def run_nccl_broadcast_double_send():
for device in self._devices:
with ops.device(device):
t = constant_op.constant(tensor_value)
collective_ops.broadcast_send(t, t.shape, t.dtype,
self._group_size, group_key,
instance_key)
def _multi_worker_init(**kwargs):
replica_ctx = get_replica_context()
global_id = replica_ctx.replica_id_in_sync_group
if global_id == 0:
unique_id = kit_lib.get_nccl_unique_id()
re = collective_ops.broadcast_send(
unique_id,
TensorShape([
32,
]),
int32,
group_size=replica_ctx.num_replicas_in_sync,
group_key=1,
instance_key=2)
else:
re = collective_ops.broadcast_recv(
TensorShape([
32,
]),
int32,
group_size=replica_ctx.num_replicas_in_sync,
group_key=1,
instance_key=2)
if global_id == 0:
global_seed = kwargs.get("seed", None) or kit_lib.gen_random_seed()
re_seed = collective_ops.broadcast_send(
global_seed,
TensorShape([
1,
]),
int64,
group_size=replica_ctx.num_replicas_in_sync,
group_key=1,
instance_key=3)
else:
global_seed = kwargs.get("seed", None)
re_seed = collective_ops.broadcast_recv(
TensorShape([
1,
]),
int64,
group_size=replica_ctx.num_replicas_in_sync,
group_key=1,
instance_key=3)
if (global_seed and global_seed != re_seed):
logging.warning(
"The seed: {} is not consistent with that from cheif-node: {}, "
"and the seed from cheif-node will be used.".format(
global_seed, re_seed))
visible_devices = _get_visible_devices()
status = kit_lib.plugin_init(
global_id,
replica_ctx.num_replicas_in_sync,
re,
re_seed,
visible_devices,
global_batch_size=kwargs['global_batch_size'])
return status
def testBasicNcclBroadcast(self):
tensor_value = [0.1, 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1]
group_size = 2
group_key = 1
instance_key = 1
devices = ['/GPU:{}'.format(i) for i in range(group_size)]
with self.session(config=self._configure(group_size)) as sess:
if not test_util.is_gpu_available(cuda_only=True):
self.skipTest('No GPU available')
collectives = []
with ops.device(devices[0]):
t = constant_op.constant(tensor_value)
collectives.append(
collective_ops.broadcast_send(t, t.shape, t.dtype,
group_size, group_key,
instance_key))
with ops.device(devices[1]):
t = constant_op.constant(tensor_value)
collectives.append(
collective_ops.broadcast_recv(t.shape, t.dtype, group_size,
group_key, instance_key))
results = sess.run(collectives)
for result in results:
self.assertAllClose(result, tensor_value, rtol=1e-5, atol=1e-5)
def initial_value_fn(): # pylint: disable=g-missing-docstring
# Only the first device participates in the broadcast of initial values.
group_key = self._collective_keys.get_group_key([device])
group_size = self._num_workers
collective_instance_key = (
self._collective_keys.get_variable_instance_key())
with ops.device(device):
initial_value = kwargs["initial_value"]
if callable(initial_value):
initial_value = initial_value()
assert not callable(initial_value)
initial_value = ops.convert_to_tensor(initial_value,
dtype=kwargs.get(
"dtype", None))
if self._num_workers > 1:
if self._is_chief:
bcast_send = collective_ops.broadcast_send(
initial_value, initial_value.shape,
initial_value.dtype, group_size, group_key,
collective_instance_key)
with ops.control_dependencies([bcast_send]):
return array_ops.identity(initial_value)
else:
return collective_ops.broadcast_recv(
initial_value.shape, initial_value.dtype,
group_size, group_key, collective_instance_key)
return initial_value
def broadcast_variables(self):
replica_ctx = tf.distribute.get_replica_context()
g_replica_id = replica_ctx.replica_id_in_sync_group
if replica_ctx.num_replicas_in_sync == 1:
return
variable = tf.identity(self._embedding_weights)
if 0 == g_replica_id:
values = collective_ops.broadcast_send(
variable,
variable.shape,
variable.dtype,
group_size=replica_ctx.num_replicas_in_sync,
group_key=2,
instance_key=2 + self._uid,
timeout=5)
else:
values = collective_ops.broadcast_recv(
variable.shape,
variable.dtype,
group_size=replica_ctx.num_replicas_in_sync,
group_key=2,
instance_key=2 + self._uid,
timeout=5)
self._embedding_weights.assign(values)
def initial_value_fn(): # pylint: disable=g-missing-docstring
# Only the first device participates in the broadcast of initial values.
group_key = self._collective_keys.get_group_key([device])
group_size = self._num_workers
collective_instance_key = (
self._collective_keys.get_variable_instance_key())
with ops.device(device):
initial_value = kwargs["initial_value"]
if callable(initial_value):
initial_value = initial_value()
assert not callable(initial_value)
initial_value = ops.convert_to_tensor(initial_value,
dtype=kwargs.get(
"dtype", None))
if self._num_workers > 1:
if self._is_chief:
