From: Павел Ильютченко/AI Tools Lab /SRR/Engineer/삼성전자 <p.iliutchenk@samsung.com>
Date: Fri, 1 Feb 2019 12:35:57 +0000 (+0300)
Subject: [nnc] Scale op NEON implementation (#2995)
X-Git-Tag: nncc_backup~885
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=5f6b080e1095e8eab511897df92093ccbd45013b;p=platform%2Fcore%2Fml%2Fnnfw.git

[nnc] Scale op NEON implementation (#2995)

* Adapt BiasAdd operation NEON implementation for Scale

Signed-off-by: Pavel Iliutchenko <p.iliutchenk@samsung.com>
---

diff --git a/contrib/nnc/passes/soft_backend/code_snippets/cpp_scale.def b/contrib/nnc/passes/soft_backend/code_snippets/cpp_scale.def
index 7026254..c380efa 100644
--- a/contrib/nnc/passes/soft_backend/code_snippets/cpp_scale.def
+++ b/contrib/nnc/passes/soft_backend/code_snippets/cpp_scale.def
@@ -18,9 +18,50 @@ void scale(Tensor& out, const char* params, const Tensor& in1, const Tensor& in2
   const auto in1_shape = shapeToRuntimeShape(in1.getShape());
   const auto in2_shape = shapeToRuntimeShape(in2.getShape());
   assert(in2_shape.DimensionsCount() == 1 &&
-    in2_shape.Dims(0) == in1_shape.Dims(in1_shape.DimensionsCount() - 1));
+         in2_shape.Dims(0) == in1_shape.Dims(in1_shape.DimensionsCount() - 1));
 
   out.reshape(in1.getShape());
+
+#ifdef USE_NEON
+  const int scale_size = in2_shape.FlatSize();
+  const int array_size = in1_shape.FlatSize();
+  TFLITE_DCHECK_EQ((array_size % scale_size), 0);
+  out.fillData(in1.getData(), array_size);
+  float* array_ptr = out.getData();
+  const float* scale_ptr = in2.getData();
+  float* array_end_ptr = array_ptr + array_size;
+  for (; array_ptr != array_end_ptr; array_ptr += scale_size) {
+    int i = 0;
+    for (; i <= scale_size - 16; i += 16) {
+      auto b0 = vld1q_f32(scale_ptr + i);
+      auto b1 = vld1q_f32(scale_ptr + i + 4);
+      auto b2 = vld1q_f32(scale_ptr + i + 8);
+      auto b3 = vld1q_f32(scale_ptr + i + 12);
+      auto a0 = vld1q_f32(array_ptr + i);
+      auto a1 = vld1q_f32(array_ptr + i + 4);
+      auto a2 = vld1q_f32(array_ptr + i + 8);
+      auto a3 = vld1q_f32(array_ptr + i + 12);
+      auto x0 = vmulq_f32(a0, b0);
+      auto x1 = vmulq_f32(a1, b1);
+      auto x2 = vmulq_f32(a2, b2);
+      auto x3 = vmulq_f32(a3, b3);
+      vst1q_f32(array_ptr + i, x0);
+      vst1q_f32(array_ptr + i + 4, x1);
+      vst1q_f32(array_ptr + i + 8, x2);
+      vst1q_f32(array_ptr + i + 12, x3);
+    }
+    for (; i <= scale_size - 4; i += 4) {
+      auto b = vld1q_f32(scale_ptr + i);
+      auto a = vld1q_f32(array_ptr + i);
+      auto x = vmulq_f32(a, b);
+      vst1q_f32(array_ptr + i, x);
+    }
+    for (; i < scale_size; i++) {
+      array_ptr[i] = array_ptr[i] * scale_ptr[i];
+    }
+  }
+#else  // not NEON
+
   const auto out_shape = shapeToRuntimeShape(out.getShape());
 
   const auto in1_mat = MapAsMatrixWithLastDimAsRows(in1.getData(), in1_shape);
@@ -28,4 +69,5 @@ void scale(Tensor& out, const char* params, const Tensor& in1, const Tensor& in2
   auto out_mat = MapAsMatrixWithLastDimAsRows(out.getData(), out_shape);
   out_mat.colwise() = in2_vec;
   out_mat.array() = out_mat.array() * in1_mat.array();
+#endif
 }