Functional Test Plugins Design, Switches and Parameters

This section describes plugin specific switches, however, it will not focus on the common switches although these switches will be mentioned here for the completeness of the command examples. To see the common plugin switches and parameters, refer to hl_qual Common Plugin Switches and Parameters.

Functional tests verify the full chip functionality while running several chip hardware modules in parallel and in a synchronized manner. All the tests described in this section verify the accuracy of calculation in parallel to performance metrics in the form of a measured frame per second. Executing the accuracy check on the host does not affect the FPS measurement of the test.

ResNet-50 Training Stress Test Plugin Design Consideration and Responsibilities

Note

• The ResNet-50 training stress test plugin is applicable for both first-gen Gaudi and Gaudi 2.

• Before running this plugin, make sure to set the following environment variable: export  __python_cmd=python3.

The ResNet-50 training stress test plugin runs a functional ResNet-50 training test as a real life training scenario. The test verifies accuracy and performance. To enable an accuracy check, you must supply a full ImageNet training dataset.

ResNet-50 Training Stress Test Plugin Testing Modes

1. The ResNet-50 training stress test plugin supports a single testing mode of batch size 256, which is 256 images in each batch.

2. (First-gen Gaudi only) Random Data - The test can run on random data tensors. When using this mode, the accuracy check is skipped and only the achievable FPS is taken into account in the pass/fail criteria. It enables pure FPS test without depending on the image augmenter. This test is limited to 1000 iterations.

3. ImageNet - When using ImageNet dataset, the test evaluates accuracy and FPS. The FPS could be influenced by the image augmenter AEON in case of first-gen Gaudi and Intel Gaudi Media Pipe in case of Gaudi 2. For more details, refer to Accuracy and FPS Evaluation.

Note

For first-gen Gaudi, running the test with ImageNet dataset is the default mode. If you want to run it with random data, use -rand switch.

The suggested number of training epochs should not exceed 90. The ResNet50 training app should converge with that range. 90 epoch runs for 20-21 hours.

Preconditions

Gaudi 2

Install sudo apt install -y habanalabs-qual-workloads.

First-gen Gaudi

The trainingApp test uses the AEON augmenter which could be a limiting factor on the achievable FPS results when running on multiple devices. Before running this testing mode with ImageNet dataset on first-gen Gaudi, make sure you have the following:

• 96 CPU cores per server (Minimum), evenly distributed between the NUMA nodes.

• A successful pass of the Memory Bandwidth Test and the PCI Bandwidth Test.

• 512 GB of RAM.

Accuracy and FPS Evaluation

When running this test with ImageNet testing mode, the FPS results may be influenced by the different augmentation and image preprocessing mechanisms used for first-gen Gaudi and Gaudi 2:

• First-gen Gaudi - Uses AEON augmenter which utilizes the host CPU.

• Gaudi 2 - Uses the Intel Gaudi Media Pipe for Jpeg decoding and image preprocessing. The preprocessing of the ImageNet dataset is done on on the decoder HW path and it does not require CPU resources.

ResNet-50 Training Stress Test Plugin Configuration Files and Requirements

Before using the plugin, make sure to perform the following:

• Download the training/validation dataset from ImageNet.

• Download the labels file from ImageNet Object Localization Challenge page.

• Untar the imagenet tar file (ILSVRC2012_img_train.tar, ILSVRC2012_img_val.tar). Use the following commands:

  • mkdir tar_folder

  • cd tar_folder

  • tar -xvf ../ILSVRC2012_img_train.tar

The above instructions assume that ILSVRC2012_img_train.tar file resides on the same folder level as tar_folder.

• Change your current directory to hl_qual bin directory.

• Run the preparation script - prepare.sh (included in the package). This script untars all the tar files (ILSVRC2012_img_train.tar file), and generates the training list file (train_list.txt).

./prepare.sh -m MODE -d EXTRACTED_DIR -f LABEL_FILE [-h]`

Parameters:

• MODE - ‘train’ or ‘val’.

• EXTRACTED_DIR - The path to the directory that contains the untared files from ILSVRC2012_img_train.tar file.

