast<T>(0); + T denominator = static_cast<T>(0); + + auto mean_vec = wrapper::vdup_n(mean, ExactTagType{}); + auto var_vec = wrapper::vdup_n(var, ExactTagType{}); + auto gamma_vec = wrapper::vdup_n(gamma, ExactTagType{}); + auto beta_vec = wrapper::vdup_n(beta, ExactTagType{}); + auto denominator_vec = wrapper::vdup_n(denominator, ExactTagType{}); + const auto epsilon_vec = wrapper::vdup_n(static_cast<T>(epsilon), ExactTagType{}); + execute_window_loop( + win_to_use, + [&](const Coordinates &id) + { + const auto input_ptr = reinterpret_cast<const T *>(input.ptr()); + const auto output_ptr = reinterpret_cast<T *>(output.ptr()); + + if (slice != id.z()) + { + mean = input_mean[id.z()]; + var = input_var[id.z()]; + mean_vec = wrapper::vdup_n(mean, ExactTagType{}); + var_vec = wrapper::vdup_n(var, ExactTagType{}); + if (input_gamma != nullptr) + { + gamma = input_gamma[id.z()]; + gamma_vec = wrapper::vdup_n(gamma, ExactTagType{}); + } + if (input_beta != nullptr) + { + beta = input_beta[id.z()]; + beta_vec = wrapper::vdup_n(beta, ExactTagType{}); + } + + // Calculate denominator + denominator_vec = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec)); + denominator = wrapper::vgetlane(denominator_vec, 0); + slice = id.z(); + } + + // Perform core calculations using vector operations + int x = window_start_x; + for (; x <= (window_end_x - window_step_x); x += window_step_x) + { + // Calculate x bar + const auto numerator = wrapper::vsub(wrapper::vloadq(input_ptr + x), mean_vec); + const auto x_bar = wrapper::vmul(numerator, denominator_vec); + auto res = wrapper::vmla(beta_vec, x_bar, gamma_vec); + + // Perform fused activation + if (fused_activation) + { + activation_functor(res); + } + + // Store results + wrapper::vstore(output_ptr + x, res); + } + + // Compute left-over elements + for (; x < window_end_x; ++x) + { + const T numerator = input_ptr[x] - mean; + const T x_bar = numerator * denominator; + T res = beta + x_bar * gamma; + + // Perform fused activation + if (fused_activation) + { + activation_functor(res); + } + + // Store results + *(output_ptr + x) = res; + } + }, + input, output); +} + template <typename T> void fused_batch_normalization_conv(const ITensor *conv_weights, const ITensor *conv_bias, |