3.1,推理适应性并行(Inference-adapted parallelism) 3.2,推理优化内核(Inference-optimized kernels) 3.2.1,通用和专用 Transformer 内核 3.3,灵活的量化支持(Flexible quantization support) 3.4,模型压缩模块(DeepSpeed Compression) 四,DeepSpeed Inf
Hi, I had some questions about the pipeline parallelism implementation in DeepSpeed. Can someone help shed some information on the following? From among the following types of pipeline scheduling, which one does DeepSpeed implement in it...
在LLM的Transformer训练中,已经出现了三种主要的分布式训练范式:数据并行(Data Parallelism, DP)、张量并行(Tensor Parallelism, TP)和流水线并行(Pipeline Parallelism, PP)。 数据并行的基本形式是每个GPU维护整个模型参数的完整副本,但处理不同的输入数据。每次训练迭代结束后,所有GPU需要同步模型参数。为了缓解LLM巨大参...
A full pipeline to finetune ChatGLM LLM with LoRA and RLHF on consumer hardware. Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the ChatGLM architecture. Basically ChatGPT but with ChatGLM pytorch llama gpt lora finetune ppo peft deepspeed llm chatgpt rlhf rewa...
The optimized GPU resources come from using inference-adapted parallelism, which allows users to adapt the model and pipeline parallelism degree from the trained model checkpoints, and shrinking model memory footprint by half with INT8 quantization. As shown in Figure...
Scientists can now train their large science models like GenSLMs with much longer sequences via a synergetic combination of our newly added memory optimization techniques on attention mask and position embedding, tensor parallelism, pipeline parallelis...
Support for Custom Model Parallelism Integration with Megatron-LM Pipeline Parallelism 3D Parallelism The Zero Redundancy Optimizer (ZeRO) Optimizer State and Gradient Partitioning Activation Partitioning Constant Buffer Optimization Contiguous Memory Optimization ...
DeepSpeed reduces the training memory footprint through a novel solution called Zero Redundancy Optimizer (ZeRO). Unlike basic data parallelism where memory states are replicated across data-parallel processes, ZeRO partitions model states to save significant memory. The current implementation (stage 1 of...
Fully general and implementation agnostic attention: DeepSpeed sequence parallelism (Ulysses) supports dense as well as sparse attention, and it works with efficient attention implementations such as FlashAttention v2(Dao,2023). • Support for massive model training: DeepSpeed sequence parallelism works ...
如前文所述,我们可以通过在多个设备上复制整个模型(数据并行 Data Parallelism)或将模型拆分,并将其不同部分存储在不同设备上(模型并行 Model Parallelism / 流水线并行 Pipeline Parallelism)来执行分布式训练。一般来说,DP 比 MP 的计算效率更高;但是,如果模型太大,单个 GPU 设备的可用显存无法容纳,那么只能使用模...