def main(args):
graph = ts.new_graph()
input_size = tuple([int(x) for x in args.input_size.split('x')])
input = graph.new_input(dims=input_size)
all_w = create_layer_weights(graph, NUM_LAYERS, args.channels)
all_arcs = parse_arcs(args.input_file)
if args.num_models is not None:
all_arcs = all_arcs[:args.num_models]
# stem conv
t = graph.conv2d(input=input,
weight=graph.new_weight(dims=(args.channels, input.dim(1),
1, 1)),
strides=(1, 1),
padding="SAME",
activation="RELU")
for arc in all_arcs:
create_architecture(arc, graph, t, all_w)
if args.save_models:
onnx_model = ts.export_onnx(graph)
onnx.save(onnx_model, 'original_model.onnx')
new_graph = ts.optimize(graph, alpha=1.0, budget=1000)
if args.save_models:
onnx_model = ts.export_onnx(new_graph)
onnx.save(onnx_model, 'optimized_model.onnx')
def main(args):
graph = ts.new_graph()
input_size = tuple([int(x) for x in args.input_size.split('x')])
input = graph.new_input(dims=input_size)
shared_resnet_model(graph, input, args.num_models, args.num_shared_blocks)
if args.save_graphs:
original_model = ts.export_onnx(graph)
onnx.save(original_model, 'original_model.onnx')
new_graph = ts.optimize(graph, alpha=1.0, budget=1000)
if args.save_graphs:
optimized_model = ts.export_onnx(new_graph)
onnx.save(optimized_model, 'optimized_model.onnx')
def graph_latency(graph: Graph,
batchsize,
warmup,
number,
repeat,
optimize,
alpha=1.0,
budget=1000):
"""
Measure the latency of TASO optimized computation graph in TASO framework.
:param graph: ios.ir.Graph
The computation graph that is going to measure the latency.
:param batchsize: int
The execution batch size.
:param warmup: int
Not used.
:param number: int
Not used.
:param repeat: int
The number of latency measurement.
:param optimize: boolean
When optimize=True, optimize the computation graph in TASO and measure the latency.
When optimize=False, directly measure the latency in TASO.
:param alpha:
The relaxation coefficient.
:param budget:
The iteration budget.
:return: List[float]
The latency measurement results.
"""
tg = graph_ios2taso(graph, batchsize)
if optimize:
tg = taso.optimize(tg, alpha=alpha, budget=budget)
# warmup and number has already implemented in taso itself
return [tg.run_time() for _ in range(repeat)]
strides = (1, 1)
unoptimized_model = ts.export_onnx(graph)
debug_dir = None
if args.debug_dir is not None:
debug_dir = args.debug_dir.resolve()
debug_dir.mkdir(parents=True)
if debug_dir is not None:
graph.export_to_file(str(debug_dir / "unoptimized.txt").encode())
if args.export:
onnx.checker.check_model(unoptimized_model)
onnx.save(
unoptimized_model,
str(args.output_dir / f"resnext50_{batch_size}_unoptimized.onnx"))
_optimized_model = ts.optimize(graph,
alpha=args.alpha,
budget=args.budget,
print_subst=args.print_subst)
if debug_dir is not None:
_optimized_model.export_to_file(
str(debug_dir / "optimized.txt").encode())
if args.export:
optimized_model = ts.export_onnx(_optimized_model)
onnx.save(
optimized_model,
str(args.output_dir / f"resnext50_{batch_size}_optimized.onnx"))
# new_graph = ts.optimize(graph, alpha=args.alpha, budget=args.budget)
# onnx_model = ts.export_onnx(new_graph)
# onnx.checker.check_model(onnx_model)
# onnx.save(onnx_model, "resnext50_xflow.onnx")
old_graph.build_graph()
# warm up
for _, data in enumerate(test_input):
old_graph.taso_forward(data)
# real run
time_sum = 0
for _, data in enumerate(test_input):
start = time.time()
old_graph.taso_forward(data)
time_sum += (time.time() - start)
print("cuDNN runtime inference time before taso: {}sec".format(time_sum / len(test_input)))
f.write("cuDNN runtime inference time before taso: {}sec\n\n".format(time_sum / len(test_input)))
print("taso.optimize()")
new_graph = taso.optimize(old_graph, alpha=1.05, budget=100)
#print("[after opt] taso runtime performance: {}ms".format(new_graph.run_time()))
#taso_tensor_input = new_graph.new_input_with_value(dims=(1, 3, 299, 299))
new_graph.build_graph()
# warm up
for _, data in enumerate(test_input):
new_graph.taso_forward(data)
# real run
time_sum = 0
for _, data in enumerate(test_input):
start = time.time()
new_graph.taso_forward(data)
time_sum += (time.time() - start)
print("cuDNN runtime inference time after taso optimization: {}sec".format(time_sum / len(test_input)))
f.write("cuDNN runtime inference time after taso optimization: {}sec\n\n".format(time_sum / len(test_input)))
import taso
import onnx
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-f", "--file", help="Path to input ONNX file", required=True)
args = parser.parse_args()
#graph = taso.load_onnx("/home/ubuntu/taso/onnx/squeezenet1.1.onnx")
#graph = taso.load_onnx("/home/ubuntu/taso/onnx/bertsquad10.onnx")
graph = taso.load_onnx(args.file)
#graph = xflow.load("/home/ubuntu/resnext-101.onnx")
