1. Marvell cnxk Machine Learning Poll Mode Driver

The cnxk ML poll mode driver provides support for offloading Machine Learning inference operations to Machine Learning accelerator units on the Marvell OCTEON cnxk SoC family.

The cnxk ML PMD code is organized into multiple files with all file names starting with cn10k, providing support for CN106XX and CN106XXS.

More information about OCTEON cnxk SoCs may be obtained from https://www.marvell.com

1.1. Supported OCTEON cnxk SoCs

• CN106XX
• CN106XXS

1.2. Features

The OCTEON cnxk ML PMD provides support for the following set of operations:

Slow-path device and ML model handling:

• Device probing, configuration and close
• Device start and stop
• Model loading and unloading
• Model start and stop
• Data quantization and dequantization

Fast-path Inference:

• Inference execution
• Error handling

1.3. Compilation Prerequisites

This driver requires external libraries to optionally enable support for models compiled using Apache TVM framework. The following dependencies are not part of DPDK and must be installed separately:

1.3.1. Jansson

This library enables support to parse and read JSON files.

1.3.2. DLPack

This library provides headers for open in-memory tensor structures.

Note

DPDK CNXK ML driver requires DLPack version 0.7

git clone https://github.com/dmlc/dlpack.git
cd dlpack
git checkout v0.7 -b v0.7
cmake -S ./ -B build \
   -DCMAKE_INSTALL_PREFIX=<install_prefix> \
   -DBUILD_MOCK=OFF
make -C build
make -C build install

When cross-compiling, compiler must be provided to CMake:

-DCMAKE_C_COMPILER=aarch64-linux-gnu-gcc \
-DCMAKE_CXX_COMPILER=aarch64-linux-gnu-g++

1.3.3. DMLC

This is a common bricks library for building scalable and portable distributed machine learning.

git clone https://github.com/dmlc/dmlc-core.git
cd dmlc-core
git checkout main
cmake -S ./ -B build \
   -DCMAKE_INSTALL_PREFIX=<install_prefix> \
   -DCMAKE_C_FLAGS="-fpermissive" \
   -DCMAKE_CXX_FLAGS="-fpermissive" \
   -DUSE_OPENMP=OFF
 make -C build
 make -C build install

When cross-compiling, compiler must be provided to CMake:

-DCMAKE_C_COMPILER=aarch64-linux-gnu-gcc \
-DCMAKE_CXX_COMPILER=aarch64-linux-gnu-g++

1.3.4. TVM

Apache TVM provides a runtime libraries used to execute models on CPU cores or hardware accelerators.

Note

DPDK CNXK ML driver requires TVM version 0.10.0

git clone https://github.com/apache/tvm.git
cd tvm
git checkout v0.11.0 -b v0.11.0
git submodule update --init
cmake -S ./ -B build \
   -DCMAKE_INSTALL_PREFIX=<install_prefix> \
   -DBUILD_STATIC_RUNTIME=OFF
make -C build
make -C build install

When cross-compiling, more options must be provided to CMake:

-DCMAKE_C_COMPILER=aarch64-linux-gnu-gcc \
-DCMAKE_CXX_COMPILER=aarch64-linux-gnu-g++ \
-DMACHINE_NAME=aarch64-linux-gnu \
-DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
-DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY

1.3.5. TVMDP

Marvell’s TVM Dataplane Library works as an interface between TVM runtime and DPDK drivers. TVMDP library provides a simplified C interface for TVM’s runtime based on C++.

Note

TVMDP library is dependent on TVM, dlpack, jansson and dmlc-core libraries.

git clone https://github.com/MarvellEmbeddedProcessors/tvmdp.git
cd tvmdp
git checkout main
cmake -S ./ -B build \
   -DCMAKE_INSTALL_PREFIX=<install_prefix> \
   -DBUILD_SHARED_LIBS=ON
make -C build
make -C build install

When cross-compiling, more options must be provided to CMake:

-DCMAKE_C_COMPILER=aarch64-linux-gnu-gcc \
-DCMAKE_CXX_COMPILER=aarch64-linux-gnu-g++ \
-DCMAKE_FIND_ROOT_PATH=<install_prefix>

1.3.6. libarchive

Apache TVM framework generates compiled models as tar archives. This library enables support to decompress and read archive files in tar, xz and other formats.

1.4. Installation

The OCTEON cnxk ML PMD may be compiled natively on an OCTEON cnxk platform or cross-compiled on an x86 platform.

In order for Meson to find the dependencies above during the configure stage, it is required to update environment variables as below:

CMAKE_PREFIX_PATH='<install_prefix>/lib/cmake/tvm:<install_prefix>/lib/cmake/dlpack:<install_prefix>/lib/cmake/dmlc'
PKG_CONFIG_PATH='<install_prefix>/lib/pkgconfig'

Refer to Marvell cnxk platform guide for instructions to build your DPDK application.

1.5. Initialization

List the ML PF devices available on cn10k platform:

lspci -d:a092

a092 is the ML device PF id. You should see output similar to:

0000:00:10.0 System peripheral: Cavium, Inc. Device a092

Bind the ML PF device to the vfio_pci driver:

cd <dpdk directory>
usertools/dpdk-devbind.py -u 0000:00:10.0
usertools/dpdk-devbind.py -b vfio-pci 0000:00:10.0

1.6. VDEV support

On platforms which don’t support ML hardware acceleration through PCI device, the Marvell ML CNXK PMD can execute inference operations on a vdev with the ML models compiled using Apache TVM framework.

