[tensor] Optimize tensor apply and multiply_strided

Optimize multiply_strided and apply to use std::transform to get the best
performance, and possibility to use par_unseq for later parallelization.

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

diff --git a/nntrainer/tensor/tensor.cpp b/nntrainer/tensor/tensor.cpp
index 8e07cd9..d8cd32a 100644 (file)
--- a/nntrainer/tensor/tensor.cpp
+++ b/nntrainer/tensor/tensor.cpp
@@ -348,13 +348,27 @@ Tensor &Tensor::multiply_strided(Tensor const &m, Tensor &output) const {
     throw std::invalid_argument(
       "Strided multiplication does not support broadcasting");
 
-  /** @todo optimize this with a tensor iterator */
-  for (unsigned int b = 0; b < batch(); ++b) {
-    for (unsigned int c = 0; c < channel(); ++c) {
-      for (unsigned int h = 0; h < height(); ++h) {
-        for (unsigned int w = 0; w < width(); ++w) {
-          output.setValue(b, c, h, w,
-                          getValue(b, c, h, w) * m.getValue(b, c, h, w));
+  if (strides[3] != 1 || m.strides[3] != 1 || output.strides[3] != 1) {
+    for (unsigned int b = 0; b < batch(); ++b) {
+      for (unsigned int c = 0; c < channel(); ++c) {
+        for (unsigned int h = 0; h < height(); ++h) {
+          for (unsigned int w = 0; w < width(); ++w) {
+            output.setValue(b, c, h, w,
+                            getValue(b, c, h, w) * m.getValue(b, c, h, w));
+          }
+        }
+      }
+    }
+  } else {
+    /** @todo optimize this with combining these loops where stride is 1 */
+    for (unsigned int b = 0; b < batch(); ++b) {
+      for (unsigned int c = 0; c < channel(); ++c) {
+        for (unsigned int h = 0; h < height(); ++h) {
+          float *out_data = output.getAddress(b, c, h, 0);
+          const float *m_data = m.getAddress(b, c, h, 0);
+          const float *in_data = getAddress(b, c, h, 0);
+          std::transform(in_data, in_data + width(), m_data, out_data,
+                         std::multiplies<float>());
         }
       }
     }
@@ -1050,8 +1064,18 @@ Tensor &Tensor::apply(std::function<float(float)> f, Tensor &output) const {
     const float *data = getData();
     float *rdata = output.getData();
     std::transform(data, data + size(), rdata, f);
+  } else if (strides[3] == 1 && output.strides[3] == 1) {
+    /** @todo optimize this with combining these loops where stride is 1 */
+    for (unsigned int b = 0; b < batch(); ++b) {
+      for (unsigned int c = 0; c < channel(); ++c) {
+        for (unsigned int h = 0; h < height(); ++h) {
+          float *out_data = output.getAddress(b, c, h, 0);
+          const float *in_data = getAddress(b, c, h, 0);
+          std::transform(in_data, in_data + width(), out_data, f);
+        }
+      }
+    }
   } else {
-    /** @todo optimize this with a tensor iterator */
     for (unsigned int b = 0; b < batch(); ++b) {
       for (unsigned int c = 0; c < channel(); ++c) {
         for (unsigned int h = 0; h < height(); ++h) {