Fuse computations into the Tensor contractions using output kernel

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

@@ -517,9 +517,15 @@ class TensorBase<Derived, ReadOnlyAccessors>
     typedef Eigen::IndexPair<Index> DimensionPair;
 
     template<typename OtherDerived, typename Dimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const TensorContractionOp<const Dimensions, const Derived, const OtherDerived>
+    const TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const NoOpOutputKernel>
     contract(const OtherDerived& other, const Dimensions& dims) const {
-      return TensorContractionOp<const Dimensions, const Derived, const OtherDerived>(derived(), other.derived(), dims);
+      return TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const NoOpOutputKernel>(derived(), other.derived(), dims);
+    }
+
+    template<typename OtherDerived, typename Dimensions, typename OutputKernel> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const OutputKernel>
+    contract(const OtherDerived& other, const Dimensions& dims, const OutputKernel& output_kernel) const {
+      return TensorContractionOp<const Dimensions, const Derived, const OtherDerived, const OutputKernel>(derived(), other.derived(), dims, output_kernel);
     }
 
     // Convolutions.

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

@@ -85,8 +85,8 @@ template<typename LhsScalar, typename RhsScalar, typename Scalar>
 #endif
 
 
-template<typename Dimensions, typename LhsXprType, typename RhsXprType>
+template<typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
-struct traits<TensorContractionOp<Dimensions, LhsXprType, RhsXprType> >
+struct traits<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> >
 {
   // Type promotion to handle the case where the types of the lhs and the rhs are different.
   typedef typename gebp_traits<typename remove_const<typename LhsXprType::Scalar>::type,
@@ -112,23 +112,24 @@ struct traits<TensorContractionOp<Dimensions, LhsXprType, RhsXprType> >
   };
 };
 
-template<typename Dimensions, typename LhsXprType, typename RhsXprType>
+template<typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
-struct eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType>, Eigen::Dense>
+struct eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>, Eigen::Dense>
 {
-  typedef const TensorContractionOp<Dimensions, LhsXprType, RhsXprType>& type;
+  typedef const TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>& type;
 };
 
-template<typename Dimensions, typename LhsXprType, typename RhsXprType>
+template<typename Dimensions, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
-struct nested<TensorContractionOp<Dimensions, LhsXprType, RhsXprType>, 1, typename eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType> >::type>
+struct nested<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType>, 1, typename eval<TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> >::type>
 {
-  typedef TensorContractionOp<Dimensions, LhsXprType, RhsXprType> type;
+  typedef TensorContractionOp<Dimensions, LhsXprType, RhsXprType, OutputKernelType> type;
 };
 
-template<typename Indices_, typename LeftArgType_, typename RightArgType_, typename Device_>
+template<typename Indices_, typename LeftArgType_, typename RightArgType_, typename OutputKernelType_, typename Device_>
-struct traits<TensorEvaluator<const TensorContractionOp<Indices_, LeftArgType_, RightArgType_>, Device_> > {
+struct traits<TensorEvaluator<const TensorContractionOp<Indices_, LeftArgType_, RightArgType_, OutputKernelType_>, Device_> > {
   typedef Indices_ Indices;
   typedef LeftArgType_ LeftArgType;
   typedef RightArgType_ RightArgType;
+  typedef OutputKernelType_ OutputKernelType;
   typedef Device_ Device;
 
   // From NumDims below.
@@ -137,8 +138,52 @@ struct traits<TensorEvaluator<const TensorContractionOp<Indices_, LeftArgType_,
 
