areusch commented on code in PR #12182: URL: https://github.com/apache/tvm/pull/12182#discussion_r930059752
########## gallery/how_to/work_with_microtvm/micro_aot.py: ########## @@ -0,0 +1,168 @@ +# 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-AoT: + +microTVM Host-Driven AoT +=========================== +**Authors**: +`Mehrdad Hessar <https://github.com/mehrdadh>`_, +`Alan MacDonald <https://github.com/alanmacd>`_ + +This tutorial is showcasing microTVM host-driven AoT compilation with +a TFLite model. This tutorial can be executed on a X86 CPU using C runtime (CRT) +or on Zephyr plarform on a microcontroller that supports Zephyr platform. +""" + +# sphinx_gallery_start_ignore +from tvm import testing + +testing.utils.install_request_hook(depth=3) +# sphinx_gallery_end_ignore + +import numpy as np +import pathlib +import json +import os + +import tvm +from tvm import relay +from tvm.relay.backend import Executor, Runtime +from tvm.contrib.download import download_testdata + +###################################################################### +# Import a TFLite model +# --------------------- +# +# To begin with, download and import a TFLite model from TinyMLPerf models. +# +# **Note:** By default this tutorial runs on X86 CPU using CRT, if you would like to run on Zephyr platform +# you need to export `TVM_MICRO_USE_HW` environment variable. +# +use_physical_hw = bool(os.getenv("TVM_MICRO_USE_HW")) +MODEL_URL = "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/model/keyword_spotting_quant.tflite"; +MODEL_PATH = download_testdata(MODEL_URL, "keyword_spotting_quant.tflite", module="model") +SAMPLE_URL = "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy"; +SAMPLE_PATH = download_testdata(SAMPLE_URL, "keyword_spotting_int8_6.pyc.npy", module="data") + +tflite_model_buf = open(MODEL_PATH, "rb").read() +try: + import tflite + + tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0) +except AttributeError: + import tflite.Model + + tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0) + +input_shape = (1, 49, 10, 1) +INPUT_NAME = "input_1" +relay_mod, params = relay.frontend.from_tflite( + tflite_model, shape_dict={INPUT_NAME: input_shape}, dtype_dict={INPUT_NAME: "int8"} +) + +###################################################################### +# Defining the target +# ------------------- +# +# Now we need to define the target, runtime and executor. In this tutorial, we focused on +# using AOT host driven executor. We use the host micro target which is for running a model Review Comment: Suggest to make this super clear: ``` # Use the C runtime (crt) and enable static linking by setting system-lib to True RUNTIME = Runtime("crt", {"system-lib": True}) # Simulate a microcontroller on the host machine. Uses the main() from src/runtime/crt/host/main.cc. To use physical hardware, replace "host" with something matching your hardware. See abc location for instructions. TARGET = tvm.target.target.micro("host") # Use the AOT executor rather than graph or vm executors. Don't use unpacked API or C calling style EXECUTOR = Executor("aot") ########## gallery/how_to/work_with_microtvm/micro_aot.py: ########## @@ -0,0 +1,168 @@ +# 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-AoT: + +microTVM Host-Driven AoT +=========================== +**Authors**: +`Mehrdad Hessar <https://github.com/mehrdadh>`_, +`Alan MacDonald <https://github.com/alanmacd>`_ + +This tutorial is showcasing microTVM host-driven AoT compilation with +a TFLite model. This tutorial can be executed on a X86 CPU using C runtime (CRT) +or on Zephyr plarform on a microcontroller that supports Zephyr platform. +""" + +# sphinx_gallery_start_ignore +from tvm import testing + +testing.utils.install_request_hook(depth=3) +# sphinx_gallery_end_ignore + +import numpy as np +import pathlib +import json +import os + +import tvm +from tvm import relay +from tvm.relay.backend import Executor, Runtime +from tvm.contrib.download import download_testdata + +###################################################################### +# Import a TFLite model +# --------------------- +# +# To begin with, download and import a TFLite model from TinyMLPerf models. +# +# **Note:** By default this tutorial runs on X86 CPU using CRT, if you would like to run on Zephyr platform +# you need to export `TVM_MICRO_USE_HW` environment variable. +# +use_physical_hw = bool(os.getenv("TVM_MICRO_USE_HW")) +MODEL_URL = "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/model/keyword_spotting_quant.tflite"; +MODEL_PATH = download_testdata(MODEL_URL, "keyword_spotting_quant.tflite", module="model") +SAMPLE_URL = "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy"; +SAMPLE_PATH = download_testdata(SAMPLE_URL, "keyword_spotting_int8_6.pyc.npy", module="data") + +tflite_model_buf = open(MODEL_PATH, "rb").read() +try: + import tflite + + tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0) +except AttributeError: + import tflite.Model + + tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0) + +input_shape = (1, 49, 10, 1) +INPUT_NAME = "input_1" +relay_mod, params = relay.frontend.from_tflite( + tflite_model, shape_dict={INPUT_NAME: input_shape}, dtype_dict={INPUT_NAME: "int8"} +) + +###################################################################### +# Defining the target +# ------------------- +# +# Now we need to define the target, runtime and executor. In this tutorial, we focused on +# using AOT host driven executor. We use the host micro target which is for running a model +# on X86 CPU using CRT runtime or running a model with Zephyr platform on qemu_x86 simulator +# board. In the case of a physical microcontoller, we get the target model for the physical +# board (E.g. nucleo_f746zg) and pass it