ezpz.__init__

• See ezpz/__init__.py

ezpz/init.py

History

A class to track and log metrics during training or evaluation.

Source code in src/ezpz/history.py

class History:
    """
    A class to track and log metrics during training or evaluation.
    """

    def __init__(self, keys: Optional[list[str]] = None) -> None:
        """
        Initialize the History object.

        Args:
            keys (Optional[list[str]]): List of keys to initialize the history with.
                If None, initializes with an empty list.
        """
        self.keys = [] if keys is None else keys
        self.history = {}

    @timeitlogit(rank=get_rank(), record=True, verbose=False, prefix="history")
    def _update(
        self,
        key: str,
        val: Union[Any, ScalarLike, list, torch.Tensor, np.ndarray],
    ):
        """
        Update the history with a new key-value pair.

        Args:
            key (str): The key to update in the history.
            val (Union[Any, ScalarLike, list, torch.Tensor, np.ndarray]): The value
                to associate with the key.
        """
        try:
            self.history[key].append(val)
        except KeyError:
            self.history[key] = [val]
        return val

    @timeitlogit(rank=get_rank(), record=True, verbose=False, prefix="history")
    def update(
        self,
        metrics: dict,
        precision: int = 6,
        use_wandb: Optional[bool] = True,
        commit: Optional[bool] = True,
        summarize: Optional[bool] = True,
    ) -> str:
        """
        Update the history with a dictionary of metrics.

        Args:
            metrics (dict): Dictionary of metrics to update the history with.
            precision (int): Precision for summarizing the metrics.
            use_wandb (Optional[bool]): Whether to log the metrics to Weights & Biases.
            commit (Optional[bool]): Whether to commit the log to Weights & Biases.
            summarize (Optional[bool]): Whether to summarize the metrics.
        """
        for key, val in metrics.items():
            # if isinstance(val, (list, np.ndarray, torch.Tensor)):
            #     val = grab_tensor(val)
            try:
                self.history[key].append(val)
            except KeyError:
                self.history[key] = [val]
        if (
            wandb is not None
            and use_wandb
            # and not WANDB_DISABLED
            and getattr(wandb, "run", None) is not None
        ):
            wandb.log(metrics, commit=commit)
        if summarize:
            return summarize_dict(metrics, precision=precision)
        return ""

    def _tplot(
        self,
        y: np.ndarray,
        x: Optional[np.ndarray] = None,
        xlabel: Optional[str] = None,
        ylabel: Optional[str] = None,
        append: bool = True,
        title: Optional[str] = None,
        verbose: bool = False,
        outfile: Optional[str] = None,
        logfreq: Optional[int] = None,
        plot_type: Optional[str] = None,
    ):
        """
        Create a text plot of the given data.

        Args:
            y (np.ndarray): The data to plot.
            x (Optional[np.ndarray]): The x-axis data.
            xlabel (Optional[str]): The x-axis label.
            ylabel (Optional[str]): The y-axis label.
            append (bool): Whether to append to an existing plot.
            title (Optional[str]): The title of the plot.
            verbose (bool): Whether to print the plot.
            outfile (Optional[str]): The path to save the plot to.
            logfreq (Optional[int]): The log frequency of the plot.
            plot_type (Optional[str]): The type of plot to create.
        """
        if xlabel is not None and ylabel == xlabel:
            return
        if len(y) > 1:
            x = x if x is not None else np.arange(len(y))
            assert x is not None
            eztplot(
                y=y,
                x=x,
                xlabel=xlabel,
                ylabel=ylabel,
                logfreq=(1 if logfreq is None else logfreq),
                append=append,
                verbose=verbose,
                outfile=outfile,
                plot_type=plot_type,
                title=title,
                # plot_type=('scatter' if 'dt' in ylabel else None),
            )
        if ylabel is not None and "dt" in ylabel:
            of = Path(outfile) if outfile is not None else None
            if of is not None:
                of = Path(of.parent).joinpath(f"{of.stem}-hist{of.suffix}")
            eztplot(
                y=y,
                xlabel=ylabel,
                title=title,
                ylabel="freq",
                append=append,
                verbose=verbose,
                outfile=(of if of is not None else None),
                plot_type="hist",
            )

    @timeitlogit(rank=get_rank(), record=True, verbose=False, prefix="history")
    def plot(
        self,
        val: np.ndarray,
        key: Optional[str] = None,
        warmup: Optional[float] = 0.0,
        num_chains: Optional[int] = 128,
        title: Optional[str] = None,
        outdir: Optional[os.PathLike] = None,
        subplots_kwargs: Optional[dict[str, Any]] = None,
        plot_kwargs: Optional[dict[str, Any]] = None,
    ):
        """
        Plot a single variable from the history.

        NOTE: The `warmup` argument can be used to drop the first `warmup`
        iterations (as a percent of the total number of iterations) from the
        plot.

        Args:
            val (np.ndarray): The data to plot.
            key (Optional[str]): The key for the data.
            warmup (Optional[float]): The percentage of iterations to drop from the
                beginning of the plot.
            num_chains (Optional[int]): The number of chains to plot.
            title (Optional[str]): The title of the plot.
            outdir (Optional[os.PathLike]): The directory to save the plot to.
            subplots_kwargs (Optional[dict[str, Any]]): Additional arguments for
                subplots.
            plot_kwargs (Optional[dict[str, Any]]): Additional arguments for plotting.
        """
        import matplotlib.pyplot as plt

        LW = plt.rcParams.get("axes.linewidth", 1.75)
        plot_kwargs = {} if plot_kwargs is None else plot_kwargs
        subplots_kwargs = {} if subplots_kwargs is None else subplots_kwargs
        figsize = subplots_kwargs.get("figsize", ezplot.set_size())
        subplots_kwargs.update({"figsize": figsize})
        num_chains = 16 if num_chains is None else num_chains

        # tmp = val[0]
        arr = np.array(val)

        subfigs = None
        steps = np.arange(arr.shape[0])
        if warmup is not None and warmup > 0:
            drop = int(warmup * arr.shape[0])
            arr = arr[drop:]
            steps = steps[drop:]

        if len(arr.shape) == 2:
            import seaborn as sns

            _ = subplots_kwargs.pop("constrained_layout", True)
            figsize = (3 * figsize[0], 1.5 * figsize[1])

            fig = plt.figure(figsize=figsize, constrained_layout=True)
            subfigs = fig.subfigures(1, 2)

            gs_kw = {"width_ratios": [1.33, 0.33]}
            (ax, ax1) = subfigs[1].subplots(
                1, 2, sharey=True, gridspec_kw=gs_kw
            )
            ax.grid(alpha=0.2)
            ax1.grid(False)
            color = plot_kwargs.get("color", None)
            label = r"$\langle$" + f" {key} " + r"$\rangle$"
            ax.plot(
                steps, arr.mean(-1), lw=1.5 * LW, label=label, **plot_kwargs
            )
            sns.kdeplot(y=arr.flatten(), ax=ax1, color=color, shade=True)
            ax1.set_xticks([])
            ax1.set_xticklabels([])
            # ax1.set_yticks([])
            # ax1.set_yticklabels([])
            sns.despine(ax=ax, top=True, right=True)
            sns.despine(ax=ax1, top=True, right=True, left=True, bottom=True)
            # ax.legend(loc='best', frameon=False)
            ax1.set_xlabel("")
            # ax1.set_ylabel('')
            # ax.set_yticks(ax.get_yticks())
            # ax.set_yticklabels(ax.get_yticklabels())
            # ax.set_ylabel(key)
            # _ = subfigs[1].subplots_adjust(wspace=-0.75)
            axes = (ax, ax1)
        else:
            if len(arr.shape) == 1:
                fig, ax = plt.subplots(**subplots_kwargs)
                # assert isinstance(ax, plt.Axes)
                ax.plot(steps, arr, **plot_kwargs)
                axes = ax
            elif len(arr.shape) == 3:
                fig, ax = plt.subplots(**subplots_kwargs)
                # assert isinstance(ax, plt.Axes)
                cmap = plt.get_cmap("viridis")
                nlf = arr.shape[1]
                for idx in range(nlf):
                    # y = arr[:, idx, :].mean(-1)
                    # pkwargs = {
                    #     'color': cmap(idx / nlf),
                    #     'label': f'{idx}',
                    # }
                    # ax.plot(steps, y, **pkwargs)
                    label = plot_kwargs.pop("label", None)
                    if label is not None:
                        label = f"{label}-{idx}"
                    y = arr[:, idx, :]
                    color = cmap(idx / y.shape[1])
                    plot_kwargs["color"] = cmap(idx / y.shape[1])
                    if len(y.shape) == 2:
                        # TOO: Plot chains
                        if num_chains > 0:
                            for idx in range(min((num_chains, y.shape[1]))):
                                _ = ax.plot(
                                    steps,
                                    y[:, idx],  # color,
                                    lw=LW / 2.0,
                                    alpha=0.8,
                                    **plot_kwargs,
                                )

                        _ = ax.plot(
                            steps,
                            y.mean(-1),  # color=color,
                            label=label,
                            **plot_kwargs,
                        )
                    else:
                        _ = ax.plot(
                            steps,
                            y,  # color=color,
                            label=label,
                            **plot_kwargs,
                        )
                axes = ax
            else:
                raise ValueError("Unexpected shape encountered")

            ax.set_ylabel(key)

        if num_chains > 0 and len(arr.shape) > 1:
            # lw = LW / 2.
            for idx in range(min(num_chains, arr.shape[1])):
                # ax = subfigs[0].subplots(1, 1)
                # plot values of invidual chains, arr[:, idx]
                # where arr[:, idx].shape = [ndraws, 1]
                ax.plot(
                    steps, arr[:, idx], alpha=0.5, lw=LW / 2.0, **plot_kwargs
                )

        ax.set_xlabel("draw")
        if title is not None:
            fig.suptitle(title)

        if outdir is not None:
            # plt.savefig(Path(outdir).joinpath(f'{key}.svg'),
            #             dpi=400, bbox_inches='tight')
            outfile = Path(outdir).joinpath(f"{key}.svg")
            if outfile.is_file():
                tstamp = ezpz.get_timestamp()
                pngdir = Path(outdir).joinpath("pngs")
                pngdir.mkdir(exist_ok=True, parents=True)
                pngfile = pngdir.joinpath(f"{key}-{tstamp}.png")
                svgfile = Path(outdir).joinpath(f"{key}-{tstamp}.svg")
                plt.savefig(pngfile, dpi=400, bbox_inches="tight")
                plt.savefig(svgfile, dpi=400, bbox_inches="tight")

        return fig, subfigs, axes

    @timeitlogit(rank=get_rank(), record=True, verbose=False, prefix="history")
    def plot_dataArray(
        self,
        val: xr.DataArray,
        key: Optional[str] = None,
        warmup: Optional[float] = 0.0,
        num_chains: Optional[int] = 0,
        title: Optional[str] = None,
        outdir: Optional[str] = None,
        subplots_kwargs: Optional[dict[str, Any]] = None,
        plot_kwargs: Optional[dict[str, Any]] = None,
        verbose: bool = False,
        line_labels: bool = False,
        logfreq: Optional[int] = None,
    ):
        """
        Plot a single variable from the history as an xarray DataArray.

