pytorch · ssnl · Oct 9, 2018 · Oct 9, 2018 · Oct 10, 2018 · Oct 10, 2018
diff --git a/aten/src/ATen/Declarations.cwrap b/aten/src/ATen/Declarations.cwrap
@@ -3266,7 +3266,9 @@
   name: alias
   return: THTensor*
   cpu_half: True
-  variants: [function]
+  variants:
+    - method
+    - function
   options:
     - cname: newWithTensor
       arguments:

diff --git a/aten/src/ATen/SparseTensorImpl.cpp b/aten/src/ATen/SparseTensorImpl.cpp
@@ -22,18 +22,18 @@ namespace {
 // a scalar and have one element)
 //
 // Thus, an empty sparse tensor should be a 1-dimensional tensor of size [0].
-// Furthermore, we have dim == sparseDims + denseDims; since this is a sparse
-// tensor, let us say that an empty sparse tensor has sparseDims == 1 and
-// denseDims == 0.  (There is a degree of freedom here, but given that this
-// is a sparse dimension, it seems reasonable to demand that sparseDims > 0).
+// Furthermore, we have dim == sparse_dim + dense_dim; since this is a sparse
+// tensor, let us say that an empty sparse tensor has sparse_dim == 1 and
+// dense_dim == 0.  (There is a degree of freedom here, but given that this
+// is a sparse dimension, it seems reasonable to demand that sparse_dim > 0).
 //
 // This means that we allocate a [1,0] size indices tensor and a [0] size
 // values tensor for such an empty tensor.
 SparseTensorImpl::SparseTensorImpl(at::TensorTypeId type_id, const caffe2::TypeMeta& data_type)
     : TensorImpl(type_id, data_type, nullptr, false)
     , size_{0}
-    , sparseDims_(1)
-    , denseDims_(0)
+    , sparse_dim_(1)
+    , dense_dim_(0)
     , indices_(at::empty({1, 0}, at::initialTensorOptions().device(sparseTensorIdToDeviceType(type_id)).dtype(ScalarType::Long)))
     , values_(at::empty({0}, at::initialTensorOptions().device(sparseTensorIdToDeviceType(type_id)).dtype(dataTypeToScalarType(data_type.id())))) {}
 
@@ -67,7 +67,7 @@ void SparseTensorImpl::set_storage_offset(int64_t storage_offset) {
 }
 
 int64_t SparseTensorImpl::dim() const {
-  return sparseDims_ + denseDims_;
+  return sparse_dim_ + dense_dim_;
 }
 TensorImpl* SparseTensorImpl::maybe_zero_dim(bool condition_when_zero_dim) {
   AT_CHECK(condition_when_zero_dim == (dim() == 0),
@@ -83,17 +83,22 @@ int64_t SparseTensorImpl::storage_offset() const {
   AT_ERROR("sparse tensors do not have storage");
 }
 void SparseTensorImpl::set_indices_and_values_unsafe(const Tensor& indices, const Tensor& values) {
+  AT_ASSERT(!indices.is_variable() && !values.is_variable());  // They should be plain tensors!
+
+  AT_CHECK(!indices.is_sparse(), "expected indices to be a dense tensor, but got indices of layout ", indices.layout());
+  AT_CHECK(!values.is_sparse(), "expected values to be a dense tensor, but got values of layout ", values.layout());
+
   AT_CHECK(values.type().toSparse() == type(), "values type must match sparse tensor type");
   AT_CHECK(indices.type().scalarType() == kLong, "indices must be an int64 tensor");
   AT_CHECK(indices.type().backend() == values.type().backend(), "backend of indices (", indices.type().backend(), ") must match backend of values (", values.type().backend(), ")");
   AT_CHECK(!indices.is_cuda() || indices.get_device() == values.get_device(), "device of indices (", indices.get_device(), ") must match device of values (", values.get_device(), ")");
 
