2df34f4aba
* deepspeed integration * style * add test * ds wants to do its own backward * fp16 assert * Update src/transformers/training_args.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * style * for clarity extract what args are being passed to deepspeed * introduce the concept of self.wrapped_model * s/self.wrapped_model/self.model_wrapped/ * complete transition to self.wrapped_model / self.model * fix * doc * give ds its own init * add custom overrides, handle bs correctly * fix test * clean up model_init logic, fix small bug * complete fix * collapse --deepspeed_config into --deepspeed * style * start adding doc notes * style * implement hf2ds optimizer and scheduler configuration remapping * oops * call get_num_training_steps absolutely when needed * workaround broken auto-formatter * deepspeed_config arg is no longer needed - fixed in deepspeed master * use hf's fp16 args in config * clean * start on the docs * rebase cleanup * finish up --fp16 * clarify the supported stages * big refactor thanks to discovering deepspeed.init_distributed * cleanup * revert fp16 part * add checkpoint-support * more init ds into integrations * extend docs * cleanup * unfix docs * clean up old code * imports * move docs * fix logic * make it clear which file it's referring to * document nodes/gpus * style * wrong format * style * deepspeed handles gradient clipping * easier to read * major doc rewrite * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * docs * switch to AdamW optimizer * style * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * clarify doc Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
589 lines
29 KiB
Python
589 lines
29 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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from dataclasses import asdict, dataclass, field
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from enum import Enum
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from typing import Any, Dict, List, Optional, Tuple
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from .file_utils import cached_property, is_torch_available, is_torch_tpu_available, torch_required
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from .trainer_utils import EvaluationStrategy, SchedulerType
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from .utils import logging
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if is_torch_available():
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import torch
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if is_torch_tpu_available():
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import torch_xla.core.xla_model as xm
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logger = logging.get_logger(__name__)
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def default_logdir() -> str:
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"""
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Same default as PyTorch
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"""
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import socket
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from datetime import datetime
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current_time = datetime.now().strftime("%b%d_%H-%M-%S")
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return os.path.join("runs", current_time + "_" + socket.gethostname())
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@dataclass
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class TrainingArguments:
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"""
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TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
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itself**.
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Using :class:`~transformers.HfArgumentParser` we can turn this class into `argparse
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<https://docs.python.org/3/library/argparse.html#module-argparse>`__ arguments that can be specified on the command
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line.
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Parameters:
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output_dir (:obj:`str`):
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The output directory where the model predictions and checkpoints will be written.
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overwrite_output_dir (:obj:`bool`, `optional`, defaults to :obj:`False`):
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If :obj:`True`, overwrite the content of the output directory. Use this to continue training if
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:obj:`output_dir` points to a checkpoint directory.
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do_train (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether to run training or not. This argument is not directly used by :class:`~transformers.Trainer`, it's
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intended to be used by your training/evaluation scripts instead. See the `example scripts
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<https://github.com/huggingface/transformers/tree/master/examples>`__ for more details.
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do_eval (:obj:`bool`, `optional`):
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Whether to run evaluation on the validation set or not. Will be set to :obj:`True` if
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:obj:`evaluation_strategy` is different from :obj:`"no"`. This argument is not directly used by
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:class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See
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the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`__ for more
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details.
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do_predict (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether to run predictions on the test set or not. This argument is not directly used by
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:class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See
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the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`__ for more
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details.
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evaluation_strategy (:obj:`str` or :class:`~transformers.trainer_utils.EvaluationStrategy`, `optional`, defaults to :obj:`"no"`):
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The evaluation strategy to adopt during training. Possible values are:
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* :obj:`"no"`: No evaluation is done during training.
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* :obj:`"steps"`: Evaluation is done (and logged) every :obj:`eval_steps`.
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* :obj:`"epoch"`: Evaluation is done at the end of each epoch.
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prediction_loss_only (:obj:`bool`, `optional`, defaults to `False`):
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When performing evaluation and generating predictions, only returns the loss.
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per_device_train_batch_size (:obj:`int`, `optional`, defaults to 8):
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The batch size per GPU/TPU core/CPU for training.
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per_device_eval_batch_size (:obj:`int`, `optional`, defaults to 8):
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The batch size per GPU/TPU core/CPU for evaluation.
