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| # 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 | ||
| # input and output header files. These header files are used to connect | ||
| # the input and output API from generated code to the rest of the | ||
| # standalone project. For this specific submission, we only need to generate | ||
| # output header file since the input API call is handled differently. | ||
| # | ||
|
|
||
| extra_tar_dir = tvm.contrib.utils.tempdir() | ||
| extra_tar_file = extra_tar_dir / "extra.tar" | ||
|
|
||
| with tarfile.open(extra_tar_file, "w:gz") as tf: | ||
| with tempfile.TemporaryDirectory() as tar_temp_dir: | ||
| model_files_path = os.path.join(tar_temp_dir, "include") | ||
| os.mkdir(model_files_path) | ||
| header_path = generate_c_interface_header( | ||
| module.libmod_name, [input_name], [output_name], [], {}, [], 0, model_files_path, {}, {} | ||
| ) | ||
| tf.add(header_path, arcname=os.path.relpath(header_path, tar_temp_dir)) | ||
|
|
||
| create_header_file( | ||
| "output_data", | ||
| np.zeros( | ||
| shape=output_shape, | ||
| dtype=output_dtype, | ||
| ), | ||
| "include", | ||
| tf, | ||
| ) | ||
|
|
||
| ###################################################################### | ||
| # Create the project, build and prepare the project tar file | ||
| # -------------------------------------------------------------------- | ||
| # | ||
| # Now that we have the compiled model as a model library format, | ||
| # we can generate the full project using Zephyr template project. First, | ||
| # we prepare the project options, then build the project. Finally, we | ||
| # cleanup the temporary files and move the submission project to the | ||
| # current working directory which could be downloaded and used on | ||
| # your development kit. | ||
| # | ||
|
|
||
| input_total_size = 1 | ||
| for i in range(len(input_shape)): | ||
| input_total_size *= input_shape[i] | ||
|
|
||
| template_project_path = pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr")) | ||
| project_options = { | ||
| "extra_files_tar": str(extra_tar_file), | ||
| "project_type": "mlperftiny", | ||
| "board": BOARD, | ||
| "compile_definitions": [ | ||
| f"-DWORKSPACE_SIZE={workspace_size + 512}", | ||
| f"-DTARGET_MODEL={MODEL_INDEX}", | ||
| f"-DTH_MODEL_VERSION=EE_MODEL_VERSION_{MODEL_SHORT_NAME}01", | ||
| f"-DMAX_DB_INPUT_SIZE={input_total_size}", | ||
| ], | ||
| } | ||
|
|
||
| if MODEL_SHORT_NAME != "AD": | ||
| project_options["compile_definitions"].append(f"-DOUT_QUANT_SCALE={quant_output_scale}") | ||
| project_options["compile_definitions"].append(f"-DOUT_QUANT_ZERO={quant_output_zero_point}") | ||
|
|
||
| if USE_CMSIS: | ||
| project_options["compile_definitions"].append(f"-DCOMPILE_WITH_CMSISNN=1") | ||
|
|
||
| if BOARD == "nrf5340dk_nrf5340_cpuapp": | ||
| config_main_stack_size = 4000 | ||
| elif BOARD == "nucleo_l4r5zi": | ||
| config_main_stack_size = 4000 | ||
| else: | ||
| raise RuntimeError("Please set the main stack size.") | ||
| project_options["config_main_stack_size"] = config_main_stack_size | ||
|
|
||
| if USE_CMSIS: | ||
| project_options["cmsis_path"] = os.environ.get("CMSIS_PATH") | ||
|
|
||
| generated_project_dir = temp_dir / "project" | ||
|
|
||
| project = tvm.micro.project.generate_project_from_mlf( | ||
| template_project_path, generated_project_dir, model_tar_path, project_options | ||
| ) | ||
| project.build() | ||
|
|
||
| # Cleanup the build directory and extra artifacts | ||
| shutil.rmtree(generated_project_dir / "build") | ||
| (generated_project_dir / "model.tar").unlink() | ||
|
|
||
| project_tar_path = pathlib.Path(os.getcwd()) / "project.tar" | ||
| with tarfile.open(project_tar_path, "w:tar") as tar: | ||
| tar.add(generated_project_dir, arcname=os.path.basename("project")) | ||
|
|
||
| print(f"The generated project is located here: {project_tar_path}") |
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