Advanced usage for Bazel users¶
This part contains the full usage of MACE.
Overview¶
As mentioned in the previous part, a model deployment file defines a case of model deployment. The building process includes parsing model deployment file, converting models, building MACE core library and packing generated model libraries.
Deployment file¶
One deployment file will generate one library normally, but if more than one ABIs are specified, one library will be generated for each ABI. A deployment file can also contain multiple models. For example, an AI camera application may contain face recognition, object recognition, and voice recognition models, all of which can be defined in one deployment file.
Example
Here is an example deployment file with two models.
# The name of library library_name: mobile_squeeze # host, armeabi-v7a or arm64-v8a target_abis: [arm64-v8a] # soc's name or all target_socs: [all] # The build mode for model(s). # 'code' for transferring model(s) into cpp code, 'file' for keeping model(s) in protobuf file(s) (.pb). model_graph_format: code # 'code' for transferring model data(s) into cpp code, 'file' for keeping model data(s) in file(s) (.data). model_data_format: code # One yaml config file can contain multi models' deployment info. 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 validation_inputs_data: - https://cnbj1.fds.api.xiaomi.com/mace/inputs/dog.npy runtime: cpu+gpu limit_opencl_kernel_time: 0 obfuscate: 0 winograd: 0 squeezenet_v11: platform: caffe model_file_path: http://cnbj1-inner-fds.api.xiaomi.net/mace/mace-models/squeezenet/SqueezeNet_v1.1/model.prototxt weight_file_path: http://cnbj1-inner-fds.api.xiaomi.net/mace/mace-models/squeezenet/SqueezeNet_v1.1/weight.caffemodel model_sha256_checksum: 625c952063da1569e22d2f499dc454952244d42cd8feca61f05502566e70ae1c weight_sha256_checksum: 72b912ace512e8621f8ff168a7d72af55910d3c7c9445af8dfbff4c2ee960142 subgraphs: - input_tensors: - data input_shapes: - 1,227,227,3 output_tensors: - prob output_shapes: - 1,1,1,1000 accuracy_validation_script: - path/to/your/script runtime: cpu+gpu limit_opencl_kernel_time: 0 obfuscate: 0 winograd: 0
Configurations
Options | Usage |
---|---|
library_name | Library name. |
target_abis | The target ABI(s) to build, could be 'host', 'armeabi-v7a' or 'arm64-v8a'. If more than one ABIs will be used, separate them by commas. |
target_socs | [optional] Build for specific SoCs. |
model_graph_format | model graph format, could be 'file' or 'code'. 'file' for converting model graph to ProtoBuf file(.pb) and 'code' for converting model graph to c++ code. |
model_data_format | model data format, could be 'file' or 'code'. 'file' for converting model weight to data file(.data) and 'code' for converting model weight to c++ code. |
model_name | model name should be unique if there are more than one models. LIMIT: if build_type is code, model_name will be used in c++ code so that model_name must comply with c++ name specification. |
platform | The source framework, tensorflow or caffe. |
model_file_path | The path of your model file which can be local path or remote URL. |
model_sha256_checksum | The SHA256 checksum of the model file. |
weight_file_path | [optional] The path of Caffe model weights file. |
weight_sha256_checksum | [optional] The SHA256 checksum of Caffe model weights file. |
subgraphs | subgraphs key. DO NOT EDIT |
input_tensors | The input tensor name(s) (tensorflow) or top name(s) of inputs' layer (caffe). If there are more than one tensors, use one line for a tensor. |
output_tensors | The output tensor name(s) (tensorflow) or top name(s) of outputs' layer (caffe). If there are more than one tensors, use one line for a tensor. |
input_shapes | The shapes of the input tensors, default is NHWC order. |
output_shapes | The shapes of the output tensors, default is NHWC order. |
input_ranges | The numerical range of the input tensors' data, default [-1, 1]. It is only for test. |
validation_inputs_data | [optional] Specify Numpy validation inputs. When not provided, [-1, 1] random values will be used. |
accuracy_validation_script | [optional] Specify the accuracy validation script as a plugin to test accuracy, see doc. |
validation_threshold | [optional] Specify the similarity threshold for validation. A dict with key in 'CPU', 'GPU' and/or 'HEXAGON' and value <= 1.0. |
backend | The onnx backend framework for validation, could be [tensorflow, caffe2, pytorch], default is tensorflow. |
runtime | The running device, one of [cpu, gpu, dsp, hta, apu]. |
data_type | [optional] The data type used for specified runtime. [fp16_fp32, fp32_fp32] for GPU and APU; [fp16_fp32, bf16_fp32, fp32_fp32, fp16_fp16] for CPU, default is fp16_fp32. |
input_data_types | [optional] The input data type for specific op(eg. gather), which can be [int32, float32], default to float32. |
input_data_formats | [optional] The format of the input tensors, one of [NONE, NHWC, NCHW]. If there is no format of the input, please use NONE. If only one single format is specified, all inputs will use that format, default is NHWC order. |
output_data_formats | [optional] The format of the output tensors, one of [NONE, NHWC, NCHW]. If there is no format of the output, please use NONE. If only one single format is specified, all inputs will use that format, default is NHWC order. |
limit_opencl_kernel_time | [optional] Whether splitting the OpenCL kernel within 1 ms to keep UI responsiveness, default is 0. |
opencl_queue_window_size | [optional] Limit the max commands in OpenCL command queue to keep UI responsiveness, default is 0. |
obfuscate | [optional] Whether to obfuscate the model operator name, default to 0. |
winograd | [optional] Which type winograd to use, could be [0, 2, 4]. 0 for disable winograd, 2 and 4 for enable winograd, 4 may be faster than 2 but may take more memory. |
Note
Some command tools:
# Get device's soc info.
adb shell getprop | grep platform
# command for generating sha256_sum
sha256sum /path/to/your/file
Advanced usage¶
- There are three common advanced use cases:
- run your model on the embedded device(ARM LINUX)
- converting model to C++ code.
- tuning GPU kernels for a specific SoC.
Run you model on the embedded device(ARM Linux)¶
The way to run your model on the ARM Linux is nearly same as with android, except you need specify a device config file.
python tools/converter.py run --config=/path/to/your/model_deployment_file.yml --device_yml=/path/to/devices.yml
There are two steps to do before run:
configure login without password
MACE use ssh to connect embedded device, you should copy your public key to embedded device with the blow command.
cat ~/.ssh/id_rsa.pub | ssh -q {user}@{ip} "cat >> ~/.ssh/authorized_keys"
write your own device yaml configuration file.
Example
Here is an device yaml config demo.
# one yaml config file can contain multi device info devices: # The name of the device nanopi: # arm64 or armhf target_abis: [arm64, armhf] # device soc, you can get it from device manual target_socs: RK3399 # device model full name models: FriendlyElec Nanopi M4 # device ip address address: 10.0.0.0 # login username username: user raspberry: target_abis: [armv7l] target_socs: BCM2837 models: Raspberry Pi 3 Model B Plus Rev 1.3 address: 10.0.0.1 username: user
- Configuration
The detailed explanation is listed in the blow table.
Options Usage target_abis Device supported abis, you can get it via dpkg --print-architecture
anddpkg --print-foreign-architectures
command, if more than one abi is supported, separate them by commas.target_socs device soc, you can get it from device manual, we haven't found a way to get it in shell. models device models full name, you can get via get lshw
command (third party package, install it via your package manager). see it's product value.address Since we use ssh to connect device, ip address is required. username login username, required.
Convert model(s) to C++ code¶
1. Change the model deployment file(.yml)
If you want to protect your model, you can convert model to C++ code. there are also two cases:
- convert model graph to code and model weight to file with below model configuration.
model_graph_format: code model_data_format: file
- convert both model graph and model weight to code with below model configuration.
model_graph_format: code model_data_format: codeNote
Another model protection method is using
obfuscate
to obfuscate names of model's operators.2. Convert model(s) to code
python tools/converter.py convert --config=/path/to/model_deployment_file.yml
The command will generate ${library_name}.a in build/${library_name}/model directory and ** .h * in build/${library_name}/include like the following dir-tree.
# model_graph_format: code # model_data_format: file build/mobilenet-v1 ├── include │ └── mace │ └── public │ ├── mace_engine_factory.h │ └── mobilenet_v1.h └── model ├── arm64-v8a/mobilenet-v1.a ├── armeabi-v7a/mobilenet-v1.a └── mobilenet_v1.data # model_graph_format: code # model_data_format: code build/mobilenet-v1 ├── include │ └── mace │ └── public │ ├── mace_engine_factory.h │ └── mobilenet_v1.h └── model ├── arm64-v8a/mobilenet-v1.a └── armeabi-v7a/mobilenet-v1.a
- 3. Deployment
- Link libmace.a and ${library_name}.a to your target.