        Args:
            val (xr.DataArray): The data to plot.
            key (Optional[str]): The key for the data.
            warmup (Optional[float]): The percentage of iterations to drop from the
                beginning of the plot.
            num_chains (Optional[int]): The number of chains to plot.
            title (Optional[str]): The title of the plot.
            outdir (Optional[str]): The directory to save the plot to.
            subplots_kwargs (Optional[dict[str, Any]]): Additional arguments for
                subplots.
            plot_kwargs (Optional[dict[str, Any]]): Additional arguments for plotting.
            verbose (bool): Whether to print the plot.
            line_labels (bool): Whether to label lines in the plot.
            logfreq (Optional[int]): The log frequency of the plot.
        """
        import matplotlib.pyplot as plt

        plot_kwargs = {} if plot_kwargs is None else plot_kwargs
        subplots_kwargs = {} if subplots_kwargs is None else subplots_kwargs
        ezplot.set_plot_style()
        plt.rcParams["axes.labelcolor"] = "#bdbdbd"
        figsize = subplots_kwargs.get("figsize", ezplot.set_size())
        subplots_kwargs.update({"figsize": figsize})
        subfigs = None
        # if key == 'dt':
        #     warmup = 0.2
        arr = val.values  # shape: [nchains, ndraws]
        # steps = np.arange(len(val.coords['draw']))
        steps = val.coords["draw"]
        if warmup is not None and warmup > 0.0:
            drop = int(warmup * arr.shape[0])
            arr = arr[drop:]
            steps = steps[drop:]
        if len(arr.shape) == 2:
            fig, axes = ezplot.plot_combined(
                val,
                key=key,
                num_chains=num_chains,
                plot_kwargs=plot_kwargs,
                subplots_kwargs=subplots_kwargs,
            )
        else:
            if len(arr.shape) == 1:
                fig, ax = ezplot.subplots(**subplots_kwargs)
                try:
                    ax.plot(steps, arr, **plot_kwargs)
                except ValueError:
                    try:
                        ax.plot(steps, arr[~np.isnan(arr)], **plot_kwargs)
                    except Exception:
                        logger.error(f"Unable to plot {key}! Continuing")
                _ = ax.grid(True, alpha=0.2)
                axes = ax
            elif len(arr.shape) == 3:
                fig, ax = ezplot.subplots(**subplots_kwargs)
                cmap = plt.get_cmap("viridis")
                y = val.mean("chain")
                for idx in range(len(val.coords["leapfrog"])):
                    pkwargs = {
                        "color": cmap(idx / len(val.coords["leapfrog"])),
                        "label": f"{idx}",
                    }
                    ax.plot(steps, y[idx], **pkwargs)
                axes = ax
            else:
                raise ValueError("Unexpected shape encountered")
            ax = plt.gca()
            # assert isinstance(ax, plt.Axes)
            assert key is not None
            _ = ax.set_ylabel(key)
            _ = ax.set_xlabel("draw")
            # if num_chains > 0 and len(arr.shape) > 1:
            #     lw = LW / 2.
            #     #for idx in range(min(num_chains, arr.shape[1])):
            #     nchains = len(val.coords['chains'])
            #     for idx in range(min(nchains, num_chains)):
            #         # ax = subfigs[0].subplots(1, 1)
            #         # plot values of invidual chains, arr[:, idx]
            #         # where arr[:, idx].shape = [ndraws, 1]
            #         ax.plot(steps, val
            #                 alpha=0.5, lw=lw/2., **plot_kwargs)
        if title is not None:
            fig = plt.gcf()
            _ = fig.suptitle(title)
        if logfreq is not None:
            ax = plt.gca()
            xticks = ax.get_xticks()  # type: ignore
            _ = ax.set_xticklabels(  # type: ignore
                [f"{logfreq * int(i)}" for i in xticks]  # type: ignore
            )
        if outdir is not None:
            dirs = {
                "png": Path(outdir).joinpath("pngs/"),
                "svg": Path(outdir).joinpath("svgs/"),
            }
            _ = [i.mkdir(exist_ok=True, parents=True) for i in dirs.values()]
            # from l2hmc.configs import PROJECT_DIR
            # from ezpz
            if verbose:
                logger.info(f"Saving {key} plot to: {Path(outdir).resolve()}")
            for ext, d in dirs.items():
                outfile = d.joinpath(f"{key}.{ext}")
                plt.savefig(outfile, dpi=400, bbox_inches="tight")
        return (fig, subfigs, axes)

    @timeitlogit(rank=get_rank(), record=True, verbose=False, prefix="history")
    def plot_dataset(
        self,
        title: Optional[str] = None,
        nchains: Optional[int] = None,
        outdir: Optional[os.PathLike] = None,
        dataset: Optional[xr.Dataset] = None,
        data: Optional[dict] = None,
        warmup: Optional[int | float] = None,
        # subplots_kwargs: Optional[dict[str, Any]] = None,
        # plot_kwargs: Optional[dict[str, Any]] = None,
    ):
        dataset = (
            dataset
            if dataset is not None
            else (
                self.get_dataset(
                    data=(data if data is not None else self.history),
                    warmup=warmup,
                )
            )
        )
        return ezplot.plot_dataset(
            dataset=dataset,
            nchains=nchains,
            title=title,
            outdir=outdir,
        )

    def plot_2d_xarr(
        self,
        xarr: xr.DataArray,
        label: Optional[str] = None,
        num_chains: Optional[int] = None,
        title: Optional[str] = None,
        outdir: Optional[os.PathLike] = None,
        subplots_kwargs: Optional[dict[str, Any]] = None,
        plot_kwargs: Optional[dict[str, Any]] = None,
    ):
        import matplotlib.pyplot as plt
        import seaborn as sns

        LW = plt.rcParams.get("axes.linewidth", 1.75)
        plot_kwargs = {} if plot_kwargs is None else plot_kwargs
        subplots_kwargs = {} if subplots_kwargs is None else subplots_kwargs
        assert len(xarr.shape) == 2
        assert "draw" in xarr.coords and "chain" in xarr.coords
        num_chains = len(xarr.chain) if num_chains is None else num_chains
        # _ = subplots_kwargs.pop('constrained_layout', True)
        figsize = plt.rcParams.get("figure.figsize", (8, 6))
        figsize = (3 * figsize[0], 1.5 * figsize[1])
        fig = plt.figure(figsize=figsize, constrained_layout=True)
        subfigs = fig.subfigures(1, 2)
        gs_kw = {"width_ratios": [1.33, 0.33]}
        (ax, ax1) = subfigs[1].subplots(1, 2, sharey=True, gridspec_kw=gs_kw)
        ax.grid(alpha=0.2)
        ax1.grid(False)
        color = plot_kwargs.get("color", f"C{np.random.randint(6)}")
        label = r"$\langle$" + f" {label} " + r"$\rangle$"
        ax.plot(
            xarr.draw.values,
            xarr.mean("chain"),
            color=color,
            lw=1.5 * LW,
            label=label,
            **plot_kwargs,
        )
        for idx in range(num_chains):
            # ax = subfigs[0].subplots(1, 1)
            # plot values of invidual chains, arr[:, idx]
            # where arr[:, idx].shape = [ndraws, 1]
            # ax0.plot(
            #     xarr.draw.values,
            #     xarr[xarr.chain == idx][0],
            #     lw=1.,
            #     alpha=0.7,
            #     color=color
            # )
            ax.plot(
                xarr.draw.values,
                xarr[xarr.chain == idx][0],
                color=color,
                alpha=0.5,
                lw=LW / 2.0,
                **plot_kwargs,
            )

        axes = (ax, ax1)
        sns.kdeplot(y=xarr.values.flatten(), ax=ax1, color=color, shade=True)
        ax1.set_xticks([])
        ax1.set_xticklabels([])
        # ax1.set_yticks([])
        # ax1.set_yticklabels([])
        sns.despine(ax=ax, top=True, right=True)
        sns.despine(ax=ax1, top=True, right=True, left=True, bottom=True)
        # ax.legend(loc='best', frameon=False)
        ax1.set_xlabel("")
        # ax1.set_ylabel('')
        # ax.set_yticks(ax.get_yticks())
        # ax.set_yticklabels(ax.get_yticklabels())
        # ax.set_ylabel(key)
        # _ = subfigs[1].subplots_adjust(wspace=-0.75)
        # if num_chains > 0 and len(arr.shape) > 1:
        # lw = LW / 2.
        # num_chains = np.min([
        #     16,
        #     len(xarr.coords['chain']),
        # ])
        sns.despine(subfigs[0])
        ax0 = subfigs[0].subplots(1, 1)
        im = xarr.plot(ax=ax0)  # type:ignore
        im.colorbar.set_label(label)  # type:ignore
        # ax0.plot(
        #     xarr.draw.values,
        #     xarr.mean('chain'),
        #     lw=2.,
        #     color=color
        # )
        # for idx in range(min(num_chains, i.shape[1])):
        ax.set_xlabel("draw")
        if title is not None:
            fig.suptitle(title)

        if outdir is not None:
            assert label is not None
            # plt.savefig(Path(outdir).joinpath(f'{key}.svg'),
            #             dpi=400, bbox_inches='tight')
            outfile = Path(outdir).joinpath(f"{label}.svg")
            if outfile.is_file():
                tstamp = get_timestamp("%Y-%m-%d-%H%M%S")
                pngdir = Path(outdir).joinpath("pngs")
                pngdir.mkdir(exist_ok=True, parents=True)
                pngfile = pngdir.joinpath(f"{label}-{tstamp}.png")
                svgfile = Path(outdir).joinpath(f"{label}-{tstamp}.svg")
                plt.savefig(pngfile, dpi=400, bbox_inches="tight")
                plt.savefig(svgfile, dpi=400, bbox_inches="tight")

    @timeitlogit(rank=get_rank(), record=True, verbose=False, prefix="history")
    def tplot_all(
        self,
        outdir: Optional[os.PathLike] = None,
        warmup: Optional[float] = 0.0,
        append: bool = True,
        xkey: Optional[str] = None,
        dataset: Optional[xr.Dataset] = None,
        data: Optional[dict] = None,
        logfreq: Optional[int] = None,
        plot_type: Optional[str] = None,
        verbose: bool = False,
    ):
        dataset = (
            dataset
            if dataset is not None
            else (
                self.get_dataset(
                    data=(data if data is not None else self.history),
                    warmup=warmup,
                )
            )
        )

        outdir = Path(os.getcwd()) if outdir is None else Path(outdir)
        logger.info(f"Saving tplots to {outdir.as_posix()}")
        for _, (key, val) in enumerate(dataset.items()):
            if (xkey is not None and key == xkey) or xkey in ["iter", "draw"]:
                continue
            if len(val.values) > 0:
                self._tplot(
                    y=val.values,
                    x=None,
                    xlabel="iter",
                    plot_type=plot_type,
                    ylabel=str(key),
                    append=append,
                    title=f"{key} [{get_timestamp()}]",
                    verbose=verbose,
                    outfile=outdir.joinpath(f"{key}.txt").as_posix(),
                    logfreq=logfreq,
                )
            else:
                logger.warning(
                    f"No data found in {key=}: {len(val.values)=} <= 0"
                )

