That means that the params weren't gathered under zero3. and when zero3 is used deepspeed puts placeholders with tensors of zero3. Please create a new issue and I will fix it. or if you feel inspired you can contribute a few lines of code that will check if the model is running und...
GPT_ARGS="--tensor-model-parallel-size ${TP} \--pipeline-model-parallel-size ${PP} \--sequence-parallel \--num-layers 32 \--hidden-size 4096 \--ffn-hidden-size 11008 \--num-attention-heads 32 \--seq-length 4096 \--max-position-embeddings 4096 \--micro-batch-size 4 \--global-b...
from ..training.utils import get_batch_on_this_cp_rank, get_batch_on_this_tp_rank, get_device_wrapper File "/home/openlab/ModelLink/modellink/training/init.py", line 16, in from .training import (get_model_wrapper, is_profile_enabled, get_profiler, setup_model_and_optimizer_wrapper, Fi...
in__init__ self.broadcast_bucket_size) File"/mnt/lustre/lirundong/Program/conda_env/torch-1.2-cuda-9.0/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 480,in_distributed_broadcast_coalesced dist._broadcast_coalesced(self.process_group, tensors, buffer_size) RuntimeError: ...
# 需要导入模块: from torch import distributed [as 别名]# 或者: from torch.distributed importget_world_size[as 别名]def__init__(self, dataset, num_replicas=None, rank=None, shuffle=True):ifnum_replicasisNone:ifnotdist.is_available():raiseRuntimeError("Requires distributed package to be avai...
LoRAPrune: Pruning meets Low-Rank Parameter-Efficient Fine-Tuning A Simple and Effective Pruning Approach for Large Language Models One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models TensorGPT: Efficient Compression of the Embedding Layer in LLMs based on the Tensor-Train Deco...
Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning LoRAPrune: Pruning meets Low-Rank Parameter-Efficient Fine-Tuning A Simple and Effective Pruning Approach for Large Language Models One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models TensorGPT: Efficient...
Source File: BertOnnxModel.py From FARM with Apache License 2.0 6 votes def get_bert_input_shape(self): graph = self.graph() bert_inputs = self.get_bert_inputs() for input in graph.input: if input.name in bert_inputs: tensor_type = input.type.tensor_type if (tensor_type.Has...
join(cfg.respth, 'model_final.pth') net.load_state_dict(torch.load(save_pth), strict=False) net.cuda() net.eval() if not args.local_rank == -1: net = nn.parallel.DistributedDataParallel(net, device_ids = [args.local_rank, ], output_device = args.local_rank ) ## evaluator ...
LoRAPrune: Pruning meets Low-Rank Parameter-Efficient Fine-Tuning A Simple and Effective Pruning Approach for Large Language Models One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models TensorGPT: Efficient Compression of the Embedding Layer in LLMs based on the Tensor-Train Deco...