guberti commented on code in PR #12182: URL: https://github.com/apache/tvm/pull/12182#discussion_r929857655
########## 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. +# Review Comment: Remove this and leave a blank line here ```suggestion ``` ########## 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")) Review Comment: We should mention the KWS dataset we're using, and probably ought to credit Google as the author (see https://ai.googleblog.com/2017/08/launching-speech-commands-dataset.html). ########## gallery/how_to/work_with_microtvm/micro_aot.py: ########## @@ -0,0 +1,162 @@ +# 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. +""" + +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 Review Comment: seconding this! ########## 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) Review Comment: nit here and in a few other places: ```suggestion a TFLite model. This tutorial can be executed on a x86 CPU using C runtime (CRT) ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