- Refer to
mace/tools/mace_run.cc
for full usage. The following list the key steps.// Include the headers #include "mace/public/mace.h" // If the model_graph_format is code #include "mace/public/${model_name}.h" #include "mace/public/mace_engine_factory.h" // ... Same with the code in basic usage // 4. Create MaceEngine instance std::shared_ptr<mace::MaceEngine> engine; MaceStatus create_engine_status; // Create Engine from compiled code create_engine_status = CreateMaceEngineFromCode(model_name.c_str(), model_data_ptr, // nullptr if model_data_format is code model_data_size, // 0 if model_data_format is code input_names, output_names, device_type, &engine); if (create_engine_status != MaceStatus::MACE_SUCCESS) { // Report error or fallback } // ... Same with the code in basic usage
Transform models after conversion¶
If
model_graph_format
ormodel_data_format
is specified as file, the model or weight file will be generated as a .pb or .data file after model conversion. After that, more transformations can be applied to the generated files, such as compression or encryption. To achieve that, the model loading is split to two stages: 1) load the file from file system to memory buffer; 2) create the MACE engine from the model buffer. So between the two stages, transformations can be inserted to decompress or decrypt the model buffer. The transformations are user defined. The following lists the key steps when bothmodel_graph_format
andmodel_data_format
are set as file.// Load model graph from file system std::unique_ptr<mace::port::ReadOnlyMemoryRegion> model_graph_data = make_unique<mace::port::ReadOnlyBufferMemoryRegion>(); if (FLAGS_model_file != "") { auto fs = GetFileSystem(); status = fs->NewReadOnlyMemoryRegionFromFile(FLAGS_model_file.c_str(), &model_graph_data); if (status != MaceStatus::MACE_SUCCESS) { // Report error or fallback } } // Load model data from file system std::unique_ptr<mace::port::ReadOnlyMemoryRegion> model_weights_data = make_unique<mace::port::ReadOnlyBufferMemoryRegion>(); if (FLAGS_model_data_file != "") { auto fs = GetFileSystem(); status = fs->NewReadOnlyMemoryRegionFromFile(FLAGS_model_data_file.c_str(), &model_weights_data); if (status != MaceStatus::MACE_SUCCESS) { // Report error or fallback } } if (model_graph_data == nullptr || model_weights_data == nullptr) { // Report error or fallback } std::vector<unsigned char> transformed_model_graph_data; std::vector<unsigned char> transformed_model_weights_data; // Add transformations here. ... // Release original model data after transformations model_graph_data.reset(); model_weights_data.reset(); // Create the MACE engine from the model buffer std::shared_ptr<mace::MaceEngine> engine; MaceStatus create_engine_status; create_engine_status = CreateMaceEngineFromProto(transformed_model_graph_data.data(), transformed_model_graph_data.size(), transformed_model_weights_data.data(), transformed_model_weights_data.size(), input_names, output_names, config, &engine); if (create_engine_status != MaceStatus::MACE_SUCCESS) { // Report error or fallback }
Tuning for specific SoC's GPU¶
If you want to use the GPU of a specific device, you can just specify the
target_socs
in your YAML file and then tune the MACE lib for it (OpenCL kernels), which may get 1~10% performance improvement.
1. Change the model deployment file(.yml)
Specify
target_socs
in your model deployment file(.yml):target_socs: [sdm845]Note
Get device's soc info: adb shell getprop | grep platform
2. Convert model(s)
python tools/converter.py convert --config=/path/to/model_deployment_file.yml
3. Tuning
The tools/converter.py will enable automatic tuning for GPU kernels. This usually takes some time to finish depending on the complexity of your model.
Note
You must specify the
target_socs
in your YAML file and plug in device(s) with the specific SoC(s).python tools/converter.py run --config=/path/to/model_deployment_file.yml
The command will generate two files in build/${library_name}/opencl, like the following dir-tree.
build └── mobilenet-v2 ├── model │ ├── mobilenet_v2.data │ └── mobilenet_v2.pb └── opencl └── arm64-v8a ├── moblinet-v2_compiled_opencl_kernel.MiNote3.sdm660.bin ├── moblinet-v2_compiled_opencl_kernel.MiNote3.sdm660.bin.cc ├── moblinet-v2_tuned_opencl_parameter.MiNote3.sdm660.bin └── moblinet-v2_tuned_opencl_parameter.MiNote3.sdm660.bin.cc
- mobilenet-v2-gpu_compiled_opencl_kernel.MI6.msm8998.bin stands for the OpenCL binaries used for your models, which could accelerate the initialization stage. Details please refer to OpenCL Specification.
