wpybtw / tutel

Tutel MoE: An Optimized Mixture-of-Experts Implementation

Tutel

Tutel MoE: An Optimized Mixture-of-Experts Implementation.

• Supported Framework: Pytorch
• Supported GPUs: CUDA(fp32 + fp16), ROCm(fp32 + fp16)

How to setup Tutel MoE for Pytorch:

* Install Online:

        $ python3 -m pip uninstall tutel -y
        $ python3 -m pip install --user --upgrade git+https://github.com/microsoft/[email protected]

* Build from Source:

        $ git clone https://github.com/microsoft/tutel --branch v0.1.x

        $ python3 -m pip uninstall tutel -y
        $ python3 ./tutel/setup.py install --user

* Quick Test on Single-GPU:

        $ python3 -m tutel.examples.helloworld --batch_size=16               # To Test Tutel-optimized MoE + manual distribution
        $ python3 -m tutel.examples.helloworld_ddp --batch_size=16           # To Test Tutel-optimized MoE + Pytorch DDP distribution (requires: Pytorch >= 1.8.0)
        $ python3 -m tutel.examples.helloworld_megatron --batch_size=16      # To Test Tutel using Megatron Gating (Tensor Parallel on Experts) + manual distribution
        $ python3 -m tutel.examples.helloworld_deepspeed --batch_size=16     # To Test Deepspeed MoE + manual distribution

        (If building from source, the following method also works:)
        $ python3 ./tutel/examples/helloworld.py --batch_size=32
        ..

How to import Tutel-optimized MoE in Pytorch:

# Input Example:
import torch
x = torch.ones([6, 1024], device='cuda:0')

# Create MoE:
from tutel import moe as tutel_moe
moe_layer = tutel_moe.moe_layer(
    gate_type={'type': 'top', 'k': 2},
    model_dim=x.shape[-1],
    experts={
        'count_per_node': 2,
        'type': 'ffn', 'hidden_size_per_expert': 2048, 'activation_fn': lambda x: torch.nn.functional.relu(x)
    },
    scan_expert_func = lambda name, param: setattr(param, 'skip_allreduce', True),
)

# Cast to GPU
moe_layer = moe_layer.to('cuda:0')

# In distributed model, you need further skip doing allreduce on global parameters that has `skip_allreduce` mask, 
# e.g.
#    for p in moe_layer.parameters():
#        if hasattr(p, 'skip_allreduce'):
#            continue
#        dist.all_reduce(p.grad)


# Forward MoE:
y = moe_layer(x)

print(y)

Full Examples in Distributed Mode & Usage:

* Running MoE Hello World Model by torch.distributed.all_reduce:

        $ python3 -m torch.distributed.launch --nproc_per_node=2 -m tutel.examples.helloworld --batch_size=32
        ..

        (For New Pytorch:)
        $ python3 -m torch.distributed.run --nproc_per_node=2 -m tutel.examples.helloworld
        ..

* Usage of MOELayer Args:

        gate_type        : dict-type gate description, e.g. {'type': 'top', 'k': 2, ..}, or {'type': 'megatron'}
        model_dim        : the number of channels for MOE's input tensor
        experts          : a dict-type config for builtin expert network, or a torch.nn.Module-type custom expert network
        scan_expert_func : allow users to specify a lambda function to iterate each experts param, e.g. `scan_expert_func = lambda name, param: setattr(param, 'expert', True)`
        result_func      : allow users to specify a lambda function to format the MoE output and aux_loss, e.g. `result_func = lambda output: (output, output.l_aux)`
        group            : specify the explicit communication group of all_to_all
        seeds            : a tuple containing a tripple of int to specify manual seed of (shared params, local params, others params after MoE's)

* Usage of dict-type Experts Config:

        count_per_node   : the number of local experts per device (by default, the value is 1 if not specified)
        type             : available built-in experts implementation, e.g: ffn
        hidden_size_per_expert : the hidden size between two linear layers for each expert (used for type == 'ffn' only)
        activation_fn    : the custom-defined activation function between two linear layers (used for type == 'ffn' only)

For Deepspeed MoE Acceleration (Deepspeed MoE Top-1 Gate has integrated Tutel acceleration):

# Without Tutel optimization:
python3 -m tutel.examples.helloworld_deepspeed --top=1

# With Tutel optimization:
python3 -m tutel.examples.helloworld_deepspeed --top=1 --use_tutel

Single-GPU Throughput (batches/sec) with default settings on NVIDIA A100 (40GB):

batch-size	helloworld (top2)	helloworld_ddp (top2)	helloworld_megatron (fc)	helloworld_deepspeed (top2)
8	672.75	672.24	970.446	188.27
16	715.86	714.95	1024.15	115.43
24	725.95	725.04	1041.89	81.02
32	729.02	729.02	1058.11	OOM
64	687.92	686.31	1056.00	OOM
128	619.75	619.03	1059.59	OOM
256	577.08	577.49	1053.93	OOM

How to reproduce these results:

$ python3 -m torch.distributed.launch --nproc_per_node=1 -m tutel.examples.helloworld --batch_size=<batch_size>
$ python3 -m torch.distributed.launch --nproc_per_node=1 -m tutel.examples.helloworld_ddp --batch_size=<batch_size>
$ python3 -m torch.distributed.launch --nproc_per_node=1 -m tutel.examples.helloworld_megatron --batch_size=<batch_size>
$ python3 -m torch.distributed.launch --nproc_per_node=1 -m tutel.examples.helloworld_deepspeed --batch_size=<batch_size>

Contributing

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

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