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使用 microTVM 进行自动调优

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单击 此处 下载完整的示例代码

作者Andrew Reusch, Mehrdad Hessar

本教程介绍如何用 C runtime 自动调优模型。

import os
import json
import numpy as np
import pathlib

import tvm
from tvm.relay.backend import Runtime

use_physical_hw = bool(os.getenv("TVM_MICRO_USE_HW"))

定义模型

首先在 Relay 中定义一个要在设备上执行的模型,然后从 Relay 模型中创建一个 IRModule,并用随机数填充参数。

data_shape = (1, 3, 10, 10)
weight_shape = (6, 3, 5, 5)

data = tvm.relay.var("data", tvm.relay.TensorType(data_shape, "float32"))
weight = tvm.relay.var("weight", tvm.relay.TensorType(weight_shape, "float32"))

y = tvm.relay.nn.conv2d(
data,
weight,
padding=(2, 2),
kernel_size=(5, 5),
kernel_layout="OIHW",
out_dtype="float32",
)
f = tvm.relay.Function([data, weight], y)

relay_mod = tvm.IRModule.from_expr(f)
relay_mod = tvm.relay.transform.InferType()(relay_mod)

weight_sample = np.random.rand(
weight_shape[0], weight_shape[1], weight_shape[2], weight_shape[3]
).astype("float32")
params = {"weight": weight_sample}

定义 target

下面定义描述执行环境的 TVM target,它与其他 microTVM 教程中的 target 定义非常相似。不同之处是用 C Runtime 来生成模型。

在物理硬件上运行时,选择一个 target 和一个描述硬件的单板。在本教程的 PLATFORM 列表中可以选择多个硬件目标。运行本教程时用 –platform 参数来选择平台。

RUNTIME = Runtime("crt", {"system-lib": True})
TARGET = tvm.target.target.micro("host")

# 为物理硬件编译
# --------------------------------------------------------------------------
# 在物理硬件上运行时,选择描述硬件的 TARGET 和 BOARD。
# 下面的示例中选择 STM32L4R5ZI Nucleo。
if use_physical_hw:
boards_file = pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr")) / "boards.json"
with open(boards_file) as f:
boards = json.load(f)

BOARD = os.getenv("TVM_MICRO_BOARD", default="nucleo_l4r5zi")
TARGET = tvm.target.target.micro(boards[BOARD]["model"])

提取调优任务

并非上面打印的 Relay 程序中的所有算子都可以调优,有些算子不是很重要,所以只定义了一个实现;其他的作为调优任务没有意义。用 extract_from_program,可以生成可调优任务列表。

因为任务提取涉及到运行编译器,所以首先配置编译器的转换 pass;之后在自动调优过程中应用相同的配置。

pass_context = tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True})
with pass_context:
tasks = tvm.autotvm.task.extract_from_program(relay_mod["main"], {}, TARGET)
assert len(tasks) > 0

配置 microTVM

自动调优前,要定义一个模块加载器,然后将它传递给 tvm.autotvm.LocalBuilder。创建一个 tvm.autotvm.LocalBuilder,并用 builder 和 runner 为自动调优器生成多个测试值。

本教程中可以选择用 x86 主机作为示例,或用来自 Zephyr RTOS 的不同 targets。若用 x86,则传递 –platform=host,也可以从 PLATFORM 列表中选择其他选项。

module_loader = tvm.micro.AutoTvmModuleLoader(
template_project_dir=pathlib.Path(tvm.micro.get_microtvm_template_projects("crt")),
project_options={"verbose": False},
)
builder = tvm.autotvm.LocalBuilder(
n_parallel=1,
build_kwargs={"build_option": {"tir.disable_vectorize": True}},
do_fork=True,
build_func=tvm.micro.autotvm_build_func,
runtime=RUNTIME,
)
runner = tvm.autotvm.LocalRunner(number=1, repeat=1, timeout=100, module_loader=module_loader)

measure_option = tvm.autotvm.measure_option(builder=builder, runner=runner)

# 为物理硬件编译
if use_physical_hw:
module_loader = tvm.micro.AutoTvmModuleLoader(
template_project_dir=pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr")),
project_options={
"zephyr_board": BOARD,
"west_cmd": "west",
"verbose": False,
"project_type": "host_driven",
},
)
builder = tvm.autotvm.LocalBuilder(
n_parallel=1,
build_kwargs={"build_option": {"tir.disable_vectorize": True}},
do_fork=False,
build_func=tvm.micro.autotvm_build_func,
runtime=RUNTIME,
)
runner = tvm.autotvm.LocalRunner(number=1, repeat=1, timeout=100, module_loader=module_loader)

measure_option = tvm.autotvm.measure_option(builder=builder, runner=runner)