• LABEL_FILE - The path to LOC_synset_mapping.txt file.

The script generates and updates the following items:

• train_list.txt - A file containing the location of all jpeg files and the labels associated with the image. Each line will contain one image path and one label. For example: For Gaudi 2, the path to train_list.txt should be specified using the -train_list switch.

@FILE   STRING
train/n01582220/n01582220_8497.JPEG     18
train/n01582220/n01582220_11460.JPEG    18
train/n01582220/n01582220_20482.JPEG    18
train/n01582220/n01582220_37512.JPEG    18
train/n01582220/n01582220_5317.JPEG     18
train/n01582220/n01582220_4839.JPEG     18

• train folder - This folder contains 1000 sub-folder each containing the images associated with the same class (label).

• (For first-gen Gaudi) training256.json - The list below changes to include the full path to the train folder and the train_list file as generated by the prepare.sh script.

  • “manifest_filename”: “/home/labuser/builds/qual_release_build/gaudi2/bin/train_list.txt”,

  • “manifest_root”: “/home/labuser/builds/qual_release_build/gaudi2/bin”,

Note

Reinstalling the package or relaunching the script will override/overwrite those files. It is highly recommended to make a copy of the following files/folders so you can restore them in case they are overwritten:

• train_list.txt

• train folder

• training256.json

For first-gen Gaudi, it is important to note that you can place the train folder and the train_list.txt file in any location within the users file system as long as the training256.json file placed under /opt/habanalabs/qual/gaudi2/bin is updated.

ResNet-50 and Linux File Cache Considerations

Linux OS has a file cache that accelerates IO operations. Once a file has been read, it is loaded into the host RAM’s file cache so that subsequent reads of the same file will be faster. As a result, when running the training plugin with the full ImageNet validations set, which has the size of 140GB, the first training epoch performance could be lower than expected. To address this issue, perform one of the following:

• Run a dummy training run for a full epoch and ignore the failure. During this dummy run, the full ImageNet dataset is uploaded to the Linux cache to ensure that the following run achieves the expected performance.

• Use the iocache_loader application supplied in the hl_qual installation folder to upload the ImageNet dataset to the Linux cache.

Note

After rebooting the host, the Linux file cache is cleared so that one of the above options can be executed to achieve the expected performance.

Running iocache_loader Application

The following is iocache_loader command line interface. To receive the applicable switches, run the command without any parameters.

$ ./iocache_loader
path to training dataset must be supplied
./iocache_loader -p <path> -t <num of threads> -e
-p <path> - path to dataset - this is mandatory
-t <num of threads> - number of threads default 20
-e enable output printouts

For example:

./iocache_loader -p /user/imagenet/train -t 40

ResNet-50 Training Stress Test - Pass/Fail Criteria

The performance and accuracy test evaluates the loss function received from the device. If the loss function shows an unexpected behavior, the test fails.

• Accuracy - loss function decreases monotonically without sharp jumps between training tests, indicating that NaN values have been propagated into the training process calculations.

• Performance (FPS) - the performance [images/sec] is calculated per training epoch. The expected results per device:

Gaudi 2

• Gaudi 2 - 225H:

  • FPS: 5900 images/sec

  • Epoch runtime 3.6 minutes

• Gaudi 2 - 225C:

  • FPS: 5150 images/sec

  • Epoch runtime 3.9 minutes

• Gaudi 2 - 225D:

  • FPS: 3058 images/sec

  • Epoch runtime 6.0 minutes

• Expected accuracy: first epoch - 0.15.

First-gen Gaudi

• FPS: 1580 images/sec

• Epoch runtime: 13.3 minutes

• Expected accuracy: first epoch - 0.12.

ResNet-50 Training Stress Test Plugin Switches and Parameters

The following lists the training test plugin switches and parameters:

Gaudi 2

**hl_qual -gaudi2 -c <pci bus id> [-rmod <serial | parallel>  [-dis_mon] [-mon_cfg <monitor INI path>]
          -trainingApp [-epoch <number of epochs>] [-n <number of iterations>] [-train_list] **

Switches and Parameters	Description
-trainingApp	Training test plugin selector.
-epoch	Defines epoch count.
-n	Defines iteration count. You must provide -epoch flag with this flag. Example: -epoch 1 -n 1000.
-train_list	Specifies path to the train_list.txt file.
-log	Writes statistics to file.

