areusch commented on a change in pull request #8990: URL: https://github.com/apache/tvm/pull/8990#discussion_r709435990
########## File path: tests/micro/zephyr/test_zephyr_armv7m.py ########## @@ -0,0 +1,293 @@ +# 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 io +import logging +import os +import pathlib +import sys +import logging +import tarfile +import tempfile + +import pytest +import numpy as np + +import tvm +import tvm.rpc +import tvm.micro +import tvm.testing +import tvm.relay as relay + +from tvm.micro.interface_api import generate_c_interface_header + +import conftest + +_LOG = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + +PLATFORMS = conftest.PLATFORMS + +TEMPLATE_PROJECT_DIR = ( + pathlib.Path(__file__).parent + / ".." + / ".." + / ".." + / "apps" + / "microtvm" + / "zephyr" + / "template_project" +).resolve() + + +def _read_line(fd, timeout_sec: int): + data = "" + new_line = False + while True: + if new_line: + break + new_data = fd.read(1, timeout_sec=timeout_sec) + logging.debug(f"read data: {new_data}") + for item in new_data: + new_c = chr(item) + data = data + new_c + if new_c == "\n": + new_line = True + break + return data + + +def _get_message(fd, expr: str, timeout_sec: int): + while True: + data = _read_line(fd, timeout_sec) + logging.debug(f"new line: {data}") + if expr in data: + return data + +def _build_project(temp_dir, zephyr_board, west_cmd, mod, build_config, extra_files_tar=None): + template_project_dir = ( + pathlib.Path(__file__).parent + / ".." + / ".." + / ".." + / "apps" + / "microtvm" + / "zephyr" + / "template_project" + ).resolve() + project_dir = temp_dir / "project" + project = tvm.micro.generate_project( + str(template_project_dir), + mod, + project_dir, + { + "extra_files_tar": extra_files_tar, + "project_type": "aot_demo", + "west_cmd": west_cmd, + "verbose": bool(build_config.get("debug")), + "zephyr_board": zephyr_board, + }, + ) + project.build() + return project, project_dir + + +def _create_header_file(tensor_name, npy_data, output_path, tar_file): + """ + This method generates a header file containing the data contained in the numpy array provided. + It is used to capture the tensor data (for both inputs and expected outputs). + """ + 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": + header_file.write(f"int8_t {tensor_name}[] =") + elif npy_data.dtype == "int32": + header_file.write(f"int32_t {tensor_name}[] = ") + elif npy_data.dtype == "uint8": + header_file.write(f"uint8_t {tensor_name}[] = ") + elif npy_data.dtype == "float32": + header_file.write(f"float {tensor_name}[] = ") + else: + raise ValueError("Data type not expected.") + + header_file.write("{") + for i in np.ndindex(npy_data.shape): + header_file.write(f"{npy_data[i]}, ") + header_file.write("};\n\n") + + header_file_bytes = bytes(header_file.getvalue(), "utf-8") + raw_path = pathlib.Path(output_path) / f"{tensor_name}.h" + ti = tarfile.TarInfo(name=str(raw_path)) + ti.size = len(header_file_bytes) + ti.mode = 0o644 + ti.type = tarfile.REGTYPE + tar_file.addfile(ti, io.BytesIO(header_file_bytes)) + + + + +def _open_tflite_model(model_path: str): + # Import TFLite 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) + + relay_mod, params = relay.frontend.from_tflite(tflite_model) + + return relay_mod, params + +def _get_test_data(testdata_dir): + + from PIL import Image + + image_files = ["digit-2.jpg"] + + for file in image_files: + img = Image.open(testdata_dir / file).resize((28, 28)) + img = np.asarray(img).astype("uint8") + sample = np.reshape(img, -1) + + output_shape = (1, 10) + + return sample, output_shape + + +def _apply_desired_layout_isa(relay_mod): + + desired_layouts = {'qnn.conv2d': ['NHWC', 'HWOI'], 'nn.conv2d': ['NHWC', 'HWOI']} + + seq = tvm.transform.Sequential([relay.transform.RemoveUnusedFunctions(), relay.transform.ConvertLayout(desired_layouts)]) + + with