运行自动调优

下面在 microTVM 设备上对每个提取的任务单独运行自动调优。

autotune_log_file = pathlib.Path("microtvm_autotune.log.txt")
if os.path.exists(autotune_log_file):
os.remove(autotune_log_file)

num_trials = 10
for task in tasks:
tuner = tvm.autotvm.tuner.GATuner(task)
tuner.tune(
n_trial=num_trials,
measure_option=measure_option,
callbacks=[
tvm.autotvm.callback.log_to_file(str(autotune_log_file)),
tvm.autotvm.callback.progress_bar(num_trials, si_prefix="M"),
],
si_prefix="M",
)

为未调优的程序计时

为了方便比较,编译并运行不实施任何自动调优 schedule 的计算图,TVM 会为每个算子选择一个随机调优的实现,其性能不如调优的算子。

with pass_context:
lowered = tvm.relay.build(relay_mod, target=TARGET, runtime=RUNTIME, params=params)

temp_dir = tvm.contrib.utils.tempdir()
project = tvm.micro.generate_project(
str(tvm.micro.get_microtvm_template_projects("crt")),
lowered,
temp_dir / "project",
{"verbose": False},
)

# 为物理硬件编译
if use_physical_hw:
temp_dir = tvm.contrib.utils.tempdir()
project = tvm.micro.generate_project(
str(tvm.micro.get_microtvm_template_projects("zephyr")),
lowered,
temp_dir / "project",
{
"zephyr_board": BOARD,
"west_cmd": "west",
"verbose": False,
"project_type": "host_driven",
},
)

project.build()
project.flash()
with tvm.micro.Session(project.transport()) as session:
debug_module = tvm.micro.create_local_debug_executor(
lowered.get_graph_json(), session.get_system_lib(), session.device
)
debug_module.set_input(**lowered.get_params())
print("########## Build without Autotuning ##########")
debug_module.run()
del debug_module

输出结果:

/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
########## Build without Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 310.0 98.73 (1, 2, 10, 10, 3) 2 1 [310.0]
tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 3.031 0.965 (1, 6, 10, 10) 1 1 [3.031]
tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.958 0.305 (1, 1, 10, 10, 3) 1 1 [0.958]
Total_time - 313.988 - - - - -

为调优程序计时

自动调优完成后,用 Debug Runtime 为整个程序的执行计时:

with tvm.autotvm.apply_history_best(str(autotune_log_file)):
with pass_context:
lowered_tuned = tvm.relay.build(relay_mod, target=TARGET, runtime=RUNTIME, params=params)

temp_dir = tvm.contrib.utils.tempdir()
project = tvm.micro.generate_project(
str(tvm.micro.get_microtvm_template_projects("crt")),
lowered_tuned,
temp_dir / "project",
{"verbose": False},
)

# 为物理硬件编译
if use_physical_hw:
temp_dir = tvm.contrib.utils.tempdir()
project = tvm.micro.generate_project(
str(tvm.micro.get_microtvm_template_projects("zephyr")),
lowered_tuned,
temp_dir / "project",
{
"zephyr_board": BOARD,
"west_cmd": "west",
"verbose": False,
"project_type": "host_driven",
},
)

project.build()
project.flash()
with tvm.micro.Session(project.transport()) as session:
debug_module = tvm.micro.create_local_debug_executor(
lowered_tuned.get_graph_json(), session.get_system_lib(), session.device
)
debug_module.set_input(**lowered_tuned.get_params())
print("########## Build with Autotuning ##########")
debug_module.run()
del debug_module

输出结果:

/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead.
"target_host parameter is going to be deprecated. "
########## Build with Autotuning ##########
Node Name Ops Time(us) Time(%) Shape Inputs Outputs Measurements(us)
--------- --- -------- ------- ----- ------ ------- ----------------
tvmgen_default_fused_nn_contrib_conv2d_NCHWc tvmgen_default_fused_nn_contrib_conv2d_NCHWc 193.2 98.657 (1, 6, 10, 10, 1) 2 1 [193.2]
tvmgen_default_fused_layout_transform_1 tvmgen_default_fused_layout_transform_1 1.778 0.908 (1, 6, 10, 10) 1 1 [1.778]
tvmgen_default_fused_layout_transform tvmgen_default_fused_layout_transform 0.851 0.435 (1, 3, 10, 10, 1) 1 1 [0.851]
Total_time - 195.83 - - - - -

下载 Python 源代码:micro_autotune.py

下载 Jupyter notebook:micro_autotune.ipynb