./hl_qual -gaudi2 -c all -rmod parallel -trainingApp -epoch 3 -train_list <path to train_list.txt>

First-gen Gaudi

**hl_qual -gaudi -c <pci bus id> [-rmod <serial | parallel>  [-dis_mon] [-mon_cfg <monitor INI path>]
          -trainingApp [-epoch <number of epochs>] [-n <number of iterations>] [rand] **

Switches and Parameters	Description
-trainingApp	Training test plugin selector.
-epoch	Defines epoch count.
-n	Defines iteration count. You must provide -epoch flag with this flag. Example: -epoch 1 -n 1000.
-rand	Random input generation. This mode disables accuracy and loss validation. Only fps is calculated. The test uses 1500 iterations preset.
-log	Writes statistics to file.

./hl_qual -gaudi -c all -rmod parallel -trainingApp -rand
./hl_qual -gaudi -c all -rmod parallel -trainingApp -epoch 3

Functional Test 2 Plugin Design Consideration and Responsibilities

Note

Functional test 2 plugin is applicable for both first-gen Gaudi and Gaudi 2.

Functional test 2 runs all hardware components on the first-gen Gaudi and Gaudi 2 SOCs simultaneously in parallel mode to test their functionality and interaction between the different units.

The functional test 2 uses a synthetic topology based on multiple operations. In these operations, all computational units and memory space are utilized while maintaining high power usage.

The test can run for long hours (more than 2) testing the following device functionalities:

• Thermal stress test, cooling system functionality, temperature dissipation and thermal protection mechanisms can be checked while running the plugin in extreme load.

• PID and clock relaxation mechanisms verification.

• Long work periods in typical power levels.

• Full bit-exact calculation.

Tested units:

• PCI links

• DMA engines – moving data between:

  • PCI ==> HBM, HBM ==>PCI

  • HBM ==> SRAM, SRAM==>HBM

• MME engines

• TPC engines

• Serdes connectivity - only when using -serdes switch

Functional Test 2 Synthetic Topology

The functional test 2 plugin builds a test topology including large tensors and multiple operators (Conv, Batchnorm).

The test application runs the topology by injecting pre-calculated inputs and verifies the output against a pre-calculated reference for each topology execution to ensure bit-exact results.

When applying the -serdes switch, the topology includes full serdes transmit/receive verification in addition to the basic topology.

Functional Test 2 Testing Modes

The test verifies the calculation result on each topology execution while enabling high power usage. All execution steps are verified for this purpose.

The test also verifies execution throughput [executions/seconds] against the performance metrics in the form of measured frame per second (FPS).

The functional test 2 contains the following sub-test modes:

Gaudi 2

• Gaudi 2 - 225H and 225C:

  • Extreme - measured power level for 54V power supply: 530-560 [watt]

  • High - measured power level for 54V power supply: 450-490 [watt]

• Gaudi 2 - 225D:

  • Extreme - measured power level for 54V power supply: 360 [watt]

  • High - measured power level for 54V power supply: 270 [watt]

First-gen Gaudi

• Extreme - measured power level: 345-355 [watt]

• Mid - measured power level: 230-240 [watt]

The measurement above is recorded from a 4 minute run. It can change depending on the on the ambient condition of the system (fan speed, server box configuration and ambient temperatures).

Note

The initialization stage can take up to 170 seconds. This time is needed to recalculate and generate the reference expected output tensors, compile the test topology and test runtime execution calibration. When using -t switch, the init time is not included in the test running duration that you have set.

Functional Test - Pass/Fail Criteria

The pass/fail criteria is composed of the following:

• The calculated value of each topology launch must be identical to a pre-calculated reference.

