MIVisionX on AMD APUs
MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX delivers highly optimized open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions along with Convolution Neural Net Model Compiler & Optimizer supporting ONNX, and Khronos NNEF™ exchange formats. The toolkit allows for rapid prototyping and deployment of optimized computer vision and machine learning inference workloads on a wide range of computer hardware, including small embedded x86 CPUs, APUs, discrete GPUs, and heterogeneous servers.
- Inference with MIVisionX
- Accelerated Processing Unit
- Pre-requisites
- Steps to Run Inference on AMD APU
MIVisionX component Neural Net Model Compiler & Optimizer converts pre-trained neural network models to MIVisionX runtime code for optimized inference
Pre-trained models in ONNX, NNEF, & Caffe formats are supported by the model compiler & optimizer. The model compiler first converts the pre-trained models to AMD Neural Net Intermediate Representation (NNIR), once the model has been translated into AMD NNIR (AMD's internal open format), the Optimizer goes through the NNIR and applies various optimizations which would allow the model to be deployed on to target hardware most efficiently. Finally, AMD NNIR is converted into OpenVX C code, which could be compiled and deployed on any targeted AMD hardware.
The AMD Accelerated Processing Unit (APU), formerly known as Fusion, is the term used for a series of 64-bit microprocessors from AMD, designed to act as a central processing unit (CPU) and graphics processing unit (GPU) on a single die.
- AMD Ryzen™ Mobile Processors with Radeon™ Graphics
- AMD Ryzen™ Embedded Processors with Radeon™ Graphics
- Hardware - ROCm OpenCL supported APU
- Operating System - Ubuntu
18.04.5LTS (Bionic Beaver)
Step 1: Install Ubuntu 18.04.5 on the system with AMD APU
% cat /etc/lsb-release
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=18.04
DISTRIB_CODENAME=bionic
DISTRIB_DESCRIPTION="Ubuntu 18.04.5 LTS"
IOMMU (I/O Memory Management Unit) is a feature supported by motherboard chipsets that provide enhanced virtual-to-physical memory mapping capabilities, including the ability to map large portions of non-contiguous memory. IOMMU can be enabled in the motherboard's BIOS, in order to resolve issues with virtual machine device drivers.
- Check your system BIOS manufacturers manual for information on how to enable IOMMU
sudo apt update
sudo apt dist-upgrade
sudo apt install libnuma-dev
sudo reboot
wget -q -O - https://repo.radeon.com/rocm/rocm.gpg.key | sudo apt-key add -
echo 'deb [arch=amd64] https://repo.radeon.com/rocm/apt/3.10/ xenial main' | sudo tee /etc/apt/sources.list.d/rocm.list
Install the ROCm meta-package. Update the appropriate repository list and install the rocm-dkms meta-package:
sudo apt update
sudo apt install rocm-dkms && sudo reboot
rocminfo
% /opt/rocm/bin/rocminfo
ROCk module is loaded
Able to open /dev/kfd read-write
=====================
HSA System Attributes
=====================
Runtime Version: 1.1
System Timestamp Freq.: 1000.000000MHz
Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count)
Machine Model: LARGE
System Endianness: LITTLE
==========
HSA Agents
==========
*******
Agent 1
*******
Name: AMD Ryzen Embedded V1605B with Radeon Vega Gfx
Uuid: CPU-XX
Marketing Name: AMD Ryzen Embedded V1605B with Radeon Vega Gfx
Vendor Name: CPU
Feature: None specified
Profile: FULL_PROFILE
Float Round Mode: NEAR
Max Queue Number: 0(0x0)
Queue Min Size: 0(0x0)
Queue Max Size: 0(0x0)
Queue Type: MULTI
Node: 0
Device Type: CPU
Cache Info:
L1: 32(0x20) KB
Chip ID: 5597(0x15dd)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 2000
BDFID: 1024
Internal Node ID: 0
Compute Unit: 8
SIMDs per CU: 4
Shader Engines: 1
Shader Arrs. per Eng.: 1
WatchPts on Addr. Ranges:4
Features: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED
Size: 16776832(0xfffe80) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
ISA Info:
N/A
*******
Agent 2
*******
Name: gfx902
Uuid: GPU-XX
Marketing Name: AMD Ryzen Embedded V1605B with Radeon Vega Gfx
Vendor Name: AMD
Feature: KERNEL_DISPATCH
Profile: FULL_PROFILE
Float Round Mode: NEAR
Max Queue Number: 128(0x80)
Queue Min Size: 4096(0x1000)
Queue Max Size: 131072(0x20000)
Queue Type: MULTI
Node: 0
Device Type: GPU
Cache Info:
L1: 16(0x10) KB
Chip ID: 5597(0x15dd)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 1100
BDFID: 1024
Internal Node ID: 0
Compute Unit: 11
SIMDs per CU: 4
Shader Engines: 1
Shader Arrs. per Eng.: 1
WatchPts on Addr. Ranges:4
Features: KERNEL_DISPATCH
Fast F16 Operation: FALSE
Wavefront Size: 64(0x40)
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Max Waves Per CU: 160(0xa0)
Max Work-item Per CU: 10240(0x2800)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
Max fbarriers/Workgrp: 32
Pool Info:
Pool 1
Segment: GROUP
Size: 64(0x40) KB
Allocatable: FALSE
Alloc Granule: 0KB
Alloc Alignment: 0KB
Accessible by all: FALSE
ISA Info:
ISA 1
Name: amdgcn-amd-amdhsa--gfx902+xnack
Machine Models: HSA_MACHINE_MODEL_LARGE
Profiles: HSA_PROFILE_BASE
Default Rounding Mode: NEAR
Default Rounding Mode: NEAR
Fast f16: TRUE
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
FBarrier Max Size: 32
*** Done ***
clinfo
% /opt/rocm/opencl/bin/clinfo
Number of platforms: 1
Platform Profile: FULL_PROFILE
Platform Version: OpenCL 2.0 AMD-APP (3212.0)
Platform Name: AMD Accelerated Parallel Processing
Platform Vendor: Advanced Micro Devices, Inc.
