Initial support of TensorBlock

1 files changed, 412 insertions, 0 deletions

diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h b/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
new file mode 100644
index 000000000..59535cd91
--- /dev/null
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
@@ -0,0 +1,412 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Andy Davis <andydavis@google.com>
+// Copyright (C) 2018 Eugene Zhulenev <ezhulenev@google.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/.
+
+#ifndef EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
+#define EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H
+
+namespace Eigen {
+namespace internal {
+
+/**
+ * \class TensorBlockShapeType
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor block shape type.
+ *
+ * Tensor block shape type defines what are the shape preference for the blocks
+ * extracted from the larger tensor.
+ *
+ * Example:
+ *
+ * We want to extract blocks of 100 elements from the large 100x100 tensor:
+ *  - tensor: 100x100
+ *  - target_block_size: 100
+ *
+ * TensorBlockShapeType:
+ *  - kUniformAllDims: 100 blocks of size 10x10
+ *  - kSkewedInnerDims: 100 blocks of size 100x1 (or 1x100 depending on a column
+ *                      or row major layout)
+ */
+enum class TensorBlockShapeType {
+  kUniformAllDims,
+  kSkewedInnerDims,
+};
+
+/**
+ * \class TensorBlock
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor block class.
+ *
+ * This class represents a tensor block specified by the index of the
+ * first block coefficient, and the size of the block in each dimension.
+ */
+template <typename Scalar, typename Index, std::size_t NumDims, int Layout>
+class TensorBlock {
+ public:
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  TensorBlock(const Index first_coeff_index, const Dimensions& block_sizes,
+              const Dimensions& block_strides, const Dimensions& tensor_strides,
+              Scalar* data)
+      : m_first_coeff_index(first_coeff_index),
+        m_block_sizes(block_sizes),
+        m_block_strides(block_strides),
+        m_tensor_strides(tensor_strides),
+        m_data(data) {}
+
+  Index first_coeff_index() const { return m_first_coeff_index; }
+
+  const Dimensions& block_sizes() const { return m_block_sizes; }
+
+  const Dimensions& block_strides() const { return m_block_strides; }
+
+  const Dimensions& tensor_strides() const { return m_tensor_strides; }
+
+  Scalar* data() { return m_data; }
+
+  const Scalar* data() const { return m_data; }
+
+ private:
+  Index m_first_coeff_index;
+  Dimensions m_block_sizes;
+  Dimensions m_block_strides;
+  Dimensions m_tensor_strides;
+  Scalar* m_data;  // Not owned.
+};
+
+/**
+ * \class TensorBlockMapper
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor block mapper class.
+ *
+ * This class is responsible for iterating over the blocks of a tensor.
+ */
+template <typename Scalar, typename Index, std::size_t NumDims, int Layout>
+class TensorBlockMapper {
+ public:
+  typedef typename internal::TensorBlock<Scalar, Index, NumDims, Layout>
+      TensorBlock;
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  TensorBlockMapper(const Dimensions& dims,
+                    const TensorBlockShapeType block_shape,
+                    size_t min_target_size)
+      : m_dimensions(dims),
+        m_block_dim_sizes(BlockDimensions(dims, block_shape, min_target_size)) {
+    // Calculate block counts by dimension and total block count.
+    DSizes<Index, NumDims> block_count;
+    for (size_t i = 0; i < block_count.rank(); ++i) {
+      block_count[i] = divup(m_dimensions[i], m_block_dim_sizes[i]);
+    }
+    m_total_block_count = array_prod(block_count);
+
+    // Calculate block strides (used for enumerating blocks).