    @timeitlogit(rank=get_rank(), record=True, verbose=False, prefix="history")
    def plot_all(
        self,
        num_chains: int = 128,
        warmup: Optional[float | int] = 0.0,
        title: Optional[str] = None,
        verbose: bool = False,
        outdir: Optional[os.PathLike] = None,
        subplots_kwargs: Optional[dict[str, Any]] = None,
        plot_kwargs: Optional[dict[str, Any]] = None,
        dataset: Optional[xr.Dataset] = None,
        data: Optional[dict] = None,
    ):
        import matplotlib.pyplot as plt
        import seaborn as sns

        plot_kwargs = {} if plot_kwargs is None else plot_kwargs
        subplots_kwargs = {} if subplots_kwargs is None else subplots_kwargs

        dataset = (
            dataset
            if dataset is not None
            else (
                self.get_dataset(
                    data=(data if data is not None else self.history),
                    warmup=warmup,
                )
            )
        )

        _ = ezplot.make_ridgeplots(
            dataset,
            outdir=outdir,
            drop_nans=True,
            drop_zeros=False,
            num_chains=num_chains,
            cmap="viridis",
            save_plot=(outdir is not None),
        )

        for idx, (key, val) in enumerate(dataset.data_vars.items()):
            color = f"C{idx % 9}"
            plot_kwargs["color"] = color

            fig, subfigs, ax = self.plot(
                val=val.values.T.real,
                key=str(key),
                title=title,
                outdir=outdir,
                warmup=warmup,
                num_chains=num_chains,
                plot_kwargs=plot_kwargs,
                subplots_kwargs=subplots_kwargs,
            )
            if fig is not None:
                _ = sns.despine(
                    fig, top=True, right=True, bottom=True, left=True
                )

            # _ = plt.grid(True, alpha=0.4)
            if subfigs is not None:
                # edgecolor = plt.rcParams['axes.edgecolor']
                plt.rcParams["axes.edgecolor"] = plt.rcParams["axes.facecolor"]
                ax = subfigs[0].subplots(1, 1)
                # ax = fig[1].subplots(constrained_layout=True)
                _ = xplt.pcolormesh(
                    val, "draw", "chain", ax=ax, robust=True, add_colorbar=True
                )
                # im = val.plot(ax=ax, cbar_kwargs=cbar_kwargs)
                # im.colorbar.set_label(f'{key}')  # , labelpad=1.25)
                sns.despine(
                    subfigs[0], top=True, right=True, left=True, bottom=True
                )
            if outdir is not None:
                dirs = {
                    "png": Path(outdir).joinpath("pngs/"),
                    "svg": Path(outdir).joinpath("svgs/"),
                }
                _ = [
                    i.mkdir(exist_ok=True, parents=True) for i in dirs.values()
                ]
                # if verbose:
                logger.info(f"Saving {key} plot to: {Path(outdir).resolve()}")
                for ext, d in dirs.items():
                    outfile = d.joinpath(f"{key}.{ext}")
                    if outfile.is_file():
                        outfile = d.joinpath(f"{key}-subfig.{ext}")
                    # logger.info(f"Saving {key}.ext to: {outfile}")
                    if verbose:
                        logger.info(
                            f"Saving {key} plot to: {outfile.resolve()}"
                        )
                    plt.savefig(outfile, dpi=400, bbox_inches="tight")
            if is_interactive():
                plt.show()

        return dataset

    def history_to_dict(self) -> dict:
        # return {k: np.stack(v).squeeze() for k, v in self.history.items()}
        return {
            k: torch.Tensor(v).numpy(force=True)
            for k, v in self.history.items()
        }

    def to_DataArray(
        self,
        x: Union[list, np.ndarray, torch.Tensor],
        warmup: Optional[float] = 0.0,
    ) -> xr.DataArray:
        if isinstance(x, list) and isinstance(x[0], torch.Tensor):
            x = torch.Tensor(x).numpy(force=True)
        try:
            arr = grab_tensor(x)
        except ValueError:
            arr = np.array(x).real
            # arr = np.array(x)
            logger.info(f"len(x): {len(x)}")
            logger.info(f"x[0].shape: {x[0].shape}")
            logger.info(f"arr.shape: {arr.shape}")
        assert isinstance(arr, np.ndarray)
        if warmup is not None and warmup > 0 and len(arr) > 0:
            if isinstance(warmup, int):
                warmup = warmup / len(arr)
            # drop = int(warmup * arr.shape[0])
            drop = int(warmup * len(arr))
            arr = arr[drop:]
        # steps = np.arange(len(arr))
        if len(arr.shape) == 1:  # [ndraws]
            ndraws = arr.shape[0]
            dims = ["draw"]
            coords = [np.arange(len(arr))]
            return xr.DataArray(arr, dims=dims, coords=coords)  # type:ignore

        if len(arr.shape) == 2:  # [nchains, ndraws]
            arr = arr.T
            nchains, ndraws = arr.shape
            dims = ("chain", "draw")
            coords = [np.arange(nchains), np.arange(ndraws)]
            return xr.DataArray(arr, dims=dims, coords=coords)  # type:ignore

        if len(arr.shape) == 3:  # [nchains, nlf, ndraws]
            arr = arr.T
            nchains, nlf, ndraws = arr.shape
            dims = ("chain", "leapfrog", "draw")
            coords = [np.arange(nchains), np.arange(nlf), np.arange(ndraws)]
            return xr.DataArray(arr, dims=dims, coords=coords)  # type:ignore

        else:
            print(f"arr.shape: {arr.shape}")
            raise ValueError("Invalid shape encountered")

    def get_dataset(
        self,
        data: Optional[
            dict[str, Union[list, np.ndarray, torch.Tensor]]
        ] = None,
        warmup: Optional[float] = 0.0,
    ):
        data = self.history_to_dict() if data is None else data
        data_vars = {}
        for key, val in data.items():
            name = key.replace("/", "_")
            try:
                data_vars[name] = self.to_DataArray(val, warmup)
            except ValueError:
                logger.error(
                    f"Unable to create DataArray for {key}! Skipping!"
                )
                logger.error(f"{key}.shape= {np.stack(val).shape}")  # type:ignore
        return xr.Dataset(data_vars)

    @timeitlogit(rank=get_rank(), record=True, verbose=False, prefix="history")
    def save_dataset(
        self,
        outdir: PathLike,
        fname: str = "dataset",
        use_hdf5: bool = True,
        data: Optional[
            dict[str, Union[list, np.ndarray, torch.Tensor]]
        ] = None,
        dataset: Optional[xr.Dataset] = None,
        warmup: Optional[int | float] = None,
        **kwargs,
    ) -> Path:
        dataset = (
            dataset
            if dataset is not None
            else (
                self.get_dataset(
                    data=(data if data is not None else self.history),
                    warmup=warmup,
                )
            )
        )
        return save_dataset(
            dataset,
            outdir=outdir,
            fname=fname,
            use_hdf5=use_hdf5,
            **kwargs,
        )

    @timeitlogit(rank=get_rank(), record=True, verbose=False, prefix="history")
    def finalize(
        self,
        outdir: Optional[PathLike] = None,
        run_name: Optional[str] = None,
        dataset_fname: Optional[str] = None,
        num_chains: int = 128,
        warmup: Optional[int | float] = 0.0,
        verbose: bool = False,
        save: bool = True,
        plot: bool = True,
        append_tplot: bool = True,
        title: Optional[str] = None,
        data: Optional[
            dict[str, Union[list, np.ndarray, torch.Tensor]]
        ] = None,
        dataset: Optional[xr.Dataset] = None,
        xkey: Optional[str] = None,
        plot_kwargs: Optional[dict[str, Any]] = None,
        subplots_kwargs: Optional[dict[str, Any]] = None,
        tplot_type: Optional[str] = None,
    ) -> xr.Dataset:
        dataset = (
            dataset
            if dataset is not None
            else (
                self.get_dataset(
                    data=(data if data is not None else self.history),
                    warmup=warmup,
                )
            )
        )
        run_name = (
            f"History-{get_timestamp()}" if run_name is None else run_name
        )
        fallback_outdir = Path(os.getcwd()).joinpath("outputs")
        if run_name is not None:
            fallback_outdir = fallback_outdir.joinpath(
                run_name, get_timestamp()
            )
        outdir = (
            # Path(os.getcwd()).joinpath('outputs')
            fallback_outdir if outdir is None else Path(outdir)
        )
        outdir = outdir.joinpath(run_name)
        if plot:
            plotdir = outdir.joinpath("plots")
            tplotdir = plotdir.joinpath("tplot")
            mplotdir = plotdir.joinpath("mplot")
            tplotdir.mkdir(exist_ok=True, parents=True)
            mplotdir.mkdir(exist_ok=True, parents=True)
            _ = self.plot_all(
                dataset=dataset,
                outdir=mplotdir,
                verbose=verbose,
                num_chains=num_chains,
                warmup=warmup,
                title=title,
                plot_kwargs=plot_kwargs,
                subplots_kwargs=subplots_kwargs,
            )
            _ = self.tplot_all(
                dataset=dataset,
                outdir=tplotdir,
                warmup=warmup,
                append=append_tplot,
                plot_type=tplot_type,
                xkey=xkey,
                verbose=verbose,
            )
        if save:
            try:
                import h5py

                use_hdf5 = True
            except ImportError:
                logger.warning(
                    "h5py not found! Saving dataset as netCDF instead."
                )
                use_hdf5 = False

            fname = "dataset" if dataset_fname is None else dataset_fname
            _ = self.save_dataset(
                dataset=dataset, outdir=outdir, fname=fname, use_hdf5=use_hdf5
            )
        return dataset

__init__(keys=None)

Initialize the History object.

Parameters:

Name	Type	Description	Default
keys	Optional[list[str]]	List of keys to initialize the history with. If None, initializes with an empty list.	None

Source code in src/ezpz/history.py

def __init__(self, keys: Optional[list[str]] = None) -> None:
    """
    Initialize the History object.

    Args:
        keys (Optional[list[str]]): List of keys to initialize the history with.
            If None, initializes with an empty list.
    """
    self.keys = [] if keys is None else keys
    self.history = {}

plot(val, key=None, warmup=0.0, num_chains=128, title=None, outdir=None, subplots_kwargs=None, plot_kwargs=None)

Plot a single variable from the history.

NOTE: The warmup argument can be used to drop the first warmup iterations (as a percent of the total number of iterations) from the plot.

Parameters:

Name	Type	Description	Default
val	ndarray	The data to plot.	required
key	Optional[str]	The key for the data.	None
warmup	Optional[float]	The percentage of iterations to drop from the beginning of the plot.	0.0
num_chains	Optional[int]	The number of chains to plot.	128
title	Optional[str]	The title of the plot.	None
outdir	Optional[PathLike]	The directory to save the plot to.	None
subplots_kwargs	Optional[dict[str, Any]]	Additional arguments for subplots.	None
plot_kwargs	Optional[dict[str, Any]]	Additional arguments for plotting.	None

Source code in src/ezpz/history.py

@timeitlogit(rank=get_rank(), record=True, verbose=False, prefix="history")
def plot(
    self,
    val: np.ndarray,
    key: Optional[str] = None,
    warmup: Optional[float] = 0.0,
    num_chains: Optional[int] = 128,
    title: Optional[str] = None,
    outdir: Optional[os.PathLike] = None,
    subplots_kwargs: Optional[dict[str, Any]] = None,
    plot_kwargs: Optional[dict[str, Any]] = None,
):
    """
    Plot a single variable from the history.

    NOTE: The `warmup` argument can be used to drop the first `warmup`
    iterations (as a percent of the total number of iterations) from the
    plot.

