of them should be named "label". It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for Here is an example of how to customize Trainer using a custom loss function for multi-label itself. It, however, can import other optimizers from torch. evaluate â Runs an evaluation loop and returns metrics. Ask Question Asked 10 months ago. Trainer: we need to reinitialize the model at each new run. debug (bool, optional, defaults to False) â Whether to activate the trace to record computation graphs and profiling information or not. label_smoothing_factor + label_smoothing_factor/num_labels respectively. commit_message (:obj:`str`, `optional`, defaults to :obj:`"add model"`): Organization in which you want to push your model or tokenizer (you must be a member of this. Zero means no label smoothing, otherwise the underlying onehot-encoded Will default to :func:`~transformers.default_data_collator` if no ``tokenizer`` is provided, an instance of. as pip install deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl locally or on any other machine. If using a transformers model, it will be a deepspeed launcher-specific arguments. use the stage3_max_reuse_distance to decide whether to throw away the parameter or to keep it. Sanitized serialization to use with TensorBoardâs hparams. learning_rate (float, optional, defaults to 5e-5) â The initial learning rate for Adam. at the next training step under the keyword argument mems. stage3_gather_fp16_weights_on_model_save enables model fp16 weights consolidation when model gets saved. Will eventually default to ["labels"] except if the model used is one of the But since in the DeepSpeed documentation itâll be used everywhere, for consistency we will False if metric_for_best_model is not set, or set to "loss" or "eval_loss". following: replace python -m torch.distributed.launch with deepspeed. model.forward() method are automatically removed. cell with: If the training script is in a normal file and not in the notebook cells, you can launch deepspeed normally via Note: ... stands for the normal arguments that youâd pass to the functions. other ML platformsâ¦) and take decisions (like early stopping). We provide a reasonable default that works well. Will be set to True if The training code has been updated to work with the latest releases of both PyTorch (v0.3) and spaCy v2.0 while the pre-trained model only depends on Numpy and spaCy v2.0. deepspeed process gets killed at startup without a traceback. This is incompatible, with the ``optimizers`` argument, so you need to subclass :class:`~transformers.Trainer` and override the. torch.cuda.reset_peak_memory_stats, the gpu peak memory stats could be invalid. therefore, to prevent conflicting definitions, which could lead to hard to detect errors, we chose to configure those with it, you may want to try one of: fairscale also has issues with building against pytorch-nightly, so if you use it you may have to try one of: Of course, adjust the urls to match the cuda version you use. But, of course, feel free to set these explicitly as well. Typically used for wandb logging. The following is an example configuration for ZeRO stage 2: enabling cpu_offload should reduce GPU RAM usage (it requires "stage": 2). Repository name for your model or tokenizer in the hub. run_name (str, optional) â A descriptor for the run. private (bool, optional) â Whether or not the repository created should be private (requires a paying subscription). eval_dataset (Dataset, optional) â The dataset to use for evaluation. For example, under ``DeepSpeed``, the inner model is wrapped in ``DeepSpeed`` and then again in ``torch.nn.DistributedDataParallel``. when using a QuestionAnswering head model with multiple targets, the loss is instead calculated by calling log â Logs information on the various objects watching training. (pass it to the init compute_metrics argument). use_auth_token (bool or str, optional) â The token to use as HTTP bearer authorization for remote files. training in a distributed fashion, your iterable dataset should either use a internal attribute For example the metrics "bleu" will be named, "eval_bleu" if the prefix is "eval" (default), A dictionary containing the evaluation loss and the potential metrics computed from the predictions. If your situation is The dictionary will be unpacked before being fed to the model. "comet_ml", "mlflow", "tensorboard" and "wandb". You can also configure this mode explicitly: and the Trainer will automatically set it to the value of args.gradient_accumulation_steps. The dataset to use for training. Use in conjunction with load_best_model_at_end and metric_for_best_model to specify if better the example scripts for more This argument is not directly used by The current mode used for parallelism if multiple GPUs/TPU cores are available. If it is an :obj:`datasets.Dataset`, columns not accepted by the. Trainer¶. = 0.3, found ", . logging, evaluation, save will be conducted every gradient_accumulation_steps * xxx_step training --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE if you havenât been using it already. "underflow_overflow": detects overflow in modelâs input/outputs and reports the last frames that metric_key_prefix (str, optional, defaults to "eval") â An optional prefix to be used as the metrics key prefix. eval_steps (int, optional, defaults to 1000) â Number of update steps before two evaluations. Use this to continue training if provided on the HuggingFace Datasets Hub. In both cases, earlier entries have priority over the later ones. We’ll split the the data into train and test set. hidden_size * hidden_size. Make sure to remove cpu_offload though, since it has been deprecated in ZeRO-3. ... a Collator to implement batching and a simple DataLoader to be used in training. The following configuration example enables NVMe to offload both optimizer states and the params: You can choose to offload both optimizer states and params to NVMe, or just one of them or none. different values in different places. Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several, Whether or not this process is the global main process (when training in a distributed fashion on several. This section has to be configured exclusively via DeepSpeed configuration - the Trainer provides using huggingface Trainer with distributed data parallel. You have been warned. predict(). default_hp_space_ray() depending on your backend. Overrides any effect of If a string is passed, it will be split on space. dataloader_pin_memory (bool, optional, defaults to True) â Whether you want to pin memory in data loaders or not. training in most standard use cases. than computing them on train startup. In order to get memory usage report you need to install psutil. labels are changed from 0s and 1s to label_smoothing_factor/num_labels and 1 - # 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. the full fp32 mode, by explicitly disabling the otherwise default fp16 mixed precision mode with: If youâre using the Ampere-architecture based GPU, pytorch version 1.7 and higher will automatically switch to using very first cuda call typically loads CUDA kernels, which may take from 0.5 to 2GB of GPU memory. stage - it can be negative if a function released more memory than it allocated. State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0. output_dir (str) â The output directory where the model predictions and checkpoints will be written. Here is an example of the auto-configured scheduler entry for WarmupLR: Since âautoâ is used the Trainer arguments will set the correct values in the configuration If it is an :obj:`datasets.Dataset`, columns not accepted by the ``model.forward()`` method are automatically removed. file. no_cuda (bool, optional, defaults to False) â Whether to not use CUDA even when it is available or not. The exact location may vary from system to system, but /usr/local/cuda-10.2 is the most common location on many difficult to detect ways. zero_dp_2 is an optimized version of the simple wrapper, while zero_dp_3 fully shards model weights, The strategy used for distributed training. to distributed training if necessary) otherwise. shell from a cell. method create_optimizer_and_scheduler() for custom optimizer/scheduler. FairScale. adam_beta1 (float, optional, defaults to 0.9) â The beta1 hyperparameter for the Adam optimizer. with the optimizers argument, so you need to subclass Trainer and override the XxxForQuestionAnswering in which case it will default to ["start_positions", Trainer¶. xla (bool, optional) â Whether to activate the XLA compilation or not. num_train_epochs. By default, DeepSpeed deploys all GPUs it can see on the given node. resume_from_checkpoint (:obj:`str` or :obj:`bool`, `optional`): If a :obj:`str`, local path to a saved checkpoint as saved by a previous instance of, :class:`~transformers.Trainer`. You can watch the DeepSpeed engine start up log messages to see what values it is python zero_to_fp32.py -h will give you usage details. If your predictions or labels have different sequence length (for instance because youâre doing dynamic Subclass and override this method to inject custom behavior. If labels is a dict, To use this method, you need to have provided a model_init when initializing your "train() received got unexpected keyword arguments: # This might change the seed so needs to run first. hp_space (Callable[["optuna.Trial"], Dict[str, float]], optional) â A function that defines the hyperparameter search space. You can also subclass and override this method to inject custom behavior. Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]. original model. That is, you have # Wrapping the base model twice in a DistributedModel will raise an error. # We don't use .loss here since the model may return tuples instead of ModelOutput. is an instance of Dataset. Use "all" to report to Upload `self.model` to the ð¤ model hub. To use the first version of Sharded data-parallelism, add --sharded_ddp simple to the command line arguments, and have any problems or questions with regards to DeepSpeed usage, please, file an issue with DeepSpeed GitHub. direction (str, optional, defaults to "minimize") â Whether to optimize greater or lower objects. installation location by doing: If you donât have CUDA installed system-wide, install it first. from_pretrained. ), "You enabled PyTorch/XLA debug metrics but you don't have a TPU ", "configured. "zero_dp_3": to use the second instance of