def receive(self, x, mesh_axis, source_pcoord):
"""Collective receive in groups.
Each group contains the processors that differ only in mesh_axis.
```python
group_size = self.shape[mesh_axis].size
```
Args:
x: a LaidOutTensor
mesh_axis: an integer
source_pcoord: a list of optional integers. Each element is either None
or an integer in [0, group_size). If source_pcoord[k] is None, then the
output for the k-th processor in each group is a zero tensor. If
source_pcoord[k] is not None, then the output for the k-th processor in
each group is equal to the input for the source_pcoord[k]-th processor
in that group.
Returns:
a LaidOutTensor
"""
x = x.to_laid_out_tensor()
t = x.one_slice
source_target_pairs = []
for pnum in xrange(self.size):
coord = mtf.pnum_to_processor_coordinates(self.shape, pnum)
k = coord[mesh_axis]
if source_pcoord[k] is not None:
coord[mesh_axis] = source_pcoord[k]
source_pnum = mtf.processor_coordinates_to_pnum(
self.shape, coord)
source_target_pairs.append(
[self.l2p(source_pnum),
self.l2p(pnum)])
if not source_target_pairs:
ret = tf.zeros_like(t, t.dtype)
elif t.dtype in [tf.float32, tf.bfloat16, tf.int32]:
ret = tpu_ops.collective_permute(t, source_target_pairs)
else:
# If t is not one of the allowed types, cast and cast back.
ret = tf.cast(
tpu_ops.collective_permute(tf.cast(t, tf.float32),
source_target_pairs), t.dtype)
return self.LaidOutTensor([ret])
def receive(self, x, mesh_axis, source_pcoord):
"""Collective receive in groups.
Each group contains the processors that differ only in mesh_axis.
```python
group_size = self.shape[mesh_axis].size
```
Args:
x: a LaidOutTensor
mesh_axis: an integer
source_pcoord: a list of optional integers. Each element is either None
or an integer in [0, group_size). If source_pcoord[k] is None, then the
output for the k-th processor in each group is a zero tensor. If
source_pcoord[k] is not None, then the output for the k-th processor in
each group is equal to the input for the source_pcoord[k]-th processor
in that group.
Returns:
a LaidOutTensor
"""
x = x.to_laid_out_tensor()
t = x.one_slice
source_target_pairs = []
for pnum in xrange(self.size):
coord = self.pnum_to_processor_coordinates(self.shape, pnum)
k = coord[mesh_axis]
if source_pcoord[k] is not None:
coord[mesh_axis] = source_pcoord[k]
target_pnum = self.processor_coordinates_to_pnum(coord)
source_target_pairs.append([pnum, target_pnum])
return tpu_ops.collective_permute(t, source_target_pairs)
