Added support for tensor reductions and concatenations

3 files changed, 260 insertions, 1 deletions

diff --git a/unsupported/test/CMakeLists.txt b/unsupported/test/CMakeLists.txt
index 8d4e7db66..e83d8b54e 100644
--- a/unsupported/test/CMakeLists.txt
+++ b/unsupported/test/CMakeLists.txt
@@ -106,14 +106,16 @@ if(EIGEN_TEST_CXX11)
   ei_add_test(cxx11_tensor_convolution "-std=c++0x")
   ei_add_test(cxx11_tensor_expr "-std=c++0x")
 #  ei_add_test(cxx11_tensor_fixed_size "-std=c++0x")
-  ei_add_test(cxx11_tensor_of_const_values "-std=c++0x")
+#  ei_add_test(cxx11_tensor_of_const_values "-std=c++0x")
   ei_add_test(cxx11_tensor_of_strings "-std=c++0x")
   ei_add_test(cxx11_tensor_intdiv "-std=c++0x")
   ei_add_test(cxx11_tensor_lvalue "-std=c++0x")
   ei_add_test(cxx11_tensor_map "-std=c++0x")
   ei_add_test(cxx11_tensor_broadcasting "-std=c++0x")
+  ei_add_test(cxx11_tensor_concatenation "-std=c++0x")
   ei_add_test(cxx11_tensor_morphing "-std=c++0x")
   ei_add_test(cxx11_tensor_padding "-std=c++0x")
+  ei_add_test(cxx11_tensor_reduction "-std=c++0x")
 #  ei_add_test(cxx11_tensor_shuffling "-std=c++0x")
   ei_add_test(cxx11_tensor_striding "-std=c++0x")
 #  ei_add_test(cxx11_tensor_device  "-std=c++0x")
diff --git a/unsupported/test/cxx11_tensor_concatenation.cpp b/unsupported/test/cxx11_tensor_concatenation.cpp
new file mode 100644
index 000000000..8fd4f5f80
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_concatenation.cpp
@@ -0,0 +1,110 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+static void test_dimension_failures()
+{
+  Tensor<int, 3> left(2, 3, 1);
+  Tensor<int, 3> right(3, 3, 1);
+  left.setRandom();
+  right.setRandom();
+
+  // Okay; other dimensions are equal.
+  Tensor<int, 3> concatenation = left.concatenate(right, 0);
+
+  // Dimension mismatches.
+  VERIFY_RAISES_ASSERT(concatenation = left.concatenate(right, 1));
+  VERIFY_RAISES_ASSERT(concatenation = left.concatenate(right, 2));
+
+  // Axis > NumDims or < 0.
+  VERIFY_RAISES_ASSERT(concatenation = left.concatenate(right, 3));
+  VERIFY_RAISES_ASSERT(concatenation = left.concatenate(right, -1));
+}
+
+static void test_static_dimension_failure()
+{
+  Tensor<int, 2> left(2, 3);
+  Tensor<int, 3> right(2, 3, 1);
+
+#ifdef CXX11_TENSOR_CONCATENATION_STATIC_DIMENSION_FAILURE
+  // Technically compatible, but we static assert that the inputs have same
+  // NumDims.
+  Tensor<int, 3> concatenation = left.concatenate(right, 0);
+#endif
+
+  // This can be worked around in this case.
+  Tensor<int, 3> concatenation = left
+      .reshape(Tensor<int, 3>::Dimensions{{2, 3, 1}})
+      .concatenate(right, 0);
+  Tensor<int, 2> alternative = left
+      .concatenate(right.reshape(Tensor<int, 2>::Dimensions{{2, 3}}), 0);
+}
+
+static void test_simple_concatenation()
+{
+  Tensor<int, 3> left(2, 3, 1);
+  Tensor<int, 3> right(2, 3, 1);
+  left.setRandom();
+  right.setRandom();
+
+  Tensor<int, 3> concatenation = left.concatenate(right, 0);
+  VERIFY_IS_EQUAL(concatenation.dimension(0), 4);
+  VERIFY_IS_EQUAL(concatenation.dimension(1), 3);
+  VERIFY_IS_EQUAL(concatenation.dimension(2), 1);
+  for (int j = 0; j < 3; ++j) {
+    for (int i = 0; i < 2; ++i) {
+      VERIFY_IS_EQUAL(concatenation(i, j, 0), left(i, j, 0));
+    }
+    for (int i = 2; i < 4; ++i) {
+      VERIFY_IS_EQUAL(concatenation(i, j, 0), right(i - 2, j, 0));
+    }
+  }
+
+  concatenation = left.concatenate(right, 1);
+  VERIFY_IS_EQUAL(concatenation.dimension(0), 2);
+  VERIFY_IS_EQUAL(concatenation.dimension(1), 6);
+  VERIFY_IS_EQUAL(concatenation.dimension(2), 1);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(concatenation(i, j, 0), left(i, j, 0));
+    }
+    for (int j = 3; j < 6; ++j) {
+      VERIFY_IS_EQUAL(concatenation(i, j, 0), right(i, j - 3, 0));
+    }
+  }
+
+  concatenation = left.concatenate(right, 2);
+  VERIFY_IS_EQUAL(concatenation.dimension(0), 2);
+  VERIFY_IS_EQUAL(concatenation.dimension(1), 3);
+  VERIFY_IS_EQUAL(concatenation.dimension(2), 2);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      VERIFY_IS_EQUAL(concatenation(i, j, 0), left(i, j, 0));
+      VERIFY_IS_EQUAL(concatenation(i, j, 1), right(i, j, 0));
+    }
+  }
+}
+
+
+// TODO(phli): Add test once we have a real vectorized implementation.