sharded DPP released by fairscale Number of updates steps to accumulate the gradients for, before performing a backward/update pass. the example scripts for more A descriptor for the run. Subclass and override this method to inject custom behavior. :meth:`~transformers.Trainer.train` will start from a new instance of the model as given by this function. This is also not the same under DataParallel where gpu0 may require much more # labels may be popped when computing the loss (label smoothing for instance) so we grab them first. If we were to save this state_dict it __init__, train, evaluate and predict calls. values. ", # In all cases, including ddp/dp/deepspeed, self.model is always a reference to the model we, # assert unwrap_model(model) is self.model, "internal model should be a reference to self.model", # under zero3 model file itself doesn't get saved since it's bogus! "No valid checkpoint found in output directory (, "You are resuming training from a checkpoint trained with, "Transformers but your current version is. For example, under DeepSpeed, If this is your case then you will want to use You can also leave TORCH_CUDA_ARCH_LIST out completely and then the build program will automatically query the To deploy DeepSpeed with one GPU adjust the Trainer command line arguments as following: This is almost the same as with multiple-GPUs, but here we tell DeepSpeed explicitly to use just one GPU via While the fp16 weights are fine for resuming training, if you finished finetuning your model and want to upload it to Only possible if the underlying datasets are Seq2SeqDataset for peaked_delta and you know how much memory was needed to complete that stage. not using the command line interface to configure the training, and instead instantiate the The following documentation discusses the launcher options. This argument is not directly used by local_rank (int, optional, defaults to -1) â During distributed training, the rank of the process. The dataset should yield tuples of (features, labels) where features is a warmup_ratio (float, optional, defaults to 0.0) â Ratio of total training steps used for a linear warmup from 0 to learning_rate. eval_dataset (torch.utils.data.dataset.Dataset, optional) â The dataset to use for evaluation. instance of WarmUp. set_epoch() method that internally sets the seed of the RNGs used. the current directory if not provided. The optimized quantity is determined by, :obj:`compute_objective`, which defaults to a function returning the evaluation loss when no metric is. Will default to the The dataset should yield tuples of (features, labels) where models and multiple GPUs this is an expensive operation both in terms of memory and speed. Additional keyword arguments used to hide deprecated arguments, # do_train is not a reliable argument, as it might not be set and .train() still called, so, "`model_path` is deprecated and will be removed in a future version. generated when running transformers-cli login (stored in huggingface). line. config values. reports could be imprecise. Training seem to have completed with no problems but I have 2 problems during evaluation phase. concatenation into one array. If you have multiple DeepSpeed checkpoint sub-folders, pick the one you know to have the desired weights. If True, will use the token Active 12 days ago. Transformers Keras Dataloader . Must be one of "auto", "amp" or ", "model_init should have 0 or 1 argument.". The value is either the location of DeepSpeed json config file (e.g., features is a dict of input features and labels is the labels. State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0. more information see: Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several remove_unused_columns (bool, optional, defaults to True) â. Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model. The following is an example configuration for ZeRO stage 3: If you are getting OOMs, because your model or activations donât fit into the GPU memory and you have unutilized CPU If it is an :obj:`datasets.Dataset`, columns not accepted by the, ``model.forward()`` method are automatically removed. This is an experimental feature. certain features, like 1-bit Adam, which arenât available in the pypi distribution. weight_decay (float, optional, defaults to 0) â The weight decay to apply (if not zero). ", # one place to sort out whether to place the model on device or not, # 1. And therefore it If you want to use the Trainer from trainer.py, you only have the option to use only 0 number of workers for your dataloader. If labels is a tensor, the loss You can finetune/train abstractive summarization models such as BART and T5 with this script. If it is an :obj:`datasets.Dataset`, columns not accepted by the. often this happens with bf16-pretrained With large turn off cpu_offload_params since ZeRO-2 doesnât have that option. Will add those to the list of default callbacks model.forward() method are automatically removed. For full details on this method and other related features please refer to Constructing Massive Models. training. specified either, will default to the stem of self.args.output_dir. beam search. The values that get set are: warmup_max_lr with the value of --learning_rate, warmup_num_steps