-  AT_CHECK(indices.dim() == 2, "indices must be nDim x nnz, but got: ", indices.sizes());
+  AT_CHECK(indices.dim() == 2, "indices must be sparse_dim x nnz, but got: ", indices.sizes());
   AT_CHECK(indices.size(1) == values.size(0), "indices and values must have same nnz, but got nnz from indices: ", indices.size(1), ", nnz from values: ", values.size(0));
-  AT_CHECK(indices.size(0) == sparseDims_, "indices has incorrect first dimension, expected ", sparseDims_, ", got ", indices.size(0));
-  AT_CHECK(values.dim() == denseDims_ + 1, "values has incorrect number of dimensions, expected ", denseDims_ + 1, ", got ", values.dim());
+  AT_CHECK(indices.size(0) == sparse_dim_, "indices has incorrect first dimension, expected ", sparse_dim_, ", got ", indices.size(0));
+  AT_CHECK(values.dim() == dense_dim_ + 1, "values has incorrect number of dimensions, expected ", dense_dim_ + 1, ", got ", values.dim());
 
-  auto dense_size_original = sizes().slice(sparseDims_);
+  auto dense_size_original = sizes().slice(sparse_dim_);
   std::vector<int64_t> expected_values_size_vec = {values.size(0)};
   expected_values_size_vec.insert(expected_values_size_vec.end(), dense_size_original.begin(), dense_size_original.end());
   IntList expected_values_size(expected_values_size_vec);

diff --git a/aten/src/ATen/SparseTensorImpl.h b/aten/src/ATen/SparseTensorImpl.h
@@ -9,18 +9,18 @@ struct CAFFE2_API SparseTensorImpl : public TensorImpl {
   // Stored in COO format, indices + values.
 
   // INVARIANTS:
-  // _sparseDims: range [0, len(shape)]; _sparseDims + _denseDims = len(shape)
-  // _denseDims : range [0, len(shape)]; _sparseDims + _denseDims = len(shape)
-  // _indices.shape: dimensionality: 2,  shape: (_sparseDims, nnz)
-  // _values.shape:  dimensionality: 1 + _denseDims.  shape: (nnz, shape[_sparseDims:])
+  // 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:])
 
   // The true size of the sparse tensor (e.g., if you called to_dense()
   // on it).  When THTensor merges into TensorImpl, this field
   // should move to the parent class.
   std::vector<int64_t> size_;
 
-  int64_t sparseDims_ = 0; // number of sparse dimensions
-  int64_t denseDims_ = 0; // number of dense dimensions
+  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_;
@@ -39,8 +39,8 @@ struct CAFFE2_API SparseTensorImpl : public TensorImpl {
   explicit SparseTensorImpl(at::TensorTypeId, const caffe2::TypeMeta&);
 
   int64_t nnz() const { return values_.size(0); }
-  int64_t sparseDims() const { return sparseDims_; }
-  int64_t denseDims() const { return denseDims_; }
+  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_; }
@@ -60,16 +60,16 @@ struct CAFFE2_API SparseTensorImpl : public TensorImpl {
   const Storage& storage() const override;
   int64_t storage_offset() const override;
 
-  // WARNING: This function does NOT preserve invariants of sparseDims/denseDims with
+  // WARNING: This function does NOT preserve invariants of sparse_dim/dense_dim with
   // respect to indices and values
-  void raw_resize_(int64_t sparseDims, int64_t denseDims, IntList size) {
+  void raw_resize_(int64_t sparse_dim, int64_t dense_dim, IntList size) {
     size_ = size.vec();
-    sparseDims_ = sparseDims;
-    denseDims_ = denseDims;
+    sparse_dim_ = sparse_dim;
+    dense_dim_ = dense_dim;
     refresh_numel();
   }
 
