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num_channels: 3 I recently tried again and was able to get it built with CUDA extensions. As far as I understand, DDP spawns one process per rank and trains the same model on different parts of the batch data. I guess the code would set the CUDA device via: and initialize the process group afterwards. By default, skip adaptation on parameters that are. deepspeed.ops.lamb.fused_lamb DeepSpeed 0.10.0 normalize: True for input. pytorch BCELoss F.binary_cross_entropy , BCEWithLogitsLoss F.binary_cross_entropy_with_logists logists . # FIXME nested where required since logical and/or not working in PT XLA, # LAMBC trust clipping, upper bound fixed at one. I dont know why this error is reported. to your account, The environment has been configured according to the installation guide, but when training the MUNIT model, an error is reported: We read every piece of feedback, and take your input very seriously. Currently GPU-only. 0 one I understand is the master GPU which will gather everything, but the -1 local_rank what does it mean? !pip install -v --no-cache-dir ./ zero_grad() [source] Clears the gradients of all optimized torch.Tensor s. apex.optimizers.FusedAdam, apex.normalization.FusedLayerNorm, etc. interpolator: BILINEAR Here is a small summary in the code I have: The DeepLearningExamples - BERT repository should give you a working example using these utils. num_channels: 35 How to install nvidia apex on Google Colab - Stack Overflow lib/timm/optim/lamb.py Roll20/pet_score at RuntimeError: apex.optimizers.FusedSGD requires cuda extension Concatenate images: # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. Concatenate images: .. _Large Batch Optimization for Deep Learning - Training BERT in 76 minutes: .. _On the Convergence of Adam and Beyond: https://openreview.net/forum?id=ryQu7f-RZ, closure (callable, optional): A closure that reevaluates the model, # because torch.where doesn't handle scalars correctly, 'Lamb does not support sparse gradients, consider SparseAdam instad. LAMB was proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_. "), "Please install apex from https://www.github.com/nvidia/apex to run this example. optimizer_name: string name of the optimizer, used for auto resolution of params. WebRuntimeError: apex.optimizers.FusedAdam requires cuda extensions. optimizer_kwargs: Either a list of strings in a specified format, or a dictionary. In general, ``opt_level="O1"`` is recommended. (default: 1e-3), betas (Tuple[float, float], optional): coefficients used for computing, running averages of gradient and its norm. The command that worked for me, after activated the environment where you have pytorch compiled with your current cuda version, and downloading the apex project use: python setup.py install --cuda_ext --cpp_ext of channels in the input image: 3 # Copyright (c) 2021, Habana Labs Ltd. All rights reserved. RuntimeError: apex.optimizers.FusedAdam requires cuda 'FusedLamb does not support the AMSGrad variant. This version of fused LAMB implements 2 fusions. File "C:\Users\Simon\v2v\imaginaire\imaginaire\utils\trainer.py", line 276, in get_optimizer_for_params Thank you very much for the resource @ptrblck ! params (iterable): iterable of parameters to optimize or dicts defining, lr (float, optional): learning rate. Then computes the gradient and performs a reduce of all of the gradients to update the model to each GPU again. All rights reserved. All rights reserved. RuntimeError: apex.optimizers.FusedSGD requires cuda extension_apex and cuda required for fused optimizers_REALLYAI-CSDN RuntimeError: apex.optimizers.FusedSGD requires cuda extension REALLYAI 2022-12-05 15:24:40 711 python python python nemo.core.optim.optimizers NVIDIA NeMo - NVIDIA optimizers , git clone 1.clone 2.git clone3.cd , UserWarning: Disabling all use of wheels due to the use of --build-opt, __init__(), https://blog.csdn.net/qq_42037273/article/details/128187470. CoCalc -- fused_lamb.py betas=(cfg_opt.adam_beta1, cfg_opt.adam_beta2)) deepspeed.ops.lamb.fused_lamb pytorch.cuda, PyTorch <- . # If `auto` is passed as name for resolution of optimizer name, # then lookup optimizer