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/usr/local/lib64/python3.6/site-packages/torch/include/ATen/

/usr/local/lib64/python3.6/site-packages/torch/include/ATen

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core/	-	0755	rm
cpu/	-	0755	rm
cuda/	-	0755	rm
cudnn/	-	0755	rm
detail/	-	0755	rm
hip/	-	0755	rm
native/	-	0755	rm
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AccumulateType.h	4438	0644	edit dl rm
ArrayRef.h	44	0644	edit dl rm
ATen.h	998	0644	edit dl rm
autocast_mode.h	6716	0644	edit dl rm
Backend.h	43	0644	edit dl rm
Backtrace.h	46	0644	edit dl rm
BatchedFallback.h	965	0644	edit dl rm
BatchedTensorImpl.h	5383	0644	edit dl rm
CompositeExplicitAutogradFunctions.h	1622	0644	edit dl rm
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Context.h	12767	0644	edit dl rm
cpp_custom_type_hack.h	5326	0644	edit dl rm
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CPUFixedAllocator.h	830	0644	edit dl rm
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InitialTensorOptions.h	445	0644	edit dl rm
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MatrixRef.h	3016	0644	edit dl rm
MemoryOverlap.h	1117	0644	edit dl rm
MetaFunctions.h	1601	0644	edit dl rm
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NativeFunctions.h	354651	0644	edit dl rm
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NumericUtils.h	2787	0644	edit dl rm
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SparseTensorImpl.h	12417	0644	edit dl rm
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Storage.h	43	0644	edit dl rm
Tensor.h	48	0644	edit dl rm
TensorAccessor.h	51	0644	edit dl rm
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WrapDimUtils.h	3438	0644	edit dl rm
WrapDimUtilsMulti.h	768	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/ATen/SparseTensorImpl.h (12417B)

#pragma once

#include <ATen/Tensor.h>
#include <c10/core/TensorImpl.h>
#include <c10/util/Exception.h>

namespace at {
struct TORCH_API SparseTensorImpl : public TensorImpl {
  // Stored in COO format, indices + values.

// INVARIANTS:
  // sparse_dim: range [0, len(shape)]; sparse_dim + dense_dim = len(shape)
  // dense_dim : range [0, len(shape)]; sparse_dim + dense_dim = len(shape)
  // _indices.shape: dimensionality: 2,  shape: (sparse_dim, nnz)
  // _values.shape:  dimensionality: 1 + dense_dim.  shape: (nnz, shape[sparse_dim:])

int64_t sparse_dim_ = 0; // number of sparse dimensions
  int64_t dense_dim_ = 0; // number of dense dimensions

Tensor indices_; // always a LongTensor
  Tensor values_;

// A sparse tensor is 'coalesced' if every index occurs at most once in
  // the indices tensor, and the indices are in sorted order.  (This means
  // that it is very easy to convert a coalesced tensor to CSR format: you
  // need only compute CSR format indices.)
  //
  // Most math operations can only be performed on coalesced sparse tensors,
  // because many algorithms proceed by merging two sorted lists (of indices).
  bool coalesced_ = false;

// compute_numel with integer multiplication overflow check, see gh-57542
  void refresh_numel() {
    TensorImpl::safe_refresh_numel();
  }

public:
  // Public for now...
  explicit SparseTensorImpl(at::DispatchKeySet, const caffe2::TypeMeta);

void release_resources() override;

int64_t nnz() const { return values_.size(0); }
  int64_t sparse_dim() const { return sparse_dim_; }
  int64_t dense_dim() const { return dense_dim_; }
  bool coalesced() const { return coalesced_; }
  Tensor indices() const { return indices_; }
  Tensor values() const { return values_; }

IntArrayRef strides() const override;
  int64_t stride(int64_t d) const override;
  void set_size(int64_t dim, int64_t new_size) override;
  void set_stride(int64_t dim, int64_t new_stride) override;
  void set_storage_offset(int64_t storage_offset) override;

#ifdef DEBUG
  bool has_storage() const override;
#endif

// WARNING: This function does NOT preserve invariants of sparse_dim/dense_dim with
  // respect to indices and values
  void raw_resize_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) {
    TORCH_CHECK(allow_tensor_metadata_change(), "raw_resize_ ", err_msg_tensor_metadata_change_not_allowed);
    sizes_and_strides_.set_sizes(size);
    sparse_dim_ = sparse_dim;
    dense_dim_ = dense_dim;
    refresh_numel();
  }