to `tvm.target.target.micro` to create a full +# micro target. +# +RUNTIME = Runtime("crt", {"system-lib": True}) +TARGET = tvm.target.target.micro("host") +EXECUTOR = Executor("aot") + +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_f746zg") + TARGET = tvm.target.target.micro(boards[BOARD]["model"]) + +###################################################################### +# Compile the model +# ----------------- +# +# Now, we compile the model for the target: +# +with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}): + module = tvm.relay.build( + relay_mod, target=TARGET, params=params, runtime=RUNTIME, executor=EXECUTOR + ) + +###################################################################### +# Create a microTVM project +# ----------------------- +# +# Now that we have the comipled model as an IRModule, we need to create a project +# with the compiled model in microTVM. To do this, we use Project API. We have defined +# CRT and Zephyr microTVM template projects which are used for X86 CPU and Zephyr platforms +# respectively. +# +template_project_path = pathlib.Path(tvm.micro.get_microtvm_template_projects("crt")) +project_options = {} # You can use options to provide platform-specific options through TVM. + +if use_physical_hw: + template_project_path = pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr")) + project_options = {"project_type": "host_driven", "zephyr_board": BOARD} + +temp_dir = tvm.contrib.utils.tempdir() +generated_project_dir = temp_dir / "project" +project = tvm.micro.generate_project( + template_project_path, module, generated_project_dir, project_options +) + +###################################################################### +# Build, flash and execute the model +# ----------------------- +# Next, we build the microTVM project and flash it. Flash step is specific to +# physical microcontrollers and it is skipped if it is using CRT runtime or running Review Comment: "and it is skipped if it is simulating a microcontroller via the host main.cc or if a Zephyr emulated board is selected as the target." ########## gallery/how_to/work_with_microtvm/micro_aot.py: ########## @@ -0,0 +1,168 @@ +# 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-AoT: + +microTVM Host-Driven AoT +=========================== +**Authors**: +`Mehrdad Hessar <https://github.com/mehrdadh>`_, +`Alan MacDonald <https://github.com/alanmacd>`_ + +This tutorial is showcasing microTVM host-driven AoT compilation with +a TFLite model. This tutorial can be executed on a X86 CPU using C runtime (CRT) +or on Zephyr plarform on a microcontroller that supports Zephyr platform. +""" + +# sphinx_gallery_start_ignore +from tvm import testing + +testing.utils.install_request_hook(depth=3) +# sphinx_gallery_end_ignore + +import numpy as np +import pathlib +import json +import os + +import tvm +from tvm import relay +from tvm.relay.backend import Executor, Runtime +from tvm.contrib.download import download_testdata + +###################################################################### +# Import a TFLite model +# --------------------- +# +# To begin with, download and import a TFLite model from TinyMLPerf models. +# +# **Note:** By default this tutorial runs on X86 CPU using CRT, if you would like to run on Zephyr platform +# you need to export `TVM_MICRO_USE_HW` environment variable. +# +use_physical_hw = bool(os.getenv("TVM_MICRO_USE_HW")) +MODEL_URL = "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/model/keyword_spotting_quant.tflite"; +MODEL_PATH = download_testdata(MODEL_URL, "keyword_spotting_quant.tflite", module="model") +SAMPLE_URL = "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy"; +SAMPLE_PATH = download_testdata(SAMPLE_URL, "keyword_spotting_int8_6.pyc.npy", module="data") + +tflite_model_buf = open(MODEL_PATH, "rb").read() +try: + import tflite + + tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0) +except AttributeError: + import tflite.Model + + tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0) + +input_shape = (1, 49, 10, 1) +INPUT_NAME = "input_1" +relay_mod, params = relay.frontend.from_tflite( + tflite_model, shape_dict={INPUT_NAME: input_shape}, dtype_dict={INPUT_NAME: "int8"} +) + +###################################################################### +# Defining the target +# ------------------- +# +# Now we need to define the target, runtime and executor. In this tutorial, we focused on +# using AOT host driven executor. We use the host micro target which is for running a model +# on X86 CPU using CRT runtime or running a model with Zephyr platform on qemu_x86 simulator +# board. In the case of a physical microcontoller, we get the target model for the physical +# board (E.g. nucleo_f746zg) and pass it to `tvm.target.target.micro` to create a full +# micro target. +# +RUNTIME = Runtime("crt", {"system-lib": True}) +TARGET = tvm.target.target.micro("host") +EXECUTOR = Executor("aot") + +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_f746zg") + TARGET = tvm.target.target.micro(boards[BOARD]["model"]) + +###################################################################### +# Compile the model +# ----------------- +# +# Now, we compile the model for the target: +# +with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}): + module = tvm.relay.build( + relay_mod, target=TARGET, params=params, runtime=RUNTIME, executor=EXECUTOR + ) + +###################################################################### +# Create a microTVM project +# ----------------------- +# +# Now that we have the comipled model as an IRModule, we need to create a project Review Comment: suggest: "firmware project" -- This is an automated message from the Apache Git Service. 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