模型拆分:将模型的权重(如线性层的矩阵)沿某个维度(行或列)切分,分配到不同GPU上。 计算拆分:每个GPU只负责自己那部分参数的计算,最后通过设备间通信(如All-Gather或All-Reduce)合并结果。 显存优化:每个GPU只需存储部分模型参数,从而降低显存占用。 2. 两个GPU的张量并行示例 3. 更复杂的模型拆分 对于更复杂的模型(如T
init_model_parallel_group() 在DeviceCommunicatorBase中,通过torch.distributed实现了all_reduce, all_gather, gather等方法。 比如all_reduce可以直接调用torch.distributed.all_reduce来实现: all_gather则因为涉及到维度的变化需要加入一些维度操作。 DeviceCommunicatorBase类示意图 vllm/distributed/communication_op.py ...
ppl.pmx/model_zoo/llama/modeling/static_batching/Model.py at master · openppl-public/ppl.pmx (github.com) Linear汇总结果 如上文,Attention层最后一个Linear、MLP层最后一个Linear都需要汇总结果,需要使用all_reduce算子。 ppl.pmx/torch_function/RowParallelLinear.py at master · openppl-public/ppl.pmx...
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...
81 81 if pipeline_parallel is None: 82 - pipeline_parallel = (mpu.get_pipeline_model_parallel_world_size() > 1) 82 + pipeline_parallel = (core.get_pipeline_model_parallel_world_size() > 1) 83 83 if tensor_rank is None: 84 - tensor_rank = mpu.get_tensor_model_parallel_rank(...
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, it...
output = tensor_model_parallel_all_reduce(output_parallel) File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/parallel_utils/communication_op.py", line 18, in tensor_model_parallel_all_reduce torch.distributed.all_reduce(input, ...
examples/distributed/parallel_opt.py 18处查看文件 @@ -56,6 +56,16 @@ def parallel_model(model: ModelProto, tp_world_size: int = 1, tp_rank: int = 0): 5656 ndim = len(vinfo[output].type.tensor_type.shape.dim) 5757 out_plc = Shard(ndim - 1) if in_plc.is_replicate() else...
revert-16650-refine_parallel_executor_3 feature/anakin-engine2 revert-16807-engine2-interface fix_lod_reset revert-16734-refine/test_imperative_transformer devel revert-16555-model_data_cryption_link_all_lib 1.4 move-code feature/anakin-engine ...
tensor_parallel: 包含tensor并行和pipeline并行实现 utils.py: 保存相关工具实现 2. parallel_state.py 除了initialize_model_parallel在“Megatron-LM源码系列(一): 模型并行初始化”已经提过,这里其他函数主要是进行通信组rank号相关操作,比如获取一个通信组的上游或下游的rank号、从通信组的local_rank转为global_ran...