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gradient_accumulation_steps (:obj:`int`, `optional`, defaults to 1):
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Number of updates steps to accumulate the gradients for, before performing a backward/update pass.
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.. warning::
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When using gradient accumulation, one step is counted as one step with backward pass. Therefore,
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logging, evaluation, save will be conducted every ``gradient_accumulation_steps * xxx_step`` training
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examples.
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eval_accumulation_steps (:obj:`int`, `optional`):
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Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If
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left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but
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requires more memory).
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learning_rate (:obj:`float`, `optional`, defaults to 5e-5):
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The initial learning rate for Adam.
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weight_decay (:obj:`float`, `optional`, defaults to 0):
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The weight decay to apply (if not zero).
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adam_beta1 (:obj:`float`, `optional`, defaults to 0.9):
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The beta1 hyperparameter for the Adam optimizer.
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adam_beta2 (:obj:`float`, `optional`, defaults to 0.999):
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The beta2 hyperparameter for the Adam optimizer.
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adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8):
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The epsilon hyperparameter for the Adam optimizer.
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max_grad_norm (:obj:`float`, `optional`, defaults to 1.0):
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Maximum gradient norm (for gradient clipping).
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num_train_epochs(:obj:`float`, `optional`, defaults to 3.0):
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Total number of training epochs to perform (if not an integer, will perform the decimal part percents of
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the last epoch before stopping training).
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max_steps (:obj:`int`, `optional`, defaults to -1):
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If set to a positive number, the total number of training steps to perform. Overrides
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:obj:`num_train_epochs`.
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lr_scheduler_type (:obj:`str` or :class:`~transformers.SchedulerType`, `optional`, defaults to :obj:`"linear"`):
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The scheduler type to use. See the documentation of :class:`~transformers.SchedulerType` for all possible
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values.
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warmup_steps (:obj:`int`, `optional`, defaults to 0):
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Number of steps used for a linear warmup from 0 to :obj:`learning_rate`.
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logging_dir (:obj:`str`, `optional`):
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`TensorBoard <https://www.tensorflow.org/tensorboard>`__ log directory. Will default to
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`runs/**CURRENT_DATETIME_HOSTNAME**`.
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logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether to log and evaluate the first :obj:`global_step` or not.
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logging_steps (:obj:`int`, `optional`, defaults to 500):
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Number of update steps between two logs.
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save_steps (:obj:`int`, `optional`, defaults to 500):
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Number of updates steps before two checkpoint saves.
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save_total_limit (:obj:`int`, `optional`):
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If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
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:obj:`output_dir`.
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no_cuda (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether to not use CUDA even when it is available or not.
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seed (:obj:`int`, `optional`, defaults to 42):
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Random seed for initialization.
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fp16 (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether to use 16-bit (mixed) precision training (through NVIDIA Apex) instead of 32-bit training.
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fp16_opt_level (:obj:`str`, `optional`, defaults to 'O1'):
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For :obj:`fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details
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on the `Apex documentation <https://nvidia.github.io/apex/amp.html>`__.
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fp16_backend (:obj:`str`, `optional`, defaults to :obj:`"auto"`):
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The backend to use for mixed precision training. Must be one of :obj:`"auto"`, :obj:`"amp"` or
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:obj:`"apex"`. :obj:`"auto"` will use AMP or APEX depending on the PyTorch version detected, while the
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other choices will force the requested backend.
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local_rank (:obj:`int`, `optional`, defaults to -1):
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Rank of the process during distributed training.
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tpu_num_cores (:obj:`int`, `optional`):
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When training on TPU, the number of TPU cores (automatically passed by launcher script).
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debug (:obj:`bool`, `optional`, defaults to :obj:`False`):
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When training on TPU, whether to print debug metrics or not.
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dataloader_drop_last (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
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or not.
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eval_steps (:obj:`int`, `optional`):
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Number of update steps between two evaluations if :obj:`evaluation_strategy="steps"`. Will default to the
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same value as :obj:`logging_steps` if not set.
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dataloader_num_workers (:obj:`int`, `optional`, defaults to 0):
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Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the
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main process.
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past_index (:obj:`int`, `optional`, defaults to -1):
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Some models like :doc:`TransformerXL <../model_doc/transformerxl>` or :doc`XLNet <../model_doc/xlnet>` can
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make use of the past hidden states for their predictions. If this argument is set to a positive int, the
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``Trainer`` will use the corresponding output (usually index 2) as the past state and feed it to the model
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at the next training step under the keyword argument ``mems``.
