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Elixir (Gemini2.0)

Elixir, also known as Gemini, is a technology designed to facilitate the training of large models on a small GPU cluster. Its goal is to eliminate data redundancy and leverage CPU memory to accommodate really large models. In addition, Elixir automatically profiles each training step prior to execution and selects the optimal configuration for the ratio of redundancy and the device for each parameter. This repository is used to benchmark the performance of Elixir. Elixir will be integrated into ColossalAI for usability.

Environment

This version is a beta release, so the running environment is somewhat restrictive. We are only demonstrating our running environment here, as we have not yet tested its compatibility. We have set the CUDA version to 11.6 and the PyTorch version to 1.13.1+cu11.6.

Three dependent package should be installed from source.

  • ColossalAI (necessary): just clone it and use pip install . from the newest master branch.
  • Apex (optional): clone it, checkout to tag 22.03, and install it.
  • Xformers (optional): clone it, checkout to tag v0.0.17, and install it.

Finally, install all packages in the requirements.txt.

Tools

CUDA Memory Profiling

Function cuda_memory_profiling in elixir.tracer.memory_tracer can help you profile each kind of memory occupation during training. It tells you the CUDA memory occupation of parameters, gradient and maximum size of activations generated during training. Moreover, it is an efficient and fast tool which enables quickly profiling OPT-175B model on a single GPU. You can try it by yourself with the folder activation in the directory example.

(I think you should have at least 16GB CUDA memory to run the OPT-175B example but that doesn't matter. Just try it first.)

Hardware Performance Profiling

See the folder profile. You can profile the aggregate bandwidth of GPU-CPU communications and the aggreagte velocity of Adam optimizers.

Examples

Here is a simple example to wrap your model and optimizer for fine-tuning.

from elixir.search import minimum_waste_search
from elixir.wrapper import ElixirModule, ElixirOptimizer

model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, eps=1e-8)

sr = minimum_waste_search(model, world_size)
model = ElixirModule(model, sr, world_group)
optimizer = ElixirOptimizer(model, optimizer)

Here is an advanced example for performance, which is used in our benchmarkhere.

import torch
import torch.distributed as dist
from colossalai.nn.optimizer import HybridAdam
from elixir.wrapper import ElixirModule, ElixirOptimizer

# get the world communication group
global_group = dist.GroupMember.WORLD
# get the communication world size
global_size = dist.get_world_size()

# initialize the model in CPU
model = get_model(model_name)
# HybridAdam allows a part of parameters updated on CPU and a part updated on GPU
optimizer = HybridAdam(model.parameters(), lr=1e-3)

sr = optimal_search(
    model,
    global_size,
    unified_dtype=torch.float16,  # enable for FP16 training
    overlap=True,  # enable for overlapping communications
    verbose=True,  # print detailed processing information
    inp=data,  # proivde an example input data in dictionary format
    step_fn=train_step  # provide an example step function
)
model = ElixirModule(
    model,
    sr,
    global_group,
    prefetch=True,  # prefetch chunks to overlap communications
    dtype=torch.float16,  # use AMP
    use_fused_kernels=True  # enable fused kernels in Apex
)
optimizer = ElixirOptimizer(
    model,
    optimizer,
    initial_scale=64,  # loss scale used in AMP
    init_step=True  # enable for the stability of training
)