 }  // end namespace internal
 
-template<typename Indices, typename LhsXprType, typename RhsXprType>
+// Tensor contraction params that should enable to get from output matrix
-class TensorContractionOp : public TensorBase<TensorContractionOp<Indices, LhsXprType, RhsXprType>, ReadOnlyAccessors>
+// 2-dimensional coordinates to the output tensor dimensions.
+struct TensorContractionParams {
+  // TensorContraction evaluator assumes that both tensors are in ColMajor
+  // layout, if tensors are in RowMajor evaluator swap lhs with rhs.
+  bool swapped_arguments;
+};
+
+// Output kernel allows to fuse operations into the tensor contraction.
+//
+// Examples:
+//   1. Elementwise Relu transformation following Conv2D.
+//   2. AddBias to the Conv2D output channels dimension.
+//
+// See expected implementation in NoOpOutputKernel.
+struct OutputKernel {
+  template <typename Index, typename Scalar>
+  using OutputMapper = internal::blas_data_mapper<Scalar, Index, ColMajor>;
+};
+
+// Output kernel that does absolutely nothing.
+struct NoOpOutputKernel {
+  /**
+   * Tensor contraction evaluator calls this kernel after finishing each block
+   * of output matrix. Output blocks belong to the 2-dimensional output tensor.
+   *
+   * TensorContractionParams contains contraction dimensions information
+   * required to map output 2-d space into the expected output tensor space
+   * (potentially higher dimensional).
+   *
+   * \param[in] output_mapper Access to output tensor memory
+   * \param[in] params   Tensor contraction parameters
+   * \param[in] i        Index of a first row available through output_mapper
+   * \param[in] j        Index of a first column available through output_mapper
+   * \param[in] num_rows Number of available rows
+   * \param[in] num_cols Number of available columns
+   */
+  template <typename Index, typename Scalar>
+  EIGEN_ALWAYS_INLINE void operator()(
+      const OutputKernel::OutputMapper<Index, Scalar>& output_mapper,
+      const TensorContractionParams& params, Index i, Index j, Index num_rows,
+      Index num_cols) const {}
+};
+
+template<typename Indices, typename LhsXprType, typename RhsXprType, typename OutputKernelType>
+class TensorContractionOp : public TensorBase<TensorContractionOp<Indices, LhsXprType, RhsXprType, OutputKernelType>, ReadOnlyAccessors>
 {
   public:
   typedef typename Eigen::internal::traits<TensorContractionOp>::Scalar Scalar;
@@ -149,8 +194,10 @@ class TensorContractionOp : public TensorBase<TensorContractionOp<Indices, LhsXp
   typedef typename Eigen::internal::traits<TensorContractionOp>::Index Index;
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionOp(
-      const LhsXprType& lhs, const RhsXprType& rhs, const Indices& dims)
+      const LhsXprType& lhs, const RhsXprType& rhs, const Indices& dims,
-      : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_indices(dims) {}
+      const OutputKernelType& output_kernel = OutputKernelType())
+      : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_indices(dims),
+        m_output_kernel(output_kernel) {}
 
   EIGEN_DEVICE_FUNC
   const Indices& indices() const { return m_indices; }
@@ -164,10 +211,14 @@ class TensorContractionOp : public TensorBase<TensorContractionOp<Indices, LhsXp
   const typename internal::remove_all<typename RhsXprType::Nested>::type&
   rhsExpression() const { return m_rhs_xpr; }
 
+  EIGEN_DEVICE_FUNC
+  const OutputKernelType& outputKernel() const { return m_output_kernel; }
+
   protected:
     typename LhsXprType::Nested m_lhs_xpr;
     typename RhsXprType::Nested m_rhs_xpr;
     const Indices m_indices;
+    const OutputKernelType m_output_kernel;
 };
 
 
@@ -177,9 +228,10 @@ struct TensorContractionEvaluatorBase
   typedef typename internal::traits<Derived>::Indices Indices;
   typedef typename internal::traits<Derived>::LeftArgType LeftArgType;
   typedef typename internal::traits<Derived>::RightArgType RightArgType;
+  typedef typename internal::traits<Derived>::OutputKernelType OutputKernelType;
   typedef typename internal::traits<Derived>::Device Device;
 
