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inferenceEngine.py
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inferenceEngine.py
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from __future__ import absolute_import, division, print_function, unicode_literals
from models.dlrm_s_caffe2 import DLRM_Wrapper
from models.wide_and_deep import Wide_and_Deep_Wrapper
from models.ncf import NCF_Wrapper
from models.din import DIN_Wrapper
from models.dien import DIEN_Wrapper
from models.multi_task_wnd import MT_Wide_and_Deep_Wrapper
import numpy as np
from utils.packets import ServiceResponse
# data generation
from data_generator.dlrm_data_caffe2 import DLRMDataGenerator
import threading
from multiprocessing import Queue
from caffe2.proto import caffe2_pb2
from caffe2.python import core,workspace
import time
import sys
import caffe2.python._import_c_extension as C
def run_model(model, args, internal_logging, responseQueue):
top_fc_layers = args.arch_mlp_top.split("-")
fc_tag = "top:::fc" + str(len(top_fc_layers)-1) + "_z"
while True:
if args.model_type == "dlrm":
model.dlrm.run()
elif args.model_type == "wnd":
model.wnd.run()
elif args.model_type == "ncf":
model.ncf.run()
elif args.model_type == "din":
model.din.run()
elif args.model_type == "mtwnd":
model.mtwnd.run()
elif args.model_type == "dien":
model.dien.run()
response = internal_logging.get()
if response == None:
return
inference_end_time = time.time()
response.inference_end_time = inference_end_time
#out_size = np.array(workspace.FetchBlob(fc_tag)).size / int(top_fc_layers[-2])
if args.model_type == "ncf":
ln_top = np.fromstring(args.arch_mlp_top, dtype=int, sep="-")
out_size = np.array(workspace.FetchBlob(model.ncf.tout)).size / ln_top[-1]
else:
out_size = np.array(workspace.FetchBlob(fc_tag)).size / int(top_fc_layers[-2])
response.out_batch_size = out_size
responseQueue.put(response)
def inferenceEngine(args,
requestQueue=None,
engine_id=None,
responseQueue=None,
inferenceEngineReadyQueue=None):
q_inference_logging = Queue()
q_inference_done = Queue()
### some basic setup ###
np.random.seed(args.numpy_rand_seed)
np.set_printoptions(precision=args.print_precision)
# #########################################################################
# Data generation
# - with multiple model implementations this should instantiate the
# particular model class' data generator
# #########################################################################
if args.model_type == "dlrm":
datagen = DLRMDataGenerator(args)
(nbatches, lX, lS_l, lS_i) = datagen.generate_input_data()
(nbatches, lT) = datagen.generate_output_data()
# construct the neural network specified by command line arguments ###
model = DLRM_Wrapper( args )
model.create(lX[0], lS_l[0], lS_i[0], lT[0])
elif args.model_type == "wnd":
datagen = DLRMDataGenerator(args)
(nbatches, lX, lS_l, lS_i) = datagen.generate_input_data()
(nbatches, lT) = datagen.generate_output_data()
model = Wide_and_Deep_Wrapper( args )
model.create(lX[0], lS_l[0], lS_i[0], lT[0])
elif args.model_type == "mtwnd":
datagen = DLRMDataGenerator(args)
(nbatches, lX, lS_l, lS_i) = datagen.generate_input_data()
(nbatches, lT) = datagen.generate_output_data()
# construct the neural network specified by command line arguments ###
model = MT_Wide_and_Deep_Wrapper( args )
model.create(lX[0], lS_l[0], lS_i[0], lT[0])
elif args.model_type == "ncf":
datagen = DLRMDataGenerator(args)
(nbatches, lX, lS_l, lS_i) = datagen.generate_input_data()
(nbatches, lT) = datagen.generate_output_data()
model = NCF_Wrapper( args )
