alanmacd commented on code in PR #13783: URL: https://github.com/apache/tvm/pull/13783#discussion_r1070187909
########## gallery/how_to/work_with_microtvm/micro_mlperftiny.py: ########## @@ -0,0 +1,285 @@ +# 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-MLPerfTiny: + +Create Your MLPerfTiny Submission with microTVM +=========================== +**Authors**: +`Mehrdad Hessar <https://github.com/mehrdadh>`_ + +This tutorial is showcasing building an MLPerTiny submission using microTVM. This +tutorial shows the steps to import a TFLite model from MLPerfTiny benchmark models, +compile it with TVM and generate a Zephyr project which can be flashed to a Zephyr +supported board to benchmark the model using EEMBC runner. + +Install CMSIS-NN only if you are interested to generate this submission +using CMSIS-NN code generator. +""" + +###################################################################### +# +# .. include:: ../../../../gallery/how_to/work_with_microtvm/install_dependencies.rst +# + +import os +import pathlib +import tarfile +import tempfile +import shutil + +###################################################################### +# +# .. include:: ../../../../gallery/how_to/work_with_microtvm/install_zephyr.rst +# + +###################################################################### +# +# .. include:: ../../../../gallery/how_to/work_with_microtvm/install_cmsis.rst +# + +###################################################################### +# Import Python dependencies +# ------------------------------- +# +import tensorflow as tf +import numpy as np + +import tvm +from tvm import relay +from tvm.relay.backend import Executor, Runtime +from tvm.contrib.download import download_testdata +from tvm.micro import export_model_library_format +from tvm.micro.model_library_format import generate_c_interface_header +from tvm.micro.testing.utils import ( + create_header_file, + mlf_extract_workspace_size_bytes, +) + +###################################################################### +# Import Visual Wake Word Model +# -------------------------------------------------------------------- +# +# To begin with, download and import Visual Wake Word (VWW) TFLite model from MLPerfTiny. +# This model is originally from `MLPerf Tiny repository <https://github.com/mlcommons/tiny>`_. +# We also capture metadata information from the TFLite model such as input/output name, +# quantization parameters and etc which will be used in following steps. +# +# We use indexing for various models to build the submission. The indices are defined as bellow. +# To build another model, you need to update the model URL, the short name and index number. +# Keyword Spotting(KWS) 1 +# Visual Wake Word(VWW) 2 +# Anomaly Detection(AD) 3 +# Image Classification(IC) 4 +# +# If you like to build the submission with CMSIS-NN, modify USE_CMSIS variable. +# + +MODEL_URL = "https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/visual_wake_words/trained_models/vww_96_int8.tflite"; +MODEL_PATH = download_testdata(MODEL_URL, "vww_96_int8.tflite", module="model") + +MODEL_SHORT_NAME = "VWW" +MODEL_INDEX = 2 + +USE_CMSIS = os.environ.get("TVM_USE_CMSIS", False) + +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) + +interpreter = tf.lite.Interpreter(model_path=str(MODEL_PATH)) +interpreter.allocate_tensors() +input_details = interpreter.get_input_details() +output_details = interpreter.get_output_details() + +input_name = input_details[0]["name"] +input_shape = tuple(input_details[0]["shape"]) +input_dtype = np.dtype(input_details[0]["dtype"]).name +output_name = output_details[0]["name"] +output_shape = tuple(output_details[0]["shape"]) +output_dtype = np.dtype(output_details[0]["dtype"]).name + +# We extract quantization information from TFLite model. +# This is required for all models except Anomaly Detection. +if MODEL_SHORT_NAME != "AD": + quant_output_scale = output_details[0]["quantization_parameters"]["scales"][0] + quant_output_zero_point = output_details[0]["quantization_parameters"]["zero_points"][0] + +relay_mod, params = relay.frontend.from_tflite( + tflite_model, shape_dict={input_name: input_shape}, dtype_dict={input_name: input_dtype} +) + +###################################################################### +# Defining Target, Runtime and Executor +# -------------------------------------------------------------------- +# +# Now we need to define the target, runtime and executor to compile this model. In this tutorial, +# we use with Ahead-of-Time (AoT) compilation and we build a standalone project. This is different +# than using AoT with host-driven mode where the target would communicate with host using host-driven +# AoT executor to run inference. +# + +# Use the C runtime (crt) +RUNTIME = Runtime("crt") + +# Use the AoT executor with unpacked-api and interface-api="c" which +# generates a simple API for standalone mode integration with a any +# microcontroller project +EXECUTOR = Executor( + "aot", + {"unpacked-api": True, "interface-api": "c", "workspace-byte-alignment": 8}, +) + +# Select a Zephyr board +BOARD = os.getenv("TVM_MICRO_BOARD", default="nucleo_l4r5zi") + +# Get the the full target description using the BOARD +TARGET = tvm.micro.testing.get_target("zephyr", BOARD) + +###################################################################### +# Compile the model and export model library format +# -------------------------------------------------------------------- +# +# Now, we compile the model for the target. Then, we generate model +# library format for the compiled model. We also need to calculate the +# workspace size that is required for the compiled model. +# +# + +config = {"tir.disable_vectorize": True} +if USE_CMSIS: + from tvm.relay.op.contrib import cmsisnn + + config["relay.ext.cmsisnn.options"] = {"mcpu": TARGET.mcpu} + relay_mod = cmsisnn.partition_for_cmsisnn(relay_mod, params, mcpu=TARGET.mcpu) + +with tvm.transform.PassContext(opt_level=3, config=config): + module = tvm.relay.build( + relay_mod, target=TARGET, params=params, runtime=RUNTIME, executor=EXECUTOR + ) + +# if USE_CMSIS: +# from tvm.relay.op.contrib import cmsisnn +# module = cmsisnn.partition_for_cmsisnn(module, params, mcpu=TARGET.mcpu) + +temp_dir = tvm.contrib.utils.tempdir() +model_tar_path = temp_dir / "model.tar" +export_model_library_format(module, model_tar_path) +workspace_size = mlf_extract_workspace_size_bytes(model_tar_path) + +###################################################################### +# Generate input/output header files +# -------------------------------------------------------------------- +# +# To create a miroTVM standalone project with AoT, we need to generate Review Comment: ```suggestion # To create a microTVM standalone project with AoT, we need to generate ``` -- This is an automated message from the Apache Git Service. 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