使用 microTVM Autotuning
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使用 microTVM Autotuning#
原作者:
本教程解释如何使用 C 运行时自动调优模型。
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 中定义要在设备上执行的模型。然后从 Relay 模型创建 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}
定义目标#
现在我们定义描述执行环境的 TVM 目标。这看起来与其他 microTVM 教程中的目标定义非常相似。与此同时，选择 C 运行时来代码生成我们的模型。
在物理硬件上运行时，选择描述该硬件的 target 和 board。本教程中可以从 PLATFORM 列表中选择多个硬件目标。在运行本教程时，您可以通过传递 –platform 参数来选择平台。
RUNTIME = Runtime("crt", {"system-lib": True})
TARGET = tvm.target.target.micro("host")
# Compiling for physical hardware
# --------------------------------------------------------------------------
# When running on physical hardware, choose a TARGET and a BOARD that describe the hardware. The
# STM32L4R5ZI Nucleo target and board is chosen in the example below.
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"])
提取优化任务#
不是所有的算子在上面打印的 Relay 程序可以调谐。有些非常简单，只定义了单个实现；其他任务作为调优任务没有意义。使用 extract_from_program，可以生成可调任务列表。
因为任务提取涉及到运行编译器，所以首先需要配置编译器的 transformation passes；将在稍后的自动调优期间应用相同的配置。
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#
在进行自动调优之前，需要定义模块加载器，并将其传递给 tvm.autotvm.LocalBuilder。然后创建 tvm.autotvm.LocalRunner，并使用构建器和运行器为自动调谐器生成多个度量值。
在本教程中，可以选择使用 x86 主机作为示例，或者使用来自 Zephyr RTOS 的不同目标。如果您选择 pass --platform=host 到本教程，它将使用 x86。您可以从 PLATFORM 列表中选择其他选项。
import tvm.micro
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)
# Compiling for physical hardware
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 Autotuning#
现在可以在 microTVM 设备上分别对每个提取任务进行自动调优。
autotune_log_file = pathlib.Path("build/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#
为了进行比较，让我们在不施加任何自动调优调度的情况下编译和运行 graph。TVM 将为每个算子随机选择调优的实现，它的性能应该不如调优后的算子。
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},
)
# Compiling for physical hardware
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
为调优后的程序计时#
一旦自动调优完成，您可以使用调试运行时对整个程序的执行进行计时：
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},
)
# Compiling for physical hardware
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