Remove TensorBlock.h and old TensorBlock/BlockMapper

1 files changed, 0 insertions, 305 deletions

diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h b/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
deleted file mode 100644
index ba11bf7a8..000000000
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
+++ /dev/null
@@ -1,305 +0,0 @@
-// 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 {
-
-namespace {
-
-// Helper template to choose between ColMajor and RowMajor values.
-template <int Layout>
-struct cond;
-
-template <>
-struct cond<ColMajor> {
-  template <typename T>
-  EIGEN_STRONG_INLINE const T& operator()(const T& col,
-                                          const T& /*row*/) const {
-    return col;
-  }
-};
-
-template <>
-struct cond<RowMajor> {
-  template <typename T>
-  EIGEN_STRONG_INLINE const T& operator()(const T& /*col*/,
-                                          const T& row) const {
-    return row;
-  }
-};
-
-}  // namespace
-
-/**
- * \enum 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 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 StorageIndex, int NumDims, int Layout>
-class TensorBlock {
- public:
-  typedef DSizes<StorageIndex, NumDims> Dimensions;
-
-  TensorBlock(const StorageIndex 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) {}
-
-  StorageIndex 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:
-  StorageIndex 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 StorageIndex, int NumDims, int Layout>
-class TensorBlockMapper {
- public:
-  typedef TensorBlock<Scalar, StorageIndex, NumDims, Layout> Block;
-  typedef DSizes<StorageIndex, NumDims> Dimensions;
-
-  TensorBlockMapper() {}
-  TensorBlockMapper(const Dimensions& dims,
-                    const TensorBlockShapeType block_shape,
-                    Index min_target_size)
-      : m_dimensions(dims),
-        m_block_dim_sizes(BlockDimensions(dims, block_shape, convert_index<StorageIndex>(min_target_size))) {
-    // Calculate block counts by dimension and total block count.
-    DSizes<StorageIndex, NumDims> block_count;
-    for (Index 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 Block
-  GetBlockForIndex(StorageIndex block_index, Scalar* data) const {
-    StorageIndex first_coeff_index = 0;
-    DSizes<StorageIndex, NumDims> coords;
-    DSizes<StorageIndex, NumDims> sizes;
-    DSizes<StorageIndex, NumDims> strides;
-    if (NumDims > 0) {
-      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-        for (int i = NumDims - 1; i > 0; --i) {
-          const StorageIndex 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 StorageIndex 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 Block(first_coeff_index, sizes, strides, m_tensor_strides, data);
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex total_block_count() const {
-    return m_total_block_count;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex
-  block_dims_total_size() const {
-    return m_block_dim_sizes.TotalSize();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions&
-  block_dim_sizes() const {
-    return m_block_dim_sizes;
-  }
-
- private:
-  static Dimensions BlockDimensions(const Dimensions& tensor_dims,
-                                    const TensorBlockShapeType block_shape,
-                                    StorageIndex min_target_size) {
-    min_target_size = numext::maxi<StorageIndex>(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 == kUniformAllDims) {
-        // Tensor will not fit within 'min_target_size' budget: calculate tensor
-        // block dimension sizes based on "square" dimension size target.
-        const StorageIndex dim_size_target = convert_index<StorageIndex>(
-          std::pow(static_cast<float>(min_target_size),
-                   1.0f / static_cast<float>(block_dim_sizes.rank())));
-        for (Index 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, tensor_dims[i]);
-        }
-        // Add any un-allocated coefficients to inner dimension(s).
-        StorageIndex total_size = block_dim_sizes.TotalSize();
-        for (int i = 0; i < NumDims; ++i) {
-          const int dim = cond<Layout>()(i, NumDims - i - 1);
-          if (block_dim_sizes[dim] < tensor_dims[dim]) {
-            const StorageIndex total_size_other_dims =
-                total_size / block_dim_sizes[dim];
-            const StorageIndex alloc_avail =
-                divup<StorageIndex>(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 == kSkewedInnerDims) {
-        StorageIndex coeff_to_allocate = min_target_size;
-        for (int i = 0; i < NumDims; ++i) {
-          const int dim = cond<Layout>()(i, NumDims - i - 1);
-          block_dim_sizes[dim] =
-              numext::mini(coeff_to_allocate, tensor_dims[dim]);
-          coeff_to_allocate = divup(
-              coeff_to_allocate,
-              numext::maxi(static_cast<StorageIndex>(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<Index>(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;
-  StorageIndex m_total_block_count;
-};
-
-}  // namespace internal
-
-}  // namespace Eigen
-
-#endif  // EIGEN_CXX11_TENSOR_TENSOR_BLOCK_H