total =sum([param.nelement()forparaminmodel.parameters()])print('Number of parameter: %.2fM'% (total/1e6)) 🐱🚀4.4Ptflops # -- coding: utf-8 --importtorchvisionfromptflopsimportget_model_complexity_info model = torchvision.models.alexnet(pretrained=False) flops, params = get_model...
trainable_num = sum(p.numel() for p in model.parameters() if p.requires_grad) The obtained results are larger than the reported one in the paper. We have recently planned to train another MambaIR-light version under the correct parameter counts setup. Stay tuned. So, the FLOPs obtained ...
batch=torch.ones(()).new_empty((1,*input_res), dtype=next(flops_model.parameters()).dtype, device=next(flops_model.parameters()).device) exceptStopIteration: batch=torch.ones(()).new_empty((1,*input_res)) _=flops_model(batch) flops_count=abs(flops_model.compute_average_flops_cost())...
Describe the bug Hi, everybody, I'm traning a llama model in step3 using deepspeed-chat. In version 0.10.1, it raised the following error(see in logs bleow). so I switch branch to HeyangQin/fix_issue_3156(#3156) and copy code into master...
() val optimalHyperparameters = optimizeHypers(instr, expertLabelsAndKernels, likelihoodAndGradient) expertLabelsAndKernels.foreach(_._2.setHyperparameters(optimalHyperparameters)) produceModel(instr, points, expertLabelsAndKernels, optimalHyperparameters) } private def likelihoodAndGradient(yAndK : (BDV[...