Datasets:
shibing624
/
source_code

Dataset card Data Studio Files
xet
 Community
2
text
stringlengths
0
2.2M
}
Tensor to_dense_backward(const Tensor& grad, const Tensor& input_) {
AT_ASSERT(input_.layout() != c10::kStrided);
if (input_.layout() == c10::kSparse) {
auto input = input_.coalesce();
return grad.sparse_mask(input);
} else if (input_.layout() == c10::kMkldnn) {
return grad.to_mkldnn(input_.scalar_type());
} else {
AT_ERROR("Unsupported input layout: ", input_.layout());
}
}
Tensor to_mkldnn_backward(const Tensor& grad, const Tensor& input_) {
AT_ASSERT(input_.layout() == c10::kStrided);
return grad.to_dense(input_.scalar_type());
}
// Computes the strides for view_dtype output when the view dtype is
// smaller than the original dtype
inline DimVector compute_strides_for_view_dtype_downsize(IntArrayRef old_strides, int64_t size_ratio, ScalarType old_dtype, ScalarType new_dtype) {
const int64_t ndim = old_strides.size();
TORCH_CHECK(
old_strides[ndim - 1] == 1,
"self.stride(-1) must be 1 to view ", old_dtype, " as ", new_dtype,
" (different element sizes), but got ", old_strides[ndim - 1]);
DimVector new_strides(ndim);
for (int64_t dim_idx = 0; dim_idx < ndim - 1; dim_idx++) {
new_strides[dim_idx] = old_strides[dim_idx] * size_ratio;
}
new_strides[ndim - 1] = 1;
return new_strides;
}
// Computes the strides for view_dtype output when the view dtype is
// larger than the original dtype
inline DimVector compute_strides_for_view_dtype_upsize(IntArrayRef old_strides, int64_t size_ratio, ScalarType old_dtype, ScalarType new_dtype) {
const int64_t ndim = old_strides.size();
TORCH_CHECK(
old_strides[ndim - 1] == 1,
"self.stride(-1) must be 1 to view ", old_dtype, " as ", new_dtype,
" (different element sizes), but got ", old_strides[ndim - 1]);
DimVector new_strides(ndim);
for (int64_t dim_idx = 0; dim_idx < ndim - 1; dim_idx++) {
TORCH_CHECK(
(old_strides[dim_idx] % size_ratio) == 0,
"self.stride(", dim_idx, ") must be divisible by ", size_ratio,
" to view ", old_dtype, " as ", new_dtype, " (different element sizes), ",
"but got ", old_strides[dim_idx]);
new_strides[dim_idx] = old_strides[dim_idx] / size_ratio;
}
new_strides[ndim - 1] = 1;
return new_strides;
}
Tensor view_dtype(const Tensor& self, ScalarType dtype) {
if (self.scalar_type() == dtype) {
return self;
}
const auto type_meta = c10::scalarTypeToTypeMeta(dtype);
TORCH_CHECK(!self.is_conj(),
"torch.Tensor.view is not supported for conjugate view tensors when converting to a different dtype.");
TORCH_CHECK(!self.is_neg(),
"torch.Tensor.view is not supported for tensors with negative bit set when converting to a different dtype.");
int64_t self_element_size = self.element_size();
int64_t new_element_size = static_cast<int64_t>(type_meta.itemsize());
Storage storage = self.storage();
auto new_tensor = detail::make_tensor<TensorImpl>(
std::move(storage), self.key_set(), type_meta);
auto* impl = new_tensor.unsafeGetTensorImpl();
if (self_element_size == new_element_size) {
impl->set_storage_offset(self.storage_offset());
impl->set_sizes_and_strides(self.sizes(), self.strides());
} else if (self.dim() == 0) {
TORCH_CHECK(false,
"self.dim() cannot be 0 to view ", self.scalar_type(), " as ",
dtype, " (different element sizes)");
} else if (self_element_size > new_element_size) {
// Downsizing element size
int64_t size_ratio = self_element_size / new_element_size;
auto new_strides = compute_strides_for_view_dtype_downsize(
self.strides(), size_ratio, self.scalar_type(), dtype);
auto old_sizes = self.sizes();
DimVector new_sizes(self.dim());
std::copy(old_sizes.begin(), old_sizes.end(), new_sizes.begin());
new_sizes[self.dim() - 1] *= size_ratio;
auto new_storage_offset = size_ratio * self.storage_offset();