# Unwrap `initial_value` if it is a `CheckpointInitialValue`.
# TODO(b/138130844): Revert the following check once
# `CheckpointInitialValue` class is removed.
if isinstance(initial_value,
trackable.CheckpointInitialValue):
initial_value = initial_value.wrapped_value
bcast_send = collective_ops.broadcast_send(
initial_value, initial_value.shape,
initial_value.dtype, group_size, group_key,
collective_instance_key)
with ops.control_dependencies([bcast_send]):
return array_ops.identity(initial_value)
else:
return collective_ops.broadcast_recv(
initial_value.shape, initial_value.dtype,
group_size, group_key, collective_instance_key)
return initial_value
def send():
s0 = collective_ops.broadcast_send(c * 3,
c.shape,
c.dtype,
group_size=2,
group_key=1,
instance_key=1)
with ops.control_dependencies([s0.op]):
return array_ops.identity(c)
def run_basic_nccl_broadcast():
collectives = []
with ops.device(self._devices[0]):
t = constant_op.constant(tensor_value)
collectives.append(collective_ops.broadcast_send(
t, t.shape, t.dtype, self._group_size, group_key, instance_key))
with ops.device(self._devices[1]):
t = constant_op.constant(tensor_value)
collectives.append(collective_ops.broadcast_recv(
t.shape, t.dtype, self._group_size, group_key, instance_key))
return collectives
def testAbortInstanceParamsResolution(self, device, communication):
if communication == "NCCL":
self.skipTest("b/171358086: cannot test multi worker NCCL")
dev0 = "/device:%s:0" % device
cluster_resolver = cluster_resolver_lib.TFConfigClusterResolver()
enable_collective_ops_with_barrier(cluster_resolver)
group_size = 2
group_key = 100
instance_key = 100
in_tensor = constant_op.constant([1.])
# First perform a normal all-reduce to complete the group resolution.
with ops.device(dev0):
collective_ops.all_reduce(in_tensor, group_size, group_key,
instance_key)
# We use broadcast to test aborting instance resolution since only broadcast
# waits for the group.
if cluster_resolver.task_id == 1:
def abort_fn():
time.sleep(2)
context.context().abort_collective_ops(errors.UNAVAILABLE,
"peer down")
t = threading.Thread(target=abort_fn)
t.start()
# Use a different instance key to trigger another instance resolution.
instance_key = 101
with self.assertRaisesRegex(errors.UnavailableError, "peer down"):
# This hangs on params resolution since we're only launching one
# collective for a group size of 2.
with ops.device(dev0):
collective_ops.broadcast_send(in_tensor, (1, ),
dtypes.float32, group_size,
group_key, instance_key)
# After abortion, subsequent collectives should fail immediately.
with self.assertRaisesRegex(errors.UnavailableError, "peer down"):
with ops.device(dev0):
collective_ops.broadcast_send(in_tensor, (1, ),
dtypes.float32, group_size,
group_key, instance_key)
t.join()
# Enable collective ops again in order to reset the collective executor.
enable_collective_ops_with_barrier(cluster_resolver)
# Reassign instance_key so that it's the same on each worker.
instance_key = 100
with ops.device(dev0):
if cluster_resolver.task_id == 0:
collective_ops.broadcast_send(in_tensor, (1, ), dtypes.float32,
group_size, group_key,
instance_key)
else:
collective_ops.broadcast_recv(
(1, ), dtypes.float32, group_size, group_key, instance_key)
def _overridden_initial_value_fn(device=d, index=i): # pylint: disable=g-missing-docstring
with ops.device(device):
initial_value = initial_value_fn()
assert not callable(initial_value)
initial_value = ops.convert_to_tensor(initial_value)
if self._is_chief and index == 0:
bcast_send = collective_ops.broadcast_send(
initial_value, initial_value.shape, initial_value.dtype,
group_size, group_key, collective_instance_key)
with ops.control_dependencies([bcast_send]):
return array_ops.identity(initial_value)
else:
return collective_ops.broadcast_recv(
initial_value.shape, initial_value.dtype, group_size,
group_key, collective_instance_key)
def _overridden_initial_value_fn(device=d, index=i): # pylint: disable=g-missing-docstring
with ops.device(device):
initial_value = initial_value_fn()
assert not callable(initial_value)
initial_value = ops.convert_to_tensor(initial_value)
if self._is_chief and index == 0:
bcast_send = collective_ops.broadcast_send(
initial_value, initial_value.shape, initial_value.dtype,
group_size, group_key, collective_instance_key)
