Megatron-LM 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...
distributed-systemsmachine-learningdeep-learningpytorchllamapipeline-parallelismtensor-parallelism UpdatedMay 14, 2025 Python Large scale 4D parallelism pre-training for 🤗 transformers in Mixture of Experts *(still work in progress)* transformersmoedata-parallelismdistributed-optimizersmodel-parallelismmegatronmi...
Hi, thanks! I use vllm to inference the llama-7B model on single gpu, and tensor-parallel on 2-gpus and 4-gpus, we found that it is 10 times faster than HF on a single GPU, but using tensor parallelism, there is no significant increase i...
on 8 GPUs from 10mn with regular PyTorch weights down to 45s. This really speeds up feedbacks loops when developing on the model. For instance you don't have to have separate copies of the weights when changing the distribution strategy (for instance Pipeline Parallelism vs Tensor Parallelism)...
data_parallelism) Create Experiment, including hooks Create Estimator T2TModel.estimator_model_fn model(features) model.model_fn model.bottom model.body model.top model.loss [TRAIN] model.estimator_spec_train train_op = model.optimize [EVAL] model.estimator_spec_eval Create metrics Create...
Both handle parallelism — but in different ways. CUDA Cores vs Tensor Cores: Side-by-Side Comparison Feature CUDA Cores Tensor Cores Primary Role General-purpose parallel processing Deep learning acceleration Architecture Purpose Built for a wide range of tasks (compute, graphics, simulations) ...
Is a medium or large size and requires larger batch sizes for training during which high parallelism is beneficial. TPU The TPU is much closer to an ASIC, providing a limited number of math functions, primarily matrix processing, expressly intended for ML tasks. A TPU is noted for high throu...
This method allows you to leverage the GPU’s parallelism to convert the data to FP16. It also enables you to fuse this operation with common pre-processing operations such as normalization or mean subtraction. Generally speaking, you can improve performance considerably if you do not mix precisi...
For example, the network demand for training an ML model, often requiring data parallelism (weak scal- ing), di ers from inference on that same model using (pipelined) model parallelism (ie. strong scaling). The multiprocessor system, interconnection network, and the...
| from cycle elements synchronously with no parallelism. If the value | `tf.data.experimental.AUTOTUNE` is used, then the number of parallel | calls is set dynamically based on available CPU. deterministic: 控制确定性 | | Returns: | Dataset: A `Dataset`. ...