#pragma once
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#include <ATen/Tensor.h>
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#include <c10/core/TensorImpl.h>
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#include <c10/util/Exception.h>
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namespace at {
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struct CAFFE2_API SparseTensorImpl : public TensorImpl {
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// Stored in COO format, indices + values.
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// INVARIANTS:
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// sparse_dim: range [0, len(shape)]; sparse_dim + dense_dim = len(shape)
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// dense_dim : range [0, len(shape)]; sparse_dim + dense_dim = len(shape)
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// _indices.shape: dimensionality: 2, shape: (sparse_dim, nnz)
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// _values.shape: dimensionality: 1 + dense_dim. shape: (nnz, shape[sparse_dim:])
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int64_t sparse_dim_ = 0; // number of sparse dimensions
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int64_t dense_dim_ = 0; // number of dense dimensions
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Tensor indices_; // always a LongTensor
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Tensor values_;
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// A sparse tensor is 'coalesced' if every index occurs at most once in
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// the indices tensor, and the indices are in sorted order. (This means
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// that it is very easy to convert a coalesced tensor to CSR format: you
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// need only compute CSR format indices.)
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//
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// Most math operations can only be performed on coalesced sparse tensors,
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// because many algorithms proceed by merging two sorted lists (of indices).
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bool coalesced_ = false;
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public:
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// Public for now...
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explicit SparseTensorImpl(at::TensorTypeSet, const caffe2::TypeMeta&);
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int64_t nnz() const { return values_.size(0); }
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int64_t sparse_dim() const { return sparse_dim_; }
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int64_t dense_dim() const { return dense_dim_; }
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bool coalesced() const { return coalesced_; }
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Tensor indices() const { return indices_; }
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Tensor values() const { return values_; }
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IntArrayRef strides() const override;
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bool is_contiguous(at::MemoryFormat memory_format=at::MemoryFormat::Contiguous) const override;
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int64_t stride(int64_t d) const override;
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void resize_dim(int64_t ndim) override;
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void set_size(int64_t dim, int64_t new_size) override;
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void set_stride(int64_t dim, int64_t new_stride) override;
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void set_storage_offset(int64_t storage_offset) override;
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int64_t dim() const override;
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TensorImpl* maybe_zero_dim(bool condition_when_zero_dim) override;
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bool has_storage() const override;
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const Storage& storage() const override;
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int64_t storage_offset() const override;
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// WARNING: This function does NOT preserve invariants of sparse_dim/dense_dim with
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// respect to indices and values
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void raw_resize_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) {
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TORCH_CHECK(allow_tensor_metadata_change(), "raw_resize_ ", err_msg_tensor_metadata_change_not_allowed);
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sizes_ = size.vec();
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sparse_dim_ = sparse_dim;
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dense_dim_ = dense_dim;
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refresh_numel();
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}
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// NOTE: This function preserves invariants of sparse_dim/dense_dim with respect to
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// indices and values.
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//
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// NOTE: This function supports the following cases:
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// 1. When we keep the number of dense dimensions unchanged, and NOT shrinking the size of
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// any of the dense dimensions.
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// 2. When we keep the number of sparse dimensions unchanged, and NOT shrinking the size of
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// any of the sparse dimensions.
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// 3. When the sparse tensor has zero nnz, in which case we are free to change the shapes of
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// both its sparse and dense dimensions.
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//
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// This function DOESN'T support (and will throw an error) the following cases:
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// 1. When we attempt to change the number of sparse dimensions on a non-empty sparse tensor
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// (such an operation will invalidate the indices stored).
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// 2. When we attempt to change the number of dense dimensions on a non-empty sparse tensor
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// (such an operation will behave differently from an equivalent dense tensor's resize method,
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// and for API consistency we don't support it).
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// 3. When we attempt to shrink the size of any of the dense dimensions on a non-empty sparse tensor
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// (such an operation will behave differently from an equivalent dense tensor's resize method,
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// and for API consistency we don't support it).
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// 4. When we attempt to shrink the size of any of the sparse dimensions on a non-empty sparse tensor
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// (this could make some of the stored indices out-of-bound and thus unsafe).
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void resize_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) {
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TORCH_CHECK(allow_tensor_metadata_change(), "resize_ ", err_msg_tensor_metadata_change_not_allowed);
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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());
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if (nnz() > 0) {
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auto alt_options_msg = "You could try the following options:\n\
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1. If you need an empty sparse tensor of this size, call `x = torch.sparse_coo_tensor(size)`.\n\
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2. If you need to resize this tensor, you have the following options:\n\
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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\
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2. Or, create a new sparse tensor with the correct indices and values from this sparse tensor.";
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TORCH_CHECK(sparse_dim == sparse_dim_,
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"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);
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TORCH_CHECK(dense_dim == dense_dim_,
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"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);
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bool shrinking_sparse_dims = false;
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bool shrinking_dense_dim = false;
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auto sparse_size_original = sizes().slice(0, sparse_dim);
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auto sparse_size_new = size.slice(0, sparse_dim);
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for (int64_t i = 0; i < sparse_dim; i++) {
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if (sparse_size_new[i] < sparse_size_original[i]) {
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shrinking_sparse_dims = true;
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break;
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}
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}
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auto dense_size_original = sizes().slice(sparse_dim);
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auto dense_size_new = size.slice(sparse_dim);
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for (int64_t i = 0; i < dense_dim; i++) {
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if (dense_size_new[i] < dense_size_original[i]) {
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shrinking_dense_dim = true;
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break;
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}
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}
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TORCH_CHECK(!shrinking_sparse_dims,
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"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);
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TORCH_CHECK(!shrinking_dense_dim,
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"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);
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}
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if ((!size.equals(sizes_)) || (sparse_dim != sparse_dim_) || (dense_dim != dense_dim_)) {
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auto nnz = values().size(0);
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std::vector<int64_t> values_size = {nnz};
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auto dense_size = size.slice(sparse_dim);
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values_size.insert(values_size.end(), dense_size.begin(), dense_size.end());
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values_.resize_(values_size);
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indices_.resize_({sparse_dim, nnz});
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}
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sizes_ = size.vec();
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sparse_dim_ = sparse_dim;
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dense_dim_ = dense_dim;
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refresh_numel();
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}
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// NOTE: this function will resize the sparse tensor and also set `indices` and `values` to empty.
