def FeedInput(self, request, context):
logger.info("server: feed_input")
if request.comp_id not in self.computations:
message = 'unknown computation id {}'.format(request.comp_id)
return hetr_pb2.FeedInputReply(status=False, message=message)
try:
values = []
for v in request.values:
if v.HasField('scalar'):
values.append(protobuf_scalar_to_python(v.scalar))
else:
values.append(pb_to_tensor(v.tensor))
computation = self.computations[request.comp_id]
if self.transformer.transformer_name == "gpu":
import pycuda.driver as drv
if self.transformer.runtime and \
not self.transformer.runtime.ctx == drv.Context.get_current():
self.transformer.runtime.ctx.push()
outputs = computation(*values)
self.transformer.runtime.ctx.pop()
else:
outputs = computation(*values)
self.results[request.comp_id] = outputs
return hetr_pb2.FeedInputReply(status=True)
except Exception:
return hetr_pb2.FeedInputReply(status=False,
message=traceback.format_exc())
def get_results(self):
logger.info("client: get results")
if self.feed_input_response_future is None:
raise RuntimeError("call feed_input before get_results")
response = self.feed_input_response_future.result()
self.feed_input_response_future = None
if not response.status:
raise RuntimeError("RPC feed_input request failed: {}".format(
response.message))
response = self.RPC.GetResults(
hetr_pb2.GetResultsRequest(comp_id=self.comp_id), _TIMEOUT_SECONDS)
if not response.status:
raise RuntimeError("RPC get_results request failed: {}".format(
response.message))
return_list = []
for r in response.results:
if r.HasField('scalar'):
return_list.append(protobuf_scalar_to_python(r.scalar))
else:
return_list.append(pb_to_tensor(r.tensor))
return_dict = {
op: return_list[mypos]
for (op, mypos) in iteritems(self.returns)
}
return return_dict
def FeedInput(self, request, context):
if request.comp_id >= len(self.computations):
return hetr_pb2.FeedInputReply(status=False)
values = []
for v in request.values:
if v.HasField('scalar'):
values.append(protobuf_scalar_to_python(v.scalar))
else:
values.append(pb_to_tensor(v.tensor))
computation = self.computations[request.comp_id]
outputs = computation(*values)
self.results[request.comp_id] = outputs
return hetr_pb2.FeedInputReply(status=True)
def get_results(self):
response = self.RPC.GetResults(
hetr_pb2.GetResultsRequest(comp_id=self.comp_id), _TIMEOUT_SECONDS)
if not response.status:
raise RuntimeError("RPC get_results request failed!")
return_list = []
for r in response.results:
if r.HasField('scalar'):
return_list.append(protobuf_scalar_to_python(r.scalar))
else:
return_list.append(pb_to_tensor(r.tensor))
return_dict = {
op: return_list[mypos]
for (op, mypos) in iteritems(self.returns)
}
return return_dict
def test_scalar_to_protobuf():
orig_tensor = np.float32(12)
pb_tensor = ser.tensor_to_protobuf(orig_tensor)
py_tensor = ser.pb_to_tensor(pb_tensor)
np.testing.assert_allclose(orig_tensor, py_tensor)
def test_tensor_to_protobuf():
orig_tensor = np.arange(12, dtype=np.float32).reshape(3, 4)
pb_tensor = ser.tensor_to_protobuf(orig_tensor)
py_tensor = ser.pb_to_tensor(pb_tensor)
np.testing.assert_allclose(orig_tensor, py_tensor)