with ops.control_dependencies([bcast_send]):
return array_ops.identity(initial_value)
else:
return collective_ops.broadcast_recv(
initial_value.shape, initial_value.dtype, group_size,
group_key, collective_instance_key)
def _testCollectiveBroadcast(self, t0):
group_key = 1
instance_key = 1
with self.test_session(
config=config_pb2.ConfigProto(device_count={'CPU': 2})) as sess:
with ops.device('/CPU:0'):
in0 = constant_op.constant(t0)
out0 = collective_ops.broadcast_send(in0, in0.shape, in0.dtype,
2, group_key, instance_key)
with ops.device('/CPU:1'):
c1 = constant_op.constant(t0)
out1 = collective_ops.broadcast_recv(c1.shape, c1.dtype,
2, group_key, instance_key)
run_options = config_pb2.RunOptions()
run_options.experimental.collective_graph_key = 1
results = sess.run([out0, out1], options=run_options)
self.assertAllClose(results[0], t0, rtol=1e-5, atol=1e-5)
self.assertAllClose(results[1], t0, rtol=1e-5, atol=1e-5)
def testNcclBroadcastDoubleSend(self):
tensor_value = [0.1, 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1]
group_size = 2
group_key = 1
instance_key = 1
devices = ['/GPU:{}'.format(i) for i in range(group_size)]
with self.session(config=self._configure(group_size)) as sess:
if not test_util.is_gpu_available(cuda_only=True):
self.skipTest('No GPU available')
collectives = []
for device in devices:
with ops.device(device):
t = constant_op.constant(tensor_value)
collectives.append(collective_ops.broadcast_send(
t, t.shape, t.dtype, group_size, group_key, instance_key))
with self.assertRaisesRegexp(errors.InternalError, 'already has source'):
sess.run(collectives)
def _testCollectiveBroadcast(self, t0):
group_key = 1
instance_key = 1
with self.session(
config=config_pb2.ConfigProto(device_count={'CPU': 2})) as sess:
with ops.device('/CPU:0'):
in0 = constant_op.constant(t0)
out0 = collective_ops.broadcast_send(in0, in0.shape, in0.dtype,
2, group_key, instance_key)
with ops.device('/CPU:1'):
c1 = constant_op.constant(t0)
out1 = collective_ops.broadcast_recv(c1.shape, c1.dtype,
2, group_key, instance_key)
run_options = config_pb2.RunOptions()
run_options.experimental.collective_graph_key = 1
results = sess.run([out0, out1], options=run_options)
self.assertAllClose(results[0], t0, rtol=1e-5, atol=1e-5)
self.assertAllClose(results[1], t0, rtol=1e-5, atol=1e-5)
def _broadcast_implementation(self, initial_value, device):
# This is an extension point for overriding, try to keep a stable API.
if self._num_workers <= 1:
return initial_value
# Only the first device participates in the broadcast of initial values.
group_key = self._collective_keys.get_group_key([device])
group_size = self._num_workers
collective_instance_key = (
self._collective_keys.get_variable_instance_key())
if self._is_chief:
bcast_send = collective_ops.broadcast_send(
initial_value, initial_value.shape, initial_value.dtype,
group_size, group_key, collective_instance_key)
with ops.control_dependencies([bcast_send]):
return array_ops.identity(initial_value)
else:
return collective_ops.broadcast_recv(initial_value.shape,
initial_value.dtype,
group_size, group_key,
collective_instance_key)
with tf.device('gpu:1'):
sum_reduce.append(collective_ops.all_reduce(v2, 2, 0, 1, 'Add', 'Id'))
print(sess.run(sum_reduce))
average_reduce = []
with tf.device('gpu:0'):
average_reduce.append(collective_ops.all_reduce(v1, 2, 1, 1, 'Add', 'Div'))
with tf.device('gpu:1'):
average_reduce.append(collective_ops.all_reduce(v2, 2, 1, 1, 'Add', 'Div'))
print(sess.run(average_reduce))
print('==========================')
bcast = []
# with tf.device('cpu:0'):
# bcast.append(collective_ops.broadcast_send(v0, v0.shape, v0.dtype, 2, 3, 1))
with tf.device('gpu:0'):
bcast.append(collective_ops.broadcast_send(v1, v1.shape, v1.dtype, 2, 3, 2))
with tf.device('gpu:1'):
bcast.append(collective_ops.broadcast_recv(v1.shape, v1.dtype, 2, 3, 2))
print(sess.run(bcast))
print('==========================')
average_reduce = []
with tf.device('gpu:0'):
average_reduce.append(collective_ops.all_reduce(v1, 2, 4, 3, 'Add', 'Div'))
with tf.device('gpu:1'):
average_reduce.append(collective_ops.all_reduce(v3, 2, 4, 3, 'Add', 'Div'))
print(sess.run(average_reduce))
def broadcast_send(t, shape, dtype, group_size, group_key, instance_key):
return collective_ops.broadcast_send(t, shape, dtype, group_size,
group_key, instance_key)