+    if (NumDims > 0) {
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        m_block_strides[0] = 1;
+        m_tensor_strides[0] = 1;
+        for (int i = 1; i < NumDims; ++i) {
+          m_block_strides[i] = m_block_strides[i - 1] * block_count[i - 1];
+          m_tensor_strides[i] = m_tensor_strides[i - 1] * m_dimensions[i - 1];
+        }
+      } else {
+        m_block_strides[NumDims - 1] = 1;
+        m_tensor_strides[NumDims - 1] = 1;
+        for (int i = NumDims - 2; i >= 0; --i) {
+          m_block_strides[i] = m_block_strides[i + 1] * block_count[i + 1];
+          m_tensor_strides[i] = m_tensor_strides[i + 1] * m_dimensions[i + 1];
+        }
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  GetBlockForIndex(Index block_index, Scalar* data) const {
+    Index first_coeff_index = 0;
+    DSizes<Index, NumDims> coords;
+    DSizes<Index, NumDims> sizes;
+    DSizes<Index, NumDims> strides;
+    if (NumDims > 0) {
+      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+        for (int i = NumDims - 1; i > 0; --i) {
+          const Index idx = block_index / m_block_strides[i];
+          coords[i] = idx * m_block_dim_sizes[i];
+          sizes[i] =
+              numext::mini((m_dimensions[i] - coords[i]), m_block_dim_sizes[i]);
+          block_index -= idx * m_block_strides[i];
+          first_coeff_index += coords[i] * m_tensor_strides[i];
+        }
+        coords[0] = block_index * m_block_dim_sizes[0];
+        sizes[0] =
+            numext::mini((m_dimensions[0] - coords[0]), m_block_dim_sizes[0]);
+        first_coeff_index += coords[0] * m_tensor_strides[0];
+
+        strides[0] = 1;
+        for (int i = 1; i < NumDims; ++i) {
+          strides[i] = strides[i - 1] * sizes[i - 1];
+        }
+      } else {
+        for (int i = 0; i < NumDims - 1; ++i) {
+          const Index idx = block_index / m_block_strides[i];
+          coords[i] = idx * m_block_dim_sizes[i];
+          sizes[i] =
+              numext::mini((m_dimensions[i] - coords[i]), m_block_dim_sizes[i]);
+          block_index -= idx * m_block_strides[i];
+          first_coeff_index += coords[i] * m_tensor_strides[i];
+        }
+        coords[NumDims - 1] = block_index * m_block_dim_sizes[NumDims - 1];
+        sizes[NumDims - 1] =
+            numext::mini((m_dimensions[NumDims - 1] - coords[NumDims - 1]),
+                         m_block_dim_sizes[NumDims - 1]);
+        first_coeff_index +=
+            coords[NumDims - 1] * m_tensor_strides[NumDims - 1];
+
+        strides[NumDims - 1] = 1;
+        for (int i = NumDims - 2; i >= 0; --i) {
+          strides[i] = strides[i + 1] * sizes[i + 1];
+        }
+      }
+    }
+
+    return TensorBlock(first_coeff_index, sizes, strides, m_tensor_strides,
+                       data);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index total_block_count() const {
+    return m_total_block_count;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index block_dims_total_size() const {
+    return m_block_dim_sizes.TotalSize();
+  }
+
+ private:
+  static int InnerDimIndex(Index i) {
+    return Layout == static_cast<int>(ColMajor) ? i : NumDims - i - 1;
+  }
+
+  static Dimensions BlockDimensions(const Dimensions& tensor_dims,
+                                    const TensorBlockShapeType block_shape,
+                                    size_t min_target_size) {
+    min_target_size = numext::maxi<size_t>(1, min_target_size);
+
+    // If tensor fully fits into the target size, we'll treat it a single block.
+    Dimensions block_dim_sizes = tensor_dims;
+
+    if (tensor_dims.TotalSize() == 0) {
+      // Corner case: one of the dimensions is zero. Logic below is too complex
+      // to handle this case on a general basis, just use unit block size.
+      // Note: we must not yield blocks with zero dimensions (recipe for
+      // overflows/underflows, divisions by zero and NaNs later).
+      for (int i = 0; i < NumDims; ++i) {
+        block_dim_sizes[i] = 1;
+      }
+    } else if (block_dim_sizes.TotalSize() > min_target_size) {
+      if (block_shape == TensorBlockShapeType::kUniformAllDims) {
+        // Tensor will not fit within 'min_target_size' budget: calculate tensor
+        // block dimension sizes based on "square" dimension size target.
