Tutel MoE: An Optimized Mixture-of-Experts Implementation.
- Supported Framework: Pytorch (recommend: >= 1.10)
- 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=16
..
* Run Tutel MoE in Distributed Mode:
(Method A - Torch launcher for `Multi-Node x Multi-GPU`:)
$ ssh <node-ip-0> python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr=<node-ip-0> -m tutel.examples.helloworld --batch_size=16
$ ssh <node-ip-1> python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr=<node-ip-0> -m tutel.examples.helloworld --batch_size=16
(Method B - Tutel launcher for `Multi-Node x Multi-GPU`, requiring package `openmpi-bin`:)
$ mpiexec -host <node-ip-0>,<node-ip-1>,.. \
-x LOCAL_SIZE=8 -x MASTER_ADDR=<node-ip-0> \
python3 -m tutel.launcher.run -m tutel.examples.helloworld --batch_size=16
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)
Usage of MOELayer:
* Usage of MOELayer Args:
gate_type : dict-type gate description, e.g. {'type': 'top', 'k': 2, ..}, or {'type': 'megatron'},
or a list of dict-type gate descriptions, e.g. [{'type': 'top', 'k', 2}, {'type': 'top', 'k', 2}],
the value of k in top-gating can be also negative, like -2, which indicates one GPU will hold 1/(-k) parameters of an expert
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| 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 tutel.examples.helloworld --batch_size=<batch_size>
$ python3 -m tutel.examples.helloworld_ddp --batch_size=<batch_size>
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