from typing import Callable, Generator, Tuple, Dict, Any
from deepmd.env import tf
from deepmd.utils.sess import run_sess
[docs]class ParallelOp:
"""Run an op with data parallelism.
Parameters
----------
builder : Callable[..., Tuple[Dict[str, tf.Tensor], Tuple[tf.Tensor]]]
returns two objects: a dict which stores placeholders by key, and a tuple with the final op(s)
nthreads : int, optional
the number of threads
config : tf.ConfigProto, optional
tf.ConfigProto
Examples
--------
>>> from deepmd.env import tf
>>> from deepmd.utils.parallel_op import ParallelOp
>>> def builder():
... x = tf.placeholder(tf.int32, [1])
... return {"x": x}, (x + 1)
...
>>> p = ParallelOp(builder, nthreads=4)
>>> def feed():
... for ii in range(10):
... yield {"x": [ii]}
...
>>> print(*p.generate(tf.Session(), feed()))
[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]
"""
def __init__(self, builder: Callable[..., Tuple[Dict[str, tf.Tensor], Tuple[tf.Tensor]]], nthreads: int = None, config: tf.ConfigProto = None) -> None:
if nthreads is not None:
self.nthreads = nthreads
elif config is not None:
self.nthreads = max(config.inter_op_parallelism_threads, 1)
else:
self.nthreads = 1
self.placeholders = []
self.ops = []
for ii in range(self.nthreads):
with tf.name_scope("task_%d" % ii) as scope:
placeholder, op = builder()
self.placeholders.append(placeholder)
self.ops.append(op)
[docs] def generate(self, sess: tf.Session, feed: Generator[Dict[str, Any], None, None]) -> Generator[Tuple, None, None]:
"""Returns a generator.
Parameters
----------
feed : Generator[dict, None, None]
generator which yields feed_dict
Yields
------
Generator[Tuple, None, None]
generator which yields session returns
"""
nn = self.nthreads
while True:
feed_dict = {}
for ii in range(self.nthreads):
try:
fd = next(feed)
except StopIteration:
if ii == 0:
return
nn = ii
break
for kk, vv in fd.items():
feed_dict[self.placeholders[ii][kk]] = vv
ops = self.ops[:nn]
for yy in run_sess(sess, ops, feed_dict=feed_dict):
yield yy