Context Parallel:在序列维度 seq_len 做“切分”,对模型单层输出做“聚合”; Tensor Parallel:在维度 hidden_dim 做“切分”,对模型单层输出做“聚合”; Pipeline Parallel:在模型层数维度 N 做“切分”,对模型最终输出做聚合。 本文将尝试从零开始实现张量并行 (Tensor Parallel, TP)。由于作者也是初学者,文章内...
MegatronLM的第一篇论文【Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism】是2020年出的,针对billion级别的模型进行训练,例如具有38亿参数的类GPT-2的transformer模型和具有39亿参数的BERT模型。 分布式训练的模型并行有两种方式,一种是层间并行(inter-layer),也就是Pipeline流水...
NVIDIA Megatron-LM 是一个基于 PyTorch 的分布式训练框架,用来训练基于Transformer的大型语言模型。Megatron-LM 综合应用了数据并行(Data Parallelism),张量并行(Tensor Parallelism)和流水线并行(Pipeline Parallelism)。很多大模型的训练过程都采用它,例如bloom、opt、智源等。 torch.distributed(dist) 为运行在一台或多台...
Describe the bug I am training the LLM with DeepSpeed Pipeline Parallel (ZeRO0 or ZeRO1 used). But I have a tricky issue: Assuming global_batch_size=4, single machine with 8GPUS, and PP=8, so DP=1, and micro_batch_size=4. Further assumin...
tensor_parallel_size=2, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(...
Tensor parallelism is a type of model parallelism in which specific model weights, gradients, and optimizer states are split across devices. In contrast to pipeline parallelism, which keeps individual weights intact but partitions the set of weights, gradients, or optimizer across devices, tensor para...
Tensor parallelism takes place at the level of nn.Modules; it partitions specific modules in the model across tensor parallel ranks. This is in addition to the existing partition of the set of modules used in pipeline parallelism. When a module is partitioned through tensor parallelism, i...
We introduce an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, specifically from one of the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly incorporates the verification and reflection patterns of R1...
CUDA cores are designed to run many tasks in parallel. They’re great for workloads like data preprocessing, simulations, video rendering, and traditional machine learning. Think of them as the flexible engine that powers a wide range of GPU-accelerated tasks. Most NVIDIA GPUs have hundreds or ...
--tensor-model-parallel-size:${TP}张量并行数,需要与训练脚本中的TP值配置一样。 --pipeline-model-paralle 来自:帮助中心 查看更多 → 训练启动脚本说明和参数配置 1_preprocess_data.sh 、2_convert_mg_hf.sh中的具体python指令,并在Notebook环境中运行执行。用户可通过Notebook中创建.ipynb文件,并...