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| # coding=utf-8 | |
| # Copyright 2020 Hugging Face | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # 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 | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import re | |
| import time | |
| from typing import Optional | |
| import IPython.display as disp | |
| from ..trainer_callback import TrainerCallback | |
| from ..trainer_utils import IntervalStrategy, has_length | |
| def format_time(t): | |
| "Format `t` (in seconds) to (h):mm:ss" | |
| t = int(t) | |
| h, m, s = t // 3600, (t // 60) % 60, t % 60 | |
| return f"{h}:{m:02d}:{s:02d}" if h != 0 else f"{m:02d}:{s:02d}" | |
| def html_progress_bar(value, total, prefix, label, width=300): | |
| # docstyle-ignore | |
| return f""" | |
| <div> | |
| {prefix} | |
| <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress> | |
| {label} | |
| </div> | |
| """ | |
| def text_to_html_table(items): | |
| "Put the texts in `items` in an HTML table." | |
| html_code = """<table border="1" class="dataframe">\n""" | |
| html_code += """ <thead>\n <tr style="text-align: left;">\n""" | |
| for i in items[0]: | |
| html_code += f" <th>{i}</th>\n" | |
| html_code += " </tr>\n </thead>\n <tbody>\n" | |
| for line in items[1:]: | |
| html_code += " <tr>\n" | |
| for elt in line: | |
| elt = f"{elt:.6f}" if isinstance(elt, float) else str(elt) | |
| html_code += f" <td>{elt}</td>\n" | |
| html_code += " </tr>\n" | |
| html_code += " </tbody>\n</table><p>" | |
| return html_code | |
| class NotebookProgressBar: | |
| """ | |
| A progress par for display in a notebook. | |
| Class attributes (overridden by derived classes) | |
| - **warmup** (`int`) -- The number of iterations to do at the beginning while ignoring `update_every`. | |
| - **update_every** (`float`) -- Since calling the time takes some time, we only do it every presumed | |
| `update_every` seconds. The progress bar uses the average time passed up until now to guess the next value | |
| for which it will call the update. | |
| Args: | |
| total (`int`): | |
| The total number of iterations to reach. | |
| prefix (`str`, *optional*): | |
| A prefix to add before the progress bar. | |
| leave (`bool`, *optional*, defaults to `True`): | |
| Whether or not to leave the progress bar once it's completed. You can always call the | |
| [`~utils.notebook.NotebookProgressBar.close`] method to make the bar disappear. | |
| parent ([`~notebook.NotebookTrainingTracker`], *optional*): | |
| A parent object (like [`~utils.notebook.NotebookTrainingTracker`]) that spawns progress bars and handle | |
| their display. If set, the object passed must have a `display()` method. | |
| width (`int`, *optional*, defaults to 300): | |
| The width (in pixels) that the bar will take. | |
| Example: | |
| ```python | |
| import time | |
| pbar = NotebookProgressBar(100) | |
| for val in range(100): | |
| pbar.update(val) | |
| time.sleep(0.07) | |
| pbar.update(100) | |
| ```""" | |
| warmup = 5 | |
| update_every = 0.2 | |
| def __init__( | |
| self, | |
| total: int, | |
| prefix: Optional[str] = None, | |
| leave: bool = True, | |
| parent: Optional["NotebookTrainingTracker"] = None, | |
| width: int = 300, | |
| ): | |
| self.total = total | |
| self.prefix = "" if prefix is None else prefix | |
| self.leave = leave | |
| self.parent = parent | |
| self.width = width | |
| self.last_value = None | |
| self.comment = None | |
| self.output = None | |
| def update(self, value: int, force_update: bool = False, comment: str = None): | |
| """ | |
| The main method to update the progress bar to `value`. | |
| Args: | |
| value (`int`): | |
| The value to use. Must be between 0 and `total`. | |
| force_update (`bool`, *optional*, defaults to `False`): | |
| Whether or not to force and update of the internal state and display (by default, the bar will wait for | |
| `value` to reach the value it predicted corresponds to a time of more than the `update_every` attribute | |
| since the last update to avoid adding boilerplate). | |
| comment (`str`, *optional*): | |
| A comment to add on the left of the progress bar. | |
| """ | |
| self.value = value | |
| if comment is not None: | |
| self.comment = comment | |
| if self.last_value is None: | |
| self.start_time = self.last_time = time.time() | |
| self.start_value = self.last_value = value | |
| self.elapsed_time = self.predicted_remaining = None | |
| self.first_calls = self.warmup | |
| self.wait_for = 1 | |
| self.update_bar(value) | |
| elif value <= self.last_value and not force_update: | |
| return | |
| elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for, self.total): | |
| if self.first_calls > 0: | |
| self.first_calls -= 1 | |
| current_time = time.time() | |
| self.elapsed_time = current_time - self.start_time | |
| # We could have value = self.start_value if the update is called twixe with the same start value. | |
| if value > self.start_value: | |
| self.average_time_per_item = self.elapsed_time / (value - self.start_value) | |
| else: | |
| self.average_time_per_item = None | |
| if value >= self.total: | |
| value = self.total | |
| self.predicted_remaining = None | |
| if not self.leave: | |
| self.close() | |
| elif self.average_time_per_item is not None: | |
| self.predicted_remaining = self.average_time_per_item * (self.total - value) | |
| self.update_bar(value) | |
| self.last_value = value | |
| self.last_time = current_time | |
| if (self.average_time_per_item is None) or (self.average_time_per_item == 0): | |
| self.wait_for = 1 | |
| else: | |
| self.wait_for = max(int(self.update_every / self.average_time_per_item), 1) | |
| def update_bar(self, value, comment=None): | |
| spaced_value = " " * (len(str(self.total)) - len(str(value))) + str(value) | |
| if self.elapsed_time is None: | |
| self.label = f"[{spaced_value}/{self.total} : < :" | |
| elif self.predicted_remaining is None: | |
| self.label = f"[{spaced_value}/{self.total} {format_time(self.elapsed_time)}" | |
| else: | |
| self.label = ( | |
| f"[{spaced_value}/{self.total} {format_time(self.elapsed_time)} <" | |
| f" {format_time(self.predicted_remaining)}" | |
| ) | |
| if self.average_time_per_item == 0: | |
| self.label += ", +inf it/s" | |
| else: | |
| self.label += f", {1/self.average_time_per_item:.2f} it/s" | |
| self.label += "]" if self.comment is None or len(self.comment) == 0 else f", {self.comment}]" | |
| self.display() | |
| def display(self): | |
| self.html_code = html_progress_bar(self.value, self.total, self.prefix, self.label, self.width) | |
| if self.parent is not None: | |
| # If this is a child bar, the parent will take care of the display. | |
| self.parent.display() | |
| return | |
| if self.output is None: | |
| self.output = disp.display(disp.HTML(self.html_code), display_id=True) | |
| else: | |
| self.output.update(disp.HTML(self.html_code)) | |
| def close(self): | |
| "Closes the progress bar." | |
| if self.parent is None and self.output is not None: | |
| self.output.update(disp.HTML("")) | |
| class NotebookTrainingTracker(NotebookProgressBar): | |
| """ | |
| An object tracking the updates of an ongoing training with progress bars and a nice table reporting metrics. | |
| Args: | |
| num_steps (`int`): The number of steps during training. column_names (`List[str]`, *optional*): | |
| The list of column names for the metrics table (will be inferred from the first call to | |
| [`~utils.notebook.NotebookTrainingTracker.write_line`] if not set). | |
| """ | |
| def __init__(self, num_steps, column_names=None): | |
| super().__init__(num_steps) | |
| self.inner_table = None if column_names is None else [column_names] | |
| self.child_bar = None | |
| def display(self): | |
| self.html_code = html_progress_bar(self.value, self.total, self.prefix, self.label, self.width) | |
| if self.inner_table is not None: | |
| self.html_code += text_to_html_table(self.inner_table) | |
| if self.child_bar is not None: | |
| self.html_code += self.child_bar.html_code | |
| if self.output is None: | |
| self.output = disp.display(disp.HTML(self.html_code), display_id=True) | |
| else: | |
| self.output.update(disp.HTML(self.html_code)) | |
| def write_line(self, values): | |
| """ | |
| Write the values in the inner table. | |
| Args: | |
| values (`Dict[str, float]`): The values to display. | |
| """ | |
| if self.inner_table is None: | |
| self.inner_table = [list(values.keys()), list(values.values())] | |
| else: | |
| columns = self.inner_table[0] | |
| for key in values.keys(): | |
| if key not in columns: | |
| columns.append(key) | |
| self.inner_table[0] = columns | |
| if len(self.inner_table) > 1: | |
| last_values = self.inner_table[-1] | |
| first_column = self.inner_table[0][0] | |
| if last_values[0] != values[first_column]: | |
| # write new line | |
| self.inner_table.append([values[c] if c in values else "No Log" for c in columns]) | |
| else: | |
| # update last line | |
| new_values = values | |
| for c in columns: | |
| if c not in new_values.keys(): | |
| new_values[c] = last_values[columns.index(c)] | |
| self.inner_table[-1] = [new_values[c] for c in columns] | |
| else: | |
| self.inner_table.append([values[c] for c in columns]) | |
