# Review — Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

## Switch Transformer, Mixture of Experts (**MoE**) With 1 Expert Selected

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity,Switch Transformer, by Google2022 JMLR, Over 660 Citations(Sik-Ho Tsang @ Medium)

Language Model1991 … 2022[GPT-NeoX-20B] [GPT-3.5, InstructGPT] [GLM] [MT-NLG 530B] [Chinchilla] [PaLM] [AlexaTM] [BLOOM] [AlexaTM 20B] [OPT]2023[GPT-4]

==== My Other Paper Readings Are Also Over Here ====

**Mixture of Experts (****MoE****)**is**select different parameters for each incoming example**, which is a**sparsely-activated model**with an outrageous number of parameters, but a constant computational cost. However, MoE is adoption has been hindered by complexity, communication costs, and training instability.**Switch Transformer**is proposed, which**simplifies the****MoE****reduced communication and computational costs.**

# Outline

**Switch Transformer****Improved Training and Fine-Tuning Techniques****Downstream Results**

**1. Switch Transformer**

## 1.1. Prior **Mixture-of-Experts (****MoE**)

**MoE****layer**takes as an input a token representation*x*and then**routes this to the best determined top-**, selected from a set {*k*experts*Ei*(*x*)} for*i*from 1 to*N*of*N*experts.- The
**router variable***Wr***logits**which are normalized via a*h*(*x*)=*Wr*⋅*x***softmax**distribution over the available N experts at that layer. - The
**gate-value for expert**is given by:*i*

- If
is the set of selected*T***top-**then the*k*indices**output**computation of the layer is the**linearly weighted combination of each expert’s computation on the token by the gate value**:

- (Please feel free to read MoE if interested.)

## 1.2. Proposed Switch Routing: Rethinking **MoE**

**Switch layer**is proposed, which is a simplified strategy where input is routed to**only a single expert, i.e.**. This simplification preserves model quality, reduces routing computation and performs better.*k*=1- The benefits for the Switch layer are
**three-fold**:

- The
**router computation is reduced**as a token is only routed to a single expert. - The
**batch size**(expert capacity) of each expert can be at least**halved**. - The routing implementation is simplified and
**communication costs are reduced**.

**Mesh-TensorFlow (MTF)**(Shazeer et al., 2018) is used, which is a library, with similar semantics and API to TensorFlow that facilitates**efficient distributed data and model parallel architectures**.

## 1.3. Expert Capacity

- If the tokens are
**unevenly dispatched**then**certain experts will overflow**(denoted by dotted red lines), resulting in these tokens not being processed by this layer. A**larger capacity factor alleviates this overflow issue**, but also increases computation and communication costs (depicted by padded white/empty slots). - One important technical consideration is how to set the expert capacity:

- A capacity factor greater than 1.0 creates additional buffer to accommodate for when tokens are not perfectly balanced across experts.

Empirically, it is found ensuring

lower rates of dropped tokensareimportant for the scalingof sparse expert-models.

## 1.4. Loss Function

- For
**each Switch layer**, this**auxiliary loss**is added to the total model loss during training. Givenindexed by*N*experts*i*= 1 to*N*and a**batch**with*B*, the auxiliary loss is computed as the*T*tokens**scaled dot-product between vectors**:*f*and*P*

- where
is the*fi***fraction of tokens dispatched to expert**:*i*

- and
is the*Pi***fraction of the router probability allocated for expert**:*i*

such that the auxiliary loss*α*=10^(-2)**Masked language modeling**task is used as in BERT, with 15% token masked.

- MoE model going from
**capacity factor 2.0 to 1.25**actually**slows down**(840 to 790).

1) Switch Transformers

outperform both carefully tuned dense models andMoETransformerson aspeed-quality basis.2) The Switch Transformer has a

smaller computational footprint than theMoEcounterpart.3) Switch Transformers perform

better at lower capacity factors (1.0, 1.25).

# 2. Improved Training and Fine-Tuning Techniques

## 2.1. Selective Precision

- The
**router input**is casted to**float32**precision. The**float32**precision is only used**within**the body of the**router**function. - The
**resulting dispatch**and**combine tensors**are**recast to bfloat16**precision at the end of the function,**no expensive float32 tensors are broadcast.**

Selective precisionobtainshigher or comparable quality.

## 2.2. Reduce Initialization

There is

improvementof the model quality andreduction of the variance (Smaller variance when weight initialization)early in training.

## 2.3. Dropout

- Overfitting arises since many fine-tuning tasks have very few examples.

The Dropout inside the experts, i.e.

expertDropout(ed),isincreased, to improve the performance.

# 3. Scaling

- The
**large C4 corpus**with**over 180B target tokens**is used for training. **Left**: Consistent scaling properties (with fixed FLOPS per token) between**sparse model parameters and test loss.**

Right:Increasing the number of experts (e)leads tomore sample efficient models.

Switch-Base 64 expert model at step 60kachievesthe same performance of theT5-Base model at step 450k, which is a7.5× speedupin terms of step time.

# 4. Downstream Results

## 4.1. Fine-Tuning

Significant downstream improvementsare obtained across many natural language tasks.Notable improvementscome fromSuperGLUE.

## 4.2. Distillation

**Initializing the dense model with the non-expert weights**yields a**modest improvement**.

A

distillationimprovementis observed usinga mixture of 0.25 for the teacher probabilities and 0.75 for the ground truth label.

Through distillation,

37% of the quality gain of the 1.1B parameter modelcan be preserved whilecompressing 82%.

- At the extreme,
**compressing the model 99%**, the model is still able to maintain**28% of the teacher’s model quality****improvement**.

**Distilling**a 7.4B parameter**Switch-Base**model,**which is fine-tuned on the****SuperGLUE****task**, obtain**76.6 on****SuperGLUE**.

## 4.3. Multilingual

- The
**multilingual variant of the Common Crawl data set (mC4)**spanning**101 languages**, is used for pretraining.

On all 101 languagesconsidered,Switch Transformer increases the final negative log perplexityover the baseline (dense).

# More: Data, Model, and Expert-Parallelism

- When
**combining both model and expert-parallelism**,**all-to-all communication costs**are still needed from routing the tokens to the correct experts along with the internal all-reduce communications from the model parallelism. **Balancing the FLOPS, communication costs and memory per core**becomes**quite complex**when combining all three methods where the**best mapping is empirically determined**.- (Please feel free to read section 5.6. of the paper directly for this part if interested.)