-  typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType;
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
   typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
   typedef typename XprType::Index Index;
   typedef typename XprType::CoeffReturnType CoeffReturnType;
@@ -221,6 +273,7 @@ struct TensorContractionEvaluatorBase
                           op.lhsExpression(), op.rhsExpression()), device),
     m_rightImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(),
                           op.rhsExpression(), op.lhsExpression()), device),
+        m_output_kernel(op.outputKernel()),
         m_device(device),
         m_result(NULL) {
     EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
@@ -391,6 +444,13 @@ struct TensorContractionEvaluatorBase
         numext::swap(m_dimensions[i], m_dimensions[j]);
       }
     }
+
+    // A set of parameters that will allow output kernel to get from output
+    // tensor dimensions (i, j) into the original tensor dimensions.
+    // TODO(ezhulenev): Add parameters required to infer output tensor index for
+    // more complex contractions than 2x2 on internal dimension.
+    m_tensor_contraction_params = {
+        /**swapped_arguments=*/static_cast<int>(Layout) == RowMajor};
   }
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
@@ -585,7 +645,15 @@ struct TensorContractionEvaluatorBase
 
           // call gebp (matrix kernel)
           // The parameters here are copied from Eigen's GEMM implementation
-          gebp(output.getSubMapper(i2, j2), blockA, blockB, actual_mc, actual_kc, actual_nc, Scalar(1), -1, -1, 0, 0);
+          const auto output_mapper = output.getSubMapper(i2, j2);
+          gebp(output_mapper, blockA, blockB, actual_mc, actual_kc, actual_nc,
+               Scalar(1), -1, -1, 0, 0);
+
+          // We are done with this [i2, j2] output block.
+          if (k2 + kc >= k) {
+            m_output_kernel(output_mapper, m_tensor_contraction_params, i2, j2,
+                            actual_mc, actual_nc);
+          }
         }
       }
     }
@@ -848,23 +916,26 @@ protected:
   Index m_j_size;
   Index m_k_size;
 
+  TensorContractionParams m_tensor_contraction_params;
+
   TensorEvaluator<EvalLeftArgType, Device> m_leftImpl;
   TensorEvaluator<EvalRightArgType, Device> m_rightImpl;
   const Device& m_device;
+  OutputKernelType m_output_kernel;
   Scalar* m_result;
   bool m_can_use_xsmm;
 };
 
 
 // evaluator for default device
-template<typename Indices, typename LeftArgType, typename RightArgType, typename Device>
+template<typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType, typename Device>
-struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> :
+struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> :
     public TensorContractionEvaluatorBase<
-      TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> > {
+      TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> > {
-  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> Self;
+  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
   typedef TensorContractionEvaluatorBase<Self> Base;
 
-  typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType;
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
   typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
   typedef typename XprType::Index Index;
   typedef typename XprType::CoeffReturnType CoeffReturnType;

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

@@ -56,16 +56,16 @@ struct packRhsAndKernelArg {
 }  // end namespace internal
 #endif  // EIGEN_USE_SIMPLE_THREAD_POOL
 
-template<typename Indices, typename LeftArgType, typename RightArgType>
+template<typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
-struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, ThreadPoolDevice> :
+struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, ThreadPoolDevice> :
-    public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, ThreadPoolDevice> > {
+    public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, ThreadPoolDevice> > {
 
   typedef ThreadPoolDevice Device;
 
-  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> Self;
+  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
   typedef TensorContractionEvaluatorBase<Self> Base;
 
-  typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType;
+  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
   typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
   typedef typename XprType::Index Index;
   typedef typename XprType::CoeffReturnType CoeffReturnType;
@@ -308,7 +308,7 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
                   this->m_k_strides);
 
