Merge branch 'dev' into 'master'

version 0.2.0 - missing DivImpl

See merge request !52

src/operator/DivImpl.cpp

@@ -55,7 +55,7 @@ void Aidge::DivImpl_cpu::forward() {
     // 2. Find the highest equal dimension -> 3
     //    Exception: if the first diverging dimension is the last one, then -> 4 (dims.size())
     // 3. Compute the highest number of contiguous data -> 7
-    // 4. Compute stride and offset step for the broadcast mechnism
+    // 4. Compute stride and offset step for the broadcast mechanism
     // 5. Call a simple kernel
     const auto& opTensor = static_cast<const Div_Op&>(mOp);
 
@@ -70,15 +70,17 @@ void Aidge::DivImpl_cpu::forward() {
     std::vector<std::size_t>        dims1   = opTensor.getInput(1)->dims();
     const std::vector<std::size_t>& outDims = opTensor.getOutput(0)->dims();
 
-    // if (dims0 == dims1) {
-    //     const std::size_t input0_contiguous_size = std::accumulate(dims0.cbegin(), dims0.cend(), std::size_t(1), std::multiplies<std::size_t>());
-    //     kernelFunc(input0_contiguous_size, input0_contiguous_size, input0_contiguous_size,
-    //                 getCPUPtr(mOp.getRawInput(0)),
-    //                 getCPUPtr(mOp.getRawInput(1)),
-    //                 getCPUPtr(mOp.getRawOutput(0)));
-    //     return;
-    // }
+    // special case for equal dimensions, the kernel is called with the entire arrays at once
+    if (dims0 == dims1) {
+        const std::size_t input0_contiguous_size = std::accumulate(dims0.cbegin(), dims0.cend(), std::size_t(1), std::multiplies<std::size_t>());
+        kernelFunc(input0_contiguous_size, input0_contiguous_size, input0_contiguous_size,
+                    getCPUPtr(mOp.getRawInput(0)),
+                    getCPUPtr(mOp.getRawInput(1)),
+                    getCPUPtr(mOp.getRawOutput(0)));
+        return;
+    }
 
+    // set dimensions to be of equal size by filling the smallest one with ones.
     if (dims0.size() > dims1.size()) {
         dims1.insert(dims1.cbegin(), dims0.size() - dims1.size(), std::size_t(1));
     }
@@ -89,8 +91,10 @@ void Aidge::DivImpl_cpu::forward() {
     const std::size_t nbDims = dims0.size();
 
     // Find the highest equal dimension
-    std::size_t contiguousIdx = nbDims - 1;
-    for (; contiguousIdx+1 > 0; --contiguousIdx) {
+    // std::size_t contiguousIdx = nbDims - 1;
+    std::size_t contiguousIdx = nbDims;
+    while (contiguousIdx-- > 0) {
+    // for (; contiguousIdx+1 > 0; --contiguousIdx) {
         if (dims0[contiguousIdx] != dims1[contiguousIdx]) {
             if (contiguousIdx == (nbDims -1)) { // last dimensions of one of the input Tensor are of size 1
                 const std::vector<std::size_t>& dims = (dims0[contiguousIdx] == 1) ? dims0 : dims1;
@@ -109,21 +113,17 @@ void Aidge::DivImpl_cpu::forward() {
     const std::size_t output_contiguous_size = std::accumulate(outDims.cbegin()+contiguousIdx, outDims.cend(), std::size_t(1), std::multiplies<std::size_t>());
 
     // initialize strides to iterate through data because of broadcasting
-    std::int32_t *stride_post0;
-    std::int32_t *stride_post1;
-    std::int32_t *stride_step0;
-    std::int32_t *stride_step1;
+    std::unique_ptr<std::int32_t[]> stride_post0 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    std::unique_ptr<std::int32_t[]> stride_post1 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    std::unique_ptr<std::int32_t[]> stride_step0 = std::make_unique<std::int32_t[]>(contiguousIdx);
+    std::unique_ptr<std::int32_t[]> stride_step1 = std::make_unique<std::int32_t[]>(contiguousIdx);
     if (contiguousIdx > 0) {
-        stride_post0 = new std::int32_t[contiguousIdx];
         stride_post0[contiguousIdx - 1] = 1;
-        stride_post1 = new std::int32_t[contiguousIdx];
         stride_post1[contiguousIdx - 1] = 1;
         for (std::size_t i = contiguousIdx - 2; i != static_cast<std::size_t>(-1); --i) {
             stride_post0[i] = stride_post0[i+1]*static_cast<std::int32_t>(dims0[i+1]);
             stride_post1[i] = stride_post1[i+1]*static_cast<std::int32_t>(dims1[i+1]);
         }
-        stride_step0 = new std::int32_t[contiguousIdx];
-        stride_step1 = new std::int32_t[contiguousIdx];
         for (std::size_t i = 0; i != contiguousIdx; ++i) {
             stride_step0[i] = (dims0[i] == 1) ? 1 - stride_post0[i] : 1;
             stride_step1[i] = (dims1[i] == 1) ? 1 - stride_post1[i] : 1;
@@ -155,10 +155,4 @@ void Aidge::DivImpl_cpu::forward() {
             dim = contiguousIdx - 1;
         }
     }
-    if (contiguousIdx > 0) {
-        delete[] stride_post0;
-        delete[] stride_post1;
-        delete[] stride_step0;
-        delete[] stride_step1;
-    }
 }