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[microTVM]Add test for MLPerfTiny models (apache#13978)
* Add test for mlperftiny models for host_driven and EEMBC runner versions * refactor mlperftiny models in different links * fix link * address comments and fix error
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| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you under the Apache License, Version 2.0 (the | ||
| # "License"); you may not use this file except in compliance | ||
| # with the License. You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, | ||
| # software distributed under the License is distributed on an | ||
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
| # KIND, either express or implied. See the License for the | ||
| # specific language governing permissions and limitations | ||
| # under the License. | ||
| 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 significantly. | ||
| "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)] | ||
|
|
||
|
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| 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) | ||
|
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||
|
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| @pytest.mark.parametrize("model_name", ["kws", "vww", "ad", "ic"]) | ||
| @pytest.mark.parametrize("project_type", ["host_driven", "mlperftiny"]) | ||
| @tvm.testing.requires_micro | ||
| @pytest.mark.skip_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): | ||
| """MLPerfTiny models test. | ||
| Testing MLPerfTiny models using host_driven project. In this case one input sample is used | ||
| to verify the end to end execution. Accuracy is not checked in this test. | ||
| Also, this test builds each model in standalone mode that can be used with EEMBC runner. | ||
| """ | ||
| if platform != "zephyr": | ||
| pytest.skip(reason="Other platforms are not supported yet.") | ||
|
|
||
| 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": | ||
| 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() | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| tvm.testing.main() |
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