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DeepSeek-V2: A Deep Dive into the Architecture of this Powerful MoE Language Model

In the rapidly evolving landscape of Natural Language Processing (NLP), large language models (LLMs) are continually pushing the boundaries of what's possible. Among the recent advancements, DeepSeek-V2 stands out as a particularly interesting development. This article delves into the architectural innovations of DeepSeek-V2.

What is DeepSeek-V2?

DeepSeek-V2 is a Mixture-of-Experts (MoE) model boasting a total of 236 billion parameters but only activating 21 billion parameters per token. It supports a context length of 128K. According to the original research paper, DeepSeek-V2 achieves better performance than its predecessor, DeepSeek 67B, while cutting training costs by 42.5%, reducing KV cache by 93.3%, and increasing maximum throughput by 5.76 times. The model was pre-trained on 8.1T tokens, followed by Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) stages. You can find the original paper on arXiv.

Key Features & Innovations

• Model Size: 236B parameters (21B active per token)
• Context Length: 128K tokens
• Training Data: 8.1T tokens

Multi-Head Latent Attention (MLA): The Core Innovation

The central architectural innovation in DeepSeek-V2 is the introduction of Multi-Head Latent Attention (MLA). Let's break down why this is important and how it works:

• The KV Cache Bottleneck: Traditional Transformer models rely on Multi-Head Attention (MHA). However, the key-value (KV) cache becomes a significant bottleneck during inference, limiting speed and efficiency.
• Existing Solutions & Their Limitations: Multi-Query Attention (MQA) and Grouped-Query Attention (GQA) offer some reduction in KV cache size, however their performance doesn't match MHA.
• MLA: A Better Approach: DeepSeek-V2's MLA utilizes low-rank key-value joint compression, achieving better results than MHA with a significantly smaller KV cache footprint.

Understanding How MLA Works

To understand MLA, it's helpful to first review the basics of MHA:

1.1 Standard MHA

• Let d represent the embedding dimension and nh the number of attention heads. Let dh be the dimension of each head.

• For a given token t, the input to the attention layer is represented as ht ∈ ℝd.

• Standard MHA uses three matrices, WQ, WK, WV ∈ ℝdhnh × d, to generate query (qt), key (kt), and value (vt) vectors:

  • qt = *WQ*ht
  • kt = *WK*ht
  • vt = *WV*ht

• In MHA, qt, kt, and vt are divided into nh heads:

  • [qt,1; qt,2; … , qt,nh] = qt
  • [kt,1; kt,2; … , kt,nh] = kt
  • [vt,1; vt,2; … , vt,nh] = vt
  • ot,i = Σj=1t Softmax((qt,iTkj,i) / √dh) vj,i
  • ut = WO[ot,1; ot,2; … , ot,nh]

  Where qt,i, kt,i, vt,i ∈ ℝdh are the query, key, and value for the i-th attention head. WO ∈ ℝd × dhnh is the output projection matrix.

• During inference, all keys and values are cached to accelerate the process. MHA requires caching 2 nh dh l elements for each token.

1.2 Low-Rank Key-Value Joint Compression

MLA reduces the KV cache by using low-rank joint compression of keys and values:

• ctKV = *WDKV*ht
• ktC = *WUK*ctKV
• vtC = *WUV*ctKV

Where:

• ctKV ∈ ℝdc is the latent vector used for compressing keys and values.
• dc (≪ dhnh) represents the dimension of KV compression.
• WDKV ∈ ℝdc × d is the down-projection matrix.
• WUK, WUV ∈ ℝdhnh × dc represent the up-projection matrices.

During inference, MLA only needs to cache ctKV, thus requiring the KV cache to store only dc l elements.

Furthermore, WUK can be absorbed into WQ, and WUV into WO during inference, removing the need to calculate keys and values explicitly.