    Args:
        val (np.ndarray): The data to plot.
        key (Optional[str]): The key for the data.
        warmup (Optional[float]): The percentage of iterations to drop from the
            beginning of the plot.
        num_chains (Optional[int]): The number of chains to plot.
        title (Optional[str]): The title of the plot.
        outdir (Optional[os.PathLike]): The directory to save the plot to.
        subplots_kwargs (Optional[dict[str, Any]]): Additional arguments for
            subplots.
        plot_kwargs (Optional[dict[str, Any]]): Additional arguments for plotting.
    """
    import matplotlib.pyplot as plt

    LW = plt.rcParams.get("axes.linewidth", 1.75)
    plot_kwargs = {} if plot_kwargs is None else plot_kwargs
    subplots_kwargs = {} if subplots_kwargs is None else subplots_kwargs
    figsize = subplots_kwargs.get("figsize", ezplot.set_size())
    subplots_kwargs.update({"figsize": figsize})
    num_chains = 16 if num_chains is None else num_chains

    # tmp = val[0]
    arr = np.array(val)

    subfigs = None
    steps = np.arange(arr.shape[0])
    if warmup is not None and warmup > 0:
        drop = int(warmup * arr.shape[0])
        arr = arr[drop:]
        steps = steps[drop:]

    if len(arr.shape) == 2:
        import seaborn as sns

        _ = subplots_kwargs.pop("constrained_layout", True)
        figsize = (3 * figsize[0], 1.5 * figsize[1])

        fig = plt.figure(figsize=figsize, constrained_layout=True)
        subfigs = fig.subfigures(1, 2)

        gs_kw = {"width_ratios": [1.33, 0.33]}
        (ax, ax1) = subfigs[1].subplots(
            1, 2, sharey=True, gridspec_kw=gs_kw
        )
        ax.grid(alpha=0.2)
        ax1.grid(False)
        color = plot_kwargs.get("color", None)
        label = r"$\langle$" + f" {key} " + r"$\rangle$"
        ax.plot(
            steps, arr.mean(-1), lw=1.5 * LW, label=label, **plot_kwargs
        )
        sns.kdeplot(y=arr.flatten(), ax=ax1, color=color, shade=True)
        ax1.set_xticks([])
        ax1.set_xticklabels([])
        # ax1.set_yticks([])
        # ax1.set_yticklabels([])
        sns.despine(ax=ax, top=True, right=True)
        sns.despine(ax=ax1, top=True, right=True, left=True, bottom=True)
        # ax.legend(loc='best', frameon=False)
        ax1.set_xlabel("")
        # ax1.set_ylabel('')
        # ax.set_yticks(ax.get_yticks())
        # ax.set_yticklabels(ax.get_yticklabels())
        # ax.set_ylabel(key)
        # _ = subfigs[1].subplots_adjust(wspace=-0.75)
        axes = (ax, ax1)
    else:
        if len(arr.shape) == 1:
            fig, ax = plt.subplots(**subplots_kwargs)
            # assert isinstance(ax, plt.Axes)
            ax.plot(steps, arr, **plot_kwargs)
            axes = ax
        elif len(arr.shape) == 3:
            fig, ax = plt.subplots(**subplots_kwargs)
            # assert isinstance(ax, plt.Axes)
            cmap = plt.get_cmap("viridis")
            nlf = arr.shape[1]
            for idx in range(nlf):
                # y = arr[:, idx, :].mean(-1)
                # pkwargs = {
                #     'color': cmap(idx / nlf),
                #     'label': f'{idx}',
                # }
                # ax.plot(steps, y, **pkwargs)
                label = plot_kwargs.pop("label", None)
                if label is not None:
                    label = f"{label}-{idx}"
                y = arr[:, idx, :]
                color = cmap(idx / y.shape[1])
                plot_kwargs["color"] = cmap(idx / y.shape[1])
                if len(y.shape) == 2:
                    # TOO: Plot chains
                    if num_chains > 0:
                        for idx in range(min((num_chains, y.shape[1]))):
                            _ = ax.plot(
                                steps,
                                y[:, idx],  # color,
                                lw=LW / 2.0,
                                alpha=0.8,
                                **plot_kwargs,
                            )

                    _ = ax.plot(
                        steps,
                        y.mean(-1),  # color=color,
                        label=label,
                        **plot_kwargs,
                    )
                else:
                    _ = ax.plot(
                        steps,
                        y,  # color=color,
                        label=label,
                        **plot_kwargs,
                    )
            axes = ax
        else:
            raise ValueError("Unexpected shape encountered")

        ax.set_ylabel(key)

    if num_chains > 0 and len(arr.shape) > 1:
        # lw = LW / 2.
        for idx in range(min(num_chains, arr.shape[1])):
            # ax = subfigs[0].subplots(1, 1)
            # plot values of invidual chains, arr[:, idx]
            # where arr[:, idx].shape = [ndraws, 1]
            ax.plot(
                steps, arr[:, idx], alpha=0.5, lw=LW / 2.0, **plot_kwargs
            )

    ax.set_xlabel("draw")
    if title is not None:
        fig.suptitle(title)

    if outdir is not None:
        # plt.savefig(Path(outdir).joinpath(f'{key}.svg'),
        #             dpi=400, bbox_inches='tight')
        outfile = Path(outdir).joinpath(f"{key}.svg")
        if outfile.is_file():
            tstamp = ezpz.get_timestamp()
            pngdir = Path(outdir).joinpath("pngs")
            pngdir.mkdir(exist_ok=True, parents=True)
            pngfile = pngdir.joinpath(f"{key}-{tstamp}.png")
            svgfile = Path(outdir).joinpath(f"{key}-{tstamp}.svg")
            plt.savefig(pngfile, dpi=400, bbox_inches="tight")
            plt.savefig(svgfile, dpi=400, bbox_inches="tight")

    return fig, subfigs, axes

plot_dataArray(val, key=None, warmup=0.0, num_chains=0, title=None, outdir=None, subplots_kwargs=None, plot_kwargs=None, verbose=False, line_labels=False, logfreq=None)

Plot a single variable from the history as an xarray DataArray.

Parameters:

Name	Type	Description	Default
val	DataArray	The data to plot.	required
key	Optional[str]	The key for the data.	None
warmup	Optional[float]	The percentage of iterations to drop from the beginning of the plot.	0.0
num_chains	Optional[int]	The number of chains to plot.	0
title	Optional[str]	The title of the plot.	None
outdir	Optional[str]	The directory to save the plot to.	None
subplots_kwargs	Optional[dict[str, Any]]	Additional arguments for subplots.	None
plot_kwargs	Optional[dict[str, Any]]	Additional arguments for plotting.	None
verbose	bool	Whether to print the plot.	False
line_labels	bool	Whether to label lines in the plot.	False
logfreq	Optional[int]	The log frequency of the plot.	None

Source code in src/ezpz/history.py

@timeitlogit(rank=get_rank(), record=True, verbose=False, prefix="history")
def plot_dataArray(
    self,
    val: xr.DataArray,
    key: Optional[str] = None,
    warmup: Optional[float] = 0.0,
    num_chains: Optional[int] = 0,
    title: Optional[str] = None,
    outdir: Optional[str] = None,
    subplots_kwargs: Optional[dict[str, Any]] = None,
    plot_kwargs: Optional[dict[str, Any]] = None,
    verbose: bool = False,
    line_labels: bool = False,
    logfreq: Optional[int] = None,
):
    """
    Plot a single variable from the history as an xarray DataArray.

    Args:
        val (xr.DataArray): The data to plot.
        key (Optional[str]): The key for the data.
        warmup (Optional[float]): The percentage of iterations to drop from the
            beginning of the plot.
        num_chains (Optional[int]): The number of chains to plot.
        title (Optional[str]): The title of the plot.
        outdir (Optional[str]): The directory to save the plot to.
        subplots_kwargs (Optional[dict[str, Any]]): Additional arguments for
            subplots.
        plot_kwargs (Optional[dict[str, Any]]): Additional arguments for plotting.
        verbose (bool): Whether to print the plot.
        line_labels (bool): Whether to label lines in the plot.
        logfreq (Optional[int]): The log frequency of the plot.
    """
    import matplotlib.pyplot as plt

    plot_kwargs = {} if plot_kwargs is None else plot_kwargs
    subplots_kwargs = {} if subplots_kwargs is None else subplots_kwargs
    ezplot.set_plot_style()
    plt.rcParams["axes.labelcolor"] = "#bdbdbd"
    figsize = subplots_kwargs.get("figsize", ezplot.set_size())
    subplots_kwargs.update({"figsize": figsize})
    subfigs = None
    # if key == 'dt':
    #     warmup = 0.2
    arr = val.values  # shape: [nchains, ndraws]
    # steps = np.arange(len(val.coords['draw']))
    steps = val.coords["draw"]
    if warmup is not None and warmup > 0.0:
        drop = int(warmup * arr.shape[0])
        arr = arr[drop:]
        steps = steps[drop:]
    if len(arr.shape) == 2:
        fig, axes = ezplot.plot_combined(
            val,
            key=key,
            num_chains=num_chains,
            plot_kwargs=plot_kwargs,
            subplots_kwargs=subplots_kwargs,
        )
    else:
        if len(arr.shape) == 1:
            fig, ax = ezplot.subplots(**subplots_kwargs)
            try:
                ax.plot(steps, arr, **plot_kwargs)
            except ValueError:
                try:
                    ax.plot(steps, arr[~np.isnan(arr)], **plot_kwargs)
                except Exception:
                    logger.error(f"Unable to plot {key}! Continuing")
            _ = ax.grid(True, alpha=0.2)
            axes = ax
        elif len(arr.shape) == 3:
            fig, ax = ezplot.subplots(**subplots_kwargs)
            cmap = plt.get_cmap("viridis")
            y = val.mean("chain")
            for idx in range(len(val.coords["leapfrog"])):
                pkwargs = {
                    "color": cmap(idx / len(val.coords["leapfrog"])),
                    "label": f"{idx}",
                }
                ax.plot(steps, y[idx], **pkwargs)
            axes = ax
        else:
            raise ValueError("Unexpected shape encountered")
        ax = plt.gca()
        # assert isinstance(ax, plt.Axes)
        assert key is not None
        _ = ax.set_ylabel(key)
        _ = ax.set_xlabel("draw")
        # if num_chains > 0 and len(arr.shape) > 1:
        #     lw = LW / 2.
        #     #for idx in range(min(num_chains, arr.shape[1])):
        #     nchains = len(val.coords['chains'])
        #     for idx in range(min(nchains, num_chains)):
        #         # ax = subfigs[0].subplots(1, 1)
        #         # plot values of invidual chains, arr[:, idx]
        #         # where arr[:, idx].shape = [ndraws, 1]
        #         ax.plot(steps, val
        #                 alpha=0.5, lw=lw/2., **plot_kwargs)
    if title is not None:
        fig = plt.gcf()
        _ = fig.suptitle(title)
    if logfreq is not None:
        ax = plt.gca()
        xticks = ax.get_xticks()  # type: ignore
        _ = ax.set_xticklabels(  # type: ignore
            [f"{logfreq * int(i)}" for i in xticks]  # type: ignore
        )
    if outdir is not None:
        dirs = {
            "png": Path(outdir).joinpath("pngs/"),
            "svg": Path(outdir).joinpath("svgs/"),
        }
        _ = [i.mkdir(exist_ok=True, parents=True) for i in dirs.values()]
        # from l2hmc.configs import PROJECT_DIR
        # from ezpz
        if verbose:
            logger.info(f"Saving {key} plot to: {Path(outdir).resolve()}")
        for ext, d in dirs.items():
            outfile = d.joinpath(f"{key}.{ext}")
            plt.savefig(outfile, dpi=400, bbox_inches="tight")
    return (fig, subfigs, axes)

update(metrics, precision=6, use_wandb=True, commit=True, summarize=True)

Update the history with a dictionary of metrics.

