300 lines
14 KiB
Python
300 lines
14 KiB
Python
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# 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 warnings
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from dataclasses import dataclass, field
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from typing import Optional, Tuple
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from .training_args import TrainingArguments
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from .utils import cached_property, is_tf_available, logging, requires_backends
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logger = logging.get_logger(__name__)
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if is_tf_available():
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import tensorflow as tf
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from .modeling_tf_utils import keras
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@dataclass
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class TFTrainingArguments(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 [`HfArgumentParser`] we can turn this class into
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[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
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command line.
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Parameters:
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output_dir (`str`):
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The output directory where the model predictions and checkpoints will be written.
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overwrite_output_dir (`bool`, *optional*, defaults to `False`):
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If `True`, overwrite the content of the output directory. Use this to continue training if `output_dir`
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points to a checkpoint directory.
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do_train (`bool`, *optional*, defaults to `False`):
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Whether to run training or not. This argument is not directly used by [`Trainer`], it's intended to be used
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by your training/evaluation scripts instead. See the [example
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scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
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do_eval (`bool`, *optional*):
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Whether to run evaluation on the validation set or not. Will be set to `True` if `evaluation_strategy` is
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different from `"no"`. This argument is not directly used by [`Trainer`], it's intended to be used by your
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training/evaluation scripts instead. See the [example
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scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
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do_predict (`bool`, *optional*, defaults to `False`):
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Whether to run predictions on the test set or not. This argument is not directly used by [`Trainer`], it's
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intended to be used by your training/evaluation scripts instead. See the [example
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scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
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evaluation_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`):
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The evaluation strategy to adopt during training. Possible values are:
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- `"no"`: No evaluation is done during training.
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- `"steps"`: Evaluation is done (and logged) every `eval_steps`.
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- `"epoch"`: Evaluation is done at the end of each epoch.
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per_device_train_batch_size (`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 (`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 (`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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<Tip warning={true}>
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When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging,
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evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training examples.
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</Tip>
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learning_rate (`float`, *optional*, defaults to 5e-5):
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The initial learning rate for Adam.
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weight_decay (`float`, *optional*, defaults to 0):
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The weight decay to apply (if not zero).
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adam_beta1 (`float`, *optional*, defaults to 0.9):
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The beta1 hyperparameter for the Adam optimizer.
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adam_beta2 (`float`, *optional*, defaults to 0.999):
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The beta2 hyperparameter for the Adam optimizer.
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adam_epsilon (`float`, *optional*, defaults to 1e-8):
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The epsilon hyperparameter for the Adam optimizer.
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max_grad_norm (`float`, *optional*, defaults to 1.0):
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Maximum gradient norm (for gradient clipping).
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num_train_epochs(`float`, *optional*, defaults to 3.0):
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Total number of training epochs to perform.
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max_steps (`int`, *optional*, defaults to -1):
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If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`.
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For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until
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`max_steps` is reached.
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warmup_ratio (`float`, *optional*, defaults to 0.0):
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Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
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warmup_steps (`int`, *optional*, defaults to 0):
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Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`.
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logging_dir (`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_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
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The logging strategy to adopt during training. Possible values are:
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- `"no"`: No logging is done during training.
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- `"epoch"`: Logging is done at the end of each epoch.
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- `"steps"`: Logging is done every `logging_steps`.
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logging_first_step (`bool`, *optional*, defaults to `False`):
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Whether to log and evaluate the first `global_step` or not.
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logging_steps (`int`, *optional*, defaults to 500):
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Number of update steps between two logs if `logging_strategy="steps"`.
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save_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
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The checkpoint save strategy to adopt during training. Possible values are:
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- `"no"`: No save is done during training.
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- `"epoch"`: Save is done at the end of each epoch.
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- `"steps"`: Save is done every `save_steps`.
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save_steps (`int`, *optional*, defaults to 500):
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Number of updates steps before two checkpoint saves if `save_strategy="steps"`.
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save_total_limit (`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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`output_dir`.
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no_cuda (`bool`, *optional*, defaults to `False`):
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Whether to not use CUDA even when it is available or not.
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seed (`int`, *optional*, defaults to 42):
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Random seed that will be set at the beginning of training.
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fp16 (`bool`, *optional*, defaults to `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 (`str`, *optional*, defaults to 'O1'):
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For `fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details on
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the [Apex documentation](https://nvidia.github.io/apex/amp).
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local_rank (`int`, *optional*, defaults to -1):
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During distributed training, the rank of the process.
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tpu_num_cores (`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 (`bool`, *optional*, defaults to `False`):
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Whether to activate the trace to record computation graphs and profiling information or not.