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run_name (:obj:`str`, `optional`):
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A descriptor for the run. Typically used for `wandb <https://www.wandb.com/>`_ logging.
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disable_tqdm (:obj:`bool`, `optional`):
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Whether or not to disable the tqdm progress bars and table of metrics produced by
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:class:`~transformers.notebook.NotebookTrainingTracker` in Jupyter Notebooks. Will default to :obj:`True`
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if the logging level is set to warn or lower (default), :obj:`False` otherwise.
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remove_unused_columns (:obj:`bool`, `optional`, defaults to :obj:`True`):
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If using :obj:`datasets.Dataset` datasets, whether or not to automatically remove the columns unused by the
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model forward method.
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(Note that this behavior is not implemented for :class:`~transformers.TFTrainer` yet.)
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label_names (:obj:`List[str]`, `optional`):
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The list of keys in your dictionary of inputs that correspond to the labels.
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Will eventually default to :obj:`["labels"]` except if the model used is one of the
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:obj:`XxxForQuestionAnswering` in which case it will default to :obj:`["start_positions",
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"end_positions"]`.
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load_best_model_at_end (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to load the best model found during training at the end of training.
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.. note::
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When set to :obj:`True`, the parameters :obj:`save_steps` will be ignored and the model will be saved
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after each evaluation.
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metric_for_best_model (:obj:`str`, `optional`):
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Use in conjunction with :obj:`load_best_model_at_end` to specify the metric to use to compare two different
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models. Must be the name of a metric returned by the evaluation with or without the prefix :obj:`"eval_"`.
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Will default to :obj:`"loss"` if unspecified and :obj:`load_best_model_at_end=True` (to use the evaluation
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loss).
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If you set this value, :obj:`greater_is_better` will default to :obj:`True`. Don't forget to set it to
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:obj:`False` if your metric is better when lower.
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greater_is_better (:obj:`bool`, `optional`):
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Use in conjunction with :obj:`load_best_model_at_end` and :obj:`metric_for_best_model` to specify if better
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models should have a greater metric or not. Will default to:
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- :obj:`True` if :obj:`metric_for_best_model` is set to a value that isn't :obj:`"loss"` or
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:obj:`"eval_loss"`.
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- :obj:`False` if :obj:`metric_for_best_model` is not set, or set to :obj:`"loss"` or :obj:`"eval_loss"`.
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ignore_skip_data (:obj:`bool`, `optional`, defaults to :obj:`False`):
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When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
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stage as in the previous training. If set to :obj:`True`, the training will begin faster (as that skipping
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step can take a long time) but will not yield the same results as the interrupted training would have.
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sharded_ddp (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Use Sharded DDP training from `FairScale <https://github.com/facebookresearch/fairscale>`__ (in distributed
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training only). This is an experimental feature.
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deepspeed (:obj:`str`, `optional`):
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Use `Deepspeed <https://github.com/microsoft/deepspeed>`__. This is an experimental feature and its API may
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evolve in the future. The value is the location of its json config file (usually ``ds_config.json``).
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label_smoothing_factor (:obj:`float`, `optional`, defaults to 0.0):
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The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
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labels are changed from 0s and 1s to :obj:`label_smoothing_factor/num_labels` and :obj:`1 -
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label_smoothing_factor + label_smoothing_factor/num_labels` respectively.
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adafactor (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to use the :class:`~transformers.Adafactor` optimizer instead of
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:class:`~transformers.AdamW`.
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"""
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output_dir: str = field(
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metadata={"help": "The output directory where the model predictions and checkpoints will be written."}
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)
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overwrite_output_dir: bool = field(
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default=False,
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metadata={
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"help": (
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"Overwrite the content of the output directory."
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"Use this to continue training if output_dir points to a checkpoint directory."
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)
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},
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)
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do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
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do_eval: bool = field(default=None, metadata={"help": "Whether to run eval on the dev set."})
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do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."})
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evaluation_strategy: EvaluationStrategy = field(
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default="no",
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metadata={"help": "The evaluation strategy to use."},
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)
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prediction_loss_only: bool = field(
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default=False,
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metadata={"help": "When performing evaluation and predictions, only returns the loss."},
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)
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per_device_train_batch_size: int = field(
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default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
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)
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per_device_eval_batch_size: int = field(
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default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
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)
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per_gpu_train_batch_size: Optional[int] = field(
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default=None,
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metadata={
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"help": "Deprecated, the use of `--per_device_train_batch_size` is preferred. "
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"Batch size per GPU/TPU core/CPU for training."
