x86 cpu卷积网络的自动调谐
x86 cpu卷积网络的自动调谐
这是一个关于如何为x86cpu调整卷积神经网络的文档。
本文不会在Windows或最新版本的macOS上运行。要让它运行，需要将主体包装在
if __name__ == "__main__": 块中。
import os
import numpy as np
import tvm
from tvm import relay, autotvm
from tvm.relay import testing
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
from tvm.autotvm.graph_tuner import DPTuner, PBQPTuner
import tvm.contrib.graph_runtime as runtime
Define network
首先需要在中继前端API中定义网络。可以从relay.testing测试或编译
relay.testing.resnet转换。也可以从MXNet、ONNX和TensorFlow加载模型。 本文选择restuning作为示例。
def get_network(name, batch_size):
"""Get the symbol definition and random weight of a network"""
input_shape = (batch_size, 3, 224, 224)
output_shape = (batch_size, 1000)
if "resnet" in name:
n_layer = int(name.split("-")[1])
mod, params = relay.testing.resnet.get_workload(
num_layers=n_layer, batch_size=batch_size, dtype=dtype
)
elif "vgg" in name:
n_layer = int(name.split("-")[1])
mod, params = relay.testing.vgg.get_workload(
num_layers=n_layer, batch_size=batch_size, dtype=dtype
)
elif name == "mobilenet":
mod, params = relay.testing.mobilenet.get_workload(batch_size=batch_size, dtype=dtype)
elif name == "squeezenet_v1.1":
mod, params = relay.testing.squeezenet.get_workload(
batch_size=batch_size, version="1.1", dtype=dtype
)
elif name == "inception_v3":
input_shape = (batch_size, 3, 299, 299)
mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype)
elif name == "mxnet":
# an example for mxnet model
from mxnet.gluon.model_zoo.vision import get_model
block = get_model("resnet18_v1", pretrained=True)
mod, params = relay.frontend.from_mxnet(block, shape={input_name: input_shape}, dtype=dtype)
net = mod["main"]
net = relay.Function(
net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs
)
mod = tvm.IRModule.from_expr(net)
else:
raise ValueError("Unsupported network: " + name)
return mod, params, input_shape, output_shape
# Replace "llvm" with the correct target of your CPU.
# For example, for AWS EC2 c5 instance with Intel Xeon
# Platinum 8000 series, the target should be "llvm -mcpu=skylake-avx512".
# For AWS EC2 c4 instance with Intel Xeon E5-2666 v3, it should be
# "llvm -mcpu=core-avx2".
target = "llvm"
batch_size = 1
dtype = "float32"
model_name = "resnet-18"
log_file = "%s.log" % model_name
graph_opt_sch_file = "%s_graph_opt.log" % model_name
# Set the input name of the graph
# For ONNX models, it is typically "0".
input_name = "data"
# Set number of threads used for tuning based on the number of
# physical CPU cores on your machine.
num_threads = 1
os.environ["TVM_NUM_THREADS"] = str(num_threads)
Configure tensor tuning settings and create tasks
为了在x86cpu上获得更好的内核执行性能，需要将卷积内核的数据布局从“NCHW”改为“NCHWc”。为了解决这种情况，在topi中定义了conv2d NCHWc运算符。将调整此运算符，而不是普通的conv2d。
将使用本地模式来优化配置。RPC跟踪器模式的设置类似于ARM CPU的卷积网络自动调谐教程中的方法。
为了进行精确测量，应该重复测量几次，并使用结果的平均值。此外，需要在重复测量之间刷新缓存中的权重张量。在端到端推断期间，这可以使一个操作符的测量延迟更接近其实际延迟。
tuning_option = {
"log_filename": log_file,
"tuner": "random",
"early_stopping": None,
"measure_option": autotvm.measure_option(
builder=autotvm.LocalBuilder(),
runner=autotvm.LocalRunner(
number=1, repeat=10, min_repeat_ms=0, enable_cpu_cache_flush=True
),
),
}
# You can skip the implementation of this function for this tutorial.
def tune_kernels(
tasks, measure_option, tuner="gridsearch", early_stopping=None, log_filename="tuning.log"
):
for i, task in enumerate(tasks):
prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
# create tuner
if tuner == "xgb" or tuner == "xgb-rank":
tuner_obj = XGBTuner(task, loss_type="rank")
elif tuner == "ga":
tuner_obj = GATuner(task, pop_size=50)
elif tuner == "random":
tuner_obj = RandomTuner(task)
elif tuner == "gridsearch":
tuner_obj = GridSearchTuner(task)
else:
raise ValueError("Invalid tuner: " + tuner)
# do tuning
n_trial = len(task.config_space)
tuner_obj.tune(
n_trial=n_trial,
early_stopping=early_stopping,
measure_option=measure_option,
callbacks=[
autotvm.callback.progress_bar(n_trial, prefix=prefix),
autotvm.callback.log_to_file(log_filename),
],
)