Parameters:

Name	Type	Description	Default
metrics	dict	Dictionary of metrics to update the history with.	required
precision	int	Precision for summarizing the metrics.	6
use_wandb	Optional[bool]	Whether to log the metrics to Weights & Biases.	True
commit	Optional[bool]	Whether to commit the log to Weights & Biases.	True
summarize	Optional[bool]	Whether to summarize the metrics.	True

Source code in src/ezpz/history.py

@timeitlogit(rank=get_rank(), record=True, verbose=False, prefix="history")
def update(
    self,
    metrics: dict,
    precision: int = 6,
    use_wandb: Optional[bool] = True,
    commit: Optional[bool] = True,
    summarize: Optional[bool] = True,
) -> str:
    """
    Update the history with a dictionary of metrics.

    Args:
        metrics (dict): Dictionary of metrics to update the history with.
        precision (int): Precision for summarizing the metrics.
        use_wandb (Optional[bool]): Whether to log the metrics to Weights & Biases.
        commit (Optional[bool]): Whether to commit the log to Weights & Biases.
        summarize (Optional[bool]): Whether to summarize the metrics.
    """
    for key, val in metrics.items():
        # if isinstance(val, (list, np.ndarray, torch.Tensor)):
        #     val = grab_tensor(val)
        try:
            self.history[key].append(val)
        except KeyError:
            self.history[key] = [val]
    if (
        wandb is not None
        and use_wandb
        # and not WANDB_DISABLED
        and getattr(wandb, "run", None) is not None
    ):
        wandb.log(metrics, commit=commit)
    if summarize:
        return summarize_dict(metrics, precision=precision)
    return ""

StopWatch

Bases: ContextDecorator

A simple stopwatch context manager for measuring time taken by a block of code.

Source code in src/ezpz/history.py

class StopWatch(ContextDecorator):
    """
    A simple stopwatch context manager for measuring time taken by a block of code.
    """

    def __init__(
        self,
        msg: str,
        wbtag: Optional[str] = None,
        iter: Optional[int] = None,
        commit: Optional[bool] = False,
        prefix: str = "StopWatch/",
        log_output: bool = True,
    ) -> None:
        """
        Initialize the StopWatch.

        Args:
            msg (str): Message to log when the stopwatch is started.
            wbtag (Optional[str]): Optional tag for logging to Weights & Biases.
            iter (Optional[int]): Optional iteration number to log.
            commit (Optional[bool]): Whether to commit the log to Weights & Biases.
            prefix (str): Prefix for the log data.
            log_output (bool): Whether to log the output message.
        """
        self.msg = msg
        self.data = {}
        self.iter = iter if iter is not None else None
        self.prefix = prefix
        self.wbtag = wbtag if wbtag is not None else None
        self.log_output = log_output
        self.commit = commit
        if wbtag is not None:
            self.data = {
                f"{self.wbtag}/dt": None,
            }
            if iter is not None:
                self.data |= {
                    f"{self.wbtag}/iter": self.iter,
                }

    def __enter__(self):
        """Start the stopwatch."""
        self.time = time.perf_counter()
        return self

    def __exit__(self, t, v, traceback):
        """Stop the stopwatch and log the time taken."""
        dt = time.perf_counter() - self.time
        # if self.wbtag is not None and wandb.run is not None:
        # if len(self.data) > 0 and wandb.run is not None:
        try:
            if (
                len(self.data) > 0
                and wandb is not None
                and (wbrun := getattr(wandb, "run", None)) is not None
            ):
                self.data |= {f"{self.wbtag}/dt": dt}
                wbrun.log({self.prefix: self.data}, commit=self.commit)
        except Exception as e:
            logger.error(f"Unable to log to wandb: {e}")
        if self.log_output:
            logger.info(f"{self.msg} took {dt:.3f} seconds")

__enter__()

Start the stopwatch.

Source code in src/ezpz/history.py

def __enter__(self):
    """Start the stopwatch."""
    self.time = time.perf_counter()
    return self

__exit__(t, v, traceback)

Stop the stopwatch and log the time taken.

Source code in src/ezpz/history.py

def __exit__(self, t, v, traceback):
    """Stop the stopwatch and log the time taken."""
    dt = time.perf_counter() - self.time
    # if self.wbtag is not None and wandb.run is not None:
    # if len(self.data) > 0 and wandb.run is not None:
    try:
        if (
            len(self.data) > 0
            and wandb is not None
            and (wbrun := getattr(wandb, "run", None)) is not None
        ):
            self.data |= {f"{self.wbtag}/dt": dt}
            wbrun.log({self.prefix: self.data}, commit=self.commit)
    except Exception as e:
        logger.error(f"Unable to log to wandb: {e}")
    if self.log_output:
        logger.info(f"{self.msg} took {dt:.3f} seconds")

__init__(msg, wbtag=None, iter=None, commit=False, prefix='StopWatch/', log_output=True)

Initialize the StopWatch.

Parameters:

Name	Type	Description	Default
msg	str	Message to log when the stopwatch is started.	required
wbtag	Optional[str]	Optional tag for logging to Weights & Biases.	None
iter	Optional[int]	Optional iteration number to log.	None
commit	Optional[bool]	Whether to commit the log to Weights & Biases.	False
prefix	str	Prefix for the log data.	'StopWatch/'
log_output	bool	Whether to log the output message.	True

Source code in src/ezpz/history.py

def __init__(
    self,
    msg: str,
    wbtag: Optional[str] = None,
    iter: Optional[int] = None,
    commit: Optional[bool] = False,
    prefix: str = "StopWatch/",
    log_output: bool = True,
) -> None:
    """
    Initialize the StopWatch.

    Args:
        msg (str): Message to log when the stopwatch is started.
        wbtag (Optional[str]): Optional tag for logging to Weights & Biases.
        iter (Optional[int]): Optional iteration number to log.
        commit (Optional[bool]): Whether to commit the log to Weights & Biases.
        prefix (str): Prefix for the log data.
        log_output (bool): Whether to log the output message.
    """
    self.msg = msg
    self.data = {}
    self.iter = iter if iter is not None else None
    self.prefix = prefix
    self.wbtag = wbtag if wbtag is not None else None
    self.log_output = log_output
    self.commit = commit
    if wbtag is not None:
        self.data = {
            f"{self.wbtag}/dt": None,
        }
        if iter is not None:
            self.data |= {
                f"{self.wbtag}/iter": self.iter,
            }

breakpoint(rank=0)

Set a breakpoint, but only on a single rank. All other ranks will wait for you to be done with the breakpoint before continuing.

Parameters:

Name	Type	Description	Default
rank	int	Which rank to break on. Default: 0	0

Source code in src/ezpz/utils.py

def breakpoint(rank: int = 0):
    """
    Set a breakpoint, but only on a single rank.  All other ranks will wait for you to be
    done with the breakpoint before continuing.

    Args:
        rank (int): Which rank to break on.  Default: ``0``
    """
    if get_rank() == rank:
        pdb = DistributedPdb()
        pdb.message(
            "\n!!! ATTENTION !!!\n\n"
            f"Type 'up' to get to the frame that called dist.breakpoint(rank={rank})\n"
        )
        pdb.set_trace()
    tdist.barrier()

check(framework='pytorch', backend='deepspeed', port='5432')

Check if the framework is installed and working

Source code in src/ezpz/dist.py

def check(
    framework: str = "pytorch",
    backend: str = "deepspeed",
    port: int | str = "5432",
):
    """Check if the framework is installed and working"""
    from ezpz.configs import FRAMEWORKS

    if framework in FRAMEWORKS["pytorch"]:
        _ = setup_torch(
            backend=backend,
            port=str(port),
        )
    elif framework in FRAMEWORKS["tensorflow"]:
        _ = setup_tensorflow()
    else:
        raise ValueError(f"Unable to parse framework: {framework}")

cleanup()

Cleanup the distributed environment. This function destroys the process group if it is initialized.

Example

cleanup()

Source code in src/ezpz/dist.py

def cleanup() -> None:
    """
    Cleanup the distributed environment.
    This function destroys the process group if it is initialized.

    Example:
        >>> cleanup()
    """
    if torch.distributed.is_initialized():  # type:ignore
        torch.distributed.destroy_process_group()  # type:ignore

destroy_tensor_parallel()

Set the groups to none.

Source code in src/ezpz/tp/__init__.py

def destroy_tensor_parallel() -> None:
    """Set the groups to none."""
    global _TENSOR_PARALLEL_GROUP
    _TENSOR_PARALLEL_GROUP = None
    global _TENSOR_PARALLEL_RANKS
    _TENSOR_PARALLEL_RANKS = None

    global _DATA_PARALLEL_GROUP
    _DATA_PARALLEL_GROUP = None
    global _DATA_PARALLEL_RANKS
    _DATA_PARALLEL_RANKS = None

    global _PIPELINE_PARALLEL_GROUP
    _PIPELINE_PARALLEL_GROUP = None
    global _PIPELINE_PARALLEL_RANKS
    _PIPELINE_PARALLEL_RANKS = None

    global _CONTEXT_PARALLEL_GROUP
    _CONTEXT_PARALLEL_GROUP = None
    global _CONTEXT_PARALLEL_GROUP_RANKS
    _CONTEXT_PARALLEL_GROUP_RANKS = None

ensure_divisibility(numerator, denominator)

Ensure that numerator is divisible by the denominator.

Source code in src/ezpz/tp/utils.py

def ensure_divisibility(numerator: int, denominator: int) -> None:
    """Ensure that numerator is divisible by the denominator."""
    assert numerator % denominator == 0, '{} is not divisible by {}'.format(
        numerator, denominator
    )

get_context_manager(rank=None, outdir=None, strict=True)

Returns a context manager for profiling code blocks using PyInstrument.

Parameters:

Name	Type	Description	Default
rank	Optional[int]	The rank of the process (default: None). If provided, the profiler will only run if rank is 0.	None
outdir	Optional[str]	The output directory for saving profiles. Defaults to ezpz.OUTPUTS_DIR.	None
strict	Optional[bool]	If True, the profiler will only run if "PYINSTRUMENT_PROFILER" is set in the environment. Defaults to True.	True

Returns:

Name	Type	Description
AbstractContextManager	AbstractContextManager	A context manager that starts and stops the PyInstrument profiler.

Source code in src/ezpz/profile.py

def get_context_manager(
    rank: Optional[int] = None,
    outdir: Optional[str] = None,
    strict: Optional[bool] = True,
) -> AbstractContextManager:
    """
    Returns a context manager for profiling code blocks using PyInstrument.

    Args:
        rank (Optional[int]): The rank of the process (default: None).
            If provided, the profiler will only run if rank is 0.
        outdir (Optional[str]): The output directory for saving profiles.
            Defaults to `ezpz.OUTPUTS_DIR`.
        strict (Optional[bool]): If True, the profiler will only run if
            "PYINSTRUMENT_PROFILER" is set in the environment.
            Defaults to True.

