Mixture of Experts for NLG models

In this tutorial, we introduce how to apply DeepSpeed Mixture of Experts (MoE) to NLG models, which reduces the training cost by 5 times and reduce the MoE model size by 3 times (details in our Blog). We use the GPT-3 like models in Megatron-LM framework as the example. Before reading this tutorial, we recommend to first read the tutorials about Mixture of Experts and Megatron-LM GPT pre-training.

1. Installation

You would need to install DeepSpeed v0.6.0 or higher to use the MoE feature. The MoE for NLG model examples are in the Megatron-DeepSpeed repo under the MoE folder.

2. Training NLG+MoE models

2.1. Changes to the model

To apply MoE to the GPT-style model, we made several changes in Megatron framework, mostly in megatron/model/ where we add the MoE layers into the model.

2.2. Pre-training the Standard MoE model

We provide example training scripts under examples_deepspeed/MoE which we used to perform the experiments in our Blog. There are a few new hyperparameters for standard MoE model:

--num-experts: the number of experts per MoE layer. In our experiments we set it to 128. Larger number of experts tend to provide better convergence, but it’s a diminishing return.

--moe-expert-parallel-size: degree of the MoE expert parallelism. In other words, there will be num-experts/moe-expert-parallel-size experts on each GPU. Thus --moe-expert-parallel-size should be no more than both number of GPUs, and --num-experts.

--moe-loss-coeff: scaling coefficient for adding MoE loss to model loss. In our experiments we find that 0.01 is a good setting.

--moe-train-capacity-factor, --moe-eval-capacity-factor, --moe-min-capacity: these configs determine how many tokens can a single expert handle. Larger numbers could lead to better convergence, but would also lead to slower training since the load would be more unbalanced on different experts.

--disable-moe-token-dropping: this will completely remove the limitation of how many tokens can a single expert handle. For the same reason as above, we only recommend using this during inference/eval.

2.3. Pre-training the PR-MoE model

PR-MoE is a new designed MoE models, standing for Pyramid-Residual-MoE, which improves the parameter efficiency up to 3x as compared to standard MoE. Please see our Blog for more details. We provide example training scripts under examples_deepspeed/MoE. There are a few different hyperparameters for PR-MoE model compared to standard MoE:

--num-experts: Instead of providing a single number, to enable Pyramid-MoE, you need to provide a list, whose length is the same as the number of MoE layers. We suggest to use more experts in the latter stage (close to output) of the model.

--mlp-type: chosen from [standard, residual]. When it is residual, Residual-MoE is enabled.

In addition to the new hyperparameters above for standard MoE and PR-MoE, for NLG+MoE models we found that it’s helpful to lower the learning rate and increase the learning rate decay duration compared to the base dense model. Details of our tuning can be found in the example training scripts.

Regarding training data, we are not able to release our internal data but any public data for Megatron-LM pre-training can be directly used to train MoE models (with the caveat that it might not provide the exact same model quality as in our experiments). For example, we evaluated The Pile dataset (pile.eleuther.ai, github.com/EleutherAI/the-pile) for both dense and MoE models. Table 1 below shows that this public data provides similar evaluation results as our internal data.

Model size LAMBADA: completion prediction PIQA: commonsense reasoning BoolQ: reading comprehension RACE-h: reading comprehension TriviaQA: question answering WebQs: question answering
Dense NLG:            
350M, internal data 0.5203 0.6931 0.5364 0.3177 0.0321 0.0157
350M, public Pile 0.5106 0.6589 0.5933 0.3196 0.0257 0.0064
Standard MoE NLG:            
350M+MoE-128, internal data 0.6270 0.7459 0.6046 0.3560 0.1658 0.0517
350M+MoE-128, public Pile 0.6128 0.7323 0.6040 0.3349 0.1111 0.0335
PR-MoE NLG:            
350M+MoE-128, internal data 0.6365 0.7399 0.5988 0.3569 0.1630 0.0473
PR-MoE + MoS NLG:            
350M+MoE-128, internal data 0.6346 0.7334 0.5807 0.3483 0.1369 0.0522

Table 1: Zero-shot evaluation results (last six columns) for different dense and MoE NLG models. All zero-shot evaluation results use the accuracy metric.

2.4. Training MoS with reduced model size

MoS, standing for Mixture-of-Students, is a staged distillation-based technique for compressing large MoE models. MoS further reduces the model size by 12.5%, leading to up 3.7x model size reduction when combined with PR-MoE over the standard MoE. The reduced model size helps reduce the latency and cost during inference. To train an MoS model, one needs to specify a few additional parameters. We will use PR-MoE as an example:

--mos: This would enable Mixture-of-Students via knowledge distillation.

--load-teacher: This specifies the path to the teacher model checkpoint. This is a mandatory argument for using MoS and the teacher model checkpoint can be obtained by either training a standard MoE or the PR-MoE.

num-layers-teacher, --hidden-size-teacher, --hidden-size-teacher, --num-experts-teacher: In addition to the teacher model checkpoint path, we also need to specify the model architecture of the teacher model such as its number of layers, hidden dimension size, and the number of experts per MoE layer. In the case of PR-MoE, we need to also provide a list of experts for the teacher model, where we remove a few expert layers from the teacher model.

In addition to the new parameters above, we observe that using the teacher PR-MoE during the entire training process may adversely impact the final student model accuracy. In our experiments, we use a staged distillation method by stopping distillation early in the training process (e.g., after 400K steps) and perform optimization only against the standard language modeling loss for the rest of the training.

We provide example training scripts under examples_deepspeed/MoE. Details of our parameter settings can be found in the example training scripts. The performance results of MoS can be seen from our blog post and our paper.