Loooooooong Sequence Lengths

Figure 1: This work was done as part of the DeepSpeed4Science project, in collaboration with Microsoft.

The new Megatron-DeepSpeed release contains a variety of improvements / optimizations to enable pre-training Transformer based architectures with significantly longer sequences than was previously possible.

Additional Details

Additional details can be found in the 📁 DeepSpeed4Science folder.

DeepSpeed4Science (09/2023)

New Features

• Enabled Megatron-LM’s sequence parallel.

• Enabled rotary positional embedding.

• Enabled FlashAttention v1 and v2.

• Enabled new fused kernels from NVIDIA.

New optimizations

• Enabled attention map memory optimization, where we first generated attention mask on CPU memory and then moved it into GPU memory to avoid out-of-memory errors when training with very large sequence lengths.

• Position embedding partitioning, where we split weights of position encoding across all GPUs when enabling sequence parallel to further reduce the memory footprint.

Initial Results

Table 1: Long sequence length support¹ from microsoft/Megatron-DeepSpeed

Sequence Length	Old Megatron-DeepSpeed (TFLOPS)	New Megatron-DeepSpeed (TFLOPS)
2k	25	68
4k	28	80
8k	OOM	86
16k	OOM	92
32k	OOM	100
64k	OOM	106
128k	OOM	119
256k	OOM	94

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Data

gpus = ('32', '64', '128')

colors = {
    'Old Megatron-DS': '#FF5252',
    'Megatron-LM': '#76b900',
    'New Megatron-DS':  '#1A8FFF',
}

data = {
    '25B': {
        'Old Megatron-DS': np.array([36, 42, 42]),
        'Megatron-LM': np.array([26, 48, 52]),
        'New Megatron-DS': np.array([192, 448, 512]),
    },
    '33B': {
        'Old Megatron-DS': np.array([28, 32, 32]),
        'Megatron-LM': np.array([14, 46, 52]),
        'New Megatron-DS': np.array([128, 384, 448]),
    },
}

Make the plots

x = np.arange(len(gpus))
width = 0.25
multiplier = 0

outdir = Path(os.getcwd()).joinpath('assets')
outdir.mkdir(exist_ok=True, parents=True)

improvement = {}
for idx, (model_size, d) in enumerate(data.items()):
    multiplier = 0
    figure, axes = plt.subplots(figsize=(7.5, 4))
    fig = plt.gcf()
    ax = plt.gca()
    for label, value in d.items():
        offset = width * multiplier
        rects = ax.barh(
          x + offset,
          value,
          width,
          label=label,
          color=colors[label],
          alpha=0.8
        )
        ax.bar_label(
          rects,
          padding=3,
          color=colors[label],
          family='monospace',
          weight='bold'
        )
        multiplier += 1
    ax.set_ylabel(
        'GPUs',
        fontsize=18,
        family='sans-serif',
        loc='center',
    )
    ax.set_yticks(x + width, gpus)
    plt.figtext(
        0.005, 0.93, f"{model_size}", fontsize=24, fontweight='bold', ha='left'
    )
    ax.set_xlabel(
        'Sequence Length (k)', fontsize=18, loc='center'
    )
    ax.legend(
        bbox_to_anchor=(0.005, 1.04, 0.99, .098),
        alignment='center',
        edgecolor="#83838320",
        frameon=True,
        ncols=3,
        fontsize=13,
        mode="expand",
        borderaxespad=0.01
    )
    save_figure(fname=f'{model_size}', outdir=outdir)
    _ = plt.show()

GPT-25B Model

GPT-33B Model

Figure 2: Pre-training with long sequence support across different model sizes and numbers of GPUs. In each case, the new (current) implementation significantly outperforms both NVIDIA/Megatron-LM as well as our previous implementation.

Installation

Using install.sh

Installation

Important
To install, simply:

git clone https://github.com/ramanthanlab/GenSLM/
cd GenSLM/examples/long-sequences/
./install.sh

Explicitly, ./install.sh will:

1. Automatically create a virtual environment on top of the latest conda module
2. Install (+ update²) / build all the required dependencies into this virtual environment

Step-by-Step

For completeness, we describe below the steps for installing and building each of the dependencies.

1. Clone GitHub repo:

   git clone https://github.com/ramanthanlab/GenSLM

2. Load conda module:

   • ThetaGPU:

     # ThetaGPU:
     if [[ "$(hostname)==theta*" ]]; then
         export MACHINE="ThetaGPU"
         export CONDA_DATE="2023-01-10"
         module load conda/2023-01-11
         conda activate base
     fi

   • Polaris:

     # Polaris:
     if [[ "$(hostname)==x3*" ]]; then
         export MACHINE="Polaris"
         export CONDA_DATE="2023-01-10"
         module load conda/2023-01-10-unstable
         conda activate base
     fi

3. Setup Virtual Environment³:

   cd ./genslm/examples/long-sequences
   # create a new virtual environment
   mkdir -p "venvs/${MACHINE}/${CONDA_DATE}"
   python3 -m venv "venvs/${MACHINE}/${CONDA_DATE}" --system-site-packages
   source "venvs/${MACHINE}/${CONDA_DATE}/bin/activate"

4. Create a new folder (genslm/examples/long-sequences/deps/${MACHINE}) where we’ll installing dependencies locally:

   mkdir -p "deps/${MACHINE}"
   cd "deps/${MACHINE}"

Dependencies

We provide below the details needed to install each of the required dependencies.

saforem2/ezpz

1. saforem2/ezpz

   pip install -e "git+https://github.com/saforem2/ezpz.git#egg=ezpz"

Microsoft/DeepSpeed

2. Microsoft/DeepSpeed

   git clone https://github.com/microsoft/DeepSpeed.git
   cd DeepSpeed
   python3 -m pip install -e .

