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The following commit(s) were added to refs/heads/main by this push:
new a3b51f11b2 [microTVM]Add test for MLPerfTiny models (#13978)
a3b51f11b2 is described below
commit a3b51f11b26d721980858091c2df4c7487388c12
Author: Mehrdad Hessar <[email protected]>
AuthorDate: Wed Feb 22 15:52:51 2023 -0800
[microTVM]Add test for MLPerfTiny models (#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
---
.../src/mlperftiny/submitter_implemented.cc | 5 +-
gallery/how_to/work_with_microtvm/micro_aot.py | 4 +-
tests/micro/common/test_mlperftiny.py | 301 +++++++++++++++++++++
.../zephyr/test_zephyr_aot_exec_standalone.py | 6 +-
tests/scripts/request_hook/request_hook.py | 13 +-
5 files changed, 314 insertions(+), 15 deletions(-)
diff --git
a/apps/microtvm/zephyr/template_project/src/mlperftiny/submitter_implemented.cc
b/apps/microtvm/zephyr/template_project/src/mlperftiny/submitter_implemented.cc
index 72b679c640..96c04b1e80 100644
---
a/apps/microtvm/zephyr/template_project/src/mlperftiny/submitter_implemented.cc
+++
b/apps/microtvm/zephyr/template_project/src/mlperftiny/submitter_implemented.cc
@@ -19,6 +19,7 @@
#include "api/submitter_implemented.h"
+#include <assert.h>
#include <string.h>
#include <tvm/runtime/crt/logging.h>
#include <tvm/runtime/crt/platform.h>
@@ -161,11 +162,7 @@ void Infer(void* input_ptr) {
struct tvmgen_default_outputs outputs = {
#if TARGET_MODEL == MODEL_KWS
-#if COMPILE_WITH_CMSISNN
.Identity = output_data,
-#else
- .output = output_data,
-#endif
#elif TARGET_MODEL == MODEL_IC
.Identity_int8 = output_data,
#elif TARGET_MODEL == MODEL_VWW
diff --git a/gallery/how_to/work_with_microtvm/micro_aot.py
b/gallery/how_to/work_with_microtvm/micro_aot.py
index f31ffa1570..f07731c183 100644
--- a/gallery/how_to/work_with_microtvm/micro_aot.py
+++ b/gallery/how_to/work_with_microtvm/micro_aot.py
@@ -74,8 +74,8 @@ from tvm.contrib.download import download_testdata
# **Note:** By default this tutorial runs on x86 CPU using CRT, if you would
like to run on Zephyr platform
# you need to export `TVM_MICRO_USE_HW` environment variable.
#
-MODEL_URL =
"https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/model/keyword_spotting_quant.tflite";
-MODEL_PATH = download_testdata(MODEL_URL, "keyword_spotting_quant.tflite",
module="model")
+MODEL_URL =
"https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/keyword_spotting/trained_models/kws_ref_model.tflite";
+MODEL_PATH = download_testdata(MODEL_URL, "kws_ref_model.tflite",
module="model")
SAMPLE_URL =
"https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy";
SAMPLE_PATH = download_testdata(SAMPLE_URL, "keyword_spotting_int8_6.pyc.npy",
module="data")
diff --git a/tests/micro/common/test_mlperftiny.py
b/tests/micro/common/test_mlperftiny.py
new file mode 100644
index 0000000000..daa3cf3405
--- /dev/null
+++ b/tests/micro/common/test_mlperftiny.py
@@ -0,0 +1,301 @@
+# 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)]
+
+
+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):
+ """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()
diff --git a/tests/micro/zephyr/test_zephyr_aot_exec_standalone.py
b/tests/micro/zephyr/test_zephyr_aot_exec_standalone.py
index 8c6bc272f0..cfc2247d91 100644
--- a/tests/micro/zephyr/test_zephyr_aot_exec_standalone.py
+++ b/tests/micro/zephyr/test_zephyr_aot_exec_standalone.py
@@ -39,8 +39,8 @@ def test_tflite(workspace_dir, board, microtvm_debug,
serial_number):
output_shape = (1, 12)
build_config = {"debug": microtvm_debug}
- model_url =
"https://github.com/tlc-pack/web-data/raw/25fe99fb00329a26bd37d3dca723da94316fd34c/testdata/microTVM/model/keyword_spotting_quant.tflite";
- model_path = download_testdata(model_url, "keyword_spotting_quant.tflite",
module="model")
+ model_url =
"https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/keyword_spotting/trained_models/kws_ref_model.tflite";
+ model_path = download_testdata(model_url, "kws_ref_model.tflite",
module="model")
# Import TFLite model
tflite_model_buf = open(model_path, "rb").read()
@@ -68,7 +68,7 @@ def test_tflite(workspace_dir, board, microtvm_debug,
serial_number):
):
lowered = relay.build(relay_mod, target, params=params,
runtime=runtime, executor=executor)
- sample_url =