+// static void test_vectorized_concatenation() {}
+
+
+void test_cxx11_tensor_concatenation()
+{
+   CALL_SUBTEST(test_dimension_failures());
+   CALL_SUBTEST(test_static_dimension_failure());
+   CALL_SUBTEST(test_simple_concatenation());
+   // CALL_SUBTEST(test_vectorized_concatenation());
+}
diff --git a/unsupported/test/cxx11_tensor_reduction.cpp b/unsupported/test/cxx11_tensor_reduction.cpp
new file mode 100644
index 000000000..27135b982
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_reduction.cpp
@@ -0,0 +1,147 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include "main.h"
+#include <limits>
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+static void test_simple_reductions()
+{
+  Tensor<float, 4> tensor(2,3,5,7);
+  tensor.setRandom();
+  array<ptrdiff_t, 2> reduction_axis;
+  reduction_axis[0] = 1;
+  reduction_axis[1] = 3;
+
+  Tensor<float, 2> result = tensor.sum(reduction_axis);
+  VERIFY_IS_EQUAL(result.dimension(0), 2);
+  VERIFY_IS_EQUAL(result.dimension(1), 5);
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 5; ++j) {
+      float sum = 0.0f;
+      for (int k = 0; k < 3; ++k) {
+        for (int l = 0; l < 7; ++l) {
+          sum += tensor(i, k, j, l);
+        }
+      }
+      VERIFY_IS_APPROX(result(i, j), sum);
+    }
+  }
+
+  reduction_axis[0] = 0;
+  reduction_axis[1] = 2;
+  result = tensor.maximum(reduction_axis);
+  VERIFY_IS_EQUAL(result.dimension(0), 3);
+  VERIFY_IS_EQUAL(result.dimension(1), 7);
+  for (int i = 0; i < 3; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      float max_val = std::numeric_limits<float>::lowest();
+      for (int k = 0; k < 2; ++k) {
+        for (int l = 0; l < 5; ++l) {
+          max_val = (std::max)(max_val, tensor(k, i, l, j));
+        }
+      }
+      VERIFY_IS_APPROX(result(i, j), max_val);
+    }
+  }
+
+  reduction_axis[0] = 0;
+  reduction_axis[1] = 1;
+  result = tensor.minimum(reduction_axis);
+  VERIFY_IS_EQUAL(result.dimension(0), 5);
+  VERIFY_IS_EQUAL(result.dimension(1), 7);
+  for (int i = 0; i < 5; ++i) {
+    for (int j = 0; j < 7; ++j) {
+      float min_val = (std::numeric_limits<float>::max)();
+      for (int k = 0; k < 2; ++k) {
+        for (int l = 0; l < 3; ++l) {
+          min_val = (std::min)(min_val, tensor(k,  l, i, j));
+        }
+      }
+      VERIFY_IS_APPROX(result(i, j), min_val);
+    }
+  }
+}
+
+
+static void test_full_reductions()
+{
+  Tensor<float, 2> tensor(2,3);
+  tensor.setRandom();
+  array<ptrdiff_t, 2> reduction_axis;
+  reduction_axis[0] = 0;
+  reduction_axis[1] = 1;
+
+  Tensor<float, 1> result = tensor.sum(reduction_axis);
+  VERIFY_IS_EQUAL(result.dimension(0), 1);
+
+  float sum = 0.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      sum += tensor(i, j);
+    }
+  }
+  VERIFY_IS_APPROX(result(0), sum);
+
+  result = tensor.square().sum(reduction_axis).sqrt();
+  VERIFY_IS_EQUAL(result.dimension(0), 1);
+
+  sum = 0.0f;
+  for (int i = 0; i < 2; ++i) {
+    for (int j = 0; j < 3; ++j) {
+      sum += tensor(i, j) * tensor(i, j);
+    }
+  }
+  VERIFY_IS_APPROX(result(0), sqrtf(sum));
+}
+
+
+struct UserReducer {
+  UserReducer(float offset) : offset_(offset), sum_(0.0f) {}
+  void reduce(const float val) {
+    sum_ += val * val;
+  }
+  float finalize() const {
+    return 1.0f / (sum_ + offset_);
+  }
+
+ private:
+  float offset_;
+  float sum_;
+};
+
+static void test_user_defined_reductions()
+{
+  Tensor<float, 2> tensor(5,7);
+  tensor.setRandom();
+  array<ptrdiff_t, 1> reduction_axis;
+  reduction_axis[0] = 1;
+
+  UserReducer reducer(10.0f);
+  Tensor<float, 1> result = tensor.reduce(reduction_axis, reducer);
+  VERIFY_IS_EQUAL(result.dimension(0), 5);
+  for (int i = 0; i < 5; ++i) {
+    float expected = 10.0f;
+    for (int j = 0; j < 7; ++j) {
+      expected += tensor(i, j) * tensor(i, j);
+    }
+    expected = 1.0f / expected;
+    VERIFY_IS_APPROX(result(i), expected);
+  }
+}
+
+
+void test_cxx11_tensor_reduction()
+{
+   CALL_SUBTEST(test_simple_reductions());
+   CALL_SUBTEST(test_full_reductions());
+   CALL_SUBTEST(test_user_defined_reductions());
+}