Microsoft/Megatron-DeepSpeed

3. Microsoft/Megatron-DeepSpeed:

   git clone https://github.com/microsoft/Megatron-DeepSpeed.git

NVIDIA/apex

4. NVIDIA/apex

   git clone https://github.com/NVIDIA/apex
   cd ../apex/
   pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" -e ./

pybind/PyBind11

5. pybind/PyBind11

   pip install pybind11

Dao-AILab/flash-attention

6. Dao-AILab/flash-attention:

   Flash Attention

   • The new release supports three different implementations of FlashAttention: (v1.0.4, v2.x, triton)
   • FlashAttention v2.x may have numerical instability issues. For the best performance, we recommend using FlashAttention + Triton

   • v1.0.4:

     python3 -m pip install flash-attn==1.0.4

   • v2.x:

     git clone https://github.com/Dao-AILab/flash-attention
     cd flash-attention
     python3 setup.py install

   • openai/triton:

     git clone -b legacy-backend https://github.com/openai/triton
     cd triton/python
     python3 -m pip install cmake
     python3 -m pip install .

Running

The ALCF/ directory contains shell scripts for setting up the environment and specifying the options to be used when launching.

Various options can be specified dynamically at runtime by setting them in your environment, e.g.:

MODEL_SIZE_KEY="GPT25B" SEQ_LEN=128000 USE_FLASH_ATTN=1 MICRO_BATCH=1 GAS=1 SP_TYPE="megatron" ZERO_STAGE=1 ./ALCF/train-gpt3.sh

Explicitly:

• ALCF/train-gpt3.sh: Main entry point for training
  • This script will automatically source the rest of the required ALCF/*.sh scripts below
• ALCF/models.sh: Contains some example model architectures for GPT3-style models
• ALCF/args.sh: Logic for parsing / setting up runtime options for Megatron and DeepSpeed
• ALCF/setup.sh: Locate and activate virtual environment to be used, ensure MPI variables are set properly
• ALCF/launch.sh: Identify available resources and build the command to be executed
  • i.e. figure out how many: {nodes, GPUs per node, GPUs total}, to pass to mpi{run,exec}
  • then, use this to build mpiexec <mpiexec-args> python3 pretrain_gpt.py

ZeRO Offloading

🚀 W&B Report: Looooooooong Sequences

These newly introduced optimizations, in combination with ZeRO-Offload allows us to go even further.

By employing ZeRO-Offloading, we are able to free up additional memory which can be used for even longer sequences.

Though work is still ongoing, this is a promising direction that will allow us to consider significantly larger genomes than previously possible.

We use Weights & Biases to track these experiments, and have aggregated our initial results in the W&B Report below.

We can evaluate the performance of our model by looking at two different metrics for throughput: samples_per_sec and TFLOPS.

Explicitly, we see that we are able to scale up to significantly longer sequences (420k / 128k ~ 3.3x) with only a minimal impact on throughput performance (81 / 105 ~ 77\%)⁴.

Table 2: Impact on TFLOPS as a function of increasing sequence length. Table from: throughput/TFLOPS

Name	Sequence Length (k)	(seq_len / min_seq_len)	TFLOPS	TFLOPS (% of peak)
GPT25B	420	3.28125	81.77225	77.867
GPT25B	400	3.125	90.62	86.297
GPT25B	360	2.8125	81.6325	77.7348
GPT25B	360	2.8125	82.6824	78.7346
GPT25B	192	1.5	115.8228	110.2927
GPT25B	128	1	106.672	101.5788
GPT25B	128	1	105.014	100.00

Figure 3: Weights & Biases Report

Footnotes

1. The described experiments were performed on 4 NVIDIA DGX A100-40GB nodes, all using TPSIZE=32[^tpsize], connected through 8 HDR InfiniBand (200Gb/s per HDR).

2. 2. deepspeed-0.10.3
   3. pytorch==2.0.0+cu118

3. Where "${MACHINE}" \in {"ThetaGPU", "Polaris"} and "${CONDA_DATE}" \in {"2023-01-10", "2023-01-11"}

4. throughput/TFLOPS

Citation

BibTeX citation:

@online{foreman2023,
  author = {Foreman, Sam},
  title = {Personal {Website}},
  date = {2023-10-31},
  url = {https://samforeman.me},
  langid = {en}
}

For attribution, please cite this work as:

Foreman, Sam. 2023. “Personal Website.” October 31, 2023. https://samforeman.me.