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},
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)
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per_gpu_eval_batch_size: Optional[int] = field(
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default=None,
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metadata={
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"help": "Deprecated, the use of `--per_device_eval_batch_size` is preferred."
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"Batch size per GPU/TPU core/CPU for evaluation."
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},
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)
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gradient_accumulation_steps: int = field(
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default=1,
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metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."},
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)
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eval_accumulation_steps: Optional[int] = field(
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default=None,
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metadata={"help": "Number of predictions steps to accumulate before moving the tensors to the CPU."},
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)
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learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for Adam."})
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weight_decay: float = field(default=0.0, metadata={"help": "Weight decay if we apply some."})
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adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for Adam optimizer"})
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adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for Adam optimizer"})
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adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for Adam optimizer."})
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max_grad_norm: float = field(default=1.0, metadata={"help": "Max gradient norm."})
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num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
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max_steps: int = field(
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default=-1,
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metadata={"help": "If > 0: set total number of training steps to perform. Override num_train_epochs."},
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)
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lr_scheduler_type: SchedulerType = field(
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default="linear",
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metadata={"help": "The scheduler type to use."},
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)
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warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
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logging_dir: Optional[str] = field(default_factory=default_logdir, metadata={"help": "Tensorboard log dir."})
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logging_first_step: bool = field(default=False, metadata={"help": "Log the first global_step"})
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logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
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save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
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save_total_limit: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"Limit the total amount of checkpoints."
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"Deletes the older checkpoints in the output_dir. Default is unlimited checkpoints"
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)
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},
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)
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no_cuda: bool = field(default=False, metadata={"help": "Do not use CUDA even when it is available"})
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seed: int = field(default=42, metadata={"help": "random seed for initialization"})
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fp16: bool = field(
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default=False,
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metadata={"help": "Whether to use 16-bit (mixed) precision (through NVIDIA Apex) instead of 32-bit"},
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)
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fp16_opt_level: str = field(
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default="O1",
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metadata={
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"help": (
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"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
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"See details at https://nvidia.github.io/apex/amp.html"
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)
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},
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)
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fp16_backend: str = field(
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default="auto",
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metadata={"help": "The backend to be used for mixed precision.", "choices": ["auto", "amp", "apex"]},
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)
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local_rank: int = field(default=-1, metadata={"help": "For distributed training: local_rank"})
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tpu_num_cores: Optional[int] = field(
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default=None, metadata={"help": "TPU: Number of TPU cores (automatically passed by launcher script)"}
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)
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tpu_metrics_debug: bool = field(
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default=False,
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metadata={"help": "Deprecated, the use of `--debug` is preferred. TPU: Whether to print debug metrics"},
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)
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debug: bool = field(default=False, metadata={"help": "Whether to print debug metrics on TPU"})
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dataloader_drop_last: bool = field(
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default=False, metadata={"help": "Drop the last incomplete batch if it is not divisible by the batch size."}
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)
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eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
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dataloader_num_workers: int = field(
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default=0,
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metadata={
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"help": "Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process."
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},
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)
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past_index: int = field(
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default=-1,
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metadata={"help": "If >=0, uses the corresponding part of the output as the past state for next step."},
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)
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run_name: Optional[str] = field(
|
||
default=None, metadata={"help": "An optional descriptor for the run. Notably used for wandb logging."}
|
||
)
|
||
disable_tqdm: Optional[bool] = field(
|
||
default=None, metadata={"help": "Whether or not to disable the tqdm progress bars."}
|
||
)
|
||
|
||
remove_unused_columns: Optional[bool] = field(
|
||
default=True, metadata={"help": "Remove columns not required by the model when using an nlp.Dataset."}
|
||
)
|
||
label_names: Optional[List[str]] = field(
|
||
default=None, metadata={"help": "The list of keys in your dictionary of inputs that correspond to the labels."}
|
||
)
|
||
|
||
load_best_model_at_end: Optional[bool] = field(
|
||
default=False,
|
||
metadata={"help": "Whether or not to load the best model found during training at the end of training."},
|
||
)
|
||
metric_for_best_model: Optional[str] = field(
|
||
default=None, metadata={"help": "The metric to use to compare two different models."}
|
||
)
|
||
greater_is_better: Optional[bool] = field(
|
||
default=None, metadata={"help": "Whether the `metric_for_best_model` should be maximized or not."}
|
||
)
|
||
ignore_data_skip: bool = field(
|
||
default=False,
|
||
metadata={
|
||
"help": "When resuming training, whether or not to skip the first epochs and batches to get to the same training data."