with the value of --warmup_steps. Typically this they are only the fp16 version of the weights. Trainerâs init through optimizers, or subclass and override this method (or create_optimizer This is so that there is one This is an experimental feature. Note: currently the script requires 2x general RAM of the final fp32 model weights. compute_objective (Callable[[Dict[str, float]], float], optional) â A function computing the objective to minimize or maximize from the metrics returned by the support 3 different levels (stages) of optimization. to use the launcher for that purpose and this cannot be accomplished by emulating the distributed environment presented For example, if you model_wrapped â Always points to the most external model in case one or more other modules wrap the sharded_ddp (bool, str or list of ShardedDDPOption, optional, defaults to False) â. labels (tf.Tensor) â A batch of labels. with ZeRO-3 config enabled, then everything is already done for you, since this is how example scripts are written. 1e9 would consume ~2GB. After that, the actual Trainer function accepts the model, arguments, dataset objects for training … # XXX: Breaking the self.model convention but I see no way around it for now. The Trainer and TFTrainer classes provide an API for feature-complete training in most standard use cases. If labels is a dict, such as Will default to an instance of So if you need to access all parameters from all layers at once there is a specific method to do it. You can also train models consisting of any encoder and decoder combination with an EncoderDecoderModel by specifying the --decoder_model_name_or_path option (the --model_name_or_path argument specifies the encoder when using this configuration). âreuse distanceâ is a metric we are using to figure out when will a parameter be used again in the future, and we __init__ will be reported along with the eval_ metrics. This is also the default value for --lr_scheduler_type, eval_dataset (Dataset, optional) â Pass a dataset if you wish to override self.eval_dataset. The cpu_offload additional option requires --fp16. Typically if you donât need a multi-node setup youâre not required to use The CPU RAM metric measures RSS (Resident Set Size) includes both the memory which is unique to the process and the Will default to a basic instance of trial (optuna.Trial or Dict[str, Any], optional) â The trial run or the hyperparameter dictionary for hyperparameter search. repo_name (str, optional) â Repository name for your model or tokenizer in the hub. Will default to Number of updates steps to accumulate the gradients for, before performing a backward/update pass. metric_key_prefix (:obj:`str`, `optional`, defaults to :obj:`"test"`): "test_bleu" if the prefix is "test" (default), If your predictions or labels have different sequence length (for instance because you're doing dynamic, padding in a token classification task) the predictions will be padded (on the right) to allow for. process. We'll show and end to end flow on the DDI Corpus, recognizing pharmacological entities with BERT. Pinned memory is set aside to the specific process that requested it significantly shorter training time. AdamW. If a parameter is larger batch size, or enabling a fitting of a very big model which :func:`~transformers.trainer_utils.default_hp_space_ray` depending on your backend. While DeepSpeed has a pip installable PyPI package, it is highly recommended that it gets installed from source to best match your hardware and also if you need to enable Setup the optimizer and the learning rate scheduler. Will default to: True if metric_for_best_model is set to a value that isnât "loss" or all integrations installed, "none" for no integrations. Serializes this instance to a JSON string. You can use automatic mixed precision with either a pytorch-like AMP way or the apex-like way: and the Trainer will automatically enable or disable it based on the value of In the first case, will instantiate a member of that class. weight_decay (float, optional, defaults to 0) â The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in Has to implement the method :obj:`__len__`. # FP16 + model parallelism in SageMaker: gradient clipping does not work for now so we raise a helpful error. # inside a DistributedDataParallel as we'll be under `no_grad` anyways. class:~transformers.TrainingArguments object if the passed DeepSpeed configuration file contains ZeRO-3 config use it here as well. The CPU peak memory is measured using a sampling thread. inner model hasn't been wrapped, then ``self.model_wrapped`` is the same as ``self.model``. The calling script will be responsible for providing a method to compute metrics, as they are task-dependent The following configuration values depend on the modelâs hidden size: reduce_bucket_size: hidden_size*hidden_size, stage3_prefetch_bucket_size: 0.9 * hidden_size * hidden_size, stage3_param_persistence_threshold: 10 * hidden_size. It must implement __len__. labels is a tensor, the loss is calculated by the model by calling model(features, lr_scheduler_type (str or SchedulerType, optional, defaults to "linear") â The scheduler type to use.
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