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BERT Large Training

BERT Large training best known configurations with Intel® Extension for PyTorch.

Model Information

Use Case Framework Model Repo Branch/Commit/Tag Optional Patch
Training PyTorch NVIDIA/DeepLearningExamples main -

Pre-Requisite

Prepare Dataset

Dataset:

Please refer to NV-bert repository to download and prepare the data.

The dataset should be like below:

|_hdf5  
      |_ eval                               # evaluation chunks in binary hdf5 format fixed length (not used in training, can delete after data preparation)  
      |_ eval_varlength                     # evaluation chunks in binary hdf5 format variable length *used for training*
      |_ training                           # 500 chunks in binary hdf5 format 
      |_ training_4320                      # 
         |_ hdf5_4320_shards_uncompressed   # sharded data in hdf5 format fixed length (not used in training, can delete after data   preparation)
         |_ hdf5_4320_shards_varlength      # sharded data in hdf5 format variable length *used for training*

We are using hdf5/hdf5_4320_shards_varlength as the our dataset.

Training

  1. git clone https://github.com/IntelAI/models.git
  2. cd models/models_v2/pytorch/bert_large/training/gpu
  3. Create virtual environment venv and activate it: 
    python3 -m venv venv
. ./venv/bin/activate
  4. Run setup.sh 
    ./setup.sh
  5. Install the latest GPU versions of torch, torchvision and intel_extension_for_pytorch: 
    python -m pip install torch==<torch_version> torchvision==<torchvision_version> intel-extension-for-pytorch==<ipex_version> --extra-index-url https://pytorch-extension.intel.com/release-whl-aitools/
  6. Setup required environment paramaters
Parameter export command
MULTI_TILE export MULTI_TILE=False (provide True for multi-tile GPU such as Max 1550, and False for single-tile GPU such as Max 1100 or Arc Series GPU)
PLATFORM export PLATFORM=Max (Max or Arc)
NUM_DEVICES export NUM_DEVICES=<num_devices> (<num_devices> is the number of GPU devices used for training. It must be equal to or smaller than the number of GPU devices attached to each node. For GPU with 2 tiles, such as Max 1550 GPU, the number of GPU devices in each node is 2 times the number of GPUs, so <num_devices> can be set as <=16 for a node with 8 Max 1550 GPUs. While for GPU with single tile, such as Max 1100 GPU or Arc Series GPU, the number of GPU devices available in each node is the same as number of GPUs, so <num_devices> can be set as <=8 for a node with 8 single-tile GPUs.)
DATASET_DIR export DATASET_DIR=</the/path/to/dataset>
OUTPUT_DIR export OUTPUT_DIR=</the/path/to/output_dir>
BATCH_SIZE (optional) export BATCH_SIZE=16
PRECISION (optional) export PRECISION=BF16 (BF16 FP8 FP32 and TF32 are supported for Max and BF16 for Arc)
NUM_ITERATIONS (optional) export NUM_ITERATIONS=20
  1. Run run_model.sh

Output

Single-device output will typically look like:

[info] construct file from initialization
[info] input dir =  /home/dataset/hdf5/hdf5_4320_shards_varlength
[info] num files =  4282
epoch: 1
Loaded 193485 samples from datafile: /home/dataset/hdf5/hdf5_4320_shards_varlength/pretrain-part-01.hdf5
bert_train latency:  0.24147300720214843  s
bert_train throughput:  66.25999396531161  sentences/s
perplexity = 11.020857810974121

Multi-device output will typically look like:

Model to device:  xpu:0
using adamw
Doing torch xpu optimize, dtype:  torch.bfloat16
Torch distributed is available.
Torch distributed is initialized.
[info] Setting seed:  123 . worker seed:  224899942


found num checkpoints:  0
resume from checkpoints:  False
resume checkpoint:  None
[info] construct file from initialization
[info] input dir =  /home/dataset/hdf5/hdf5_4320_shards_varlength
[info] num files =  4282
epoch: 1
Loaded 194779 samples from datafile: /home/dataset/hdf5/hdf5_4320_shards_varlength/pretrain-part-00.hdf5
bert_train latency:  0.2703933477401733  s
bert_train throughput:  59.17305338212218  sentences/s
perplexity = 11.018452644348145
Setting seed to ensure same model master weight at the beginning.
world_size:2, rank:1, device:xpu:1
args.world_size=2, args.rank=1
Get config from config_name bert_config.json
Set different weight_decay for model parameters
GroupSizes:  [335869952, 356156]
Model to device:  xpu:1
using adamw
Doing torch xpu optimize, dtype:  torch.bfloat16
Torch distributed is available.
Torch distributed is initialized.
[info] Setting seed:  123 . worker seed:  1749090055


found num checkpoints:  0
resume from checkpoints:  False
resume checkpoint:  None
[info] construct file from initialization
[info] input dir =  /home/dataset/hdf5/hdf5_4320_shards_varlength
[info] num files =  4282
Loaded 193485 samples from datafile: /home/dataset/hdf5/hdf5_4320_shards_varlength/pretrain-part-01.hdf5
bert_train latency:  0.2730390548706055  s
bert_train throughput:  58.599675447831  sentences/s
perplexity = 11.12635612487793

Final results of the inference run can be found in results.yaml file.

results:
 - key: throughput
   value: 66.259994
   unit: sent/s
 - key: latency
   value: 0.2414730072021484
   unit: s
 - key: accuracy
   value: 11.021
   unit: perplexity