leandron commented on a change in pull request #8715:
URL: https://github.com/apache/tvm/pull/8715#discussion_r688308950
##########
File path: python/tvm/micro/build.py
##########
@@ -17,10 +17,14 @@
"""Defines top-level glue functions for building microTVM artifacts."""
+import contextlib
Review comment:
nit: It looks like this is unused here.
##########
File path: python/tvm/autotvm/measure/measure_methods.py
##########
@@ -511,7 +523,13 @@ def __call__(self, measure_input, tmp_dir, **kwargs):
)
# TODO(tvm-team) consider linline _build_func_common
func, arg_info = _build_func_common(measure_input, **kwargs)
- func.export_library(filename, self.build_func)
+ if self.build_func.output_format == "tar.gz":
+ # Late import to preserve autoTVM with USE_MICRO OFF
+ from tvm import micro # pylint:
disable=import-outside-toplevel
Review comment:
I think it makes sense here to `try:` import, rather than leave it to
crash when `USE_MICRO OFF`. Also maybe add an error message.
##########
File path: tutorials/micro/micro_autotune.py
##########
@@ -0,0 +1,268 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+.. _tutorial-micro-autotune:
+
+Autotuning with micro TVM
+=========================
+**Author**: `Andrew Reusch <https://github.com/areusch>`_, `Mehrdad Hessar
<https://github.com/mehrdadh>`
+
+This tutorial explains how to autotune a model using the C runtime.
+"""
+
+import argparse
+
+PLATFORMS = {
+ "host": ("host", None),
+ "qemu_x86": ("host", "qemu_x86"),
+ "nrf5340dk": ("nrf5340dk", "nrf5340dk_nrf5340_cpuapp"),
+ "stm32f746xx_disco": ("stm32f746xx", "stm32f746g_disco"),
+ "stm32f746xx_nucleo": ("stm32f746xx", "nucleo_f746zg"),
+ "stm32l4r5zi_nucleo": ("stm32l4r5zi", "nucleo_l4r5zi"),
+}
Review comment:
It would be good to have a bit more context on what this dictionary
contains. Is that the physical board target and qemu target?
##########
File path: tutorials/micro/micro_autotune.py
##########
@@ -0,0 +1,268 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""
+.. _tutorial-micro-autotune:
+
+Autotuning with micro TVM
+=========================
+**Author**: `Andrew Reusch <https://github.com/areusch>`_, `Mehrdad Hessar
<https://github.com/mehrdadh>`
+
+This tutorial explains how to autotune a model using the C runtime.
+"""
+
+import argparse
+
+PLATFORMS = {
+ "host": ("host", None),
+ "qemu_x86": ("host", "qemu_x86"),
+ "nrf5340dk": ("nrf5340dk", "nrf5340dk_nrf5340_cpuapp"),
+ "stm32f746xx_disco": ("stm32f746xx", "stm32f746g_disco"),
+ "stm32f746xx_nucleo": ("stm32f746xx", "nucleo_f746zg"),
+ "stm32l4r5zi_nucleo": ("stm32l4r5zi", "nucleo_l4r5zi"),
+}
+
+
+def main(args):
+ ####################
+ # Defining the model
+ ####################
+ #
+ # To begin with, define a model in Relay to be executed on-device. Then
create an IRModule from relay model and
+ # fill parameters with random numbers.
+ #
+
+ import tvm
+
+ 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)
+
+ import numpy as np
+
+ weight_sample = np.random.rand(
+ weight_shape[0], weight_shape[1], weight_shape[2], weight_shape[3]
+ ).astype("float32")
+ params = {"weight": weight_sample}
+
+ #######################
+ # Defining the target #
+ #######################
+ # Now we define the TVM target that describes the execution environment.
This looks very similar
+ # to target definitions from other microTVM tutorials.
+ #
+ # When running on physical hardware, choose a target and a board that
+ # describe the hardware. There are multiple hardware targets that could be
selected from
+ # PLATFORM list in this tutorial. You can chose the platform by passing
--platform argument when running
+ # this tutorial.
+ #
+ target = tvm.target.target.micro(PLATFORMS[args.platform][0])
+ board = PLATFORMS[args.platform][1]
+
+ #########################
+ # Extracting tuning tasks
+ #########################
+ # Not all operators in the Relay program printed above can be tuned. Some
are so trivial that only
+ # a single implementation is defined; others don't make sense as tuning
tasks. Using
+ # `extract_from_program`, you can produce a list of tunable tasks.
