def generate_returns_placeholders():
pb_returns = []
pb_placeholders = []
for op in returns:
pb_returns.append(op_to_protobuf(op))
for op in placeholders:
pb_placeholders.append(op_to_protobuf(op))
return pb_returns, pb_placeholders
def generate_messages():
pb_returns = [op_to_protobuf(o) for o in returns]
pb_placeholders = [op_to_protobuf(o) for o in placeholders]
for pb_ops, pb_edges in pb_graph:
msg = make_computation_request(
pb_ops, pb_edges, pb_returns, pb_placeholders)
yield msg
pb_returns, pb_placeholders = [], []
def test_op_to_protobuf():
axis = ng.make_axis(name='C', length=2)
axes = ng.make_axes([axis])
orig_op = ng.placeholder(axes)
# Test attributes
orig_op.test0 = 'stringval_attr'
orig_op.test1 = [-1.0, 4]
orig_op.test2 = dict(foo=2, you='bar')
orig_op.test3 = dict()
orig_op.test4 = slice(1, 3, 5)
orig_op.test5 = slice(1, 3)
orig_op.test6 = slice(1, None, 3)
orig_op.test7 = axis
orig_op.test8 = axes
# Test metadata
orig_op.metadata['test0'] = 'stringval'
orig_op.metadata['test1'] = [1, 4.0]
orig_op.metadata['test2'] = dict(hey=1, you=4.0)
orig_op.metadata['test4'] = dict()
orig_op.metadata['test5'] = slice(1, 3, 5)
orig_op.metadata['test6'] = slice(1, 3)
orig_op.metadata['test7'] = slice(1, None, 5)
orig_op.metadata['test8'] = axis
orig_op.metadata['test9'] = axes
pb_op = ser.op_to_protobuf(orig_op)
py_op = ser.protobuf_to_op(pb_op)
assert_object_equality(py_op, orig_op)
def computation(self, returns, placeholders):
if not self.initialized:
raise RuntimeError("RPC build_transformer request failed!")
update_comm_deps(returns)
pb_subgraph = _serialize_graph(returns + list(placeholders))
pb_returns = []
pb_placeholders = []
for op in returns:
pb_returns.append(op_to_protobuf(op))
for op in placeholders:
pb_placeholders.append(op_to_protobuf(op))
response = self.RPC.Computation(
hetr_pb2.ComputationRequest(
subgraph=pb_subgraph,
returns=pb_returns,
placeholders=pb_placeholders),
_TIMEOUT_SECONDS)
if response.comp_id >= 0:
rpcComputationClient = RPCComputationClient(response.comp_id, self.RPC)
return rpcComputationClient
else:
raise RuntimeError("RPC computation request failed!")
def generate_messages():
pb_ops, pb_edges = [], []
pb_returns, pb_placeholders = generate_returns_placeholders()
ops = Op.all_op_references(returns + list(placeholders))
for i, op in enumerate(ops):
pb_ops.append(op_to_protobuf(op))
add_edges(pb_edges, pb_ops, op)
if (i != 0 and i % _OPS_PER_MSG == 0) or (i == len(ops) - 1):
msg = make_computation_request(pb_ops, pb_edges,
pb_returns, pb_placeholders)
yield msg
pb_ops, pb_edges = [], []
pb_returns, pb_placeholders = [], []
def __init__(self, hetr, computation_op):
self.child_computations = dict()
self.transformer = hetr
# clear send_nodes for multiple computations
if hetr.send_nodes:
hetr.send_nodes.clear()
self.send_nodes = hetr.send_nodes
self.computation_op = computation_op
# self.returns could be replaced by comp_op.returns if it were expressed as a set
self.returns = OrderedSet()
if isinstance(computation_op.returns, collections.Container):
self.returns.update(list(computation_op.returns))
elif isinstance(computation_op.returns, Op):
self.returns.update(list([computation_op.returns]))
# if one of the requested results is marked as distributed across devices,
# wrap it in a ResultOp to facilitate DistributedPass inserting a gather operation
new_returns = OrderedSet()
for op in self.returns:
if 'device_id' in op.metadata and \
isinstance(op.metadata['device_id'], (list, tuple)):
op.metadata['is_split_op'] = True
new_result = ResultOp(device_id=0, args=tuple([op]))
op.metadata['hetr_replaced_by'] = new_result
new_result.metadata['replaces_op'] = op
new_returns.add(new_result)
else:
new_returns.add(op)
# Do Hetr passes
logger.info('Running graph passes'),
pass_ops = new_returns | OrderedSet(self.computation_op.parameters)
for graph_pass in self.transformer.graph_passes:
pass_ops = pass_ops | OrderedSet(hetr.send_nodes)
graph_pass.do_pass(ops=pass_ops)
# hack around new TensorValueOp that wraps AssignableTensorOp
# autogenerated by creating a ComputationOp:
for p in self.computation_op.parameters:
if isinstance(p, TensorValueOp):
p.metadata.update(p.states_read[0].metadata)
logger.info('Launching child processes'),
# assume all children are the same type
# and all GPUs are in one chassis
num_process = len(self.transformer.child_transformers)
ppn = 1 if self.transformer.default_device == 'cpu' else num_process
self.transformer.mpilauncher.launch(num_process, ppn)
self.transformer.setup_child_transformers(num_process)
def is_my_op(op, name):
op_trans = op.metadata['transformer']
return name == op_trans or name in op_trans
logger.info('Serializaing computation graph'),
# build whole_graph once to avoid slow serialization once per worker
# split whole pb message into list of smaller chunks
# gRPC prefers sending smaller messages
placeholders = [p for p in self.computation_op.parameters]
all_returns = [o for o in self.send_nodes | new_returns]
transform_returns = [
o.args[0] if isinstance(o, ResultOp) else o for o in all_returns
]
whole_graph = Op.all_op_references(transform_returns + placeholders)
pb_whole_graph = []
pb_ops, pb_edges = [], []
for i, o in enumerate(whole_graph):
pb_ops.append(op_to_protobuf(o))
add_edges(pb_edges, pb_ops, o)
if (i != 0 and i % _OPS_PER_MSG == 0) or (i
== len(whole_graph) - 1):
pb_whole_graph.append((pb_ops, pb_edges))
pb_ops, pb_edges = [], []
t_placeholders, t_returns = {}, {}
for t_name in self.transformer.child_transformers.keys():
t_placeholders[t_name] = [
p for p in placeholders if is_my_op(p, t_name)
]
t_returns[t_name] = [r for r in all_returns if is_my_op(r, t_name)]
# create_computation is an async call using gPRC future
# allowing child transformers to create computation simultaneously
# get_computation waits the corresponding request to finish
logger.info('Creating remote computations'),
for t_name, trans in iteritems(self.transformer.child_transformers):
logger.debug('child transformer: {}'.format(t_name))
trans.build_transformer()
transform_ops = [
r.args[0] if isinstance(r, ResultOp) else r
for r in t_returns[t_name]
]
trans.create_computation(pb_whole_graph, transform_ops,
t_placeholders[t_name])
for t_name, trans in iteritems(self.transformer.child_transformers):
comp = trans.get_computation()
comp.param_idx = [
g_pos for g_pos, p in enumerate(self.computation_op.parameters)
if is_my_op(p, t_name)
]
# when there is a ResultOp, hack around it
comp.returns = dict()
for i, op in enumerate(t_returns[t_name]):
if op in self.returns and 'hetr_replaced_by' not in op.metadata:
comp.returns[op] = i
elif 'replaces_op' in op.metadata and op.metadata[
'replaces_op'] in self.returns:
comp.returns[op.metadata['replaces_op']] = i
self.child_computations[t_name] = comp