VDEV can be enabled by passing the EAL arguments

--vdev ml_mvtvm

VDEV can also be used on platforms with ML HW accelerator. However to use vdev in this case, the PCI device has to be unbound. When PCI device is bound, creation of vdev is skipped.

1.7. Runtime Config Options

Firmware file path (default /lib/firmware/mlip-fw.bin)

Path to the firmware binary to be loaded during device configuration. The parameter fw_path can be used by the user to load ML firmware from a custom path.

This option is supported only on PCI HW accelerator.

For example:

-a 0000:00:10.0,fw_path="/home/user/ml_fw.bin"

With the above configuration, driver loads the firmware from the path /home/user/ml_fw.bin.

Enable DPE warnings (default 1)

ML firmware can be configured during load to handle the DPE errors reported by ML inference engine. When enabled, firmware would mask the DPE non-fatal hardware errors as warnings. The parameter enable_dpe_warnings is used fo this configuration.

This option is supported only on PCI HW accelerator.

For example:

-a 0000:00:10.0,enable_dpe_warnings=0

With the above configuration, DPE non-fatal errors reported by HW are considered as errors.

Model data caching (default 1)

Enable caching model data on ML ACC cores. Enabling this option executes a dummy inference request in synchronous mode during model start stage. Caching of model data improves the inferencing throughput / latency for the model. The parameter cache_model_data is used to enable data caching.

This option is supported on PCI HW accelerator and vdev.

For example:

-a 0000:00:10.0,cache_model_data=0

With the above configuration, model data caching is disabled on HW accelerator.

For example:

--vdev ml_mvtvm,cache_model_data=0

With the above configuration, model data caching is disabled on vdev.

OCM allocation mode (default lowest)

Option to specify the method to be used while allocating OCM memory for a model during model start. Two modes are supported by the driver. The parameter ocm_alloc_mode is used to select the OCM allocation mode.

lowest

Allocate OCM for the model from first available free slot. Search for the free slot is done starting from the lowest tile ID and lowest page ID.

largest

Allocate OCM for the model from the slot with largest amount of free space.

This option is supported only on PCI HW accelerator.

For example:

-a 0000:00:10.0,ocm_alloc_mode=lowest

With the above configuration, OCM allocation for the model would be done from the first available free slot / from the lowest possible tile ID.

OCM page size (default 16384)

Option to specify the page size in bytes to be used for OCM management. Available OCM is split into multiple pages of specified sizes and the pages are allocated to the models. The parameter ocm_page_size is used to specify the page size to be used.

Supported page sizes by the driver are 1 KB, 2 KB, 4 KB, 8 KB and 16 KB. Default page size is 16 KB.

This option is supported only on PCI HW accelerator.

For example:

-a 0000:00:10.0,ocm_page_size=8192

With the above configuration, page size of OCM is set to 8192 bytes / 8 KB.

Enable hardware queue lock (default 0)

Option to select the job request enqueue function to use to queue the requests to hardware queue. The parameter hw_queue_lock is used to select the enqueue function.

0

Disable (default), use lock-free version of hardware enqueue function for job queuing in enqueue burst operation. To avoid race condition in request queuing to hardware, disabling hw_queue_lock restricts the number of queue-pairs supported by cnxk driver to 1.

1

Enable, use spin-lock version of hardware enqueue function for job queuing. Enabling spinlock version would disable restrictions on the number of queue-pairs that can be supported by the driver.

This option is supported only on PCI HW accelerator.

For example:

-a 0000:00:10.0,hw_queue_lock=1

With the above configuration, spinlock version of hardware enqueue function is used in the fast path enqueue burst operation.

Maximum queue pairs (default 1)

VDEV supports additional EAL arguments to configure the maximum number of queue-pairs on the ML device through the option max_qps.

This option is supported only on vdev.

For example:

--vdev ml_mvtvm,max_qps=4

With the above configuration, 4 queue-pairs are created on the vdev.

1.8. Debugging Options

Table 1.12 OCTEON cnxk ML PMD debug options

#	Component	EAL log command
1	ML	–log-level=’pmd.common.cnxk.ml,8’

1.9. Extended stats

Marvell cnxk ML PMD supports reporting the device and model extended statistics.

PMD supports the below list of 4 device extended stats.

Table 1.13 OCTEON cnxk ML PMD device xstats names

#	Type	Description
1	nb_models_loaded	Number of models loaded
2	nb_models_unloaded	Number of models unloaded
3	nb_models_started	Number of models started
4	nb_models_stopped	Number of models stopped

PMD supports the below list of 6 extended stats types per each model.

Table 1.14 OCTEON cnxk ML PMD model xstats names

#	Type	Description
1	Avg-HW-Latency	Average hardware latency
2	Min-HW-Latency	Minimum hardware latency
3	Max-HW-Latency	Maximum hardware latency
4	Avg-FW-Latency	Average firmware latency
5	Min-FW-Latency	Minimum firmware latency
6	Max-FW-Latency	Maximum firmware latency

Latency values reported by the PMD through xstats can have units, either in cycles or nano seconds. The units of the latency is determined during DPDK initialization and would depend on the availability of SCLK. Latencies are reported in nano seconds when the SCLK is available and in cycles otherwise. Application needs to initialize at least one RVU for the clock to be available.

xstats names are dynamically generated by the PMD and would have the format Model-<model_id>-Type-<units>.

For example:

Model-1-Avg-FW-Latency-ns

The above xstat name would report average firmware latency in nano seconds for model ID 1.

The number of xstats made available by the PMD change dynamically. The number would increase with loading a model and would decrease with unloading a model. The application needs to update the xstats map after a model is either loaded or unloaded.