Platform Extensions: cl_khr_icd cl_amd_event_callback
Platform Name: AMD Accelerated Parallel Processing
Number of devices: 1
Device Type: CL_DEVICE_TYPE_GPU
Vendor ID: 1002h
Board name: AMD Ryzen Embedded V1605B with Radeon Vega Gfx
Device Topology: PCI[ B#4, D#0, F#0 ]
Max compute units: 11
Max work items dimensions: 3
Max work items[0]: 1024
Max work items[1]: 1024
Max work items[2]: 1024
Max work group size: 256
Preferred vector width char: 4
Preferred vector width short: 2
Preferred vector width int: 1
Preferred vector width long: 1
Preferred vector width float: 1
Preferred vector width double: 1
Native vector width char: 4
Native vector width short: 2
Native vector width int: 1
Native vector width long: 1
Native vector width float: 1
Native vector width double: 1
Max clock frequency: 1100Mhz
Address bits: 64
Max memory allocation: 6695813120
Image support: Yes
Max number of images read arguments: 128
Max number of images write arguments: 8
Max image 2D width: 16384
Max image 2D height: 16384
Max image 3D width: 16384
Max image 3D height: 16384
Max image 3D depth: 8192
Max samplers within kernel: 5597
Max size of kernel argument: 1024
Alignment (bits) of base address: 1024
Minimum alignment (bytes) for any datatype: 128
Single precision floating point capability
Denorms: Yes
Quiet NaNs: Yes
Round to nearest even: Yes
Round to zero: Yes
Round to +ve and infinity: Yes
IEEE754-2008 fused multiply-add: Yes
Cache type: Read/Write
Cache line size: 64
Cache size: 16384
Global memory size: 7877427200
Constant buffer size: 6695813120
Max number of constant args: 8
Local memory type: Scratchpad
Local memory size: 65536
Max pipe arguments: 16
Max pipe active reservations: 16
Max pipe packet size: 2400845824
Max global variable size: 6695813120
Max global variable preferred total size: 7877427200
Max read/write image args: 64
Max on device events: 1024
Queue on device max size: 8388608
Max on device queues: 1
Queue on device preferred size: 262144
SVM capabilities:
Coarse grain buffer: Yes
Fine grain buffer: Yes
Fine grain system: Yes
Atomics: No
Preferred platform atomic alignment: 0
Preferred global atomic alignment: 0
Preferred local atomic alignment: 0
Kernel Preferred work group size multiple: 64
Error correction support: 0
Unified memory for Host and Device: 1
Profiling timer resolution: 1
Device endianess: Little
Available: Yes
Compiler available: Yes
Execution capabilities:
Execute OpenCL kernels: Yes
Execute native function: No
Queue on Host properties:
Out-of-Order: No
Profiling : Yes
Queue on Device properties:
Out-of-Order: Yes
Profiling : Yes
Platform ID: 0x7ff4a1f7fcf0
Name: gfx902+xnack
Vendor: Advanced Micro Devices, Inc.
Device OpenCL C version: OpenCL C 2.0
Driver version: 3212.0 (HSA1.1,LC)
Profile: FULL_PROFILE
Version: OpenCL 2.0
Extensions: cl_khr_fp64 cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_fp16 cl_khr_gl_sharing cl_amd_device_attribute_query cl_amd_media_ops cl_amd_media_ops2 cl_khr_image2d_from_buffer cl_khr_subgroups cl_khr_depth_images cl_amd_copy_buffer_p2p cl_amd_assembly_program
git clone https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX.git
python MIVisionX/MIVisionX-setup.py --rali no
mkdir build && cd build
cmake ../MIVisionX
make
sudo make install
cd MIVisionX/apps/dg_test
mkdir build && cd build
cmake ../
make
./DGTest ../data/weights.bin
In this sample, we will learn how to run inference efficiently using OpenVX and OpenVX Extensions. The sample will go over each step required to convert a pre-trained neural net model into an OpenVX Graph and run this graph efficiently on any target hardware. In this sample, we will also learn about AMD MIVisionX which delivers an open-source implementation of OpenVX and OpenVX Extensions along with MIVisionX Neural Net Model Compiler & Optimizer.
- Sample-1: Classification Using Pre-Trained ONNX Model
- Sample-2: Detection Using Pre-Trained Caffe Model
- Sample-3: Classification Using Pre-Trained NNEF Model
- Sample-4: Classification Using Pre-Trained Caffe Model