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>

3 files changed, 134 insertions, 136 deletions

diff --git a/src/cpu/kernels/fuse_batch_normalization/generic/fp16.cpp b/src/cpu/kernels/fuse_batch_normalization/generic/fp16.cpp
index 2821af32ce..8f47ecba8f 100644
--- a/src/cpu/kernels/fuse_batch_normalization/generic/fp16.cpp
+++ b/src/cpu/kernels/fuse_batch_normalization/generic/fp16.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021-2022 Arm Limited.
+ * Copyright (c) 2021-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -43,6 +43,21 @@ void fused_batch_normalization_conv_f16(const ITensor *conv_weights,
     return fused_batch_normalization_conv<float16_t>(conv_weights, conv_bias, fused_weights, fused_bias, bn_mean,
                                                      bn_var, bn_beta, bn_gamma, epsilon, window);
 }
+
+void fused_batch_normalization_dwc_nchw_f16(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)
+{
+    return fused_batch_normalization_dwc_nchw<float16_t>(dwc_weights, dwc_bias, fused_weights, fused_bias, bn_mean,
+                                                         bn_var, bn_beta, bn_gamma, epsilon, window);
+}
 } // namespace cpu
 } // namespace arm_compute
 #endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */
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
diff --git a/src/cpu/kernels/fuse_batch_normalization/nchw/all.cpp b/src/cpu/kernels/fuse_batch_normalization/nchw/all.cpp
index c0b0dfd4dc..25580e1bec 100644
--- a/src/cpu/kernels/fuse_batch_normalization/nchw/all.cpp
+++ b/src/cpu/kernels/fuse_batch_normalization/nchw/all.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2022 Arm Limited.
+ * Copyright (c) 2018-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -28,120 +28,6 @@ namespace arm_compute
 {
 namespace cpu
 {
-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);
-}
-
 void fused_batch_normalization_dwc_nchw_f32(const ITensor *dwc_weights,
                                             const ITensor *dwc_bias,
                                             ITensor       *fused_weights,
@@ -157,22 +43,5 @@ void fused_batch_normalization_dwc_nchw_f32(const ITensor *dwc_weights,
                                                          bn_var, bn_beta, bn_gamma, epsilon, window);
 }
 
-#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS)
-void fused_batch_normalization_dwc_nchw_f16(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)
-{
-    return fused_batch_normalization_dwc_nchw<float16_t>(dwc_weights, dwc_bias, fused_weights, fused_bias, bn_mean,
-                                                         bn_var, bn_beta, bn_gamma, epsilon, window);
-}
-#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && defined(ENABLE_FP16_KERNELS) */
-
 } // namespace cpu
 } // namespace arm_compute