torchrun --nproc_per_node=2 test_dist.py torchrun或者launch对上面ENV的初始化方法支持最完善,TCP初始化方法的可能会出现问题,因此尽量使用env来初始化dist 3. 分布式做evaluation 分布式做evaluation的时候,一般需要先所有进程的输出结果进行gather,再进行指标的计算,两个常用的函数: dist.all_gather(tensor_list,...
import torch import torch_npu import os import torch.distributed as dist def all_gather_func(): rank = int(os.getenv('LOCAL_RANK')) # torch.npu.set_device(rank) dist.init_process_group(backend='hccl', init_method='env://') #,world_size=2 rank=rank, world_size=2, # rank = dist...
print("***test_gather***") print('Rank ', rank, ' has data ', output) # 结果是 [[1,1,1,1]] def test_all_gather(rank, size): output = [torch.zeros(1) for _ in range(size)] tensor = torch.ones(1) dist.all_gather(output, tensor) print("***test_all_gather***") prin...
我没有想到要这样做，因为文档中指出all_gather()是一个阻塞调用。也许它们的意思是阻塞，如notasync;...
CUDA_VISIBLE_DEVICES=2,3tools/dist_train.sh./projects/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py2--work-dirtools/test_cache Did you make any modifications on the code or config? Did you understand what you have modified?
lax.all_gather(input_tensor._elem, axis_name="torch_dist") output_size = jax.numpy.shape(output)[0] assert len(output_tensors) == output_size for i, t in enumerate(output_tensors): assert isinstance(t, torch_xla2.tensor.XLATensor2) t._elem = output[i] fut = torch.futures....
python def ddp_setup_torchrun(): dist.init_process_group(backend="nccl") def main(): args = parser.parse_args() ddp_setup_torchrun() args.world_size = int(os.environ["WORLD_SIZE"]) args.gpu = int(os.environ['LOCAL_RANK']) args.rank = int(os.environ['RANK'])...
dist.all_gather(gathered_losses, total_loss) total_loss = sum([t.item() for t in gathered_losses]) / world_size return total_loss, evaluated_on_tokens def main(args): torch.manual_seed(args.seed) np.random.seed(args.seed) random.seed(args.seed) assert "LOCAL_RANK" in os...
dist.init_process_group(backend=”nccl”) model = FSDP( model, device_id=torch.cuda.current_device(), auto_wrap_policy=partial( transformer_auto_wrap_policy, transformer_layer_cls={ TransformerEncoderLayer, ImageTransformer, BERTTextEncoder, ...
[self.batch_size * self.world_size, self.embedding_size], device=self.device) dist.all_gather(list(total_features.chunk(self.world_size, dim=0)), features.data) total_features.requires_grad = True # 矩阵相乘得到cosine 相似度,含有负数项[乘-1的原因] logits = self.forward(total_features,...