    Returns:
        AbstractContextManager: A context manager that starts and stops
            the PyInstrument profiler.
    """
    d = ezpz.OUTPUTS_DIR if outdir is None else outdir
    fp = Path(d)
    fp = fp.joinpath("ezpz", "pyinstrument_profiles")

    if strict:
        if os.environ.get("PYINSTRUMENT_PROFILER", None) is not None:
            return PyInstrumentProfiler(rank=rank, outdir=fp.as_posix())
        return nullcontext()
    if rank is None or rank == 0:
        return PyInstrumentProfiler(rank=rank, outdir=fp.as_posix())
    # if rank == 0:
    #     return PyInstrumentProfiler(rank=rank, outdir=outdir)
    return nullcontext()

get_context_parallel_group()

Get the context parallel group the caller rank belongs to.

Source code in src/ezpz/tp/__init__.py

def get_context_parallel_group() -> tdist.ProcessGroup:
    """Get the context parallel group the caller rank belongs to."""
    assert (
        _CONTEXT_PARALLEL_GROUP is not None
    ), "context parallel group is not initialized"
    return _CONTEXT_PARALLEL_GROUP

get_context_parallel_rank()

Return my rank for the context parallel group.

Source code in src/ezpz/tp/__init__.py

def get_context_parallel_rank() -> int:
    """Return my rank for the context parallel group."""
    return tdist.get_rank(group=get_context_parallel_group())

get_context_parallel_ranks()

Return context parallel ranks for the context parallel group.

Source code in src/ezpz/tp/__init__.py

def get_context_parallel_ranks() -> List[int]:
    """Return context parallel ranks for the context parallel group."""
    assert (
        _CONTEXT_PARALLEL_GROUP_RANKS is not None
    ), "context parallel group is not initialized"
    return _CONTEXT_PARALLEL_GROUP_RANKS

get_context_parallel_world_size()

Return world size for the context parallel group.

Source code in src/ezpz/tp/__init__.py

def get_context_parallel_world_size() -> int:
    """Return world size for the context parallel group."""
    return tdist.get_world_size(group=get_context_parallel_group())

get_cpus_per_node()

Get the number of CPUs per node

Source code in src/ezpz/dist.py

def get_cpus_per_node() -> int:
    """Get the number of CPUs per node"""
    from sh import getconf as sh_getconf  # type:ignore noqa

    return int(sh_getconf("_NPROCESSORS_ONLN").rstrip("\n"))

get_data_parallel_group()

Get the data parallel group the caller rank belongs to.

Source code in src/ezpz/tp/__init__.py

def get_data_parallel_group() -> tdist.ProcessGroup:
    """Get the data parallel group the caller rank belongs to."""
    assert _DATA_PARALLEL_GROUP is not None, "data parallel group is not initialized"
    return _DATA_PARALLEL_GROUP

get_data_parallel_rank()

Return my rank for the data parallel group.

Source code in src/ezpz/tp/__init__.py

def get_data_parallel_rank() -> int:
    """Return my rank for the data parallel group."""
    return tdist.get_rank(group=get_data_parallel_group())

get_data_parallel_world_size()

Return world size for the data parallel group.

Source code in src/ezpz/tp/__init__.py

def get_data_parallel_world_size() -> int:
    """Return world size for the data parallel group."""
    return tdist.get_world_size(group=get_data_parallel_group())

get_device(type=None, as_torch_device=None)

Alias for get_torch_device.

Source code in src/ezpz/dist.py

def get_device(type: Optional[str] = None, as_torch_device: Optional[bool] = None) -> str | torch.device:
    """Alias for `get_torch_device`."""
    return get_torch_device(device_type=type, as_torch_device=as_torch_device)

get_dist_info(framework=None, verbose=None, hostfile=None)

Get distributed info.

Parameters:

Name	Type	Description	Default
framework	str	Framework to use. Defaults to None.	None
verbose	bool	Whether to print the info. Defaults to None.	None
hostfile	PathLike	Path to the hostfile. Defaults to None.	None

Returns:

Name	Type	Description
dict	dict[str, str | int | list]	Dictionary containing the distributed info.

Source code in src/ezpz/dist.py

def get_dist_info(
    framework: Optional[str] = None,
    verbose: Optional[bool] = None,
    hostfile: Optional[PathLike] = None,
) -> dict[str, str | int | list]:
    """Get distributed info.

    Args:
        framework (str, optional): Framework to use. Defaults to None.
        verbose (bool, optional): Whether to print the info. Defaults to None.
        hostfile (PathLike, optional): Path to the hostfile. Defaults to None.

    Returns:
        dict: Dictionary containing the distributed info.
    """
    dist_info = _get_dist_info(
        hostfile=hostfile,
        framework=framework,
    )
    if verbose:
        import json

        logger.info(
            f"DistInfo={json.dumps(dist_info, indent=4, sort_keys=True)}"
        )
    return dist_info

get_gpus_per_node()

Get the number of GPUs per node

Source code in src/ezpz/dist.py

def get_gpus_per_node() -> int:
    """Get the number of GPUs per node"""
    # return torch.cuda.device_count() if torch.cuda.is_available() else (
    #     (
    #         ipex.xpu.device_count() if ipex is not None else (
    #             get_cpus_per_node()
    #         )
    #     )
    # )
    # if _assert:
    #     raise RuntimeError(
    #         'No {X, G}pus found; but _assert specified. Returning !!'
    #     )
    # logger.warning('No {x,g}-pus found, returning' + f'{cpus_per_node}')
    ngpu_per_host = os.environ.get("NGPU_PER_HOST", None)
    if ngpu_per_host is not None:
        return int(ngpu_per_host)
    if torch.cuda.is_available():
        return torch.cuda.device_count()
    if torch.xpu.is_available():
        return torch.xpu.device_count()
    if ipex is not None and torch.xpu.is_available():
        return ipex.xpu.device_count()
    if torch.backends.mps.is_available():
        # XXX: Maybe we're running MPI with multiple MPS devices?
        return get_world_size_in_use()
    return 0

get_hostname()

Get the hostname of the current machine.

Returns:

Name	Type	Description
str	str	The hostname of the current machine.

Source code in src/ezpz/dist.py

def get_hostname() -> str:
    """Get the hostname of the current machine.

    Returns:
        str: The hostname of the current machine.
    """
    import socket

    try:
        hostname = socket.gethostbyaddr(socket.gethostname())[0].lower()
    # except socket.herror as exc:
    except Exception:
        from sh import hostname as sh_hostname  # type:ignore[missingTypeStubs]

        hostname = sh_hostname()
        # if get_rank() == 0:
        #     logger.debug('Unable to determine hostname with `socket`.')
        #     logger.debug(f'hostname from`sh`: {hostname}')
        #     # logger.exception(exc)
    return hostname.rstrip("\n")

get_hosts_from_hostfile(hostfile=None)

Get hosts from the hostfile or environment variables.

Parameters:

Name	Type	Description	Default
hostfile	str | Path	Path to the hostfile. Defaults to None.	None

Returns:

Type	Description
tuple[str, list[str]]	tuple[str, list[str]]: Tuple containing the hostfile path and a list of hosts.

Example

get_hosts_from_hostfile("/path/to/hostfile") ('/path/to/hostfile', ['host1', 'host2', ...])

Source code in src/ezpz/dist.py

def get_hosts_from_hostfile(
    hostfile: Optional[str | Path] = None,  # type:ignore[reportDeprecated]
) -> tuple[str, list[str]]:
    """
    Get hosts from the hostfile or environment variables.

    Args:
        hostfile (str | Path, optional): Path to the hostfile. Defaults to None.

    Returns:
        tuple[str, list[str]]: Tuple containing the hostfile path and a list of hosts.

    Example:
        >>> get_hosts_from_hostfile("/path/to/hostfile")
        ('/path/to/hostfile', ['host1', 'host2', ...])
    """
    # hostfile = '' if hostfile is None else hostfile
    hostname = get_hostname()
    hostfile = os.environ.get(
        "HOSTFILE",
        os.environ.get(
            "PBS_NODEFILE",
            os.environ.get(
                "COBALT_NODEFILE",
                None,
            ),
        ),
    )
    hosts: list[str] = []
    assert hostfile is not None
    if Path(hostfile).is_file():
        if get_rank() == 0:
            logger.debug(f"Reading hosts from {hostfile}")
        hpath = Path(hostfile).resolve().absolute()
        with hpath.open("r") as f:
            hosts.extend([h.rstrip("\n") for h in f.readlines()])
    else:
        hosts.append(hostname)
    return Path(hostfile).as_posix(), hosts

get_local_rank()

Return get_rank() % get_gpus_per_node()

Returns:

Name	Type	Description
int	int	The local rank of the current process within its node. This is calculated as the current rank modulo the number of GPUs per node.

Example

local_rank = get_local_rank() print(f"Local rank of the current process: {local_rank}")

Source code in src/ezpz/dist.py

def get_local_rank() -> int:
    """Return `get_rank() % get_gpus_per_node()`

    Returns:
        int: The local rank of the current process within its node.
            This is calculated as the current rank modulo the number of GPUs per node.

    Example:
        >>> local_rank = get_local_rank()
        >>> print(f"Local rank of the current process: {local_rank}")
    """
    return int(get_rank() % get_gpus_per_node()) if get_world_size() > 1 else 0

get_machine(hostname=None)

Get the machine name from the hostname.

Parameters:

Name	Type	Description	Default
hostname	str	The hostname to check. Defaults to None.	None

Returns:

Name	Type	Description
str	str	The machine name.

Example

get_machine("frontier") "Frontier"

Source code in src/ezpz/dist.py

def get_machine(hostname: Optional[str] = None) -> str:
    """Get the machine name from the hostname.

    Args:
        hostname (str, optional): The hostname to check. Defaults to None.

    Returns:
        str: The machine name.

    Example:
        >>> get_machine("frontier")
        "Frontier"
    """

    if hostname is None:
        try:
            hostname = socket.gethostbyaddr(socket.gethostname())[0]
        except Exception:
            try:
                hostname = socket.gethostname()
            except Exception:
                logger.warning("Unable to determine hostname!")
                hostname = "unknown"
    if hostname.startswith("frontier"):
        return "Frontier"
    if hostname.startswith("sophia"):
        return "Sophia"
    if hostname.startswith("theta"):
        return "ThetaGPU"
    if hostname.startswith("x1"):
        return "SunSpot"
    if hostname.startswith("x3"):
        if (pbs_host := os.environ.get("PBS_O_HOST", None)) is not None:
            if str(pbs_host).startswith("sirius"):
                return "Sirius"
            return "Polaris"
        return "Polaris"
    if hostname.startswith("x4"):
        return "Aurora"
    if hostname.startswith("login"):
        return "Perlmutter"
    if hostname.startswith("nid"):
        return "Perlmutter"
    return f"{hostname}"

get_max_memory_allocated(device)

Get the maximum memory allocated on the specified device.

Parameters:

Name	Type	Description	Default
device	device	The device to check memory allocation for.	required

Source code in src/ezpz/utils.py

def get_max_memory_allocated(device: torch.device) -> float:
    """
    Get the maximum memory allocated on the specified device.

    Args:
        device (torch.device): The device to check memory allocation for.
    """
    if torch.cuda.is_available():
        return torch.cuda.max_memory_allocated(device)
    elif torch.xpu.is_available():  #  and ipex is not None:
        try:
            import intel_extension_for_pytorch as ipex

            return ipex.xpu.max_memory_allocated(device)
        except ImportError:
            return -1.0
    raise RuntimeError(f"Memory allocation not available for {device=}")

get_node_index()

Get the index of the current node in the hostfile

Source code in src/ezpz/dist.py

def get_node_index() -> int:
    """Get the index of the current node in the hostfile"""
    return get_rank() % get_num_nodes()

get_nodes_from_hostfile(hostfile)

Get the nodes from the hostfile.