• The execution throughput [executions/seconds] must not fall below an existing predefined threshold:

Gaudi 2

Regular functional test - 225H:

• Extreme - FPS 621 [Frame/Sec], measured on HLS2 server

• High - FPS 729 [Frame/Sec], measured on HLS2 server

Functional + Serdes test - 225H:

• Extreme - FPS 1233 [Frame/Sec], measured on HLS2 server

• High - FPS 1450 [Frame/Sec], measured on HLS2 server

Regular functional test - 225C:

• Extreme - FPS 621 [Frame/Sec], measured on HLS2 server

• High - FPS 657 [Frame/Sec], measured on HLS2 server

Functional + Serdes test - 225C:

• Extreme - FPS 129 [Frame/Sec], measured on HLS2 server

• High - FPS 1296 [Frame/Sec], measured on HLS2 server

Regular functional test - 225D:

• Extreme - FPS 585 [Frame/Sec], measured on HLS2 server

• High - FPS 585 [Frame/Sec], measured on HLS2 server

Functional + Serdes test - 225D:

• Extreme - FPS 1151 [Frame/Sec], measured on HLS2 server

• High - FPS 1151 [Frame/Sec], measured on HLS2 server

First-gen Gaudi

• Extreme - FPS 260 [Frame/Sec]

• High - FPS 270 [Frame/Sec]

The measurement above is recorded from a 4 minute run.

Functional Test 2 Plugin Switches and Parameters

Gaudi 2

hl_qual -gaudi2 -c <pci bus id> [-t <time in seconds>]  -rmod <serial | parallel>  [-dis_mon] [-mon_cfg <monitor INI path>]
      -f2 -l <extreme | high> [-d] [-dis_val] [-serdes] [-enable_ports_check <all | int>]

Switches and Parameters	Description
-t	Test duration in seconds.
-f2	Functional test 2 plugin selector.
-d	Download once option. The input tensors are downloaded at the beginning of the test and reused for each test run. This switch is useful when you suspect that functional test performance degradation is due to PCI low BW.
-dis_val	Disables output tensor validation. The test will not fail on bit-exact test, but it may fail on low FPS. When using this switch, the test performance becomes higher as the data is not uploaded to the host for verification.
-serdes	Runs allreduce collective operation to test serdes connectivity along with the other components tested using the functional 2 test.
-l <extreme | high>	Power level selector for 54V power supply: extreme - 530-560 [w] measured on HL-225H high - 450-490 [w] measured on HL-225H
-enable_ports_check <all / int>	Indicates whether the ports are UP or DOWN. If they are are DOWN, it will result in a test failure. all - Checks all the external and internal ports. int - Checks the internal ports only.

First-gen Gaudi

hl_qual -gaudi -c <pci bus id> [-t <time in seconds>]  -rmod <serial | parallel>  [-dis_mon] [-mon_cfg <monitor INI path>]
   -f2 -l <extreme | mid> [-d] [-dis_val] [-serdes] [-enable_ports_check <all | int>]

Switches and Parameters	Description
-t	Test duration in seconds.
-f2	Functional test 2 plugin selector.
-d	Download once option. The input tensors are downloaded at the beginning of the test and reused for each test run. This switch is useful when you suspect that functional test performance degradation is due to PCI low BW.
-dis_val	Disables output tensor validation. The test will not fail on bit-exact test, but it may fail on low FPS. When using this switch, the test performance becomes higher as the data is not uploaded to the host for verification.
-serdes	Runs allreduce collective operation to test serdes connectivity along with the other components tested using the functional 2 test.
-l <extreme | mid>	Power level selector: extreme - 345-355 [w] measured on HL-205 mid - 230-240 [w] measured on HL-205 If the value is not specified, the default value is MID.
-enable_ports_check <all / int>	Indicates whether the ports are UP or DOWN. If they are are DOWN, it will result in a test failure. all - Checks all the external and internal ports. int - Checks the internal ports only.

For example:

./hl_qual -gaudi -c all -rmod parallel -f2 -d -l mid
./hl_qual -gaudi2 -c all -rmod parallel -f2 -l extreme -t 450
./hl_qual -gaudi2 -c all -rmod parallel -f2 -l extreme -t 450 -serdes