model.create(lX[0], lS_l[0], lS_i[0], lT[0])
elif args.model_type == "din":
datagen = DLRMDataGenerator(args)
(nbatches, lX, lS_l, lS_i) = datagen.generate_input_data()
(nbatches, lT) = datagen.generate_output_data()
model = DIN_Wrapper( args )
model.create(lX[0], lS_l[0], lS_i[0], lT[0])
elif args.model_type == "dien":
datagen = DLRMDataGenerator(args)
(nbatches, lX, lS_l, lS_i) = datagen.generate_input_data()
(nbatches, lT) = datagen.generate_output_data()
# construct the neural network specified by command line arguments ###
model = DIEN_Wrapper( args )
model.create(lX[0], lS_l[0], lS_i[0], lT[0])
if requestQueue == None:
total_time = 0
dload_time = 0
time_start = time.time()
for k in range(args.nepochs):
for j in range(nbatches):
if args.model_type == "dlrm":
time_load_start = time.time()
time_load_end = model.dlrm.run(lX[j], lS_l[j], lS_i[j])
dload_time += (time_load_end - time_load_start)
elif args.model_type == "wnd":
time_load_start = time.time()
time_load_end = model.wnd.run(lX[j], lS_l[j], lS_i[j])
dload_time += (time_load_end - time_load_start)
elif args.model_type == "mtwnd":
time_load_start = time.time()
time_load_end = model.mtwnd.run(lX[j], lS_l[j], lS_i[j])
dload_time += (time_load_end - time_load_start)
elif args.model_type == "ncf":
time_load_start = time.time()
time_load_end = model.ncf.run(lX[j], lS_l[j], lS_i[j])
dload_time += (time_load_end - time_load_start)
elif args.model_type == "din":
model.din.run(lX[j], lS_l[j], lS_i[j])
elif args.model_type == "dien":
model.dien.run(lX[j], lS_l[j], lS_i[j])
time_end = time.time()
dload_time *= 1000.
total_time += (time_end - time_start) * 1000.
print("Total data loading time: ***", dload_time, " ms")
print("Total data loading time: ***", dload_time / (args.nepochs * nbatches), " ms/iter")
print("Total computation time: ***", (total_time - dload_time), " ms")
print("Total computation time: ***", (total_time - dload_time) / (args.nepochs * nbatches), " ms/iter")
print("Total execution time: ***", total_time, " ms")
print("Total execution time: ***", total_time / (args.nepochs * nbatches), " ms/iter")
else:
# Run DLRM model inferences in a separate thread in order to decouple input
# and inference run-times (non-blocking FeedBlob() caffe2 call)
inference_thread = threading.Thread( target=run_model,
args = (model,
args,
q_inference_logging,
responseQueue
)
)
inference_thread.daemon = True
inference_thread.start()
total_time = 0
while True:
inferenceEngineReadyQueue.put(True)
request = requestQueue.get()
if request is None:
time.sleep(4)
q_inference_logging.put(None)
responseQueue.put(None)
return
batch_id = request.batch_id
lS_l_curr = np.transpose(np.array(lS_l[batch_id]))
lS_l_curr = np.transpose(np.array(lS_l_curr[:request.batch_size]))
lS_ids_curr = np.array(lS_i[batch_id])
lS_ids_curr = np.array(lS_ids_curr[:][:, :request.batch_size * args.num_indices_per_lookup])
start_time = time.time()
# Parameterized on batch_size
model.run_queues(lS_ids_curr,
lS_l_curr,
lX[batch_id][:request.batch_size],
request.batch_size
)
end_time = time.time()
response = ServiceResponse( consumer_id = engine_id,
epoch = request.epoch,
batch_id = request.batch_id,
batch_size = request.batch_size,
arrival_time = request.arrival_time,
process_start_time = start_time,
queue_end_time = end_time,
total_sub_batches = request.total_sub_batches,
exp_packet = request.exp_packet,
sub_id = request.sub_id
)
q_inference_logging.put(response)
return
if __name__=="__main__":
from utils.utils import cli
args = cli()
inferenceEngine(args)