1.3 Decoupled RoPE

RoPE (Rotary Positional Embeddings) is typically used to provide positional information. However, it's incompatible with low-rank KV compression directly. DeepSeek-V2 introduces an additional multi-head query (qRt,i ∈ ℝdRh) and a shared key (kRt ∈ ℝdRh) to carry RoPE information.

• qRt = [qRt,1; qRt,2; … ; qRt,nh] = RoPE(*WQR*cQt)
• kRt = RoPE(*WKR*ht)
• qt,i = [qCt,i; qRt,i]
• kt,i = [kCt,i; kRt]
• ot,i = Σj=1t Softmax((qt,i⊤kj,i) / √(dh+dRh)) vCj,i
• ut = WO[ot,1; ot,2; … ; ot,nh]

Where WQR and WKR are matrices used to generate the decoupled query and key, and RoPE(⋅) indicates the application of RoPE.

In short, MLA achieves better performance with less KV cache.

2. Overall Architecture

2.1 Core Structure:

DeepSeek-V2 uses a DeepSeekMoE architecture for the Feed-Forward Network (FFN) layers. This involves dividing experts into finer-grained units to achieve greater specialization and more accurate knowledge representation. DeepSeekMoE outperforms traditional MoE architectures with the same activation and total expert parameters.

• Input to FFN: ut

• FFN Output: h’t

  • h’t = ut + Σi=1Ns FFN(s)i(ut) + Σi=1Nr gi,t FFN(r)i(ut)
  • gi,t = { si,t, if si,t ∈ Topk({sj,t | 1 ≤ j ≤ Nr}, Kr) ; 0, otherwise }
  • si,t = Softmaxi(ut⊤ ei)

  Where:

  • Ns and Nr represent the numbers of shared and routing experts.
  • FFN(s)i(⋅) and FFN(r)i(⋅) denote the i-th shared and routing expert.
  • Kr is the number of activated routing experts.
  • gi,t is the gate value for the i-th expert.
  • ei is the center of the i-th routing expert in the current layer.

2.2 Device-Constrained Routing

The model employs a device-constrained routing mechanism to control MoE-related communication costs. To ensure that target experts are distributed on a maximum of M devices, the model initially selects the M devices containing the experts with the highest scores. Then, a top-K selection is performed on these M devices. In practice, performance comparable to unconstrained top-K routing is achieved when M ≥ 3.

2.3 Auxiliary Loss for Load Balancing

To combat issues arising from imbalanced loads, DeepSeek-V2 incorporates three auxiliary loss functions:

• Expert-level Load Balancing ( LExpBal )
• Device-level Load Balancing (LDevBal)
• Communication Balancing (LCommBal)

These losses help prevent routing collapse and improve computational efficiency, especially when using expert parallelism.

2.4 Token-Dropping Strategy

Even with the balancing losses identified above, a device-level token-dropping strategy is used during training as well to further mitigate imbalances. Devices drop low-affinity tokens until the allocated computational budget is achieved.

3. Pre-training & Alignment

DeepSeek-V2 was pre-trained on a massive dataset of 8.1T tokens, with a carefully balanced mix of English and Chinese. The model then undergoes both Supervised Fine-Tuning (SFT) using 1.5 million samples of fine-tuning data and Reinforcement Learning (RL) using the GRPO approach.

Conclusion

DeepSeek-V2 represents a significant stride forward in LLM development. By tackling the KV cache bottleneck with its innovative MLA architecture and incorporating expert-level optimizations, DeepSeek-V2 achieves impressive performance and efficiency. Its MoE design, combined with load-balancing strategies, highlights a trend towards more specialized and scalable language models.

Keywords: DeepSeek-V2, Large Language Model, LLM, Mixture of Experts, MoE, Multi-Head Latent Attention, MLA, Natural Language Processing, NLP, KV Cache, DeepSeekMoE, Device-Constrained Routing, Load Balancing, Token-Dropping, SFT, GRPO, transformer architecture.