- mobilenet-v2-gpu_compiled_opencl_kernel.MI6.msm8998.bin.cc contains C++ source code which defines OpenCL binary data as const array.
- mobilenet-v2-tuned_opencl_parameter.MI6.msm8998.bin stands for the tuned OpenCL parameters for the SoC.
- mobilenet-v2-tuned_opencl_parameter.MI6.msm8998.bin.cc contains C++ source code which defines OpenCL binary data as const array.
- 4. Deployment
- Change the names of files generated above for not collision and push them to your own device's directory.
- Use like the previous procedure, below lists the key steps differently.
// Include the headers #include "mace/public/mace.h" // 0. Declare the device type (must be same with ``runtime`` in configuration file) DeviceType device_type = DeviceType::GPU; // 1. configuration MaceStatus status; MaceEngineConfig config; std::shared_ptr<OpenclContext> opencl_context; const std::string storage_path ="path/to/storage"; opencl_context = GPUContextBuilder() .SetStoragePath(storage_path) .SetOpenCLBinaryPaths(path/to/opencl_binary_paths) .SetOpenCLParameterPath(path/to/opencl_parameter_file) .Finalize(); config.SetGPUContext(opencl_context); config.SetGPUHints( static_cast<GPUPerfHint>(GPUPerfHint::PERF_NORMAL), static_cast<GPUPriorityHint>(GPUPriorityHint::PRIORITY_LOW)); // ... Same with the code in basic usage.
Validate accuracy of MACE model¶
MACE supports python validation script as a plugin to test the accuracy, the plugin script could be used for below two purpose.
- Test the accuracy(like Top-1) of MACE model(specifically quantization model) converted from other framework(like tensorflow)
- Show some real output if you want to see it.
The script define some interfaces like preprocess and postprocess to deal with input/outut and calculate the accuracy, you could refer to the sample code for detail. the sample code show how to calculate the Top-1 accuracy with imagenet validation dataset.
Useful Commands¶
- run the model
# Test model run time
python tools/converter.py run --config=/path/to/model_deployment_file.yml --round=100
# Validate the correctness by comparing the results against the
# original model and framework, measured with cosine distance for similarity.
python tools/converter.py run --config=/path/to/model_deployment_file.yml --validate
# Check the memory usage of the model(**Just keep only one model in deployment file**)
python tools/converter.py run --config=/path/to/model_deployment_file.yml --round=10000 &
sleep 5
adb shell dumpsys meminfo | grep mace_run
kill %1
Warning
run
rely on convert
command, you should convert
before run
.
- benchmark and profile model
the detailed information is in Benchmark usage.
# Benchmark model, get detailed statistics of each Op.
python tools/converter.py run --config=/path/to/model_deployment_file.yml --benchmark
Warning
benchmark
rely on convert
command, you should benchmark
after convert
.
Common arguments
option type default commands explanation --num_threads int -1 run
number of threads --cpu_affinity_policy int 1 run
0:AFFINITY_NONE/1:AFFINITY_BIG_ONLY/2:AFFINITY_LITTLE_ONLY --gpu_perf_hint int 3 run
0:DEFAULT/1:LOW/2:NORMAL/3:HIGH --gpu_priority_hint int 3 run
/benchmark
0:DEFAULT/1:LOW/2:NORMAL/3:HIGH
Use -h
to get detailed help.
python tools/converter.py -h
python tools/converter.py build -h
python tools/converter.py run -h
Reduce Library Size¶
Build for your own usage purpose. Some configuration variables in tools/bazel_build_standalone_lib.sh are set to
true
by default, you can change them tofalse
to reduce the library size.dynamic library
- If the models don't need to run on device
dsp
, change the build option--define hexagon=true
tofalse
. And the library will be decreased about100KB
. - Futher more, if only
cpu
device needed, change--define opencl=true
tofalse
. This way will reduce half of library size to about700KB
forarmeabi-v7a
and1000KB
forarm64-v8a
- About
300KB
can be reduced when add--config symbol_hidden
building option. It will change the visibility of inner apis in libmace.so and lead to linking error when load model(s) incode
but no effection forfile
mode.
