FuseBatchNorm changes to enable fp16 in armv8a multi_isa builds

* FP16 kernels must be instantiated in fp16.cpp.
* Partially resolves MLCE-1102

Change-Id: Ie652203876a0ac12b025e96d20990b6efb21e772
Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10477
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 117 insertions, 3 deletions

diff --git a/src/cpu/kernels/fuse_batch_normalization/generic/impl.h b/src/cpu/kernels/fuse_batch_normalization/generic/impl.h
index 6fa843263a..d807148e37 100644
--- a/src/cpu/kernels/fuse_batch_normalization/generic/impl.h
+++ b/src/cpu/kernels/fuse_batch_normalization/generic/impl.h
@@ -21,8 +21,8 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef SRC_CORE_NEON_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H
-#define SRC_CORE_NEON_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H
+#ifndef ACL_SRC_CPU_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H
+#define ACL_SRC_CPU_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H
 
 #include "arm_compute/core/Helpers.h"
 
@@ -144,6 +144,120 @@ void fused_batch_normalization_conv(const ITensor *conv_weights,
         },
         conv_w_in, conv_w_out);
 }
+template <typename T>
+void fused_batch_normalization_dwc_nchw(const ITensor *dwc_weights,
+                                        const ITensor *dwc_bias,
+                                        ITensor       *fused_weights,
+                                        ITensor       *fused_bias,
+                                        const ITensor *bn_mean,
+                                        const ITensor *bn_var,
+                                        const ITensor *bn_beta,
+                                        const ITensor *bn_gamma,
+                                        float          epsilon,
+                                        const Window  &window)
+{
+    using ScalarType   = T;
+    const int size     = 16 / dwc_weights->info()->element_size();
+    using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
+
+    const bool run_in_place_weights = (fused_weights == nullptr) || (fused_weights == dwc_weights);
+    const bool run_in_place_bias    = (fused_bias == nullptr) || (dwc_bias != nullptr && fused_bias == dwc_bias);
+
+    // Set build options
+    Window win = window;
+    win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+    const int  window_step_x  = size;
+    const auto window_start_x = static_cast<int>(window.x().start());
+    const auto window_end_x   = static_cast<int>(window.x().end());
+
+    Iterator dwc_w_in(dwc_weights, win);
+    Iterator dwc_w_out(run_in_place_weights ? dwc_weights : fused_weights, win);
+
+    const auto dwc_bias_in =
+        (dwc_bias != nullptr ? reinterpret_cast<ScalarType *>(dwc_bias->ptr_to_element(Coordinates(0, 0))) : nullptr);
+    auto dwc_bias_out =
+        (run_in_place_bias ? dwc_bias_in
+                           : reinterpret_cast<ScalarType *>(fused_bias->ptr_to_element(Coordinates(0, 0))));
+
+    const auto input_mean  = reinterpret_cast<const ScalarType *>(bn_mean->ptr_to_element(Coordinates(0, 0)));
+    const auto input_var   = reinterpret_cast<const ScalarType *>(bn_var->ptr_to_element(Coordinates(0, 0)));
+    const auto input_gamma = (bn_gamma != nullptr)
+                                 ? reinterpret_cast<const ScalarType *>(bn_gamma->ptr_to_element(Coordinates(0, 0)))
+                                 : nullptr;
+    const auto input_beta  = (bn_beta != nullptr)
+                                 ? reinterpret_cast<const ScalarType *>(bn_beta->ptr_to_element(Coordinates(0, 0)))
+                                 : nullptr;
+
+    auto       mean_vec    = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+    auto       var_vec     = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+    auto       gamma_vec   = wrapper::vdup_n(ScalarType(1), ExactTagType{});
+    auto       beta_vec    = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+    auto       rvar_vec    = wrapper::vdup_n(ScalarType(0), ExactTagType{});
+    const auto epsilon_vec = wrapper::vdup_n(ScalarType(epsilon), ExactTagType{});
+
+    auto mean               = ScalarType(0.0);
+    auto var                = ScalarType(0.0);
+    auto gamma              = ScalarType(1.0);
+    auto beta               = ScalarType(0.0);
+    auto dwc_bias_in_scalar = ScalarType(0.0);
+    execute_window_loop(
+        win,
+        [&](const Coordinates &id)
+        {
+            var = input_var[id[2]];
+            if (input_gamma != nullptr)
+            {
+                gamma = input_gamma[id[2]];
+            }
+
+            if (id[1] == 0)
+            {
+                mean = input_mean[id[2]];
+
+                // Construct vectors
+                mean_vec = wrapper::vdup_n(mean, ExactTagType{});
+                if (input_beta != nullptr)
+                {
+                    beta     = input_beta[id[2]];
+                    beta_vec = wrapper::vdup_n(beta, ExactTagType{});
+                }
+
+                if (dwc_bias_in != nullptr)
+                {
+                    dwc_bias_in_scalar = dwc_bias_in[id[2]];
+                }
+
+                auto dwc_bias_tmp_scalar = (dwc_bias_in_scalar - mean) / std::sqrt(var + ScalarType(epsilon));
+                dwc_bias_out[id[2]]      = (dwc_bias_tmp_scalar * gamma) + beta;
+            }
+
+            int  x             = window_start_x;
+            auto dwc_w_in_ptr  = reinterpret_cast<const ScalarType *>(dwc_w_in.ptr());
+            auto dwc_w_out_ptr = reinterpret_cast<ScalarType *>(dwc_w_out.ptr());
+            var_vec            = wrapper::vdup_n(var, ExactTagType{});
+            gamma_vec          = wrapper::vdup_n(gamma, ExactTagType{});
+            rvar_vec           = wrapper::vinvsqrt(wrapper::vadd(var_vec, epsilon_vec));
+
+            for (; x <= (window_end_x - window_step_x); x += window_step_x)
+            {
+                auto wn = wrapper::vloadq(dwc_w_in_ptr + x);
+                wn      = wrapper::vmul(wn, rvar_vec);
+                wn      = wrapper::vmul(wn, gamma_vec);
+
+                // Store results
+                wrapper::vstore(dwc_w_out_ptr + x, wn);
+            }
+
+            // Compute left-over elements
+            for (; x < window_end_x; ++x)
+            {
+                *(dwc_w_out_ptr + x) = *(dwc_w_in_ptr + x) / std::sqrt(var + ScalarType(epsilon)) * gamma;
+            }
+        },
+        dwc_w_in, dwc_w_out);
+}
+
 } // namespace cpu
 } // namespace arm_compute
-#endif //SRC_CORE_NEON_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H
+#endif // ACL_SRC_CPU_KERNELS_FUSE_BATCH_NORMALIZATION_GENERIC_IMPL_H