-  // NOTE: This function preserves invariants of sparseDims/denseDims with respect to
+  // NOTE: This function preserves invariants of sparse_dim/dense_dim with respect to
   // indices and values.
   //
   // NOTE: This function supports the following cases:
@@ -91,75 +91,73 @@ struct CAFFE2_API SparseTensorImpl : public TensorImpl {
   // 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 sparseDims, int64_t denseDims, IntList size) {
-    AT_CHECK(sparseDims + denseDims == size.size(), "number of dimensions must be sparseDims (", sparseDims, ") + denseDims (", denseDims, "), but got ", size.size());
+  void resize_(int64_t sparse_dim, int64_t dense_dim, IntList size) {
+    AT_CHECK(sparse_dim + dense_dim == 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\
+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.";
 
-      AT_CHECK(sparseDims == sparseDims_,
-        "changing the number of sparse dimensions (from ", sparseDims_, " to ", sparseDims, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg);
+      AT_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);
 
-      AT_CHECK(denseDims == denseDims_,
-        "changing the number of dense dimensions (from ", denseDims_, " to ", denseDims, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg);
+      AT_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_dims = false;
-      auto sparse_size_original = sizes().slice(0, sparseDims);
-      auto sparse_size_new = size.slice(0, sparseDims);
-      for (int i = 0; i < sparseDims; i++) {
+      bool shrinking_dense_dim = false;
+      auto sparse_size_original = sizes().slice(0, sparse_dim);
+      auto sparse_size_new = size.slice(0, sparse_dim);
+      for (int 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(sparseDims);
-      auto dense_size_new = size.slice(sparseDims);
-      for (int i = 0; i < denseDims; i++) {
+      auto dense_size_original = sizes().slice(sparse_dim);
+      auto dense_size_new = size.slice(sparse_dim);
+      for (int i = 0; i < dense_dim; i++) {
         if (dense_size_new[i] < dense_size_original[i]) {
-          shrinking_dense_dims = true;
+          shrinking_dense_dim = true;
           break;
         }
       }
 
       AT_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);
 
-      AT_CHECK(!shrinking_dense_dims,
+      AT_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);
     }
 
-    if ((!size.equals(size_)) || (sparseDims != sparseDims_) || (denseDims != denseDims_)) {
-      std::vector<int64_t> values_size = {values().size(0)};
-      auto dense_size = size.slice(sparseDims);
+    if ((!size.equals(size_)) || (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);
-
-      std::vector<int64_t> indices_size = indices().sizes().vec();
-      indices_size[0] = sparseDims;
-      indices_.resize_(indices_size);
+      indices_.resize_({sparse_dim, nnz});
     }
 
     size_ = size.vec();
-    sparseDims_ = sparseDims;
-    denseDims_ = denseDims;
+    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 sparseDims, int64_t denseDims, IntList size) {
-    AT_CHECK(sparseDims + denseDims == size.size(), "number of dimensions must be sparseDims (", sparseDims, ") + denseDims (", denseDims, "), but got ", size.size());
+  void resize_and_clear_(int64_t sparse_dim, int64_t dense_dim, IntList size) {
+    AT_CHECK(sparse_dim + dense_dim == size.size(), "number of dimensions must be sparse_dim (", sparse_dim, ") + dense_dim (", dense_dim, "), but got ", size.size());
 
     size_ = size.vec();
-    sparseDims_ = sparseDims;
-    denseDims_ = denseDims;
+    sparse_dim_ = sparse_dim;
+    dense_dim_ = dense_dim;
 
-    auto empty_indices = at::empty({sparseDims, 0}, indices().options());
+    auto empty_indices = at::empty({sparse_dim, 0}, indices().options());
     std::vector<int64_t> values_size = {0};
-    auto dense_size = sizes().slice(sparseDims);
+    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);
@@ -169,9 +167,10 @@ struct CAFFE2_API SparseTensorImpl : public TensorImpl {
   void set_coalesced(bool coalesced) { coalesced_ = coalesced; }
 