Parameters:

Name	Type	Description	Default
hostfile	PathLike	The path to the hostfile.	required

Returns:

Type	Description
list[str]	list[str]: A list of nodes from the hostfile.

Source code in src/ezpz/dist.py

def get_nodes_from_hostfile(
    hostfile: PathLike,
) -> list[str]:
    """Get the nodes from the hostfile.

    Args:
        hostfile (PathLike): The path to the hostfile.

    Returns:
        list[str]: A list of nodes from the hostfile.
    """
    # cobalt_nodefile = get_cobalt_nodefile()
    fpath = Path(hostfile)
    assert fpath.is_file()
    with fpath.open("r") as f:
        nodes = [i.rstrip("\n") for i in f.readlines()]
    return nodes

get_num_nodes(hostfile=None)

Get the number of nodes from the hostfile

Source code in src/ezpz/dist.py

def get_num_nodes(hostfile: Optional[PathLike] = None) -> int:
    """Get the number of nodes from the hostfile"""
    num_nodes = os.environ.get("SLURM_NNODES", None)
    if num_nodes is not None:
        return int(num_nodes)
    hfp = get_hostfile_with_fallback(hostfile)
    hosts = [h.split(".")[0] for h in get_nodes_from_hostfile(hfp)]
    return len(hosts)

get_pipeline_parallel_group()

Get the pipeline parallel group the caller rank belongs to.

Source code in src/ezpz/tp/__init__.py

def get_pipeline_parallel_group() -> tdist.ProcessGroup:
    """Get the pipeline parallel group the caller rank belongs to."""
    assert (
        _PIPELINE_PARALLEL_GROUP is not None
    ), "pipeline parallel group is not initialized"
    return _PIPELINE_PARALLEL_GROUP

get_pipeline_parallel_ranks()

Get the pipeline parallel group the caller rank belongs to.

Source code in src/ezpz/tp/__init__.py

def get_pipeline_parallel_ranks() -> List[int]:
    """Get the pipeline parallel group the caller rank belongs to."""
    assert (
        _PIPELINE_PARALLEL_RANKS is not None
    ), "pipeline parallel group is not initialized"
    return _PIPELINE_PARALLEL_RANKS

get_rank()

Get current MPI rank.

Returns:

Name	Type	Description
int	int	The rank of the current process in the MPI world.

Example

rank = get_rank() print(f"Current MPI rank: {rank}")

Source code in src/ezpz/dist.py

def get_rank() -> int:
    """Get current MPI rank.

    Returns:
        int: The rank of the current process in the MPI world.

    Example:
        >>> rank = get_rank()
        >>> print(f"Current MPI rank: {rank}")
    """
    return int(MPI.COMM_WORLD.Get_rank())

get_tensor_parallel_group()

Get the tensor parallel group the caller rank belongs to.

Source code in src/ezpz/tp/__init__.py

def get_tensor_parallel_group() -> tdist.ProcessGroup:
    """Get the tensor parallel group the caller rank belongs to."""
    assert (
        _TENSOR_PARALLEL_GROUP is not None
    ), "tensor parallel group is not initialized"
    return _TENSOR_PARALLEL_GROUP

get_tensor_parallel_rank()

Return my rank for the tensor parallel group.

Source code in src/ezpz/tp/__init__.py

def get_tensor_parallel_rank() -> int:
    """Return my rank for the tensor parallel group."""
    return tdist.get_rank(group=get_tensor_parallel_group())

get_tensor_parallel_src_rank()

Calculate the global rank corresponding to local rank 0 in the TP group.

Source code in src/ezpz/tp/__init__.py

def get_tensor_parallel_src_rank() -> int:
    """
    Calculate the global rank corresponding to local rank 0 in the TP group.
    """
    global_rank = tdist.get_rank()
    local_world_size = get_tensor_parallel_world_size()
    return (global_rank // local_world_size) * local_world_size

get_tensor_parallel_world_size()

Return world size for the tensor parallel group.

Source code in src/ezpz/tp/__init__.py

def get_tensor_parallel_world_size() -> int:
    """Return world size for the tensor parallel group."""
    return tdist.get_world_size(group=get_tensor_parallel_group())

get_timestamp(fstr=None)

Get formatted timestamp.

Source code in src/ezpz/utils.py

def get_timestamp(fstr: Optional[str] = None) -> str:
    """Get formatted timestamp."""
    import datetime

    now = datetime.datetime.now()
    return (
        now.strftime("%Y-%m-%d-%H%M%S") if fstr is None else now.strftime(fstr)
    )

get_torch_backend()

Get the current PyTorch backend.

Returns:

Name	Type	Description
str	str	The current PyTorch backend.

Source code in src/ezpz/dist.py

def get_torch_backend() -> str:
    """
    Get the current PyTorch backend.

    Returns:
        str: The current PyTorch backend.
    """
    backend_from_env = os.environ.get("TORCH_BACKEND", None)
    if backend_from_env is not None:
        return backend_from_env
    return (
        "nccl"
        if torch.cuda.is_available()
        else (
            get_torch_backend_on_xpu() if torch.xpu.is_available() else "gloo"
        )
    )

get_torch_device(*, device_type=None, as_torch_device=None)

Get the current PyTorch device.

Parameters:

Name	Type	Description	Default
device_type	str	The type of device to return. If None, it will be determined automatically. Defaults to None.	None
as_torch_device	bool	If True, return a torch.device object. If False, return a string representing the device type. Defaults to False.	None

Returns:

Type	Description
str | device	str | torch.device: The current PyTorch device. If as_torch_device is True, returns a torch.device object. If as_torch_device is False, returns a string representing the device type.

Example

get_torch_device() # Returns the current device type as a string

Source code in src/ezpz/dist.py

def get_torch_device(
    *,
    device_type: Optional[str] = None,
    as_torch_device: Optional[bool] = None,
) -> str | torch.device:
    """Get the current PyTorch device.

    Args:
        device_type (str, optional): The type of device to return.
            If None, it will be determined automatically.
            Defaults to None.
        as_torch_device (bool, optional): If True, return a torch.device object.
            If False, return a string representing the device type.
            Defaults to False.

    Returns:
        str | torch.device: The current PyTorch device.
            If as_torch_device is True, returns a torch.device object.
            If as_torch_device is False, returns a string representing the device type.

    Example:
        >>> get_torch_device()  # Returns the current device type as a string
    """
    device_type = get_torch_device_type(device_type)
    return torch.device(device_type) if as_torch_device else device_type

get_torch_device_type(device_type=None)

Get the current PyTorch device type.

Parameters:

Name	Type	Description	Default
device_type	str	The type of device to return. If None, it will be determined automatically. Defaults to None.	None

Returns:

Name	Type	Description
str	str	The current PyTorch device type. Possible values are "cpu", "mps", "xpu", or "cuda".

Example

get_torch_device_type() # Returns the current device type as a string

Source code in src/ezpz/dist.py

def get_torch_device_type(device_type: Optional[str] = None) -> str:
    """Get the current PyTorch device type.

    Args:
        device_type (str, optional): The type of device to return.
            If None, it will be determined automatically.
            Defaults to None.

    Returns:
        str: The current PyTorch device type.
            Possible values are "cpu", "mps", "xpu", or "cuda".

    Example:
        >>> get_torch_device_type()  # Returns the current device type as a string
    """
    if device_type is not None:
        assert device_type in (
            "cpu",
            "mps",
            "xpu",
            "cuda",
        )
        logger.warning(
            " ".join(
                [
                    f"device_type: {device_type} passed to",
                    "ezpz.dist.get_torch_device_type",
                ]
            )
        )
        return device_type
    if (tdevice := os.environ.get("TORCH_DEVICE")) is not None:
        if get_rank() == 0:
            logger.warning(f"Caught TORCH_DEVICE={tdevice} from environment!")
        tdevice = tdevice.lower()
        assert tdevice is not None and tdevice in (
            "cpu",
            "mps",
            "xpu",
            "cuda",
        )
        return tdevice.lower()
    return (
        "xpu"
        if torch.xpu.is_available()
        else (
            "cuda"
            if torch.cuda.is_available()
            else (
                "mps"
                if (
                    torch.backends.mps.is_available()
                    and torch.get_default_dtype() != torch.float64
                )
                else "cpu"
            )
        )
    )

get_world_size(total=None, in_use=None)

Get the total number of *PUs available or currently in use. Args: total (bool, optional): If True, return the total number of *PUs available. Defaults to None. in_use (bool, optional): If True, return the number of *PUs currently in use. Defaults to None.

Returns:

Name	Type	Description
int	int	The total number of *PUs available or currently in use. If both total and in_use are None, it returns the number of *PUs currently in use by the MPI communicator.

Example

world_size = get_world_size(total=True) print(f"Total number of *PUs available: {world_size}") world_size_in_use = get_world_size(in_use=True) print(f"Number of *PUs currently in use: {world_size_in_use}")

Source code in src/ezpz/dist.py

def get_world_size(
    total: Optional[bool] = None,
    in_use: Optional[bool] = None,
) -> int:
    """
    Get the total number of *PUs available or currently in use.
    Args:
        total (bool, optional): If True, return the total number of *PUs available.
            Defaults to None.
        in_use (bool, optional): If True, return the number of *PUs currently in use.
            Defaults to None.

    Returns:
        int: The total number of *PUs available or currently in use.
            If both `total` and `in_use` are None, it returns the number of *PUs
            currently in use by the MPI communicator.

    Example:
        >>> world_size = get_world_size(total=True)
        >>> print(f"Total number of *PUs available: {world_size}")
        >>> world_size_in_use = get_world_size(in_use=True)
        >>> print(f"Number of *PUs currently in use: {world_size_in_use}")
    """
    if total:
        return get_world_size_total()
    if in_use:
        return get_world_size_in_use()
    # TODO: Deal with subtlety between:
    # 1. 'world_size' == total AVAILABLE gpus (for record keeping)
    # 2. 'world_size' == number of gpus CURRENTLY IN USE (from {`mpi`, ...})
    # ¯\_(ツ)_/¯
    try:
        world_size = int(MPI.COMM_WORLD.Get_size())
    except Exception:
        num_nodes = get_num_nodes()
        gpus_per_node = get_gpus_per_node()
        world_size = num_nodes * gpus_per_node
        logger.warning(
            "MPI not initialized !!"
            "Calculating (and using!! ??) "
            "[world_size]=[(num_nodes) x (num_*pus_per_node)]=[num_*pus_total]"
            f"[{world_size}]=[({num_nodes}) x ({gpus_per_node})]"
        )
    # if world_size == 1:
    #     gpus_per_node = get_gpus_per_node()
    #     num_nodes = get_num_nodes()
    #     world_size = num_nodes * gpus_per_node
    return world_size

include_file(f)

Check if a file should be included based on its extension.

Parameters:

Name	Type	Description	Default
f	PathLike	The file path to check.	required

Returns:

Name	Type	Description
bool		True if the file should be included, False otherwise.

Source code in src/ezpz/dist.py

def include_file(f: PathLike):
    """
    Check if a file should be included based on its extension.

    Args:
        f (PathLike): The file path to check.

