Currently, when using FSDP, the model is loaded for each of the N processes completely on CPU leading to huge CPU RAM usage. When training models like Flacon-40B with FSDP on a dgx node with 8 GPUs, it would lead to CPU RAM getting out of memory because each process is loading 160GB...
aligned_numel) [rank2]: File "/data0/home/ening/software/miniconda3/envs/cogmllm/lib/python3.10/site-packages/torch/distributed/fsdp/_flat_param.py", line 844, in flatten_tensors [rank2]: return torch.cat(flat_tensors, dim=0) [rank2]: torch.OutOfMemoryError: CUDA out of memory. ...
test_batch_size, 'sampler': sampler2} cuda_kwargs = {'num_workers': 2, 'pin_memory': True, 'shuffle': False} train_kwargs.update(cuda_kwargs) test_kwargs.update(cuda_kwargs) train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs) test_loader = torch.utils.data....
例如`"zero_optimization": {"offload_optimizer": {"device": "cpu", "pin_memory": true}}`。
Memory-efficient attention, SwiGLU, sparse and more won't be available. Set AIGCODE_MORE_DETAILS=1 for more details WARNING[AIGCODE]: Need to compile C++ extensions to use all AIGCode features. Please install aigcode properly (see https://github.com/facebookresearch/aigcode#installing-aigcode...
Using FSDP results in a significantly smaller GPU memory footprint compared to DDP across all workers, enabling the training of very large models or using larger batch sizes for training jobs. This, however, comes at the cost of increased communication overhead, which is mitigate...
CPU utilization on one of the nodes in the cluster. In this case, we are looking at a P5.48xlarge instance, which has 192 vCPUs. The processor cores are idle while the model weights are downloaded, and we see rising utilization while the model weights are being loaded to ...
First, GPT-2 Large(762M) model is used wherein DDP works with certain batch sizes without throwing Out Of Memory (OOM) errors. Next, GPT-2 XL (1.5B) model is used wherein DDP fails with OOM error even on batch size of 1. We observe that FSDP enables larger batch sizes for GPT-...
First, GPT-2 Large(762M) model is used wherein DDP works with certain batch sizes without throwing Out Of Memory (OOM) errors. Next, GPT-2 XL (1.5B) model is used wherein DDP fails with OOM error even on batch size of 1. We observe that FSDP enables larger batch sizes for ...
Using FSDP results in a significantly smaller GPU memory footprint compared to DDP across all workers, enabling the training of very large models or using larger batch sizes for training jobs. This, however, comes at the cost of increased communication overhead, which is mitigat...