   // NOTE: this function is only used internally and not exposed to Python frontend
-  void set_nnz_and_narrow(int64_t nnz) {
-    indices_ = indices_.narrow(1, 0, nnz);
-    values_ = values_.narrow(0, 0, nnz);
+  void set_nnz_and_narrow(int64_t new_nnz) {
+    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.

diff --git a/aten/src/ATen/SparseTensorUtils.h b/aten/src/ATen/SparseTensorUtils.h
@@ -0,0 +1,111 @@
+#include <ATen/ATen.h>
+#include <ATen/SparseTensorImpl.h>
+
+namespace at { namespace sparse {
+
+// Just for documentary purposes
+using SparseTensor = Tensor;
+using LongTensor = Tensor;
+using IntTensor = Tensor;
+using SparseType = Type;
+
+// This is an internal utility function for getting at the SparseTensorImpl,
+// so that we can write sparse tensor specific accessors for special fields
+// in SparseTensor.  You should only use this for writing low level
+// setters/getters for SparseTensorImpl fields; otherwise, you should use
+// the low level setters/getters that were implemented using this.
+//
+// This may be called repeatedly, so make sure it's pretty cheap.
+inline SparseTensorImpl* get_sparse_impl(const SparseTensor& self) {
+  AT_ASSERTM(!self.is_variable(), "_internal_get_SparseTensorImpl: should not be a variable");
+  AT_ASSERTM(self.is_sparse(), "_internal_get_SparseTensorImpl: not a sparse tensor");
+  return static_cast<SparseTensorImpl*>(self.unsafeGetTensorImpl());
+}
+
+// Port of the old THCSTensor_(checkGPU), but it doesn't really belong here
+// because it is more general
+// NB: I dropped kernelP2PEnabled support
+// NB: This only works if the tensors are KNOWN to be CUDA.
+// TODO: Generalize it so it works on CPU as well
+inline bool check_device(ArrayRef<Tensor> ts) {
+  if (ts.empty()) {
+    return true;
+  }
+  int64_t curDevice = current_device();
+  for (const Tensor& t : ts) {
+    if (t.get_device() != curDevice) return false;
+  }
+  return true;
+}
+
+// Takes indices and values and directly puts them into the sparse tensor, no
+// copy.  This used to be called THSTensor_(_move)
+inline void alias_into_sparse(const SparseTensor& self, const LongTensor& indices, const Tensor& values) {
+  get_sparse_impl(self)->set_indices_and_values_unsafe(indices, values);
+}
+
+// Take indices and values and makes a (data) copy of them to put into the sparse
+// indices/values.  This used to be called THSTensor_(_set)
+inline void copy_into_sparse(const SparseTensor& self, const LongTensor& indices, const Tensor& values, bool non_blocking) {
+  alias_into_sparse(self, self._indices().type().copy(indices, non_blocking), self._values().type().copy(values, non_blocking));
+}
+
+// TODO: put this into the public API
+inline bool is_same_tensor(const Tensor& lhs, const Tensor& rhs) {
+  return lhs.unsafeGetTensorImpl() == rhs.unsafeGetTensorImpl();
+}
+
+inline bool is_same_density(const SparseTensor& self, const SparseTensor& src) {
+  return self.sparse_dim() == src.sparse_dim() && self.dense_dim() == src.dense_dim();
+}
+
+// Give us a new values tensor, with the same dimensionality
+// as 'values' but with a new number of non-zero elements.
+// TODO: Expose this for real in ATen, some day?
+// NB: Doesn't preserve data.
+inline Tensor new_values_with_size_of(const Tensor& values, int64_t nnz) {
+  std::vector<int64_t> size = values.sizes().vec();
+  size[0] = nnz;
+  return at::empty(size, values.options());
+}
+