# Use graph tuner to achieve graph level optimal schedules
# Set use_DP=False if it takes too long to finish.
def tune_graph(graph, dshape, records, opt_sch_file, use_DP=True):
target_op = [
relay.op.get("nn.conv2d"),
]
Tuner = DPTuner if use_DP else PBQPTuner
executor = Tuner(graph, {input_name: dshape}, records, target_op, target)
executor.benchmark_layout_transform(min_exec_num=2000)
executor.run()
executor.write_opt_sch2record_file(opt_sch_file)
最后，启动优化作业并评估端到端性能。
def tune_and_evaluate(tuning_opt):
# extract workloads from relay program
print("Extract tasks...")
mod, params, data_shape, out_shape = get_network(model_name, batch_size)
tasks = autotvm.task.extract_from_program(
mod["main"], target=target, params=params, ops=(relay.op.get("nn.conv2d"),)
)
# run tuning tasks
tune_kernels(tasks, **tuning_opt)
tune_graph(mod["main"], data_shape, log_file, graph_opt_sch_file)
# compile kernels with graph-level best records
with autotvm.apply_graph_best(graph_opt_sch_file):
print("Compile...")
with tvm.transform.PassContext(opt_level=3):
lib = relay.build_module.build(mod, target=target, params=params)
# upload parameters to device
ctx = tvm.cpu()
data_tvm = tvm.nd.array((np.random.uniform(size=data_shape)).astype(dtype))
module = runtime.GraphModule(lib["default"](ctx))
module.set_input(input_name, data_tvm)
# evaluate
print("Evaluate inference time cost...")
ftimer = module.module.time_evaluator("run", ctx, number=100, repeat=3)
prof_res = np.array(ftimer().results) * 1000 # convert to millisecond
print(
"Mean inference time (std dev): %.2f ms (%.2f ms)"
% (np.mean(prof_res), np.std(prof_res))
)
# We do not run the tuning in our webpage server since it takes too long.
# Uncomment the following line to run it by yourself.
# tune_and_evaluate(tuning_option)
Sample Output
调整需要编译许多程序并从中提取特性。因此建议使用高性能CPU。下面列出了一个示例输出。
Extract tasks...
Tuning...
[Task 1/12] Current/Best: 598.05/2497.63 GFLOPS | Progress: (252/252) | 1357.95 s Done.
[Task 2/12] Current/Best: 522.63/2279.24 GFLOPS | Progress: (784/784) | 3989.60 s Done.
[Task 3/12] Current/Best: 447.33/1927.69 GFLOPS | Progress: (784/784) | 3869.14 s Done.
[Task 4/12] Current/Best: 481.11/1912.34 GFLOPS | Progress: (672/672) | 3274.25 s Done.
[Task 5/12] Current/Best: 414.09/1598.45 GFLOPS | Progress: (672/672) | 2720.78 s Done.
[Task 6/12] Current/Best: 508.96/2273.20 GFLOPS | Progress: (768/768) | 3718.75 s Done.
[Task 7/12] Current/Best: 469.14/1955.79 GFLOPS | Progress: (576/576) | 2665.67 s Done.
[Task 8/12] Current/Best: 230.91/1658.97 GFLOPS | Progress: (576/576) | 2435.01 s Done.
[Task 9/12] Current/Best: 487.75/2295.19 GFLOPS | Progress: (648/648) | 3009.95 s Done.
[Task 10/12] Current/Best: 182.33/1734.45 GFLOPS | Progress: (360/360) | 1755.06 s Done.
[Task 11/12] Current/Best: 372.18/1745.15 GFLOPS | Progress: (360/360) | 1684.50 s Done.
[Task 12/12] Current/Best: 215.34/2271.11 GFLOPS | Progress: (400/400) | 2128.74 s Done.
Compile...
Evaluate inference time cost...
Mean inference time (std dev): 3.16 ms (0.03 ms)
https://tvm.apache.org/docs/tutorials/autotvm/tune_relay_x86.html
下载Python源代码：tune_relay_x86.py
下载Jupyter笔记本：tune_relay_x86.ipynbDownload Python source code: tune_relay_x86.py
Download Jupyter notebook: tune_relay_x86.ipynb