tvm.transform.PassContext(opt_level=3): + return seq(relay_mod) + +def _apply_desired_layout_no_isa(relay_mod): + + desired_layouts = {'qnn.conv2d': ['NHWC', 'HWIO'], 'nn.conv2d': ['NHWC', 'HWIO']} + + seq = tvm.transform.Sequential([relay.transform.RemoveUnusedFunctions(), relay.transform.ConvertLayout(desired_layouts)]) + + with tvm.transform.PassContext(opt_level=3): + return seq(relay_mod) + +def _generate_project(temp_dir, board, west_cmd, lowered, build_config, sample, output_shape): + + with tempfile.NamedTemporaryFile() as tar_temp_file: + with tarfile.open(tar_temp_file.name, "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( + lowered.libmod_name, ["input_1"], ["output"], model_files_path + ) + tf.add(header_path, arcname=os.path.relpath(header_path, tar_temp_dir)) + + _create_header_file("input_data", sample, "include", tf) + _create_header_file("output_data", np.zeros(shape=output_shape, dtype="float32"), "include", tf) + + project, _ = _build_project( + temp_dir, + board, + west_cmd, + lowered, + build_config, + extra_files_tar=tar_temp_file.name, + ) + + return project + + +def _run_model(temp_dir, board, west_cmd, lowered, build_config, sample, output_shape): + + project = _generate_project(temp_dir, board, west_cmd, lowered, build_config, sample, output_shape) + + project.flash() + + with project.transport() as transport: + timeout_read = 60 + # _get_message(transport, "#wakeup", timeout_sec=timeout_read) + transport.write(b"start\n", timeout_sec=5) + result_line = _get_message(transport, "#result", timeout_sec=timeout_read) + + result_line = result_line.strip("\n") + result_line = result_line.split(":") + result = int(result_line[1]) + time = int(result_line[2]) + logging.info(f"Result: {result}\ttime: {time} ms") + + return result, time + + +@tvm.testing.requires_micro +def test_armv7m_intrinsic(temp_dir, board, west_cmd, tvm_debug): + """Testing a ARM v7m SIMD extension.""" + + if board not in [ + "nrf5340dk", + "stm32f746xx_disco", + "stm32f746xx_nucleo", + "stm32l4r5zi_nucleo", + ]: + pytest.skip(msg="Platform does not support ARM v7m SIMD extenion.") + + model = conftest.ZEPHYR_BOARDS[board] + + build_config = {"debug": tvm_debug} + + this_dir = pathlib.Path(os.path.dirname(__file__)) + testdata_dir = this_dir.parent / "testdata" / "armv7m" + + relay_mod, params = _open_tflite_model(testdata_dir / "mnist_model_quant.tflite") + + sample, output_shape = _get_test_data(testdata_dir) + + relay_mod_isa = _apply_desired_layout_isa(relay_mod) + # kernel layout "HWIO" is not supported by arm_cpu SIMD extension (see tvm\python\relay\op\strategy\arm_cpu.py) + relay_mod_no_isa = _apply_desired_layout_no_isa(relay_mod) + + target = tvm.target.target.micro( + model, options=["-keys=arm_cpu,cpu", "-link-params=1", "--executor=aot", "--unpacked-api=1", "--interface-api=c"] + ) + + temp_dir_isa = temp_dir / "isa" + temp_dir_no_isa = temp_dir / "noisa" + + os.makedirs(temp_dir_isa, exist_ok=True) + os.makedirs(temp_dir_no_isa, exist_ok=True) + + with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}): + lowered_isa = relay.build(relay_mod_isa, target, params=params) + lowered_no_isa = relay.build(relay_mod_no_isa, target, params=params) + result_isa, time_isa = _run_model(temp_dir_isa, board, west_cmd, lowered_isa, build_config, sample, output_shape) + result_no_isa, time_no_isa = _run_model(temp_dir_no_isa, board, west_cmd, lowered_no_isa, build_config, sample, output_shape) + + assert result_no_isa == result_isa + assert time_no_isa > time_isa Review comment: @u99127 i'm remembering now i'm not sure if we intended to add Corstone to regression. what do you think of using one of the Zephyr QEMU targets? -- 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: commits-unsubscr...@tvm.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