name and resolve its parameter config, # Override arguments provided in the config yaml file, # If optimizer kwarg overrides are wrapped in yaml `params`, # If the kwargs themselves are a DictConfig, # If we are provided just a Config object, simply return the dictionary of that object. Requires Apex to be installed via"," ``pip install -v --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" ./``. normalize: True for input. Num. # copies or substantial portions of the Software. net_D parameter count: 32,322,498 I use paperspace, and this worked for me: !pip install git+https://github.com/NVIDIA/apex (default: 1e-3), bias_correction (bool, optional): bias correction (default: True), betas (Tuple[float, float], optional): coefficients used for computing, running averages of gradient and its square. File "G:\Anaconda3\envs\xyy_imagenaire\lib\site-packages\apex\optimizers\fused_adam.py", line 80, in init I am training a BERT model using PyTorch and after endless research on different versions I cant be sure which should be the correct implementation of DDP (DistributedDataParallel). interpolator: BILINEAR Epoch length: 1 +, # "Pytorch binaries were compiled with Cuda {}.\n".format(torch.version.cuda) +, # "In some cases, a minor-version mismatch will not cause later errors: " +, # "https://github.com/NVIDIA/apex/pull/323#discussion_r287021798. net_G parameter count: 30,258,966 GitHub - NVIDIA/apex: A PyTorch Extension: Tools for ext: png # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. WebFor example: x = torch.ones(1, device="cuda") # GPU Migration changes the argument `device` from "cuda" to "hpu". Hey guys, I am using apex.optimizers FusedLamb and its working well. of channels in the input label: 35 ', closure (callable, optional): A closure that reevaluates the model, grads (list of tensors, optional): weight gradient to use for the, optimizer update. This version of fused Adam implements 2 fusions. is the final parsed value, and simply returned. I can now train bert-mini on lambdalabs 8x Tesla V100 single machine in about 3 hours and 40 min. # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. normalize: True for input. return get_optimizer_for_params(cfg_opt, params) If you wish to use :class:`FusedLAMB` with Amp, model, opt = amp.initialize(model, opt, opt_level="O0" or "O1 or "O2"). `"Cuda extensions are being compiled with a version of Cuda that does not`, , GPU GPU : nvcc pytorch.cuda 10.0, GPU GPU : nvcc pytorch.cuda 9.2. File "G:\Anaconda3\envs\xyy_imagenaire\lib\site-packages\apex\optimizers\fused_adam.py", line 80, in init raise apex.optimizers.fused_lamb Apex 0.1.0 documentation - GitHub optimizer_params: The parameters as a dataclass of the optimizer, "Cannot override pre-existing optimizers. installing Apex with CUDA and C++ extensions Currently GPU-only. Change the config file, adding fused_opt: False here: ext: png Habana GPU Migration APIs Gaudi Documentation You can built apex on Colab using the following simple steps: Query the version Ubuntu Colab is running on: !lsb_release -a No LSB modules are available. IN NO EVENT SHALL THE, # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER. net_G parameter count: 346,972,262 In addition to some cleanup, this Lamb impl has been modified to support PyTorch XLA and has been tested on TPU. WebPerforms a single optimization step. apex.optimizers.fused_adam Apex 0.1.0 documentation By clicking Sign up for GitHub, you agree to our terms of service and Parameters closure ( callable, optional) A closure that reevaluates the model and returns the loss. This version of fused LAMB implements 2 fusions. cuda. git clone https://github.com/ Num. File "H:\19xyy\project\imaginaire-master\imaginaire\utils\trainer.py", line 115, in get_model_optimizer_and_scheduler Initialize net_G and net_D weights using type: xavier gain: 0.02 (default: 1e-3), betas (Tuple[float, float], optional): coefficients used for computing, running averages of gradient and its norm. The args.local_rank is set by the torch.distributed.launch call which passes these arguments (or sets the env variables). similar in behaviour to APEX FusedLamb if you aren't using NVIDIA GPUs or cannot install/use