mehrdadh commented on code in PR #13783: URL: https://github.com/apache/tvm/pull/13783#discussion_r1072566733
########## 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. Review Comment: done ########## 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. Review Comment: done ########## 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. Review Comment: done ########## 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 +# 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 Review Comment: done, added a note that they might need to adjust it based on their board ########## 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. Review Comment: done ########## 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. Review Comment: done ########## 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 Review Comment: done ########## python/tvm/micro/testing/utils.py: ########## @@ -133,3 +136,40 @@ def get_conv2d_relay_module(): mod = tvm.IRModule.from_expr(f) mod = relay.transform.InferType()(mod) return mod + + +def create_header_file(tensor_name: str, npy_data: np.array, output_path: str, tar_file: str): + """ + This method generates a header file containing the data contained in the numpy array provided + and adds the header file to a tar file. + It is used to capture the tensor data (for both inputs and output). + """ + header_file = io.StringIO() + header_file.write("#include <stddef.h>\n") + header_file.write("#include <stdint.h>\n") + header_file.write("#include <dlpack/dlpack.h>\n") + header_file.write(f"const size_t {tensor_name}_len = {npy_data.size};\n") + + if npy_data.dtype == "int8": Review Comment: done ########## 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 Review Comment: done ########## 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 Review Comment: done ########## 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: done ########## 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 Review Comment: done ########## 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"; Review Comment: I think it would redundant here. We can make a micro/test api call that returns the address to the model. ########## 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: Review Comment: nope, removed them ########## 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 Review Comment: done ########## 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 Review Comment: done ########## 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 Review Comment: added more info ########## 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 +# 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}", Review Comment: done. ########## 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 +# 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}") Review Comment: I explained it in top of the tutorial ########## 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 +# 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", "/content/cmsis") + +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}") Review Comment: I'll add info on how to build the generated project again and flash it. And also refer to the website on how to setup the EEMBC runner. I don't think it makes sense to bring those details here ########## python/tvm/micro/testing/utils.py: ########## @@ -17,13 +17,16 @@ """Defines the test methods used with microTVM.""" +import io from functools import lru_cache import json import logging from pathlib import Path import tarfile import time from typing import Union +import numpy as np +import pathlib Review Comment: done -- 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]
