자유게시판

• 목록
• 답변
• 글쓰기
• 게시판 리스트 옵션
  • 수정
  • 삭제

DeepSeek actually made two models: R1 and R1-Zero. V2 and V3 Models: These are additionally optimized for NLP duties such as summarization, translation, and sentiment analysis. Overall, underneath such a communication technique, only 20 SMs are sufficient to completely make the most of the bandwidths of IB and NVLink. The important thing concept of DualPipe is to overlap the computation and communication within a pair of particular person ahead and backward chunks. As well as, each dispatching and combining kernels overlap with the computation stream, so we also consider their impact on other SM computation kernels. This overlap also ensures that, as the model additional scales up, so long as we maintain a constant computation-to-communication ratio, we will nonetheless employ fantastic-grained experts across nodes while reaching a near-zero all-to-all communication overhead. In addition, even in more common situations with no heavy communication burden, DualPipe still exhibits efficiency benefits. In January 2024, this resulted in the creation of extra advanced and efficient models like DeepSeekMoE, which featured a complicated Mixture-of-Experts architecture, and a new model of their Coder, DeepSeek-Coder-v1.5.财联社 (29 January 2021). "幻方量化"萤火二号"堪比76万台电脑？两个月规模猛增200亿".

Fedus et al. (2021) W. Fedus, DeepSeek Chat B. Zoph, and N. Shazeer. Compared with Chimera (Li and Hoefler, 2021), DualPipe solely requires that the pipeline phases and micro-batches be divisible by 2, with out requiring micro-batches to be divisible by pipeline stages. In addition, for DualPipe, neither the bubbles nor activation reminiscence will increase as the variety of micro-batches grows. Given the efficient overlapping technique, the complete DualPipe scheduling is illustrated in Figure 5. It employs a bidirectional pipeline scheduling, which feeds micro-batches from each ends of the pipeline concurrently and a significant portion of communications could be totally overlapped. With the DualPipe strategy, we deploy the shallowest layers (together with the embedding layer) and deepest layers (together with the output head) of the model on the identical PP rank. In this overlapping strategy, we will be certain that each all-to-all and PP communication could be fully hidden during execution. For DeepSeek-V3, the communication overhead introduced by cross-node expert parallelism results in an inefficient computation-to-communication ratio of approximately 1:1. To tackle this challenge, we design an progressive pipeline parallelism algorithm referred to as DualPipe, which not solely accelerates model training by successfully overlapping forward and backward computation-communication phases, but also reduces the pipeline bubbles. With a view to facilitate efficient coaching of DeepSeek-V3, we implement meticulous engineering optimizations.

Besides, some low-cost operators may utilize a higher precision with a negligible overhead to the overall coaching price. Despite the efficiency advantage of the FP8 format, sure operators still require a higher precision as a result of their sensitivity to low-precision computations. We validate the proposed FP8 combined precision framework on two model scales just like DeepSeek-V2-Lite and DeepSeek-V2, coaching for approximately 1 trillion tokens (see more particulars in Appendix B.1). We'll continue testing and poking this new AI mannequin for more outcomes and keep you updated. ARG times. Although DualPipe requires preserving two copies of the model parameters, this doesn't considerably improve the memory consumption since we use a large EP size throughout coaching. Building upon widely adopted methods in low-precision training (Kalamkar et al., 2019; Narang et al., 2017), we suggest a blended precision framework for FP8 coaching. Specially, for a backward chunk, both consideration and MLP are further break up into two elements, backward for enter and backward for weights, like in ZeroBubble (Qi et al., 2023b). In addition, now we have a PP communication component.

In detail, we employ the warp specialization approach (Bauer et al., 2014) and partition 20 SMs into 10 communication channels. Additionally, we leverage the IBGDA (NVIDIA, 2022) technology to further reduce latency and enhance communication effectivity. To successfully leverage the totally different bandwidths of IB and NVLink, we limit every token to be dispatched to at most 4 nodes, thereby lowering IB traffic. Once it reaches the target nodes, we'll endeavor to ensure that it is instantaneously forwarded by way of NVLink to particular GPUs that host their target experts, without being blocked by subsequently arriving tokens. Similarly, throughout the combining course of, (1) NVLink sending, (2) NVLink-to-IB forwarding and accumulation, and (3) IB receiving and accumulation are also handled by dynamically adjusted warps. In the course of the dispatching process, (1) IB sending, (2) IB-to-NVLink forwarding, and (3) NVLink receiving are handled by respective warps. In this manner, communications via IB and NVLink are fully overlapped, and every token can efficiently select a mean of 3.2 consultants per node with out incurring extra overhead from NVLink. These models divide the feedforward blocks of a Transformer into multiple distinct experts and add a routing mechanism which sends every token to a small number of those experts in a context-dependent method.