+ #
+ # Because task extraction involves running the compiler, we first
configure the compiler's
+ # transformation passes; we'll apply the same configuration later on
during autotuning.
+
+ 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
+
+ ######################
+ # Configuring microTVM
+ ######################
+ # Before autotuning, we need to define a module loader and then pass that
to
+ # a `tvm.autotvm.LocalBuilder`. Then we create a `tvm.autotvm.LocalRunner`
and use
+ # both builder and runner to generates multiple measurements for auto
tunner.
+ #
+ # In this tutorial, we have the option to use x86 host as an example or
use different targets
+ # from Zephyr RTOS. If you choose pass `--platform=host` to this tutorial
it will uses x86. You can
+ # choose other options by choosing from `PLATFORM` list.
+ #
+
+ import subprocess
+ import pathlib
+
+ repo_root = pathlib.Path(
+ subprocess.check_output(["git", "rev-parse", "--show-toplevel"],
encoding="utf-8").strip()
+ )
+
+ if args.platform == "host":
+ module_loader = tvm.micro.AutoTvmModuleLoader(
+ template_project_dir=repo_root / "src" / "runtime" / "crt" /
"host",
+ project_options={},
+ )
+ 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,
+ ) # do_fork=False needed to persist stateful builder.
+ runner = tvm.autotvm.LocalRunner(number=1, repeat=1, timeout=0,
module_loader=module_loader)
+
+ measure_option = tvm.autotvm.measure_option(builder=builder,
runner=runner)
+
+ else:
+ module_loader = tvm.micro.AutoTvmModuleLoader(
+ template_project_dir=repo_root / "apps" / "microtvm" / "zephyr" /
"template_project",
+ project_options={
+ "zephyr_board": board,
+ "west_cmd": "west",
+ "verbose": 1,
+ "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,
+ )
+ runner = tvm.autotvm.LocalRunner(number=1, repeat=1, timeout=0,
module_loader=module_loader)
+
+ measure_option = tvm.autotvm.measure_option(builder=builder,
runner=runner)
+
+ ################
+ # Run Autotuning
+ ################
+ # Now we can run autotuning separately on each extracted task.
+
+ 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("microtvm_autotune.log"),
+ tvm.autotvm.callback.progress_bar(num_trials, si_prefix="M"),
+ ],
+ si_prefix="M",
+ )
+
+ ############################
+ # Timing the untuned program
+ ############################
+ # For comparison, let's compile and run the graph without imposing any
autotuning schedules. TVM
+ # will select a randomly-tuned implementation for each operator, which
should not perform as well as
+ # the tuned operator.
+
+ with pass_context:
+ lowered = tvm.relay.build(relay_mod, target=target, params=params)
+
+ temp_dir = tvm.contrib.utils.tempdir()
+ if args.platform == "host":
+ project = tvm.micro.generate_project(
+ str(repo_root / "src" / "runtime" / "crt" / "host"), lowered,
temp_dir / "project"
+ )
+
+ else:
+ project = tvm.micro.generate_project(
+ str(repo_root / "apps" / "microtvm" / "zephyr" /
"template_project"),
+ lowered,
+ temp_dir / "project",
+ {
+ "zephyr_board": board,
+ "west_cmd": "west",
+ "verbose": 1,
+ "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
+
+ ##########################
+ # Timing the tuned program
+ ##########################
+ # Once autotuning completes, you can time execution of the entire program
using the Debug Runtime:
+
+ with tvm.autotvm.apply_history_best("microtvm_autotune.log"):
+ with pass_context:
+ lowered_tuned = tvm.relay.build(relay_mod, target=target,
params=params)
+
+ temp_dir = tvm.contrib.utils.tempdir()
+ if args.platform == "host":
+ project = tvm.micro.generate_project(
+ str(repo_root / "src" / "runtime" / "crt" / "host"),
lowered_tuned, temp_dir / "project"
+ )
+
+ else:
+ project = tvm.micro.generate_project(
+ str(repo_root / "apps" / "microtvm" / "zephyr" /
"template_project"),
+ lowered_tuned,
+ temp_dir / "project",
+ {
+ "zephyr_board": board,
+ "west_cmd": "west",
+ "verbose": 1,
+ "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
+
+
+def parse_args():
+ parser = argparse.ArgumentParser()
Review comment:
nit: good to have a simple description of the program
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