merging updates from upstream

unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h

@@ -161,6 +161,22 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
         }
       }
     }
+
+    // Handle special format like NCHW, its input shape is '[1, N..., 1]' and
+    // broadcast shape is '[N, 1..., N]'
+    if (!oneByN && !nByOne) {
+      if (input_dims[0] == 1 && input_dims[NumDims-1] == 1 && NumDims > 2) {
+        nByOne = true;
+        oneByN = true;
+        for (int i = 1; i < NumDims-1; ++i) {
+          if (broadcast[i] != 1) {
+            nByOne = false;
+            oneByN = false;
+            break;
+          }
+        }
+      }
+    }
   }
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
@@ -256,24 +272,70 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
     }
 
     if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      if (oneByN) {
+      if (oneByN && !nByOne) {
         return packetNByOne<LoadMode>(index);
-      } else if (nByOne) {
+      } else if (!oneByN && nByOne) {
         return packetOneByN<LoadMode>(index);
+      } else if (oneByN && nByOne) {
+        return packetOneByNByOne<LoadMode>(index);
       } else {
         return packetColMajor<LoadMode>(index);
       }
     } else {
-      if (oneByN) {
+      if (oneByN && !nByOne) {
         return packetOneByN<LoadMode>(index);
-      } else if (nByOne) {
+      } else if (!oneByN && nByOne) {
         return packetNByOne<LoadMode>(index);
+      } else if (oneByN && nByOne) {
+        return packetOneByNByOne<LoadMode>(index);
       } else {
         return packetRowMajor<LoadMode>(index);
       }
     }
   }
 
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetOneByNByOne
+  (Index index) const
+  {
+    EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
+    eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
+
+    EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
+    Index startDim, endDim;
+    Index inputIndex, outputOffset, batchedIndex;
+
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      startDim = NumDims - 1;
+      endDim = 1;
+    } else {
+      startDim = 0;
+      endDim = NumDims - 2;
+    }
+
+    batchedIndex = index % m_outputStrides[startDim];
+    inputIndex   = batchedIndex / m_outputStrides[endDim];
+    outputOffset = batchedIndex % m_outputStrides[endDim];
+
+    if (outputOffset + PacketSize <= m_outputStrides[endDim]) {
+      values[0] = m_impl.coeff(inputIndex);
+      return internal::pload1<PacketReturnType>(values);
+    } else {
+      for (int i = 0, cur = 0; i < PacketSize; ++i, ++cur) {
+        if (outputOffset + cur < m_outputStrides[endDim]) {
+          values[i] = m_impl.coeff(inputIndex);
+        } else {
+          ++inputIndex;
+          inputIndex = (inputIndex == m_inputStrides[startDim] ? 0 : inputIndex);
+          values[i] = m_impl.coeff(inputIndex);
+          outputOffset = 0;
+          cur = 0;
+        }
+      }
+      return internal::pload<PacketReturnType>(values);
+    }
+  }
+
   template<int LoadMode>
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetOneByN(Index index) const
   {

unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h

@@ -189,9 +189,11 @@ struct ThreadPoolDevice {
     // of blocks to be evenly dividable across threads.
 
     double block_size_f = 1.0 / CostModel::taskSize(1, cost);
-    Index block_size = numext::mini(n, numext::maxi<Index>(1, block_size_f));
-    const Index max_block_size =
-        numext::mini(n, numext::maxi<Index>(1, 2 * block_size_f));
+    const Index max_oversharding_factor = 4;
+    Index block_size = numext::mini(
+        n, numext::maxi<Index>(divup<Index>(n, max_oversharding_factor * numThreads()),
+                               block_size_f));
+    const Index max_block_size = numext::mini(n, 2 * block_size);
     if (block_align) {
       Index new_block_size = block_align(block_size);
       eigen_assert(new_block_size >= block_size);
@@ -205,7 +207,8 @@ struct ThreadPoolDevice {
         (divup<int>(block_count, numThreads()) * numThreads());
     // Now try to increase block size up to max_block_size as long as it
     // doesn't decrease parallel efficiency.
-    for (Index prev_block_count = block_count; prev_block_count > 1;) {
+    for (Index prev_block_count = block_count;
+         max_efficiency < 1.0 && prev_block_count > 1;) {
       // This is the next block size that divides size into a smaller number
       // of blocks than the current block_size.
       Index coarser_block_size = divup(n, prev_block_count - 1);