+// This helper function flattens a sparse indices tensor (a LongTensor) into a 1D
+// indices tensor. E.g.,
+//   input = [[2, 4, 0],
+//            [3, 1, 10]]
+//   full_size = [2, 12]
+//   output = [ 2 * 12 + 3, 4 * 12 + 1, 0 * 12 + 10 ] = [27, 49, 10]
+//
+// In other words, assuming that each `indices[i, :]` is a valid index to a
+// tensor `t` of shape `full_size`. This returns the corresponding indices to
+// the flattened tensor `t.reshape( prod(full_size[:indices.size(0)]), -1 )`.
+// if forceClone is true, the result will forced to be a clone of self.
+// if force_clone is true, the result will forced to be a clone of self.
+inline LongTensor flatten_indices(const Tensor& indices, IntList full_size, bool force_clone = false) {
+  int64_t sparse_dim = indices.size(0);
+  if (sparse_dim == 1) {
+    if (force_clone) {
+      return indices.squeeze(0).clone();
+    } else {
+      return indices.squeeze(0);
+    }
+  } else {
+    std::vector<int64_t> indices_mult_cpu_vec;
+    indices_mult_cpu_vec.reserve(sparse_dim);
+    int64_t mult = 1;
+    for (int64_t i = sparse_dim - 1; i >= 0; i--) {
+      indices_mult_cpu_vec[i] = mult;
+      mult *= full_size[i];
+    }
+    auto indices_mult_cpu = indices.type().cpu()
+                                   .tensorFromBlob(indices_mult_cpu_vec.data(), /*size=*/{sparse_dim, 1});
+    // NB: must be blocking because this blob may be freed after this closure,
+    //     and non_blocking copy will see garbage.
+    auto indices_mult = indices_mult_cpu.to(indices.device(), /*non_blocking=*/false);
+    // Ideally we want matmul but matmul is slow on CPU Long and not implemented
+    // on CUDA Long. So mul is faster.
+    return indices.mul(indices_mult).sum(0);
+  }
+}
+
+}} // namespace at::sparse
diff --git a/aten/src/ATen/core/Tensor.h b/aten/src/ATen/core/Tensor.h
@@ -404,6 +404,7 @@ class CAFFE2_API Tensor {
   Tensor & log_normal_(double mean=1, double std=2, Generator * generator=nullptr);
   Tensor & exponential_(double lambd=1, Generator * generator=nullptr);
   Tensor & geometric_(double p, Generator * generator=nullptr);
+  Tensor alias() const;
   Tensor abs() const;
   Tensor & abs_();
   Tensor acos() const;
@@ -621,17 +622,22 @@ class CAFFE2_API Tensor {
   Tensor & sub_(Scalar other, Scalar alpha=1);
   Tensor addmm(const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1) const;
   Tensor & addmm_(const Tensor & mat1, const Tensor & mat2, Scalar beta=1, Scalar alpha=1);
-  Tensor & sparse_resize_(IntList size, int64_t sparseDims, int64_t denseDims);
-  Tensor & sparse_resize_and_clear_(IntList size, int64_t sparseDims, int64_t denseDims);
+  Tensor & sparse_resize_(IntList size, int64_t sparse_dim, int64_t dense_dim);
+  Tensor & sparse_resize_and_clear_(IntList size, int64_t sparse_dim, int64_t dense_dim);
   Tensor sparse_mask(SparseTensorRef mask) const;
   Tensor to_dense() const;
-  int64_t _sparseDims() const;
-  int64_t _denseDims() const;
+  int64_t sparse_dim() const;
+  int64_t _dimI() const;
+  int64_t dense_dim() const;
+  int64_t _dimV() const;
   int64_t _nnz() const;
   Tensor coalesce() const;
   bool is_coalesced() const;
   Tensor _indices() const;
   Tensor _values() const;
+  Tensor & _coalesced_(bool coalesced);
+  Tensor indices() const;
+  Tensor values() const;
   int64_t numel() const;
   std::vector<Tensor> unbind(int64_t dim=0) const;
   int64_t get_device() const;