"https://github.com/tlc-pack/web-data/raw/967fc387dadb272c5a7f8c3461d34c060100dbf1/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy";
+ sample_url =
"https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy";
sample_path = download_testdata(sample_url,
"keyword_spotting_int8_6.pyc.npy", module="data")
sample = np.load(sample_path)
diff --git a/tests/scripts/request_hook/request_hook.py
b/tests/scripts/request_hook/request_hook.py
index f093ab789b..500200557f 100644
--- a/tests/scripts/request_hook/request_hook.py
+++ b/tests/scripts/request_hook/request_hook.py
@@ -142,10 +142,6 @@ URL_MAP = {
+ quote("?raw=true"),
"https://github.com/shicai/MobileNet-Caffe/raw/master/mobilenet_v2_deploy.prototxt":
f"{BASE}/shicai/MobileNet-Caffe/raw/master/mobilenet_v2_deploy.prototxt",
"https://github.com/tensorflow/tflite-micro/raw/main/tensorflow/lite/micro/examples/micro_speech/micro_speech.tflite":
f"{BASE}/tensorflow/tflite-micro/raw/main/tensorflow/lite/micro/examples/micro_speech/micro_speech.tflite",
-
"https://github.com/tlc-pack/web-data/raw/25fe99fb00329a26bd37d3dca723da94316fd34c/testdata/microTVM/model/keyword_spotting_quant.tflite":
f"{BASE}/tlc-pack/web-data/raw/25fe99fb00329a26bd37d3dca723da94316fd34c/testdata/microTVM/model/keyword_spotting_quant.tflite",
-
"https://github.com/tlc-pack/web-data/raw/967fc387dadb272c5a7f8c3461d34c060100dbf1/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy":
f"{BASE}/tlc-pack/web-data/raw/967fc387dadb272c5a7f8c3461d34c060100dbf1/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy",
-
"https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy":
f"{BASE}/tlc-pack/web-data/raw/main/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy",
-
"https://github.com/tlc-pack/web-data/raw/main/testdata/microTVM/model/keyword_spotting_quant.tflite":
f"{BASE}/tlc-pack/web-data/raw/main/testdata/microTVM/model/keyword_spotting_quant.tflite",
"https://github.com/mlcommons/tiny/raw/v0.7/benchmark/training/visual_wake_words/trained_models/vww_96_int8.tflite":
f"{BASE}/mlcommons/tiny/raw/v0.7/benchmark/training/visual_wake_words/trained_models/vww_96_int8.tflite",
"https://github.com/uwsampl/web-data/raw/main/vta/models/synset.txt":
f"{BASE}/2022-10-05/synset.txt",
"https://homes.cs.washington.edu/~cyulin/media/gnn_model/gcn_cora.torch":
f"{BASE}/gcn_cora.torch",
@@ -181,8 +177,6 @@ URL_MAP = {
"https://raw.githubusercontent.com/tlc-pack/tophub/main/tophub/mali_v0.06.log":
f"{BASE}/2022-10-05/mali_v0.06.log",
"https://raw.githubusercontent.com/tlc-pack/tophub/main/tophub/opencl_v0.04.log":
f"{BASE}/tlc-pack/tophub/main/tophub/opencl_v0.04.log",
"https://raw.githubusercontent.com/tlc-pack/tophub/main/tophub/vta_v0.10.log":
f"{BASE}/tlc-pack/tophub/main/tophub/vta_v0.10.log",
-
"https://raw.githubusercontent.com/tlc-pack/web-data/main/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy":
f"{BASE}/tlc-pack/web-data/main/testdata/microTVM/data/keyword_spotting_int8_6.pyc.npy",
-
"https://raw.githubusercontent.com/tlc-pack/web-data/main/testdata/microTVM/model/keyword_spotting_quant.tflite":
f"{BASE}/tlc-pack/web-data/main/testdata/microTVM/model/keyword_spotting_quant.tflite",
"https://s3.amazonaws.com/model-server/inputs/kitten.jpg":
f"{BASE}/2022-10-05/kitten.jpg",
"https://s3.amazonaws.com/onnx-model-zoo/synset.txt":
f"{BASE}/2022-10-05/synset-s3.txt",
"https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_224_quant_20181026.tgz":
f"{BASE}/download.tensorflow.org/models/inception_v1_224_quant_20181026.tgz",
@@ -209,6 +203,13 @@ URL_MAP = {
"https://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels.h5":
f"{BASE}/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels.h5",
"https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz":
f"{BASE}/tensorflow/tf-keras-datasets/mnist.npz",
"https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/visual_wake_words/trained_models/vww_96_int8.tflite":
f"{BASE}/mlcommons/tiny/benchmark/training/visual_wake_words/trained_models/vww_96_int8.tflite",
+
"https://github.com/mlcommons/tiny/raw/bceb91c5ad2e2deb295547d81505721d3a87d578/benchmark/training/keyword_spotting/trained_models/kws_ref_model.tflite":
f"{BASE}/mlcommons/tiny/benchmark/training/keyword_spotting/trained_models/kws_ref_model.tflite",
+
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