|
||
},
|
||
)
|
||
sharded_ddp: bool = field(
|
||
default=False,
|
||
metadata={"help": "Whether or not to use sharded DDP training (in distributed training only)."},
|
||
)
|
||
deepspeed: Optional[str] = field(
|
||
default=None,
|
||
metadata={"help": "Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json)"},
|
||
)
|
||
label_smoothing_factor: float = field(
|
||
default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."}
|
||
)
|
||
adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace Adam by Adafactor."})
|
||
_n_gpu: int = field(init=False, repr=False, default=0)
|
||
|
||
def __post_init__(self):
|
||
if self.disable_tqdm is None:
|
||
self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN
|
||
self.evaluation_strategy = EvaluationStrategy(self.evaluation_strategy)
|
||
self.lr_scheduler_type = SchedulerType(self.lr_scheduler_type)
|
||
if self.do_eval is False and self.evaluation_strategy != EvaluationStrategy.NO:
|
||
self.do_eval = True
|
||
if self.eval_steps is None:
|
||
self.eval_steps = self.logging_steps
|
||
|
||
if self.load_best_model_at_end and self.metric_for_best_model is None:
|
||
self.metric_for_best_model = "loss"
|
||
if self.greater_is_better is None and self.metric_for_best_model is not None:
|
||
self.greater_is_better = self.metric_for_best_model not in ["loss", "eval_loss"]
|
||
if self.run_name is None:
|
||
self.run_name = self.output_dir
|
||
|
||
if is_torch_available() and self.device.type != "cuda" and self.fp16:
|
||
raise ValueError("Mixed precision training with AMP or APEX (`--fp16`) can only be used on CUDA devices.")
|
||
self._n_gpu = torch.cuda.device_count()
|
||
|
||
def __repr__(self):
|
||
# We override the default repr to remove deprecated arguments from the repr. This method should be removed once
|
||
# those deprecated arguments are removed form TrainingArguments. (TODO: v5)
|
||
self_as_dict = asdict(self)
|
||
del self_as_dict["per_gpu_train_batch_size"]
|
||
del self_as_dict["per_gpu_eval_batch_size"]
|
||
attrs_as_str = [f"{k}={v}" for k, v in self_as_dict.items()]
|
||
return f"{self.__class__.__name__}({', '.join(attrs_as_str)})"
|
||
|
||
@property
|
||
def train_batch_size(self) -> int:
|
||
"""
|
||
The actual batch size for training (may differ from :obj:`per_gpu_train_batch_size` in distributed training).
|
||
"""
|
||
if self.per_gpu_train_batch_size:
|
||
logger.warning(
|
||
"Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
|
||
"version. Using `--per_device_train_batch_size` is preferred."
|
||
)
|
||
per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size
|
||
train_batch_size = per_device_batch_size * max(1, self.n_gpu)
|
||
return train_batch_size
|
||
|
||
@property
|
||
def eval_batch_size(self) -> int:
|
||
"""
|
||
The actual batch size for evaluation (may differ from :obj:`per_gpu_eval_batch_size` in distributed training).
|
||
"""
|
||
if self.per_gpu_eval_batch_size:
|
||
logger.warning(
|
||
"Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
|
||
"version. Using `--per_device_eval_batch_size` is preferred."
