# How to debug¶

## Debug correctness¶

MACE provides tools to examine correctness of model execution by comparing model's output of MACE with output of training platform (e.g., Tensorflow, Caffe). Three metrics are used as comparison results:

• Cosine Similarity:
$Cosine\ Similarity = \frac{X \cdot X'}{\|X\| \|X'\|}$

This metric will be approximately equal to 1 if output is correct.

• SQNR (Signal-to-Quantization-Noise Ratio):
$SQNR = \frac{P_{signal}}{P_{noise}} = \frac{\|X\|^2}{\|X - X'\|^2}$

It is usually used to measure quantization accuracy. The higher SQNR is, the better accuracy will be.

• Pixel Accuracy:
$Pixel\ Accuracy = \frac{\sum^{batch}_{b=1} equal(\mathrm{argmax} X_b, \mathrm{argmax} X'_b)}{batch}$

It is usually used to measure classification accuracy. The higher the better.

where $$X$$ is expected output (from training platform) whereas $$X'$$ is actual output (from MACE) .

You can validate it by specifying --validate option while running the model.

MACE automatically validate these metrics by running models with synthetic inputs. If you want to specify input data to use, you can add an option in yaml config under 'subgraphs', e.g.,

models:
mobilenet_v1:
platform: tensorflow
model_file_path: https://cnbj1.fds.api.xiaomi.com/mace/miai-models/mobilenet-v1/mobilenet-v1-1.0.pb
model_sha256_checksum: 71b10f540ece33c49a7b51f5d4095fc9bd78ce46ebf0300487b2ee23d71294e6
subgraphs:
- input_tensors:
- input
input_shapes:
- 1,224,224,3
output_tensors:
- MobilenetV1/Predictions/Reshape_1
output_shapes:
- 1,1001
check_tensors:
check_shapes:
- 1,1,1,1001
validation_inputs_data:
- https://cnbj1.fds.api.xiaomi.com/mace/inputs/dog.npy


If model's output is suspected to be incorrect, it might be useful to debug your model layer by layer by specifying an intermediate layer as output, or use binary search method until suspicious layer is found.

You can also specify --layers after --validate to validate all or some of the layers of the model(excluding some layers changed by MACE, e.g., BatchToSpaceND), it only supports TensorFlow now. You can find validation results in build/your_model/model/runtime_in_yaml/log.csv.

For quantized model, if you want to check one layer, you can add check_tensors and check_shapes like in the yaml above. You can only specify MACE op's output.

## Debug with crash¶

When MACE crashes, a complete stacktrace is useful in debugging. But because of selinux problem, symbols table is not loaded in memory, which leading to no symbol in stack trace. To circumvent this problem, you can rebuild mace_run with --debug_mode option to reserve debug symbols, e.g.,

For CMake users, modify the cmake configuration located in tools/cmake/ as -DCMAKE_BUILD_TYPE=Debug, and rebuild the engine.

For Bazel users,

python tools/converter.py run --config=/path/to/config.yml --debug_mode


For android, you can use ndk-stack tools to symbolize stack trace, e.g.,

adb logcat | $ANDROID_NDK_HOME/ndk-stack -sym /path/to/local/binary/directory/  ## Debug memory usage¶ The simplest way to debug process memory usage is to use top command. With -H option, it can also show thread info. For android, if you need more memory info, e.g., memory used of all categories, adb shell dumpsys meminfo will help. By watching memory usage, you can check if memory usage meets expectations or if any leak happens. ## Debug performance¶ Using MACE, you can benchmark a model by examining each layer's duration as well as total duration. Or you can benchmark a single op. The detailed information is in Benchmark usage. ## Debug model conversion¶ After model is converted to MACE model, a literal model graph is generated in directory mace/codegen/models/your_model. You can refer to it when debugging model conversion. MACE also provides model visualization HTML generated in build directory, generated after converting model. ## Debug engine using log¶ MACE implements a similar logging mechanism like glog. There are two types of logs, LOG for normal logging and VLOG for debugging. LOG includes four levels, sorted by severity level: INFO, WARNING, ERROR, FATAL. The logging severity threshold can be configured via environment variable, e.g. MACE_CPP_MIN_LOG_LEVEL=WARNING to set as WARNING. Only the log messages with equal or above the specified severity threshold will be printed, the default threshold is INFO. We don't support integer log severity value like glog, because they are confusing with VLOG. VLOG is verbose logging which is logged as LOG(INFO). VLOG also has more detailed integer verbose levels, like 0, 1, 2, 3, etc. The threshold can be configured through environment variable, e.g. MACE_CPP_MIN_VLOG_LEVEL=2 to set as 2. With VLOG, the lower the verbose level, the more likely messages are to be logged. For example, when the threshold is set to 2, both VLOG(1), VLOG(2) log messages will be printed, but VLOG(3) and highers won't. By using mace_run tool, VLOG level can be easily set by option, e.g., --vlog_level=2 If models are run on android, you might need to use adb logcat to view logs. ## Debug engine using GDB¶ GDB can be used as the last resort, as it is powerful that it can trace stacks of your process. If you run models on android, things may be a little bit complicated. # push gdbserver to your phone adb push$ANDROID_NDK_HOME/prebuilt/android-arm64/gdbserver/gdbserver /data/local/tmp/

# set system env, pull system libs and bins to host
export SYSTEM_LIB=/path/to/android/system_lib
export SYSTEM_BIN=/path/to/android/system_bin
mkdir -p $SYSTEM_LIB adb pull /system/lib/.$SYSTEM_LIB
mkdir -p $SYSTEM_BIN adb pull /system/bin/.$SYSTEM_BIN

# Suppose ndk compiler used to compile Mace is of android-21
export PLATFORMS_21_LIB=$ANDROID_NDK_HOME/platforms/android-21/arch-arm/usr/lib/ # start gdbserver，make gdb listen to port 6000 # adb shell /data/local/tmp/gdbserver :6000 /path/to/binary/on/phone/example_bin adb shell LD_LIBRARY_PATH=/dir/to/dynamic/library/on/phone/ /data/local/tmp/gdbserver :6000 /data/local/tmp/mace_run/example_bin # or attach a running process adb shell /data/local/tmp/gdbserver :6000 --attach 8700 # forward tcp port adb forward tcp:6000 tcp:6000 # use gdb on host to execute binary$ANDROID_NDK_HOME/prebuilt/linux-x86_64/bin/gdb [/path/to/binary/on/host/example_bin]

# connect remote port after starting gdb command
target remote :6000

# set lib path
set solib-search-path $SYSTEM_LIB:$SYSTEM_BIN:\$PLATFORMS_21_LIB

# then you can use it as host gdb, e.g.,
bt