// NOTE: This function preserves invariants of sparse_dim/dense_dim with respect to
  // indices and values.
  //
  // NOTE: This function supports the following cases:
  // 1. When we keep the number of dense dimensions unchanged, and NOT shrinking the size of
  // any of the dense dimensions.
  // 2. When we keep the number of sparse dimensions unchanged, and NOT shrinking the size of
  // any of the sparse dimensions.
  // 3. When the sparse tensor has zero nnz, in which case we are free to change the shapes of
  // both its sparse and dense dimensions.
  //
  // This function DOESN'T support (and will throw an error) the following cases:
  // 1. When we attempt to change the number of sparse dimensions on a non-empty sparse tensor
  // (such an operation will invalidate the indices stored).
  // 2. When we attempt to change the number of dense dimensions on a non-empty sparse tensor
  // (such an operation will behave differently from an equivalent dense tensor's resize method,
  // and for API consistency we don't support it).
  // 3. When we attempt to shrink the size of any of the dense dimensions on a non-empty sparse tensor
  // (such an operation will behave differently from an equivalent dense tensor's resize method,
  // and for API consistency we don't support it).
  // 4. When we attempt to shrink the size of any of the sparse dimensions on a non-empty sparse tensor
  // (this could make some of the stored indices out-of-bound and thus unsafe).
  void resize_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) {
    TORCH_CHECK(allow_tensor_metadata_change(), "resize_ ", err_msg_tensor_metadata_change_not_allowed);
    TORCH_CHECK(sparse_dim + dense_dim == static_cast<int64_t>(size.size()), "number of dimensions must be sparse_dim (", sparse_dim, ") + dense_dim (", dense_dim, "), but got ", size.size());
    if (nnz() > 0) {
      auto alt_options_msg = "You could try the following options:\n\
1. If you need an empty sparse tensor of this size, call `x = torch.sparse_coo_tensor(size)`.\n\
2. If you need to resize this tensor, you have the following options:\n\
    1. For both sparse and dense dimensions, keep the number of them constant and the size of them non-shrinking, and then try the same call again.\n\
    2. Or, create a new sparse tensor with the correct indices and values from this sparse tensor.";

TORCH_CHECK(sparse_dim == sparse_dim_,
        "changing the number of sparse dimensions (from ", sparse_dim_, " to ", sparse_dim, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg);

TORCH_CHECK(dense_dim == dense_dim_,
        "changing the number of dense dimensions (from ", dense_dim_, " to ", dense_dim, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg);

bool shrinking_sparse_dims = false;
      bool shrinking_dense_dim = false;
      auto sparse_size_original = sizes().slice(0, sparse_dim);
      auto sparse_size_new = size.slice(0, sparse_dim);
      for (int64_t i = 0; i < sparse_dim; i++) {
        if (sparse_size_new[i] < sparse_size_original[i]) {
          shrinking_sparse_dims = true;
          break;
        }
      }
      auto dense_size_original = sizes().slice(sparse_dim);
      auto dense_size_new = size.slice(sparse_dim);
      for (int64_t i = 0; i < dense_dim; i++) {
        if (dense_size_new[i] < dense_size_original[i]) {
          shrinking_dense_dim = true;
          break;
        }
      }

TORCH_CHECK(!shrinking_sparse_dims,
        "shrinking the size of sparse dimensions (from ", sparse_size_original, " to ", sparse_size_new, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg);

TORCH_CHECK(!shrinking_dense_dim,
        "shrinking the size of dense dimensions (from ", dense_size_original, " to ", dense_size_new, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg);
    }

const bool size_equals_sizes = std::equal(size.begin(), size.end(), sizes_and_strides_.sizes_begin(), sizes_and_strides_.sizes_end());
    if ((!size_equals_sizes) || (sparse_dim != sparse_dim_) || (dense_dim != dense_dim_)) {
      auto nnz = values().size(0);
      std::vector<int64_t> values_size = {nnz};
      auto dense_size = size.slice(sparse_dim);
      values_size.insert(values_size.end(), dense_size.begin(), dense_size.end());
      values_.resize_(values_size);
      indices_.resize_({sparse_dim, nnz});
    }

if (!size_equals_sizes) {
      sizes_and_strides_.set_sizes(size);
    }
    sparse_dim_ = sparse_dim;
    dense_dim_ = dense_dim;
    refresh_numel();
  }

