mehrdadh commented on code in PR #13978: URL: https://github.com/apache/tvm/pull/13978#discussion_r1106206901
########## tests/micro/common/test_mlperftiny.py: ########## @@ -0,0 +1,295 @@ +# 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. +import os +from urllib.parse import urlparse + +import pytest +import tensorflow as tf +import numpy as np +import tarfile +import tempfile +import pathlib + +import tvm +import tvm.testing +from tvm import relay +from tvm.relay.backend import Executor, Runtime +from tvm.micro.project_api import server +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 import create_aot_session, predict_labels_aot +from tvm.micro.testing.utils import ( + create_header_file, + mlf_extract_workspace_size_bytes, +) + +MLPERF_TINY_MODELS = { + "kws": { + "name": "Keyword Spotting", + "index": 1, + "url": "https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/keyword_spotting/trained_models/kws_ref_model.tflite";, + "sample": "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy";, + }, + "vww": { + "name": "Visual Wake Words", + "index": 2, + "url": "https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/visual_wake_words/trained_models/vww_96_int8.tflite";, + "sample": "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/visual_wake_word_int8_1.npy";, + }, + # Note: The reason we use quantized model with float32 I/O is + # that TVM does not handle the int8 I/O correctly and accuracy + # would drop signigicantly. Review Comment: done ########## apps/microtvm/zephyr/template_project/src/mlperftiny/submitter_implemented.cc: ########## @@ -19,6 +19,7 @@ #include "api/submitter_implemented.h" +#include <assert.h> Review Comment: yeah, it is used in `TVMBackendAllocWorkspace` ########## tests/micro/common/test_mlperftiny.py: ########## @@ -0,0 +1,295 @@ +# 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. +import os +from urllib.parse import urlparse + +import pytest +import tensorflow as tf +import numpy as np +import tarfile +import tempfile +import pathlib + +import tvm +import tvm.testing +from tvm import relay +from tvm.relay.backend import Executor, Runtime +from tvm.micro.project_api import server +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 import create_aot_session, predict_labels_aot +from tvm.micro.testing.utils import ( + create_header_file, + mlf_extract_workspace_size_bytes, +) + +MLPERF_TINY_MODELS = { + "kws": { + "name": "Keyword Spotting", + "index": 1, + "url": "https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/keyword_spotting/trained_models/kws_ref_model.tflite";, + "sample": "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy";, + }, + "vww": { + "name": "Visual Wake Words", + "index": 2, + "url": "https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/visual_wake_words/trained_models/vww_96_int8.tflite";, + "sample": "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/visual_wake_word_int8_1.npy";, + }, + # Note: The reason we use quantized model with float32 I/O is + # that TVM does not handle the int8 I/O correctly and accuracy + # would drop signigicantly. + "ad": { + "name": "Anomaly Detection", + "index": 3, + "url": "https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/anomaly_detection/trained_models/ToyCar/baseline_tf23/model/model_ToyCar_quant_fullint_micro.tflite";, + "sample": "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/anomaly_detection_normal_id_01.npy";, + # This model takes in a (1, 640) vector, so it must be called 40 times - + # once for each time slice. + }, + "ic": { + "name": "Image Classification", + "index": 4, + "url": "https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/image_classification/trained_models/pretrainedResnet_quant.tflite";, + "sample": "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/image_classification_int8_0.npy";, + }, +} + + +def mlperftiny_get_module(model_name: str): + model_url = MLPERF_TINY_MODELS[model_name]["url"] + url = urlparse(model_url) + model_path = download_testdata(model_url, os.path.basename(url.path), module="model") + + 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() + + model_info = { + "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, + } + + if model_name != "ad": + model_info["quant_output_scale"] = output_details[0]["quantization_parameters"]["scales"][0] + model_info["quant_output_zero_point"] = output_details[0]["quantization_parameters"][ + "zero_points" + ][0] + + relay_mod, params = relay.frontend.from_tflite( + tflite_model, + shape_dict={model_info["input_name"]: model_info["input_shape"]}, + dtype_dict={model_info["input_name"]: model_info["input_dtype"]}, + ) + return relay_mod, params, model_info + + +def get_test_data(model_name: str) -> list: + sample_url = MLPERF_TINY_MODELS[model_name]["sample"] + url = urlparse(sample_url) + sample_path = download_testdata(sample_url, os.path.basename(url.path), module="data") + return [np.load(sample_path)] + + +def predict_ad_labels_aot(session, aot_executor, input_data, runs_per_sample=1): + """A special version of tvm/micro/testing/evaluation.py's predict_labels_aot. + The runtime returned for each sample is the median of the runtimes for all slices + in that sample.""" + + assert runs_per_sample > 0 + assert aot_executor.get_num_inputs() == 1 + assert aot_executor.get_num_outputs() == 1 + + sample_counter = 0 + for sample in input_data: + output_fp32 = np.empty_like(sample) + slice_runtimes = [] + + for i, time_slice in enumerate(sample): + aot_executor.get_input(0).copyfrom(time_slice.reshape((1, 640))) + result = aot_executor.module.time_evaluator( + "run", session.device, number=runs_per_sample + )() + slice_runtimes.append(result.mean) + output_fp32[i, :] = aot_executor.get_output(0).numpy() + + sample_counter += 1 + errors = np.mean(np.square(sample - output_fp32), axis=1) + yield np.mean(errors), np.median(slice_runtimes) + + [email protected]("model_name", ["kws", "vww", "ad", "ic"]) [email protected]("project_type", ["host_driven", "mlperftiny"]) [email protected]_micro [email protected]_boards( + ["mps2_an521", "mps3_an547", "stm32f746g_disco", "nucleo_f746zg", "nrf5340dk_nrf5340_cpuapp"] +) +def test_mlperftiny_models(platform, board, workspace_dir, serial_number, model_name, project_type): + use_cmsis_nn = False + relay_mod, params, model_info = mlperftiny_get_module(model_name) + target = tvm.micro.testing.get_target(platform, board) + project_options = {"config_main_stack_size": 4000, "serial_number": serial_number} + + if use_cmsis_nn: + project_options["cmsis_path"] = os.getenv("CMSIS_PATH") + + if model_name == "ad": + predictor = predict_ad_labels_aot + else: + predictor = predict_labels_aot + + if project_type == "host_driven": + with create_aot_session( + platform, + board, + target, + relay_mod, + params, + build_dir=workspace_dir, + # The longest models take ~5 seconds to infer, but running them + # ten times (with NUM_TESTING_RUNS_PER_SAMPLE) makes that 50 + timeout_override=server.TransportTimeouts( + session_start_retry_timeout_sec=300, + session_start_timeout_sec=150, + session_established_timeout_sec=150, + ), + project_options=project_options, + use_cmsis_nn=use_cmsis_nn, + ) as session: + aot_executor = tvm.runtime.executor.aot_executor.AotModule( + session.create_aot_executor() + ) + args = { + "session": session, + "aot_executor": aot_executor, + "input_data": get_test_data(model_name), + "runs_per_sample": 10, + } + predicted_labels, runtimes = zip(*predictor(**args)) + + avg_runtime = float(np.mean(runtimes) * 1000) + print(f"Model {model_name} average runtime: {avg_runtime}") + + elif project_type == "mlperftiny": + if platform != "zephyr": + pytest.skip(reason="MLPerTiny package is not supported.") + + runtime = Runtime("crt") + executor = Executor( + "aot", {"unpacked-api": True, "interface-api": "c", "workspace-byte-alignment": 8} + ) + + config = {"tir.disable_vectorize": True} + if use_cmsis_nn: + 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 + ) + + 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) + + 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, + [model_info["input_name"]], + [model_info["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=model_info["output_shape"], + dtype=model_info["output_dtype"], + ), + "include/tvm", + tf, + ) + + input_total_size = 1 + input_shape = model_info["input_shape"] + for i in range(len(input_shape)): + input_total_size *= input_shape[i] + + template_project_path = pathlib.Path(tvm.micro.get_microtvm_template_projects(platform)) + project_options.update( + { + "extra_files_tar": str(extra_tar_file), + "project_type": project_type, + "board": board, + "compile_definitions": [ + f"-DWORKSPACE_SIZE={workspace_size + 512}", # Memory workspace size, 512 is a temporary offset + # since the memory calculation is not accurate. + f'-DTARGET_MODEL={MLPERF_TINY_MODELS[model_name]["index"]}', # Sets the model index for project compilation. + f"-DTH_MODEL_VERSION=EE_MODEL_VERSION_{model_name.upper()}01", # Sets model version. This is required by MLPerfTiny API. + f"-DMAX_DB_INPUT_SIZE={input_total_size}", # Max size of the input data array. + ], + } + ) + + if model_name != "ad": + project_options["compile_definitions"].append( + f'-DOUT_QUANT_SCALE={model_info["quant_output_scale"]}' + ) + project_options["compile_definitions"].append( + f'-DOUT_QUANT_ZERO={model_info["quant_output_zero_point"]}' + ) + + project = tvm.micro.project.generate_project_from_mlf( + template_project_path, workspace_dir / "project", model_tar_path, project_options + ) + project.build() Review Comment: yeah, we are testing accuracy here. I have added that to the doc string ########## tests/micro/common/test_mlperftiny.py: ########## @@ -0,0 +1,295 @@ +# 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. +import os +from urllib.parse import urlparse + +import pytest +import tensorflow as tf +import numpy as np +import tarfile +import tempfile +import pathlib + +import tvm +import tvm.testing +from tvm import relay +from tvm.relay.backend import Executor, Runtime +from tvm.micro.project_api import server +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 import create_aot_session, predict_labels_aot +from tvm.micro.testing.utils import ( + create_header_file, + mlf_extract_workspace_size_bytes, +) + +MLPERF_TINY_MODELS = { + "kws": { + "name": "Keyword Spotting", + "index": 1, + "url": "https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/keyword_spotting/trained_models/kws_ref_model.tflite";, + "sample": "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy";, + }, + "vww": { + "name": "Visual Wake Words", + "index": 2, + "url": "https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/visual_wake_words/trained_models/vww_96_int8.tflite";, + "sample": "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/visual_wake_word_int8_1.npy";, + }, + # Note: The reason we use quantized model with float32 I/O is + # that TVM does not handle the int8 I/O correctly and accuracy + # would drop signigicantly. + "ad": { + "name": "Anomaly Detection", + "index": 3, + "url": "https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/anomaly_detection/trained_models/ToyCar/baseline_tf23/model/model_ToyCar_quant_fullint_micro.tflite";, + "sample": "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/anomaly_detection_normal_id_01.npy";, + # This model takes in a (1, 640) vector, so it must be called 40 times - + # once for each time slice. + }, + "ic": { + "name": "Image Classification", + "index": 4, + "url": "https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/image_classification/trained_models/pretrainedResnet_quant.tflite";, + "sample": "https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/image_classification_int8_0.npy";, + }, +} + + +def mlperftiny_get_module(model_name: str): + model_url = MLPERF_TINY_MODELS[model_name]["url"] + url = urlparse(model_url) + model_path = download_testdata(model_url, os.path.basename(url.path), module="model") + + 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() + + model_info = { + "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, + } + + if model_name != "ad": + model_info["quant_output_scale"] = output_details[0]["quantization_parameters"]["scales"][0] + model_info["quant_output_zero_point"] = output_details[0]["quantization_parameters"][ + "zero_points" + ][0] + + relay_mod, params = relay.frontend.from_tflite( + tflite_model, + shape_dict={model_info["input_name"]: model_info["input_shape"]}, + dtype_dict={model_info["input_name"]: model_info["input_dtype"]}, + ) + return relay_mod, params, model_info + + +def get_test_data(model_name: str) -> list: + sample_url = MLPERF_TINY_MODELS[model_name]["sample"] + url = urlparse(sample_url) + sample_path = download_testdata(sample_url, os.path.basename(url.path), module="data") + return [np.load(sample_path)] + + +def predict_ad_labels_aot(session, aot_executor, input_data, runs_per_sample=1): + """A special version of tvm/micro/testing/evaluation.py's predict_labels_aot. + The runtime returned for each sample is the median of the runtimes for all slices + in that sample.""" + + assert runs_per_sample > 0 + assert aot_executor.get_num_inputs() == 1 + assert aot_executor.get_num_outputs() == 1 + + sample_counter = 0 + for sample in input_data: + output_fp32 = np.empty_like(sample) + slice_runtimes = [] + + for i, time_slice in enumerate(sample): + aot_executor.get_input(0).copyfrom(time_slice.reshape((1, 640))) + result = aot_executor.module.time_evaluator( + "run", session.device, number=runs_per_sample + )() + slice_runtimes.append(result.mean) + output_fp32[i, :] = aot_executor.get_output(0).numpy() + + sample_counter += 1 + errors = np.mean(np.square(sample - output_fp32), axis=1) + yield np.mean(errors), np.median(slice_runtimes) + + [email protected]("model_name", ["kws", "vww", "ad", "ic"]) [email protected]("project_type", ["host_driven", "mlperftiny"]) [email protected]_micro [email protected]_boards( + ["mps2_an521", "mps3_an547", "stm32f746g_disco", "nucleo_f746zg", "nrf5340dk_nrf5340_cpuapp"] +) +def test_mlperftiny_models(platform, board, workspace_dir, serial_number, model_name, project_type): Review Comment: done -- This is an automated message from the Apache Git Service. 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