modified/utils/notebook.py

# 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