     Context<LhsPacker, RhsPacker, GebpKernel, LhsMapper, RhsMapper,
-            OutputMapper>(this->m_device, num_threads, lhs, rhs, buffer, m, n,
+            OutputMapper>(this, num_threads, lhs, rhs, buffer, m, n,
                           k, bm, bn, bk, nm, nn, nk, gm, gn, nm0, nn0,
                           shard_by_col, parallel_pack)
         .run();
@@ -319,16 +319,18 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
             typename LhsMapper, typename RhsMapper, typename OutputMapper>
   class Context {
    public:
-    Context(const Device& device, int num_threads, LhsMapper& lhs,
+    Context(const Self* self, int num_threads, LhsMapper& lhs,
             RhsMapper& rhs, Scalar* buffer, Index tm, Index tn, Index tk, Index bm,
             Index bn, Index bk, Index nm, Index nn, Index nk, Index gm,
             Index gn, Index nm0, Index nn0, bool shard_by_col,
             bool parallel_pack)
-        : device_(device),
+        : device_(self->m_device),
           lhs_(lhs),
           rhs_(rhs),
           buffer_(buffer),
           output_(buffer, tm),
+          output_kernel_(self->m_output_kernel),
+          tensor_contraction_params_(self->m_tensor_contraction_params),
           num_threads_(num_threads),
           shard_by_col_(shard_by_col),
           parallel_pack_(parallel_pack),
@@ -420,6 +422,8 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
     RhsMapper& rhs_;
     Scalar* const buffer_;
     OutputMapper output_;
+    OutputKernelType output_kernel_;
+    TensorContractionParams tensor_contraction_params_;
     const int num_threads_;
     const bool shard_by_col_;
     const bool parallel_pack_;
@@ -536,19 +540,32 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
       const Index mend = m * gm_ + gm(m);
       if (shard_by_col_) {
         for (Index n1 = n * gn_; n1 < nend; n1++) {
-          for (Index m1 = m * gm_; m1 < mend; m1++)
+          for (Index m1 = m * gm_; m1 < mend; m1++) {
-            GebpKernel()(output_.getSubMapper(m1 * bm_, n1 * bn_),
+            const auto output_mapper = output_.getSubMapper(m1 * bm_, n1 * bn_);
-                         packed_lhs_[k % (P - 1)][m1],
+            GebpKernel()(output_mapper, packed_lhs_[k % (P - 1)][m1],
                          packed_rhs_[k % (P - 1)][n1], bm(m1), bk(k), bn(n1),
                          Scalar(1), -1, -1, 0, 0);
+
+            // We are done with the last task for the [m1, n1] block.
+            if (k + 1 == nk_) {
+              output_kernel_(output_mapper, tensor_contraction_params_,
+                             m1 * bm_, n1 * bn_, bm(m1), bn(n1));
+            }
+          }
         }
       } else {
         for (Index m1 = m * gm_; m1 < mend; m1++)
           for (Index n1 = n * gn_; n1 < nend; n1++) {
-            GebpKernel()(output_.getSubMapper(m1 * bm_, n1 * bn_),
+            const auto output_mapper = output_.getSubMapper(m1 * bm_, n1 * bn_);
-                         packed_lhs_[k % (P - 1)][m1],
+            GebpKernel()(output_mapper, packed_lhs_[k % (P - 1)][m1],
                          packed_rhs_[k % (P - 1)][n1], bm(m1), bk(k), bn(n1),
                          Scalar(1), -1, -1, 0, 0);
+
+            // We are done with the last task for the [m1, n1] block.
+            if (k + 1 == nk_) {
+              output_kernel_(output_mapper, tensor_contraction_params_,
+                             m1 * bm_, n1 * bn_, bm(m1), bn(n1));
+            }
           }
       }
       signal_kernel(m, n, k + 1, false);
@@ -747,6 +764,10 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
   }
 
 #else  // EIGEN_USE_SIMPLE_THREAD_POOL
+  // TODO(ezhulenev): SimpleThreadPool will be removed in the future, and seems
+  // like it's not worth adding output kernel support here.
+  static_assert(std::is_same<OutputKernelType, const NoOpOutputKernel>::value,
+                "SimpleThreadPool does not support contraction output kernels.");
 
   template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
   void evalProduct(Scalar* buffer) const {
@@ -1065,6 +1086,10 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
   }
 