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dataloader_drop_last (`bool`, *optional*, defaults to `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 (`int`, *optional*, defaults to 1000):
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Number of update steps before two evaluations.
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past_index (`int`, *optional*, defaults to -1):
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Some models like [TransformerXL](../model_doc/transformerxl) or :doc*XLNet <../model_doc/xlnet>* can make
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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 at
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the next training step under the keyword argument `mems`.
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tpu_name (`str`, *optional*):
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The name of the TPU the process is running on.
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tpu_zone (`str`, *optional*):
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The zone of the TPU the process is running on. If not specified, we will attempt to automatically detect
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from metadata.
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gcp_project (`str`, *optional*):
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Google Cloud Project name for the Cloud TPU-enabled project. If not specified, we will attempt to
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automatically detect from metadata.
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run_name (`str`, *optional*):
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A descriptor for the run. Notably used for wandb logging.
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xla (`bool`, *optional*):
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Whether to activate the XLA compilation or not.
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"""
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framework = "tf"
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tpu_name: Optional[str] = field(
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default=None,
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metadata={"help": "Name of TPU"},
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)
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tpu_zone: Optional[str] = field(
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default=None,
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metadata={"help": "Zone of TPU"},
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)
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gcp_project: Optional[str] = field(
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default=None,
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metadata={"help": "Name of Cloud TPU-enabled project"},
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)
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poly_power: float = field(
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default=1.0,
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metadata={"help": "Power for the Polynomial decay LR scheduler."},
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)
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xla: bool = field(default=False, metadata={"help": "Whether to activate the XLA compilation or not"})
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@cached_property
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def _setup_strategy(self) -> Tuple["tf.distribute.Strategy", int]:
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requires_backends(self, ["tf"])
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logger.info("Tensorflow: setting up strategy")
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gpus = tf.config.list_physical_devices("GPU")
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# Set to float16 at first
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if self.fp16:
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keras.mixed_precision.set_global_policy("mixed_float16")
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if self.no_cuda:
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strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
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else:
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try:
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if self.tpu_name:
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tpu = tf.distribute.cluster_resolver.TPUClusterResolver(
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self.tpu_name, zone=self.tpu_zone, project=self.gcp_project
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)
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else:
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tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
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except ValueError:
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if self.tpu_name:
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raise RuntimeError(f"Couldn't connect to TPU {self.tpu_name}!")
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else:
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tpu = None
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if tpu:
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# Set to bfloat16 in case of TPU
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if self.fp16:
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keras.mixed_precision.set_global_policy("mixed_bfloat16")
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tf.config.experimental_connect_to_cluster(tpu)
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tf.tpu.experimental.initialize_tpu_system(tpu)
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strategy = tf.distribute.TPUStrategy(tpu)
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elif len(gpus) == 0:
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strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
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elif len(gpus) == 1:
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strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
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elif len(gpus) > 1:
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# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
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strategy = tf.distribute.MirroredStrategy()
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else:
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raise ValueError("Cannot find the proper strategy, please check your environment properties.")
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return strategy
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@property
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def strategy(self) -> "tf.distribute.Strategy":
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"""
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The strategy used for distributed training.
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"""
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requires_backends(self, ["tf"])
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return self._setup_strategy
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@property
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def n_replicas(self) -> int:
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"""
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The number of replicas (CPUs, GPUs or TPU cores) used in this training.
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"""
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requires_backends(self, ["tf"])
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return self._setup_strategy.num_replicas_in_sync
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@property
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def should_log(self):
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"""
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Whether or not the current process should produce log.
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"""
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return False # TF Logging is handled by Keras not the Trainer
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@property
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def train_batch_size(self) -> int:
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"""
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The actual batch size for training (may differ from `per_gpu_train_batch_size` in distributed training).
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"""
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if self.per_gpu_train_batch_size:
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logger.warning(
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"Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
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"version. Using `--per_device_train_batch_size` is preferred."
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)
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per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size
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return per_device_batch_size * self.n_replicas
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@property
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def eval_batch_size(self) -> int:
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"""
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The actual batch size for evaluation (may differ from `per_gpu_eval_batch_size` in distributed training).
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"""
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if self.per_gpu_eval_batch_size:
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logger.warning(
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"Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
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"version. Using `--per_device_eval_batch_size` is preferred."
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)
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per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
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return per_device_batch_size * self.n_replicas
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@property
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def n_gpu(self) -> int:
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"""
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The number of replicas (CPUs, GPUs or TPU cores) used in this training.
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"""
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requires_backends(self, ["tf"])
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warnings.warn(
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"The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.",
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FutureWarning,
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)
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return self._setup_strategy.num_replicas_in_sync
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