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benchmark_autotvm.py
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benchmark_autotvm.py
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import os
import argparse
import numpy as np
import tvm
from tvm import relay, autotvm
import tvm.contrib.graph_runtime as runtime
from utils import get_network, make_network_key, use_graph_tuner
def benchmark(network, batch_size, dtype, target, log_prefix, repeat):
layout = "NCHW"
mod, params, input_name, input_shape, output_shape = get_network(
network, batch_size, dtype, layout
)
if use_graph_tuner(network, batch_size, dtype, target):
log_file = log_prefix + ".graph.log"
history_best_context = autotvm.apply_graph_best(log_file)
else:
log_file = log_prefix + ".kernel.log"
history_best_context = autotvm.apply_history_best(log_file)
assert os.path.exists(log_file), "The log file '%s' does not exist." % log_file
print("Use log file %s" % log_file)
if network in ["bert"]:
# Build module
with history_best_context:
with tvm.transform.PassContext(opt_level=3):
lib = relay.build(mod, target=target, params=params)
ctx = tvm.context(str(target), 0)
module = runtime.GraphModule(lib["default"](ctx))
# Feed input data
seq_length = input_shape[0][1]
data = np.random.uniform(size=input_shape[0])
token_types = np.random.uniform(size=input_shape[1])
valid_length = np.array([seq_length] * batch_size)
module.set_input(data0=data, data1=token_types, data2=valid_length)
else:
# Build module
with history_best_context:
with tvm.transform.PassContext(opt_level=3):
lib = relay.build(mod, target=target, params=params)
ctx = tvm.context(str(target), 0)
module = runtime.GraphModule(lib["default"](ctx))
# Feed input data
data = np.random.uniform(size=input_shape)
module.set_input(input_name, data)
# Evaluate
ftimer = module.module.time_evaluator("run", ctx, min_repeat_ms=500, repeat=repeat)
return np.array(ftimer().results)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--network",
type=str,
choices=["resnet_50", "mobilenet_v2", "bert", "all"],
default="all",
help="The name of the neural network.",
)
parser.add_argument("--batch-size", type=int, default=1, help="The batch size")
parser.add_argument(
"--target",
type=str,
default="llvm -model=platinum-8124m -mcpu=skylake-avx512",
help="The compilation target.",
)
parser.add_argument("--dtype", type=str, default="float32", help="The data type.")
parser.add_argument(
"--logdir", type=str, default="tmp_logs/", help="Log file directory."
)
parser.add_argument("--repeat", type=int, default=3)
args = parser.parse_args()
if args.network == "all":
networks = ["resnet_50", "mobilenet_v2", "bert"]
else:
networks = [args.network]
batch_sizes = [args.batch_size]
dtypes = [args.dtype]
target = tvm.target.Target(args.target)
# Benchmark
result_messages = []
for network in networks:
for batch_size in batch_sizes:
for dtype in dtypes:
network_key = make_network_key(network, batch_size, dtype)
print("Benchmark %s ..." % network_key)
log_prefix = os.path.join(
args.logdir, "autotvm", target.model, network_key
)
prof_res = benchmark(
network, batch_size, dtype, target, log_prefix, args.repeat
)
prof_res *= 1000 # convert to millisecond
message = "%-18s %-12s %-19s (%s)" % (
network,
batch_size,
"%.2f ms" % np.mean(prof_res),
"%.2f ms" % np.std(prof_res),
)
result_messages.append(message)
# Print result
print("-------------------------------------------------------------")
print(
"%-18s %-12s %-20s"
% ("Network Name", "Batch size", "Mean Inference Time (std dev)")
)
print("-------------------------------------------------------------")
for line in result_messages:
print(line)
print("-------------------------------------------------------------")