 #if defined(EIGEN_VECTORIZE_AVX) && defined(EIGEN_USE_LIBXSMM)
+  // TODO(ezhulenev): Add support for output kernels and LIBXSMM.
+  static_assert(std::is_same<OutputKernelType, const NoOpOutputKernel>::value,
+                "XSMM does not support contraction output kernels.");
+
   template<int Alignment>
   class ContextXsmm {
    public:

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

@@ -65,7 +65,7 @@ template<typename Op, typename Dims, typename XprType, template <class> class Ma
 template<typename XprType> class TensorIndexTupleOp;
 template<typename ReduceOp, typename Dims, typename XprType> class TensorTupleReducerOp;
 template<typename Axis, typename LeftXprType, typename RightXprType> class TensorConcatenationOp;
-template<typename Dimensions, typename LeftXprType, typename RightXprType> class TensorContractionOp;
+template<typename Dimensions, typename LeftXprType, typename RightXprType, typename OutputKernelType> class TensorContractionOp;
 template<typename TargetType, typename XprType> class TensorConversionOp;
 template<typename Dimensions, typename InputXprType, typename KernelXprType> class TensorConvolutionOp;
 template<typename FFT, typename XprType, int FFTDataType, int FFTDirection> class TensorFFTOp;
@@ -97,6 +97,8 @@ template<typename XprType> class TensorForcedEvalOp;
 template<typename ExpressionType, typename DeviceType> class TensorDevice;
 template<typename Derived, typename Device> struct TensorEvaluator;
 
+class NoOpOutputKernel;
+
 struct DefaultDevice;
 struct ThreadPoolDevice;
 struct GpuDevice;

unsupported/test/cxx11_tensor_contraction.cpp

@@ -510,6 +510,55 @@ static void test_const_inputs()
   VERIFY_IS_APPROX(mat3(1,1), mat1(1,0)*mat2(0,1) + mat1(1,1)*mat2(1,1) + mat1(1,2)*mat2(2,1));
 }
 
+// Apply Sqrt to all output elements.
+struct SqrtOutputKernel {
+  template <typename Index, typename Scalar>
+  EIGEN_ALWAYS_INLINE void operator()(
+      const OutputKernel::OutputMapper<Index, Scalar>& output_mapper,
+      const TensorContractionParams&, Index, Index, Index num_rows,
+      Index num_cols) const {
+    for (int i = 0; i < num_rows; ++i) {
+      for (int j = 0; j < num_cols; ++j) {
+        output_mapper(i, j) = std::sqrt(output_mapper(i, j));
+      }
+    }
+  }
+};
+
+template <int DataLayout>
+static void test_large_contraction_with_output_kernel() {
+  Tensor<float, 4, DataLayout> t_left(30, 50, 8, 31);
+  Tensor<float, 5, DataLayout> t_right(8, 31, 7, 20, 10);
+  Tensor<float, 5, DataLayout> t_result(30, 50, 7, 20, 10);
+
+  t_left.setRandom();
+  t_right.setRandom();
+  // Put trash in mat4 to verify contraction clears output memory.
+  t_result.setRandom();
+
+  // Add a little offset so that the results won't be close to zero.
+  t_left += t_left.constant(1.0f);
+  t_right += t_right.constant(1.0f);
+
+  typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 1500, 248);
+  MapXf m_right(t_right.data(), 248, 1400);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(1500, 1400);
+
+  // this contraction should be equivalent to a single matrix multiplication
+  Eigen::array<DimPair, 2> dims({{DimPair(2, 0), DimPair(3, 1)}});
+
+  // compute results by separate methods
+  t_result = t_left.contract(t_right, dims, SqrtOutputKernel());
+
+  m_result = m_left * m_right;
+
+  for (size_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY(&t_result.data()[i] != &m_result.data()[i]);
+    VERIFY_IS_APPROX(t_result.data()[i], std::sqrt(m_result.data()[i]));
+  }
+}
+
 void test_cxx11_tensor_contraction()
 {
   CALL_SUBTEST(test_evals<ColMajor>());
@@ -542,4 +591,6 @@ void test_cxx11_tensor_contraction()
   CALL_SUBTEST(test_tensor_product<RowMajor>());
   CALL_SUBTEST(test_const_inputs<ColMajor>());
   CALL_SUBTEST(test_const_inputs<RowMajor>());
+  CALL_SUBTEST(test_large_contraction_with_output_kernel<ColMajor>());
+  CALL_SUBTEST(test_large_contraction_with_output_kernel<RowMajor>());
 }