unsupported/Eigen/src/FFT/ei_kissfft_impl.h

@@ -316,8 +316,8 @@ struct kissfft_impl
 
         // use optimized mode for even real
         fwd( dst, reinterpret_cast<const Complex*> (src), ncfft);
-        Complex dc = dst[0].real() +  dst[0].imag();
-        Complex nyquist = dst[0].real() -  dst[0].imag();
+        Complex dc(dst[0].real() +  dst[0].imag());
+        Complex nyquist(dst[0].real() -  dst[0].imag());
         int k;
         for ( k=1;k <= ncfft2 ; ++k ) {
           Complex fpk = dst[k];

unsupported/test/CMakeLists.txt

@@ -124,6 +124,7 @@ ei_add_test(polynomialsolver)
 ei_add_test(polynomialutils)
 ei_add_test(splines)
 ei_add_test(gmres)
+ei_add_test(dgmres)
 ei_add_test(minres)
 ei_add_test(levenberg_marquardt)
 ei_add_test(kronecker_product)

unsupported/test/cxx11_tensor_broadcasting.cpp

@@ -238,6 +238,59 @@ static void test_simple_broadcasting_n_by_one()
   }
 }
 
+template <int DataLayout>
+static void test_simple_broadcasting_one_by_n_by_one_1d()
+{
+  Tensor<float, 3, DataLayout> tensor(1,7,1);
+  tensor.setRandom();
+  array<ptrdiff_t, 3> broadcasts;
+  broadcasts[0] = 5;
+  broadcasts[1] = 1;
+  broadcasts[2] = 13;
+  Tensor<float, 3, DataLayout> broadcasted;
+  broadcasted = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(broadcasted.dimension(0), 5);
+  VERIFY_IS_EQUAL(broadcasted.dimension(1), 7);
+  VERIFY_IS_EQUAL(broadcasted.dimension(2), 13);
+
+  for (int i = 0; i < 5; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      for (int k = 0; k < 13; ++k) {
+        VERIFY_IS_EQUAL(tensor(0,j%7,0), broadcasted(i,j,k));
+      }
+    }
+  }
+}
+
+template <int DataLayout>
+static void test_simple_broadcasting_one_by_n_by_one_2d()
+{
+  Tensor<float, 4, DataLayout> tensor(1,7,13,1);
+  tensor.setRandom();
+  array<ptrdiff_t, 4> broadcasts;
+  broadcasts[0] = 5;
+  broadcasts[1] = 1;
+  broadcasts[2] = 1;
+  broadcasts[3] = 19;
+  Tensor<float, 4, DataLayout> broadcast;
+  broadcast = tensor.broadcast(broadcasts);
+
+  VERIFY_IS_EQUAL(broadcast.dimension(0), 5);
+  VERIFY_IS_EQUAL(broadcast.dimension(1), 7);
+  VERIFY_IS_EQUAL(broadcast.dimension(2), 13);
+  VERIFY_IS_EQUAL(broadcast.dimension(3), 19);
+
+  for (int i = 0; i < 5; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      for (int k = 0; k < 13; ++k) {
+        for (int l = 0; l < 19; ++l) {
+          VERIFY_IS_EQUAL(tensor(0,j%7,k%13,0), broadcast(i,j,k,l));
+        }
+      }
+    }
+  }
+}
 
 void test_cxx11_tensor_broadcasting()
 {
@@ -253,4 +306,8 @@ void test_cxx11_tensor_broadcasting()
   CALL_SUBTEST(test_simple_broadcasting_n_by_one<RowMajor>());
   CALL_SUBTEST(test_simple_broadcasting_one_by_n<ColMajor>());
   CALL_SUBTEST(test_simple_broadcasting_n_by_one<ColMajor>());
+  CALL_SUBTEST(test_simple_broadcasting_one_by_n_by_one_1d<ColMajor>());
+  CALL_SUBTEST(test_simple_broadcasting_one_by_n_by_one_2d<ColMajor>());
+  CALL_SUBTEST(test_simple_broadcasting_one_by_n_by_one_1d<RowMajor>());
+  CALL_SUBTEST(test_simple_broadcasting_one_by_n_by_one_2d<RowMajor>());
 }