- If the models don't need to run on device
static library
- The methods in dynamic library can be useful for static library too. In additional, the static
library may also contain model graph and model datas if the configs
model_graph_format
andmodel_data_format
in deployment file are set tocode
. - It is recommended to use
version script
andstrip
feature when linking mace static library. The effect is remarkable.
- The methods in dynamic library can be useful for static library too. In additional, the static
library may also contain model graph and model datas if the configs
Remove the unused ops.
Remove the registration of the ops unused for your models in the mace/ops/ops_register.cc
,
which will reduce the library size significantly. the final binary just link the registered ops' code.
#include "mace/ops/ops_register.h"
namespace mace {
namespace ops {
// Just leave the ops used in your models
...
} // namespace ops
OpRegistry::OpRegistry() {
// Just leave the ops used in your models
...
ops::RegisterMyCustomOp(this);
...
}
} // namespace mace
Reduce Model Size¶
Model file size can be a bottleneck for the deployment of neural networks on mobile devices, so MACE provides several ways to reduce the model size with no or little performance or accuracy degradation.
1. Save model weights in half-precision floating point format
The data type of a regular model is float (32bit). To reduce the model weights size, half (16bit) can be used to reduce it by half with negligible accuracy degradation. Therefore, the default storage type for a regular model in MACE is half. However, if the model is very sensitive to accuracy, storage type can be changed to float.
In the deployment file, data_type
is fp16_fp32
by default and can be changed to fp32_fp32
,
for CPU it can also be changed to bf16_fp32
and fp16_fp16
( fp16_fp16
can only be used on armv8.2 or higher version).
For CPU, fp16_fp32
means that the weights are saved in half and actual inference is in float; while bf16_fp32
means that the weights are saved in bfloat16 and actual inference is in float,85G
and fp16_fp16
means that the weights are saved in half and actual inference is in half.
For GPU, fp16_fp32
means that the ops in GPU take half as inputs and outputs while kernel execution in float.
2. Save model weights in quantized fixed point format
Weights of convolutional (excluding depthwise) and fully connected layers take up a major part of model size.
These weights can be quantized to 8bit to reduce the size to a quarter, whereas the accuracy usually decreases only by 1%-3%.
For example, the top-1 accuracy of MobileNetV1 after quantization of weights is 68.2% on the ImageNet validation set.
quantize_large_weights
can be specified as 1 in the deployment file to save these weights in 8bit and actual inference in float.
It can be used for both CPU and GPU.
Reduce Memory Occupation¶
MACE creates intermediate memory for inference, which maybe large size, so MACE provides several ways to reduce the intermediate memory size.
1. Release intermediate memory between two inferences
If the interval of app inferences is long, the intermediate memory of MACE can be released temporarily to reduce the memory occupation. Before the next inference, MACE will rebuild the intermediate memory, which will take some time, so essentially this is a strategy of trading time for space. The API for temporarily releasing intermediate memory of MACE is:
MaceEngine::ReleaseIntermediateBuffer();
2. Share intermediate memory among multiple MACE engines
If app has multiple MACE engines in a process, and these engines will not be called at the same time, such as not concurrent in two threads, then we can let these engines share their intermediate memory.
By doing so, multiple engines will use only one copy of the intermediate memory, thus greatly saving memory.
When an engine A
is initialized, if an engine B
wants to share the memory of engine A
, it only needs to use engine A
as the tutor of engine B
.
You can set engine A
as the engine B
's tutor by CreateMaceEngineFromProto
or CreateMaceEngineFromCode
, the code is as follows:
std::shared_ptr<mace::MaceEngine> A;
MaceStatus create_engine_status;
// Create Engine from model file
create_engine_status =
CreateMaceEngineFromProto(model_graph_proto,
model_graph_proto_size,
model_weights_data,
model_weights_data_size,
input_names,
output_names,
device_type,
&A);
MACE_CHECK(create_engine_status == MaceStatus::MACE_SUCCESS);
std::shared_ptr<mace::MaceEngine> B;
create_engine_status =
CreateMaceEngineFromProto(model_graph_proto,
model_graph_proto_size,
model_weights_data,
model_weights_data_size,
input_names,
output_names,
device_type,
&B,
nullptr,
A.get());
MACE_CHECK(create_engine_status == MaceStatus::MACE_SUCCESS);
Warning
Before passing engine A
as a tutor of engine B
, A
must be initialized first.
Both CreateMaceEngineFromProto
and CreateMaceEngineFromCode
initialize the MACE engine after it is created.
You can use any engine as a tutor of other engines. Two engines with the same runtime can share more intermediate memory.