| def add_child(self, total, prefix=None, width=300): | |
| """ | |
| Add a child progress bar displayed under the table of metrics. The child progress bar is returned (so it can be | |
| easily updated). | |
| Args: | |
| total (`int`): The number of iterations for the child progress bar. | |
| prefix (`str`, *optional*): A prefix to write on the left of the progress bar. | |
| width (`int`, *optional*, defaults to 300): The width (in pixels) of the progress bar. | |
| """ | |
| self.child_bar = NotebookProgressBar(total, prefix=prefix, parent=self, width=width) | |
| return self.child_bar | |
| def remove_child(self): | |
| """ | |
| Closes the child progress bar. | |
| """ | |
| self.child_bar = None | |
| self.display() | |
| class NotebookProgressCallback(TrainerCallback): | |
| """ | |
| A [`TrainerCallback`] that displays the progress of training or evaluation, optimized for Jupyter Notebooks or | |
| Google colab. | |
| """ | |
| def __init__(self): | |
| self.training_tracker = None | |
| self.prediction_bar = None | |
| self._force_next_update = False | |
| def on_train_begin(self, args, state, control, **kwargs): | |
| self.first_column = "Epoch" if args.evaluation_strategy == IntervalStrategy.EPOCH else "Step" | |
| self.training_loss = 0 | |
| self.last_log = 0 | |
| column_names = [self.first_column] + ["Training Loss"] | |
| if args.evaluation_strategy != IntervalStrategy.NO: | |
| column_names.append("Validation Loss") | |
| self.training_tracker = NotebookTrainingTracker(state.max_steps, column_names) | |
| def on_step_end(self, args, state, control, **kwargs): | |
| epoch = int(state.epoch) if int(state.epoch) == state.epoch else f"{state.epoch:.2f}" | |
| self.training_tracker.update( | |
| state.global_step + 1, | |
| comment=f"Epoch {epoch}/{state.num_train_epochs}", | |
| force_update=self._force_next_update, | |
| ) | |
| self._force_next_update = False | |
| def on_prediction_step(self, args, state, control, eval_dataloader=None, **kwargs): | |
| if not has_length(eval_dataloader): | |
| return | |
| if self.prediction_bar is None: | |
| if self.training_tracker is not None: | |
| self.prediction_bar = self.training_tracker.add_child(len(eval_dataloader)) | |
| else: | |
| self.prediction_bar = NotebookProgressBar(len(eval_dataloader)) | |
| self.prediction_bar.update(1) | |
| else: | |
| self.prediction_bar.update(self.prediction_bar.value + 1) | |
| def on_predict(self, args, state, control, **kwargs): | |
| if self.prediction_bar is not None: | |
| self.prediction_bar.close() | |
| self.prediction_bar = None | |
| def on_log(self, args, state, control, logs=None, **kwargs): | |
| # Only for when there is no evaluation | |
| if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs: | |
| values = {"Training Loss": logs["loss"]} | |
| # First column is necessarily Step sine we're not in epoch eval strategy | |
| values["Step"] = state.global_step | |
| self.training_tracker.write_line(values) | |
| def on_evaluate(self, args, state, control, metrics=None, **kwargs): | |
| if self.training_tracker is not None: | |
| values = {"Training Loss": "No log", "Validation Loss": "No log"} | |
| for log in reversed(state.log_history): | |
| if "loss" in log: | |
| values["Training Loss"] = log["loss"] | |
| break | |
| if self.first_column == "Epoch": | |
| values["Epoch"] = int(state.epoch) | |
| else: | |
| values["Step"] = state.global_step | |
| metric_key_prefix = "eval" | |
| for k in metrics: | |
| if k.endswith("_loss"): | |
| metric_key_prefix = re.sub(r"\_loss$", "", k) | |
| _ = metrics.pop("total_flos", None) | |
| _ = metrics.pop("epoch", None) | |
| _ = metrics.pop(f"{metric_key_prefix}_runtime", None) | |
| _ = metrics.pop(f"{metric_key_prefix}_samples_per_second", None) | |
| _ = metrics.pop(f"{metric_key_prefix}_steps_per_second", None) | |
| _ = metrics.pop(f"{metric_key_prefix}_jit_compilation_time", None) | |
| for k, v in metrics.items(): | |
| splits = k.split("_") | |
| name = " ".join([part.capitalize() for part in splits[1:]]) | |
| if name == "Loss": | |
| # Single dataset | |
| name = "Validation Loss" | |
| values[name] = v | |
| self.training_tracker.write_line(values) | |
| self.training_tracker.remove_child() | |
| self.prediction_bar = None | |
| # Evaluation takes a long time so we should force the next update. | |
| self._force_next_update = True | |
| def on_train_end(self, args, state, control, **kwargs): | |
| self.training_tracker.update( | |
| state.global_step, | |
| comment=f"Epoch {int(state.epoch)}/{state.num_train_epochs}", | |
| force_update=True, | |
| ) | |
| self.training_tracker = None | |