Module): """linear layer with column parallelism The linear layer is defined as Y = XA + b. A is parallelized along its second dimension as A = [A_1, ..., A_p]. """ def __init__( self, input_size: int, output_
在LLM推理上,张量并行(Tensor Parallelism, TP)是一种重要的模型加速手段:将模型的权重矩阵按照一定的规则(如列分割或行分割)拆分为多个部分,在每个GPU上分别完成部分计算,从而可以提高计算速度以及降低单个GPU的内存需求。在VLLM中,张量并行主要涉及到进程(worker)管理、行并行、列并行,以及Reduce通信. 下面会结合代码...
Tensor parallelism is required in cases in which a single parameter consumes most of the GPU memory (such as large embedding tables with a large vocabulary size or a large softmax layer with a large number of classes). In this case, treating this large tensor or operation as an atomic unit...
其将计算任务分解为数据并行(Data Parallelism)与任务并行(Task Parallelism)的混合模型,通过主机(Host)与设备(Device)的协同,实现计算密集团队(Compute-Intensive)与访存密集团队(Memory-Intensive)的分离。例如,在深度学习训练中,前向传播(高计算密度)由 GPU 的 Tensor Core 处理,而数据预处理(高访存...
Tensor analytics lays the mathematical basis for the prosperous promotion of multiway signal processing. To increase computing throughput, mainstream processors transform tensor convolutions into matrix multiplications to enhance the parallelism of computing. However, such order-reducing transformation produces...
hparams.no_data_parallelism = True hparams.use_fixed_batch_size = True hparams.add_hparam("mtf_mode", True) hparams.batch_size = 64 hparams.max_length = 256 hparams.add_hparam("d_model", 512) hparams.add_hparam("d_kv", 128) hparams.add_hparam("local_attention_window_size", 128)...
a Parallelism. """ assert device_names_or_functions self._devices = device_names_or_functions self._n = len(device_names_or_functions) self._reuse = reuse self._caching_devices = self._maybe_repeat(caching_devices) self._daisy_chain_variables = daisy_chain_variables...
4. Better Compute Utilization: While large model training needs to be distributed across many GPUs, the small model tuning can happen on individual GPUs, greatly increasing the level of parallelism for tuning (and in the context of organizational compute clusters, better scheduling and utilization ...
(e.g. GPUs). It subdivides the neural network architecture intocellsformed by one or more consecutive layers and assigns each cell to a separate accelerator. It furthermore employs pipeline parallelism by also splitting each mini-batch of training samples into severalmicro-batches that are ...
在合理的 batching 后可以利用好 tensor core；attention 是batched gemv，batching 只能增加 parallelism...