|
||
)
|
||
per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
|
||
eval_batch_size = per_device_batch_size * max(1, self.n_gpu)
|
||
return eval_batch_size
|
||
|
||
@cached_property
|
||
@torch_required
|
||
def _setup_devices(self) -> Tuple["torch.device", int]:
|
||
logger.info("PyTorch: setting up devices")
|
||
if self.no_cuda:
|
||
device = torch.device("cpu")
|
||
n_gpu = 0
|
||
elif is_torch_tpu_available():
|
||
device = xm.xla_device()
|
||
n_gpu = 0
|
||
elif self.local_rank == -1:
|
||
# if n_gpu is > 1 we'll use nn.DataParallel.
|
||
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
|
||
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
|
||
# trigger an error that a device index is missing. Index 0 takes into account the
|
||
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
|
||
# will use the first GPU in that env, i.e. GPU#1
|
||
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||
n_gpu = self._n_gpu
|
||
else:
|
||
# Here, we'll use torch.distributed.
|
||
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
|
||
#
|
||
# deepspeed performs its own DDP internally, and requires the program to be started with:
|
||
# deepspeed ./program.py
|
||
# rather than:
|
||
# python -m torch.distributed.launch --nproc_per_node=2 ./program.py
|
||
if self.deepspeed:
|
||
from .integrations import is_deepspeed_available
|
||
|
||
if not is_deepspeed_available():
|
||
raise ImportError("--deepspeed requires deepspeed: `pip install deepspeed`.")
|
||
import deepspeed
|
||
|
||
deepspeed.init_distributed()
|
||
else:
|
||
torch.distributed.init_process_group(backend="nccl")
|
||
device = torch.device("cuda", self.local_rank)
|
||
n_gpu = 1
|
||
|
||
if device.type == "cuda":
|
||
torch.cuda.set_device(device)
|
||
|
||
return device, n_gpu
|
||
|
||
@property
|
||
@torch_required
|
||
def device(self) -> "torch.device":
|
||
"""
|
||
The device used by this process.
|
||
"""
|
||
return self._setup_devices[0]
|
||
|
||
@property
|
||
@torch_required
|
||
def n_gpu(self):
|
||
"""
|
||
The number of GPUs used by this process.
|
||
|
||
Note:
|
||
This will only be greater than one when you have multiple GPUs available but are not using distributed
|
||
training. For distributed training, it will always be 1.
|
||
"""
|
||
return self._setup_devices[1]
|
||
|
||
@property
|
||
@torch_required
|
||
def parallel_mode(self):
|
||
"""
|
||
The current mode used for parallelism if multiple GPUs/TPU cores are available. One of:
|
||
|
||
- :obj:`ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU).
|
||
- :obj:`ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses :obj:`torch.nn.DataParallel`).
|
||
- :obj:`ParallelMode.DISTRIBUTED`: several GPUs, each ahving its own process (uses
|
||
:obj:`torch.nn.DistributedDataParallel`).
|
||
- :obj:`ParallelMode.TPU`: several TPU cores.
|
||
"""
|
||
if is_torch_tpu_available():
|
||
return ParallelMode.TPU
|
||
elif self.local_rank != -1:
|
||
return ParallelMode.DISTRIBUTED
|
||
elif self.n_gpu > 1:
|
||
return ParallelMode.NOT_DISTRIBUTED
|
||
else:
|
||
return ParallelMode.NOT_PARALLEL
|
||
|
||
def to_dict(self):
|
||
"""
|
||
Serializes this instance while replace `Enum` by their values (for JSON serialization support).
|
||
"""
|
||
d = asdict(self)
|
||
for k, v in d.items():
|
||
if isinstance(v, Enum):
|
||
d[k] = v.value
|
||
return d
|
||
|
||
def to_json_string(self):
|
||
"""
|
||
Serializes this instance to a JSON string.
|
||
"""
|
||
return json.dumps(self.to_dict(), indent=2)
|
||
|
||
def to_sanitized_dict(self) -> Dict[str, Any]:
|
||
"""
|
||
Sanitized serialization to use with TensorBoard’s hparams
|
||
"""
|
||
d = self.to_dict()
|
||
d = {**d, **{"train_batch_size": self.train_batch_size, "eval_batch_size": self.eval_batch_size}}
|
||
|
||
valid_types = [bool, int, float, str]
|
||
if is_torch_available():
|
||
valid_types.append(torch.Tensor)
|
||
|
||
return {k: v if type(v) in valid_types else str(v) for k, v in d.items()}
|
||
|
||
|
||
class ParallelMode(Enum):
|
||
NOT_PARALLEL = "not_parallel"
|
||
NOT_DISTRIBUTED = "not_distributed"
|
||
DISTRIBUTED = "distributed"
|
||
TPU = "tpu"
|