APEX. Requires Apex to be installed via. Make software development more efficient, Also welcome to join our telegram. raise RuntimeError('apex.optimizers.FusedAdam requires cuda extensions') ext: png WebFor performance and full functionality, we recommend installing Apex with CUDA and C++ extensions via git clone https://github.com/NVIDIA/apex cd apex # if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with # You may obtain a copy of the License at, # http://www.apache.org/licenses/LICENSE-2.0, # Unless required by applicable law or agreed to in writing, software. Will be used as key to retrieve the optimizer. Initialize net_G and net_D weights using type: orthogonal gain: 1 * Fusion of the LAMB update's elementwise operations. I guess the code would set the CUDA device via: torch.cuda.set_device (args.local_rank) device = torch.device ("cuda", args.local_rank) and initialize the process Thank you very much again for your answers! ext: png Currently, the FusedAdam implementation in Apex flattens the parameters for the optimization step, then carries out the optimization step itself via a fused kernel that ext: png Sorry to bother you again I have one naive question about the local_rank argument. # distributed under the License is distributed on an "AS IS" BASIS. WebFor example: x = torch.ones(1, device="cuda") # GPU Migration changes the argument `device` from "cuda" to "hpu". Habana GPU Migration APIs Gaudi Documentation Web `"Cuda extensions are being compiled with a version of Cuda that does not`, . main() RuntimeError: apex.optimizers.FusedAdam requires cuda extensions, Hi, I just run fs_vid2vid inferring successfully. is_available (): raise ValueError (f 'CUDA must be available to use cudnn deterministic: False Concatenate seg_maps: The LAMB optimizer has been shown to stabilize pre-training of large models using large batch sizes. require CUDA and C++ extensions (see e.g., here). File "H:\19xyy\project\imaginaire-master\train.py", line 60, in main Web `"Cuda extensions are being compiled with a version of Cuda that does not`, . Requires Apex to be installed via ``pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./``. I foud solution in previous issues: Hi, I just run fs_vid2vid inferring successfully. There also seems to be a "FusedAdam" optimizer: I tried a few options, but I liked the one in this website , which worked very well with fast_bert and torch: try: This allows custom optimizers to be added and called by name during instantiation. ext: png Sign in Max sequence length: 30 Modifications Copyright 2021 Ross Wightman. [docs] class FusedAdam(torch.optim.Optimizer): """Implements Adam algorithm. get_model_optimizer_and_scheduler(cfg, seed=args.seed) To see all available qualifiers, see our documentation. [docs] class FusedAdam(torch.optim.Optimizer): """Implements Adam algorithm. opt_G = get_optimizer(cfg.gen_opt, net_G) Pre-training with Lamb optimizer - Hugging Face Forums (wanted to just add a comment but I don't have enough reputation) it works for me but the cd is actually not required. Also, I needed the two keys ()} ") if name == 'fused_adam': if not torch. For """Implements the LAMB algorithm. simon-eda simon-eda NONE Created 2 years ago. (default: (0.9, 0.999)), eps (float, optional): term added to the denominator to improve, weight_decay (float, optional): weight decay (L2 penalty) (default: 0), grad_averaging (bool, optional): whether apply (1-beta2) to grad when, calculating running averages of gradient. Found 1 sequences Requires Apex to be installed via"," ``pip install -v --no-cache-dir --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext\" ./``. Num. except Exception: Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Currently GPU-only. I also checked apex, and it is (default: True), set_grad_none (bool, optional): whether set grad to None when zero_grad(), max_grad_norm (float, optional): value used to clip global grad norm, use_nvlamb (boolean, optional): Apply adaptive learning rate to 0.0. I cant find a good example where my desired specificities (torch-based mixed-precision, apex FusedLAMB optimizer and DDP) are implemented and its hard to know if my implementation is good.