#graph = xflow.load("/home/ubuntu/ONNXModel/inception_v2/model.onnx")
new_graph = taso.optimize(graph, alpha = 1.0, budget = 100, print_subst = True)
onnx_model = taso.export_onnx(new_graph)
onnx.checker.check_model(onnx_model)
onnx.save(onnx_model, "{}.taso.onnx".format(args.file))
t.append(combine(graph, x, input))
midt = list()
midt.append(graph.add(graph.relu(t[0]), graph.sigmoid(t[3])))
midt.append(graph.add(graph.sigmoid(t[1]), graph.tanh(t[2])))
midt.append(graph.mul(graph.sigmoid(t[4]), graph.tanh(t[5])))
midt.append(graph.mul(graph.sigmoid(t[6]), graph.relu(t[7])))
midt.append(graph.add(graph.sigmoid(midt[1]), graph.tanh(midt[2])))
midt.append(graph.mul(graph.tanh(midt[0]), graph.tanh(midt[3])))
midt.append(graph.mul(graph.tanh(midt[4]), graph.tanh(midt[5])))
return graph.tanh(midt[6])
graph = taso.new_graph()
xs = list()
for i in range(length):
xs.append(graph.new_input(dims=(1, hidden_size)))
state = graph.new_weight(dims=(1, hidden_size))
for i in range(length):
state = nas_node(graph, state, xs[i])
old_time = graph.run_time()
new_graph = taso.optimize(graph, alpha=1.0, budget=100)
new_time = new_graph.run_time()
print("Run time of original graph is: {}".format(old_time))
print("Run time of optimized graph is: {}".format(new_time))
args = get_args()
with open(args.result_file, "a") as f:
f.write("{}\t{}\n".format(old_time, new_time))
# transpose the output back
output = graph.transpose(output,perm=(1,0,2), shuffle=True)
output = graph.reshape(output, shape=(64, 1024))
# a final linear layer
linear = graph.new_weight(dims=(d_model, d_model))
output = graph.matmul(input, linear)
return output
if __name__ == '__main__':
test_input = list()
for i in range(500):
test_input.append(np.random.randn(seq_length, hidden_dims))
graph = ts.new_graph()
input = graph.new_input(dims=(seq_length, hidden_dims))
input = graph.relu(input)
t = input
for i in range(12):
t = attention(graph, t, 16)
print(t)
before_model = ts.export_onnx(graph)
onnx.save(before_model, "./onnx_models/bert.onnx")
new_graph = ts.optimize(graph, alpha=1.05, budget=100)
after_model = ts.export_onnx(new_graph)
onnx.save(after_model, "./onnx_models/bert_taso.onnx")
ts.append(graph.maxpool2d(input=cur, kernels=(3,3), strides=(2,2), padding="SAME"))
ts.append(seperable_conv(graph, input=outputs[0], out_channels=out_channels,
kernels=(3,3), strides=(1,1), padding="SAME"))
outputs.append(graph.add(ts[6], ts[7]))
ts.append(graph.avgpool2d(input=outputs[0], kernels=(3,3), strides=(1,1), padding="SAME"))
ts.append(outputs[1])
outputs.append(graph.add(ts[8], ts[9]))
return graph.concat(1, outputs)
graph = ts.new_graph()
input = graph.new_input(dims=(1,3,224,224))
weight = graph.new_weight(dims=(64,3,7,7))
input = graph.conv2d(input=input, weight=weight, strides=(2,2),
padding="SAME", activation="RELU")
input = graph.maxpool2d(input=input, kernels=(3,3), strides=(2,2), padding="SAME")
out_channels = 128
for i in range(3):
prev = input
cur = input
for j in range(5):
t = normal_cell(graph, prev, cur, out_channels)
prev = cur
cur = t
out_channels *= 2
input = reduction_cell(graph, prev, cur, out_channels)
new_graph = ts.optimize(graph, alpha=1.0, budget=-1)
onnx_model = ts.export_onnx(new_graph)
onnx.checker.check_model(onnx_model)
onnx.save(onnx_model, "nasneta_taso.onnx")
import taso import onnx #Build DNN model graph = taso.new_graph() input = graph.new_input(dims=(1,128,56,56)) w1 = graph.new_weight(dims=(128,128,3,3)) w2 = graph.new_weight(dims=(128,128,1,1)) w3 = graph.new_weight(dims=(128,128,3,3)) left = graph.conv2d(input=input, weight=w1, strides=(1,1), padding="SAME", activation="RELU") left = graph.conv2d(input=input, weight=w3, strides=(1,1), padding="SAME") right = graph.conv2d(input=input, weight=w2, strides=(1,1), padding="SAME", activation="RELU") output = graph.add(left, right) output = graph.relu(output) #Optimize DNN model new_graph = taso.optimize(graph) onnx_model = taso.export_onnx(new_graph) onnx.save(onnx_model, "arbitrary_DNN.onnx")
def inception_logits(graph, v):
return pool2d(graph, v, pool_type='global_avg')
def inception_v3(batch_size=1):
graph = taso.new_graph()
v = graph.new_input(dims=(batch_size, 3, 299, 299))
v = inception_front(graph, v)
v = inception_a(graph, v, 32)
v = inception_a(graph, v, 64)
v = inception_a(graph, v, 64)
v = inception_b(graph, v)
v = inception_c(graph, v, 128)
v = inception_c(graph, v, 160)
v = inception_c(graph, v, 160)
v = inception_c(graph, v, 192)
v = inception_d(graph, v)
v = inception_e(graph, v)
v = inception_e(graph, v)
v = inception_logits(graph, v)
return graph
graph = inception_v3(
batch_size=32) # change batch_size from 4 to 8 would cause error.
opt_graph = taso.optimize(graph, alpha=1.0, budget=30)
print(graph.run_time())
print(opt_graph.run_time())