+        const size_t dim_size_target = static_cast<const size_t>(
+            std::pow(static_cast<float>(min_target_size),
+                     1.0 / static_cast<float>(block_dim_sizes.rank())));
+        for (size_t i = 0; i < block_dim_sizes.rank(); ++i) {
+          // TODO(andydavis) Adjust the inner most 'block_dim_size' to make it
+          // a multiple of the packet size. Note that reducing
+          // 'block_dim_size' in this manner can increase the number of
+          // blocks, and so will amplify any per-block overhead.
+          block_dim_sizes[i] = numext::mini(
+              dim_size_target, static_cast<size_t>(tensor_dims[i]));
+        }
+        // Add any un-allocated coefficients to inner dimension(s).
+        Index total_size = block_dim_sizes.TotalSize();
+        for (int i = 0; i < NumDims; ++i) {
+          const int dim = InnerDimIndex(i);
+          if (block_dim_sizes[dim] < tensor_dims[dim]) {
+            const Index total_size_other_dims =
+                total_size / block_dim_sizes[dim];
+            const Index alloc_avail =
+                divup<Index>(min_target_size, total_size_other_dims);
+            if (alloc_avail == block_dim_sizes[dim]) {
+              // Insufficient excess coefficients to allocate.
+              break;
+            }
+            block_dim_sizes[dim] = numext::mini(tensor_dims[dim], alloc_avail);
+            total_size = total_size_other_dims * block_dim_sizes[dim];
+          }
+        }
+      } else if (block_shape == TensorBlockShapeType::kSkewedInnerDims) {
+        Index coeff_to_allocate = min_target_size;
+        for (int i = 0; i < NumDims; ++i) {
+          const int dim = InnerDimIndex(i);
+          block_dim_sizes[dim] =
+              numext::mini(coeff_to_allocate, tensor_dims[dim]);
+          coeff_to_allocate =
+              divup(coeff_to_allocate,
+                    numext::maxi(static_cast<Index>(1), block_dim_sizes[dim]));
+        }
+        eigen_assert(coeff_to_allocate == 1);
+      } else {
+        eigen_assert(false);  // someone added new block shape type
+      }
+    }
+
+    eigen_assert(
+        block_dim_sizes.TotalSize() >=
+        numext::mini<size_t>(min_target_size, tensor_dims.TotalSize()));
+
+    return block_dim_sizes;
+  }
+
+  Dimensions m_dimensions;
+  Dimensions m_block_dim_sizes;
+  Dimensions m_block_strides;
+  Dimensions m_tensor_strides;
+  Index m_total_block_count;
+};
+
+/**
+ * \class TensorSliceBlockMapper
+ * \ingroup CXX11_Tensor_Module
+ *
+ * \brief Tensor slice block mapper class.
+ *
+ * This class is responsible for iterating over the blocks of
+ * a slice of a tensor. Supports shuffling of the block strides
+ * for callers that want to reduce strides for dimensions to be
+ * processed together.
+ *
+ */
+template <typename Scalar, typename Index, std::size_t NumDims, int Layout>
+class TensorSliceBlockMapper {
+ public:
+  typedef typename internal::TensorBlock<Scalar, Index, NumDims, Layout>
+      TensorBlock;
+  typedef DSizes<Index, NumDims> Dimensions;
+
+  TensorSliceBlockMapper(const Dimensions& tensor_dims,
+                         const Dimensions& tensor_slice_offsets,
+                         const Dimensions& tensor_slice_extents,
+                         const Dimensions& block_dim_sizes,
+                         const Dimensions& block_stride_order)
+      : m_tensor_dimensions(tensor_dims),
+        m_tensor_slice_offsets(tensor_slice_offsets),
+        m_tensor_slice_extents(tensor_slice_extents),
+        m_block_dim_sizes(block_dim_sizes),
+        m_block_stride_order(block_stride_order),
+        m_total_block_count(1) {
+    // Calculate block counts by dimension and total block count.
+    DSizes<Index, NumDims> block_count;
+    for (size_t i = 0; i < block_count.rank(); ++i) {
+      block_count[i] = divup(m_tensor_slice_extents[i], m_block_dim_sizes[i]);
+    }
+    m_total_block_count = array_prod(block_count);
+
+    // Calculate block strides (used for enumerating blocks).
