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import os |
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from transformers import TrainerCallback, is_tensorboard_available |
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from transformers.integrations import rewrite_logs |
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class LogFlosCallback(TrainerCallback): |
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""" |
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A :class:`~transformers.TrainerCallback` that adds current flos to every log. |
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""" |
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def on_log(self, args, state, control, logs=None, **kwargs): |
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logs["total_flos"] = state.total_flos |
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class TensorBoardFloIndexedCallback(TrainerCallback): |
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""" |
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A :class:`~transformers.TrainerCallback` that sends the logs to `TensorBoard |
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<https://www.tensorflow.org/tensorboard>`__. |
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Args: |
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tb_writer (:obj:`SummaryWriter`, `optional`): |
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The writer to use. Will instantiate one if not set. |
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""" |
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def __init__(self, tb_writer=None): |
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has_tensorboard = is_tensorboard_available() |
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assert ( |
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has_tensorboard |
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), "TensorBoardCallback requires tensorboard to be installed. Either update your PyTorch version or install tensorboardX." |
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if has_tensorboard: |
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try: |
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from torch.utils.tensorboard import SummaryWriter |
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self._SummaryWriter = SummaryWriter |
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except ImportError: |
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try: |
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from tensorboardX import SummaryWriter |
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self._SummaryWriter = SummaryWriter |
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except ImportError: |
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self._SummaryWriter = None |
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else: |
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self._SummaryWriter = None |
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self.tb_writer = tb_writer |
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def _init_summary_writer(self, args, log_dir=None): |
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log_dir = log_dir or args.logging_dir |
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if self._SummaryWriter is not None: |
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self.tb_writer = self._SummaryWriter(log_dir=log_dir) |
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def on_train_begin(self, args, state, control, **kwargs): |
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if not state.is_world_process_zero: |
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return |
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log_dir = None |
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if state.is_hyper_param_search: |
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trial_name = state.trial_name |
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if trial_name is not None: |
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log_dir = os.path.join(args.logging_dir, trial_name) |
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self._init_summary_writer(args, log_dir) |
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if self.tb_writer is not None: |
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self.tb_writer.add_text("args", args.to_json_string()) |
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if "model" in kwargs: |
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model = kwargs["model"] |
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if hasattr(model, "config") and model.config is not None: |
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model_config_json = model.config.to_json_string() |
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self.tb_writer.add_text("model_config", model_config_json) |
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if hasattr(self.tb_writer, "add_hparams"): |
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self.tb_writer.add_hparams(args.to_sanitized_dict(), metric_dict={}) |
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def on_log(self, args, state, control, logs=None, **kwargs): |
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if not state.is_world_process_zero: |
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return |
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if self.tb_writer is None: |
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self._init_summary_writer(args) |
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if self.tb_writer is not None: |
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logs = rewrite_logs(logs) |
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self.tb_writer.add_scalar("Conversion/x steps - y flos", state.total_flos, state.global_step) |
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self.tb_writer.add_scalar("Conversion/x flos - y steps", state.global_step, state.total_flos) |
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for k, v in logs.items(): |
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if isinstance(v, (int, float)): |
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self.tb_writer.add_scalar(f"Flos/{k}", v, state.total_flos) |
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self.tb_writer.add_scalar(f"Steps/{k}", v, state.global_step) |
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self.tb_writer.flush() |
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def on_train_end(self, args, state, control, **kwargs): |
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if self.tb_writer: |
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self.tb_writer.close() |
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