// NOTE: this function will resize the sparse tensor and also set `indices` and `values` to empty.
  void resize_and_clear_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) {
    TORCH_CHECK(allow_tensor_metadata_change(), "resize_and_clear_ ", err_msg_tensor_metadata_change_not_allowed);
    TORCH_CHECK(sparse_dim + dense_dim == static_cast<int64_t>(size.size()), "number of dimensions must be sparse_dim (", sparse_dim, ") + dense_dim (", dense_dim, "), but got ", size.size());

sizes_and_strides_.set_sizes(size);
    sparse_dim_ = sparse_dim;
    dense_dim_ = dense_dim;

auto empty_indices = at::empty({sparse_dim, 0}, indices().options());
    std::vector<int64_t> values_size = {0};
    auto dense_size = sizes().slice(sparse_dim);
    values_size.insert(values_size.end(), dense_size.begin(), dense_size.end());
    auto empty_values = at::empty(values_size, values().options());
    set_indices_and_values_unsafe(empty_indices, empty_values);
    refresh_numel();
  }

void set_coalesced(bool coalesced) {
    TORCH_CHECK(allow_tensor_metadata_change(), "set_coalesced ", err_msg_tensor_metadata_change_not_allowed);
    coalesced_ = coalesced;
  }

// NOTE: this function is only used internally and not exposed to Python frontend
  void set_nnz_and_narrow(int64_t new_nnz) {
    TORCH_CHECK(allow_tensor_metadata_change(), "set_nnz_and_narrow ", err_msg_tensor_metadata_change_not_allowed);
    AT_ASSERT(new_nnz <= nnz());
    indices_ = indices_.narrow(1, 0, new_nnz);
    values_ = values_.narrow(0, 0, new_nnz);
  }

// Takes indices and values and directly puts them into the sparse tensor, no copy.
  // NOTE: this function is unsafe because it doesn't check whether any indices are
  // out of boundaries of `sizes`, so it should ONLY be used where we know that the
  // indices are guaranteed to be within bounds.
  // This used to be called THSTensor_(_move)
  // NB: This used to be able to avoid a refcount bump, but I was too lazy to
  // make it happen
  void set_indices_and_values_unsafe(const Tensor& indices, const Tensor& values);

/**
   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`,
   * see NOTE [ TensorImpl Shallow-Copying ].
   */
  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
      const c10::VariableVersion& version_counter,
      bool allow_tensor_metadata_change) const override {
    auto impl = c10::make_intrusive<SparseTensorImpl>(key_set(), dtype());
    copy_tensor_metadata(
      /*src_impl=*/this,
      /*dest_impl=*/impl.get(),
      /*version_counter=*/version_counter,
      /*allow_tensor_metadata_change=*/allow_tensor_metadata_change);
    impl->refresh_numel();
    return impl;
  }

/**
   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`,
   * see NOTE [ TensorImpl Shallow-Copying ].
   */
  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
      c10::VariableVersion&& version_counter,
      bool allow_tensor_metadata_change) const override {
    auto impl = c10::make_intrusive<SparseTensorImpl>(key_set(), dtype());
    copy_tensor_metadata(
      /*src_impl=*/this,
      /*dest_impl=*/impl.get(),
      /*version_counter=*/std::move(version_counter),
      /*allow_tensor_metadata_change=*/allow_tensor_metadata_change);
    impl->refresh_numel();
    return impl;
  }

/**
   * Shallow-copies data from another TensorImpl into this TensorImpl.
   *
   * For why this function doesn't check this TensorImpl's `allow_tensor_metadata_change_`,
   * see NOTE [ TensorImpl Shallow-Copying ].
   */
  void shallow_copy_from(const c10::intrusive_ptr<TensorImpl>& impl) override {
    AT_ASSERT(has_compatible_shallow_copy_type(impl->key_set()));
    auto sparse_impl = static_cast<const SparseTensorImpl*>(impl.get());
    copy_tensor_metadata(
      /*src_impl=*/sparse_impl,
      /*dest_impl=*/this,
      /*version_counter=*/version_counter(),
      /*allow_tensor_metadata_change=*/allow_tensor_metadata_change());
    refresh_numel();
  }
private:
    explicit SparseTensorImpl(at::DispatchKeySet, const caffe2::TypeMeta, at::Tensor indices, at::Tensor values);

/**
   * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer / storage_offset)
   * from one TensorImpl to another TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE [ TensorImpl Shallow-Copying ].
   */
  static void copy_tensor_metadata(
      const SparseTensorImpl* src_sparse_impl,
      SparseTensorImpl* dest_sparse_impl,
      const c10::VariableVersion& version_counter,
      bool allow_tensor_metadata_change) {
    TensorImpl::copy_tensor_metadata(src_sparse_impl, dest_sparse_impl, version_counter, allow_tensor_metadata_change);

// Sparse-specific fields
    dest_sparse_impl->sparse_dim_ = src_sparse_impl->sparse_dim();
    dest_sparse_impl->dense_dim_ = src_sparse_impl->dense_dim();
    dest_sparse_impl->indices_ = src_sparse_impl->indices();
    dest_sparse_impl->values_ = src_sparse_impl->values();
    dest_sparse_impl->coalesced_ = src_sparse_impl->coalesced();
  }

const char* tensorimpl_type_name() const override;
};

} // namespace at

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