7ca9b35c96b9f9d4300fe32e0c417cfc95e4d094,t3f/ops.py,,tt_sparse_flat_inner,#Any#Any#,401

Before Change

      core_slices = tf.gather(curr_core, curr_elements_idx)
      tt_a_elements = tf.matmul(tt_a_elements, core_slices)
  else:
    for core_idx in range(tt_a.ndims()):
      curr_elements_idx = sparse_b.indices[:, core_idx]
      // TODO: probably a very slow way to do it, wait for a reasonable gather
      // implementation
      // https://github.com/tensorflow/tensorflow/issues/206
      curr_core = tt_a.tt_cores[core_idx]
      curr_core = tf.transpose(curr_core, (1, 0, 2))
      core_slices = tf.gather(curr_core, curr_elements_idx)
      tt_a_elements = tf.matmul(tt_a_elements, core_slices)
  tt_a_elements = tf.reshape(tt_a_elements, (1, -1))
  sparse_b_elements = tf.reshape(sparse_b.values, (-1, 1))
  result = tf.matmul(tt_a_elements, sparse_b_elements)
  // Convert a 1x1 matrix into a number.

After Change

      core_slices = tf.gather(curr_core, curr_elements_idx)
      tt_a_elements = tf.matmul(tt_a_elements, core_slices)
  else:
    tt_a_elements = gather_nd(tt_a, sparse_b.indices)
  tt_a_elements = tf.reshape(tt_a_elements, (1, -1))
  sparse_b_elements = tf.reshape(sparse_b.values, (-1, 1))
  result = tf.matmul(tt_a_elements, sparse_b_elements)
  // Convert a 1x1 matrix into a number.

Instances