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      m_block_strides[0] = 1;
+      m_tensor_strides[0] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        m_block_strides[i] = m_block_strides[i - 1] * block_count[i - 1];
+        m_tensor_strides[i] =
+            m_tensor_strides[i - 1] * m_tensor_dimensions[i - 1];
+      }
+    } else {
+      m_block_strides[NumDims - 1] = 1;
+      m_tensor_strides[NumDims - 1] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        m_block_strides[i] = m_block_strides[i + 1] * block_count[i + 1];
+        m_tensor_strides[i] =
+            m_tensor_strides[i + 1] * m_tensor_dimensions[i + 1];
+      }
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
+  GetBlockForIndex(Index block_index, Scalar* data) const {
+    Index first_coeff_index = 0;
+    DSizes<Index, NumDims> coords;
+    DSizes<Index, NumDims> sizes;
+    DSizes<Index, NumDims> strides;
+    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
+      for (int i = NumDims - 1; i > 0; --i) {
+        const Index idx = block_index / m_block_strides[i];
+        coords[i] = m_tensor_slice_offsets[i] + idx * m_block_dim_sizes[i];
+        sizes[i] = numext::mini(
+            m_tensor_slice_offsets[i] + m_tensor_slice_extents[i] - coords[i],
+            m_block_dim_sizes[i]);
+        block_index -= idx * m_block_strides[i];
+        first_coeff_index += coords[i] * m_tensor_strides[i];
+      }
+      coords[0] =
+          m_tensor_slice_offsets[0] + block_index * m_block_dim_sizes[0];
+      sizes[0] = numext::mini(
+          m_tensor_slice_offsets[0] + m_tensor_slice_extents[0] - coords[0],
+          m_block_dim_sizes[0]);
+      first_coeff_index += coords[0] * m_tensor_strides[0];
+
+      Index prev_dim = m_block_stride_order[0];
+      strides[prev_dim] = 1;
+      for (int i = 1; i < NumDims; ++i) {
+        const Index curr_dim = m_block_stride_order[i];
+        strides[curr_dim] = strides[prev_dim] * sizes[prev_dim];
+        prev_dim = curr_dim;
+      }
+    } else {
+      for (int i = 0; i < static_cast<int>(NumDims) - 1; ++i) {
+        const Index idx = block_index / m_block_strides[i];
+        coords[i] = m_tensor_slice_offsets[i] + idx * m_block_dim_sizes[i];
+        sizes[i] = numext::mini(
+            m_tensor_slice_offsets[i] + m_tensor_slice_extents[i] - coords[i],
+            m_block_dim_sizes[i]);
+        block_index -= idx * m_block_strides[i];
+        first_coeff_index += coords[i] * m_tensor_strides[i];
+      }
+      coords[NumDims - 1] = m_tensor_slice_offsets[NumDims - 1] +
+                            block_index * m_block_dim_sizes[NumDims - 1];
+      sizes[NumDims - 1] = numext::mini(
+          m_tensor_slice_offsets[NumDims - 1] +
+              m_tensor_slice_extents[NumDims - 1] - coords[NumDims - 1],
+          m_block_dim_sizes[NumDims - 1]);
+      first_coeff_index += coords[NumDims - 1] * m_tensor_strides[NumDims - 1];
+
+      Index prev_dim = m_block_stride_order[NumDims - 1];
+      strides[prev_dim] = 1;
+      for (int i = NumDims - 2; i >= 0; --i) {
+        const Index curr_dim = m_block_stride_order[i];
+        strides[curr_dim] = strides[prev_dim] * sizes[prev_dim];
+        prev_dim = curr_dim;
+      }
+    }
+
+    return TensorBlock(first_coeff_index, sizes, strides, m_tensor_strides,
+                       data);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index total_block_count() const {
+    return m_total_block_count;
+  }
+
+ private:
+  Dimensions m_tensor_dimensions;
+  Dimensions m_tensor_slice_offsets;
+  Dimensions m_tensor_slice_extents;
+  Dimensions m_tensor_strides;
+  Dimensions m_block_dim_sizes;
+  Dimensions m_block_stride_order;
+  Dimensions m_block_strides;
+  Index m_total_block_count;
+};
+
+}  // namespace internal
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H