Lines Matching refs:x_tensor

154 def _iter_tensor(x_tensor):  argument
171     if _is_sparse_any_tensor(x_tensor):
182         x_nnz = x_tensor._nnz()
183         x_size = list(x_tensor.size())
184         if x_tensor.layout is torch.sparse_coo:
185             x_indices = x_tensor._indices().t()
186             x_values = x_tensor._values()
187         elif x_tensor.layout is torch.sparse_csr:
189                 x_tensor.crow_indices(), x_tensor.col_indices()
191             x_values = x_tensor.values()
192         elif x_tensor.layout is torch.sparse_csc:
194                 x_tensor.ccol_indices(), x_tensor.row_indices(), transpose=True
196             x_values = x_tensor.values()
197         elif x_tensor.layout is torch.sparse_bsr:
198             x_block_values = x_tensor.values()
202                     x_tensor.crow_indices(), x_tensor.col_indices()
205                 .mul_(torch.tensor(x_blocksize, device=x_tensor.device).reshape(2, 1))
208                         torch.where(torch.ones(x_blocksize, device=x_tensor.device))
215         elif x_tensor.layout is torch.sparse_bsc:
216             x_block_values = x_tensor.values()
220                     x_tensor.ccol_indices(), x_tensor.row_indices(), transpose=True
223                 .mul_(torch.tensor(x_blocksize, device=x_tensor.device).reshape(2, 1))
226                         torch.where(torch.ones(x_blocksize, device=x_tensor.device))
244     elif x_tensor.layout == torch._mkldnn:  # type: ignore[attr-defined]
245         for d_idx, x_idx in enumerate(product(*[range(m) for m in x_tensor.size()])):
248             x_tensor_dense = x_tensor.to_dense()
252         x_tensor = x_tensor.data
253         for d_idx, x_idx in enumerate(product(*[range(m) for m in x_tensor.size()])):
254             yield x_tensor, x_idx, d_idx