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void resize_and_clear_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) {
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TORCH_CHECK(allow_tensor_metadata_change(), "resize_and_clear_ ", err_msg_tensor_metadata_change_not_allowed);
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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());
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sizes_ = size.vec();
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sparse_dim_ = sparse_dim;
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dense_dim_ = dense_dim;
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auto empty_indices = at::empty({sparse_dim, 0}, indices().options());
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std::vector<int64_t> values_size = {0};
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auto dense_size = sizes().slice(sparse_dim);
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values_size.insert(values_size.end(), dense_size.begin(), dense_size.end());
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auto empty_values = at::empty(values_size, values().options());
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set_indices_and_values_unsafe(empty_indices, empty_values);
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refresh_numel();
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}
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void set_coalesced(bool coalesced) {
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TORCH_CHECK(allow_tensor_metadata_change(), "set_coalesced ", err_msg_tensor_metadata_change_not_allowed);
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coalesced_ = coalesced;
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}
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// NOTE: this function is only used internally and not exposed to Python frontend
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void set_nnz_and_narrow(int64_t new_nnz) {
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TORCH_CHECK(allow_tensor_metadata_change(), "set_nnz_and_narrow ", err_msg_tensor_metadata_change_not_allowed);
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AT_ASSERT(new_nnz <= nnz());
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indices_ = indices_.narrow(1, 0, new_nnz);
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values_ = values_.narrow(0, 0, new_nnz);
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}
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// Takes indices and values and directly puts them into the sparse tensor, no copy.
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// NOTE: this function is unsafe because it doesn't check whether any indices are
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// out of boundaries of `sizes`, so it should ONLY be used where we know that the
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// indices are guaranteed to be within bounds.
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// This used to be called THSTensor_(_move)
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// NB: This used to be able to avoid a refcount bump, but I was too lazy to
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// make it happen
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void set_indices_and_values_unsafe(const Tensor& indices, const Tensor& values);
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/**
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* Return a TensorImpl that is a shallow-copy of this TensorImpl.
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*
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* For usage of `version_counter` and `allow_tensor_metadata_change`,
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* see NOTE [ TensorImpl Shallow-Copying ].
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*/
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c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
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const c10::VariableVersion& version_counter,
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bool allow_tensor_metadata_change) const override {
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auto impl = c10::make_intrusive<SparseTensorImpl>(type_set(), dtype());
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copy_tensor_metadata(
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/*src_impl=*/this,
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/*dest_impl=*/impl.get(),
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/*version_counter=*/version_counter,
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/*allow_tensor_metadata_change=*/allow_tensor_metadata_change);
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impl->refresh_numel();
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return impl;
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}
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/**
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* Shallow-copies data from another TensorImpl into this TensorImpl.
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*
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* For why this function doesn't check this TensorImpl's `allow_tensor_metadata_change_`,
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* see NOTE [ TensorImpl Shallow-Copying ].
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*/
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void shallow_copy_from(const c10::intrusive_ptr<TensorImpl>& impl) override {
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AT_ASSERT(has_compatible_shallow_copy_type(impl->type_set()));
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auto sparse_impl = static_cast<const SparseTensorImpl*>(impl.get());
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copy_tensor_metadata(
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/*src_impl=*/sparse_impl,
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/*dest_impl=*/this,
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/*version_counter=*/version_counter(),
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/*allow_tensor_metadata_change=*/allow_tensor_metadata_change());
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refresh_numel();
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}
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private:
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explicit SparseTensorImpl(at::TensorTypeSet, const caffe2::TypeMeta&, at::Tensor indices, at::Tensor values);
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/**
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* Copy the tensor metadata fields (e.g. sizes / strides / storage pointer / storage_offset)
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* from one TensorImpl to another TensorImpl.
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*
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* For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE [ TensorImpl Shallow-Copying ].
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*/
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static void copy_tensor_metadata(
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const SparseTensorImpl* src_sparse_impl,
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SparseTensorImpl* dest_sparse_impl,
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const c10::VariableVersion& version_counter,
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bool allow_tensor_metadata_change) {
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TensorImpl::copy_tensor_metadata(src_sparse_impl, dest_sparse_impl, version_counter, allow_tensor_metadata_change);
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// Sparse-specific fields
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dest_sparse_impl->sparse_dim_ = src_sparse_impl->sparse_dim();
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dest_sparse_impl->dense_dim_ = src_sparse_impl->dense_dim();
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dest_sparse_impl->indices_ = src_sparse_impl->indices();
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dest_sparse_impl->values_ = src_sparse_impl->values();
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dest_sparse_impl->coalesced_ = src_sparse_impl->coalesced();
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}
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};
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} // namespace at
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