unsupported/test/cxx11_tensor_thread_pool.cpp

@@ -232,6 +232,60 @@ void test_multithread_contraction_agrees_with_singlethread() {
   }
 }
 
+// Apply Sqrt to all output elements.
+struct SqrtOutputKernel {
+  template <typename Index, typename Scalar>
+  EIGEN_ALWAYS_INLINE void operator()(
+      const OutputKernel::OutputMapper<Index, Scalar>& output_mapper,
+      const TensorContractionParams&, Index, Index, Index num_rows,
+      Index num_cols) const {
+    for (int i = 0; i < num_rows; ++i) {
+      for (int j = 0; j < num_cols; ++j) {
+        output_mapper(i, j) = std::sqrt(output_mapper(i, j));
+      }
+    }
+  }
+};
+
+template <int DataLayout>
+static void test_multithread_contraction_with_output_kernel() {
+  typedef Tensor<float, 1>::DimensionPair DimPair;
+
+  const int num_threads = internal::random<int>(2, 11);
+  ThreadPool threads(num_threads);
+  Eigen::ThreadPoolDevice device(&threads, num_threads);
+
+  Tensor<float, 4, DataLayout> t_left(30, 50, 8, 31);
+  Tensor<float, 5, DataLayout> t_right(8, 31, 7, 20, 10);
+  Tensor<float, 5, DataLayout> t_result(30, 50, 7, 20, 10);
+
+  t_left.setRandom();
+  t_right.setRandom();
+  // Put trash in mat4 to verify contraction clears output memory.
+  t_result.setRandom();
+
+  // Add a little offset so that the results won't be close to zero.
+  t_left += t_left.constant(1.0f);
+  t_right += t_right.constant(1.0f);
+
+  typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
+  MapXf m_left(t_left.data(), 1500, 248);
+  MapXf m_right(t_right.data(), 248, 1400);
+  Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(1500, 1400);
+
+  // this contraction should be equivalent to a single matrix multiplication
+  Eigen::array<DimPair, 2> dims({{DimPair(2, 0), DimPair(3, 1)}});
+
+  // compute results by separate methods
+  t_result.device(device) = t_left.contract(t_right, dims, SqrtOutputKernel());
+
+  m_result = m_left * m_right;
+
+  for (size_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
+    VERIFY(&t_result.data()[i] != &m_result.data()[i]);
+    VERIFY_IS_APPROX(t_result.data()[i], std::sqrt(m_result.data()[i]));
+  }
+}
 
 template<int DataLayout>
 void test_full_contraction() {
@@ -355,6 +409,8 @@ void test_cxx11_tensor_thread_pool()
 
   CALL_SUBTEST_3(test_multithread_contraction_agrees_with_singlethread<ColMajor>());
   CALL_SUBTEST_3(test_multithread_contraction_agrees_with_singlethread<RowMajor>());
+  CALL_SUBTEST_3(test_multithread_contraction_with_output_kernel<ColMajor>());
+  CALL_SUBTEST_3(test_multithread_contraction_with_output_kernel<RowMajor>());
 
   // Exercise various cases that have been problematic in the past.
   CALL_SUBTEST_4(test_contraction_corner_cases<ColMajor>());