    Returns:
        bool: True if the file should be included, False otherwise.
    """
    fpath = Path(f)
    return fpath.suffix in {
        ".py",
        ".yaml",
        ".sh",
        ".md",
        ".qmd",
        ".yml",
        ".toml",
    }

init_deepspeed(dist_backend=None, auto_mpi_discovery=True, distributed_port=29500, verbose=True, timeout=None, init_method=None, dist_init_required=None, config=None, rank=None, world_size=None)

Initialize DeepSpeed distributed environment.

Parameters:

Name	Type	Description	Default
dist_backend	str	The distributed backend to use. Defaults to None.	None
auto_mpi_discovery	bool	Whether to automatically discover MPI. Defaults to True.	True
distributed_port	int | str	The port for distributed communication. Defaults to 29500.	29500
verbose	bool	Whether to print verbose logs. Defaults to True.	True
timeout	int | None	Timeout in seconds for distributed initialization. Defaults to None.	None
init_method	str	Initialization method for distributed training. Defaults to None.	None
dist_init_required	bool	Whether distributed initialization is required. Defaults to None.	None
config	dict	DeepSpeed configuration dictionary. Defaults to None.	None
rank	int | None	Rank of the current process. Defaults to None.	None
world_size	int | None	Total number of processes. Defaults to None.	None

Raises:

Type	Description
ImportError	If DeepSpeed is not installed.
Exception	If there is an error during DeepSpeed initialization.

Example

init_deepspeed( ... dist_backend="nccl", ... distributed_port=29500, ... verbose=True, ... timeout=3600, ... rank=0, ... world_size=4, ... )

Source code in src/ezpz/dist.py

def init_deepspeed(
    dist_backend: Optional[str] = None,
    auto_mpi_discovery: bool = True,
    distributed_port: int | str = 29500,
    verbose: bool = True,
    timeout: Optional[int] = None,
    init_method: Optional[str] = None,
    dist_init_required: Optional[bool] = None,
    config: Optional[dict] = None,
    rank: Optional[int] = None,
    world_size: Optional[int] = None,
):
    """
    Initialize DeepSpeed distributed environment.

    Args:
        dist_backend (str, optional): The distributed backend to use.
            Defaults to None.
        auto_mpi_discovery (bool, optional): Whether to automatically discover MPI.
            Defaults to True.
        distributed_port (int | str, optional): The port for distributed communication.
            Defaults to 29500.
        verbose (bool, optional): Whether to print verbose logs. Defaults to True.
        timeout (int | None, optional): Timeout in seconds for distributed initialization.
            Defaults to None.
        init_method (str, optional): Initialization method for distributed training.
            Defaults to None.
        dist_init_required (bool, optional): Whether distributed initialization is required.
            Defaults to None.
        config (dict, optional): DeepSpeed configuration dictionary. Defaults to None.
        rank (int | None, optional): Rank of the current process. Defaults to None.
        world_size (int | None, optional): Total number of processes. Defaults to None.

    Raises:
        ImportError: If DeepSpeed is not installed.
        Exception: If there is an error during DeepSpeed initialization.

    Example:
        >>> init_deepspeed(
        ...     dist_backend="nccl",
        ...     distributed_port=29500,
        ...     verbose=True,
        ...     timeout=3600,
        ...     rank=0,
        ...     world_size=4,
        ... )
    """
    try:
        import deepspeed  # noqa type:ignore

        os.environ["DEEPSPEED_VERSION"] = deepspeed.__version__
    except ImportError as e:
        logger.warning(
            "Unable to import deepspeed. Please install it to use DeepSpeed features."
        )
        raise ImportError(
            "DeepSpeed is not installed. Install with 'pip install deepspeed'"
        ) from e

    rank = get_rank() if rank is None else rank
    world_size = get_world_size() if world_size is None else world_size
    os.environ["WORLD_SIZE"] = str(world_size)
    try:
        import deepspeed  # type:ignore

        # logger.warning(f'Setting {timeout=}')
        dt = 3600 if timeout is None else timeout
        deepspeed.init_distributed(
            dist_backend=dist_backend,
            auto_mpi_discovery=auto_mpi_discovery,
            distributed_port=int(distributed_port),
            verbose=verbose,
            timeout=datetime.timedelta(seconds=dt),
            init_method=init_method,
            dist_init_required=dist_init_required,
            config=config,
            rank=rank,
            world_size=world_size,
        )
    except Exception as exc:
        logger.warning("Unable to `import deepspeed`. Exiting!")
        logger.exception(exc)
        raise exc

init_process_group(rank, world_size, timeout, backend=None)

Initialize the PyTorch distributed process group.

Parameters:

Name	Type	Description	Default
rank	int | str	The rank of the current process.	required
world_size	int | str	The total number of processes.	required
timeout	str | int | timedelta	Timeout for the process group initialization.	required
backend	str	The backend to use for distributed training. Defaults to None, which will use the default backend based on the device.	None

Source code in src/ezpz/dist.py

def init_process_group(
    rank: int | str,
    world_size: int | str,
    timeout: str | int | timedelta,
    backend: Optional[str] = None,
) -> None:
    """
    Initialize the PyTorch distributed process group.

    Args:
        rank (int | str): The rank of the current process.
        world_size (int | str): The total number of processes.
        timeout (str | int | timedelta): Timeout for the process group initialization.
        backend (str, optional): The backend to use for distributed training.
            Defaults to None, which will use the default backend based on the device.
    """
    backend = get_torch_backend() if backend is None else backend
    if get_rank() == 0:
        logger.info(
            " ".join(
                [
                    "Calling torch.distributed.init_process_group_with:",
                    f"rank={rank}",
                    f"world_size={world_size}",
                    f"backend={backend}",
                ]
            )
        )
    if not isinstance(timeout, timedelta):
        env_timeout = os.environ.get("TORCH_DDP_TIMEOUT", timeout)
        timeout = timedelta(
            seconds=int(env_timeout),
        )
    if not torch.distributed.is_initialized():  # type:ignore
        torch.distributed.init_process_group(  # type:ignore
            backend=backend,
            timeout=timeout,
            rank=int(rank),
            world_size=int(world_size),
            init_method="env://",
        )

initialize_tensor_parallel(tensor_parallel_size=1, pipeline_parallel_size=1, context_parallel_size=1, tensor_parallel_backend=None, pipeline_parallel_backend=None, context_parallel_backend=None, data_parallel_backend=None, timeout=None)

Initialize tensor data parallel groups.

Parameters:

Name	Type	Description	Default
tensor_parallel_size	int	number of GPUs used to parallelize model.	1

Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we use 2 GPUs to parallelize the model. The present function will create 4 tensor parallel groups and 2 data parallel groups as:

• 4 tensor parallel groups:

[g0, g1], [g2, g3], [g4, g5], [g6, g7]

• 2 data parallel groups:

  [g0, g2, g4, g6], [g1, g3, g5, g7]
  

Note that for efficiency, the caller should make sure adjacent ranks are on the same DGX box. For example if we are using 2 DGX-1 boxes with a total of 16 GPUs, rank 0 to 7 belong to the first box and ranks 8 to 15 belong to the second box.

process groups initialized in the order of TP, CP, PP, DP.

Let's say we have a total of 16 GPUs denoted by g0 ... g15 and we use 2 GPUs to parallelize the tensor tensor, 2 GPUs to parallelize context(seq len), and 2 GPUs to parallelize the tensor pipeline. The present function will create 8 tensor model-parallel groups, 8 context-parallel group, 8 pipeline model-parallel groups and 8 data-parallel groups as: when alternate_pp_config = False,

• 8 data_parallel groups: [g0, g4], [g1, g5], [g2, g6], [g3, g7], [g8, g12], [g9, g13], [g10, g14], [g11, g15]
• 8 tensor model-parallel groups: [g0, g1], [g2, g3], [g4, g5], [g6, g7], [g8, g9], [g10, g11], [g12, g13], [g14, g15]
• 8 context-parallel groups: [g0, g2], [g1, g3], [g4, g6], [g5, g7], [g8, g10], [g9, g11], [g12, g14], [g13, g15]
• 8 pipeline model-parallel groups: [g0, g8], [g1, g9], [g2, g10], [g3, g11], [g4, g12], [g5, g13], [g6, g16], [g7, g15]

Source code in src/ezpz/tp/__init__.py

def initialize_tensor_parallel(
    tensor_parallel_size: int = 1,
    pipeline_parallel_size: int = 1,
    context_parallel_size: int = 1,
    tensor_parallel_backend: Optional[str] = None,
    pipeline_parallel_backend: Optional[str] = None,
    context_parallel_backend: Optional[str] = None,
    data_parallel_backend: Optional[str] = None,
    timeout: Optional[timedelta] = None,
) -> None:
    """
    Initialize tensor data parallel groups.

    Arguments:
        tensor_parallel_size: number of GPUs used to parallelize model.

    Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we
    use 2 GPUs to parallelize the model. The present function will
    create 4 tensor parallel groups and 2 data parallel groups as:

    - 4 tensor parallel groups:

      ```
      [g0, g1], [g2, g3], [g4, g5], [g6, g7]
      ```

    - 2 data parallel groups:

        ```
        [g0, g2, g4, g6], [g1, g3, g5, g7]
        ```

    Note that for efficiency, the caller should make sure adjacent ranks
    are on the same DGX box. For example if we are using 2 DGX-1 boxes
    with a total of 16 GPUs, rank 0 to 7 belong to the first box and
    ranks 8 to 15 belong to the second box.

    process groups initialized in the order of TP, CP, PP, DP.

    Let's say we have a total of 16 GPUs denoted by g0 ... g15 and we
    use 2 GPUs to parallelize the tensor tensor, 2 GPUs to parallelize context(seq len), and 2 GPUs to parallelize
    the tensor pipeline. The present function will
    create 8 tensor model-parallel groups, 8 context-parallel group, 8 pipeline model-parallel groups
    and 8 data-parallel groups as:
    when alternate_pp_config = False,

    - 8 data_parallel groups:
        [g0, g4], [g1, g5], [g2, g6], [g3, g7], [g8, g12], [g9, g13], [g10, g14], [g11, g15]
    - 8 tensor model-parallel groups:
        [g0, g1], [g2, g3], [g4, g5], [g6, g7], [g8, g9], [g10, g11], [g12, g13], [g14, g15]
    - 8 context-parallel groups:
        [g0, g2], [g1, g3], [g4, g6], [g5, g7], [g8, g10], [g9, g11], [g12, g14], [g13, g15]
    - 8 pipeline model-parallel groups:
        [g0, g8], [g1, g9], [g2, g10], [g3, g11], [g4, g12], [g5, g13], [g6, g16], [g7, g15]
    """
    # Get world size and rank. Ensure some consistencies.
    assert tdist.is_initialized()
    world_size = tdist.get_world_size()
    tensor_parallel_size = int(min(tensor_parallel_size, world_size))
    ensure_divisibility(world_size, tensor_parallel_size)
    ensure_divisibility(world_size, context_parallel_size)
    ensure_divisibility(
        world_size,
        tensor_parallel_size * pipeline_parallel_size * context_parallel_size,
    )
    rank = tdist.get_rank()

    dpsize = int(
        world_size
        / (tensor_parallel_size * pipeline_parallel_size * context_parallel_size)
    )

    if tdist.get_rank() == 0:
        pstr = ", ".join(
            [
                f"TP: {tensor_parallel_size}",
                f"PP: {pipeline_parallel_size}",
                f"CP: {context_parallel_size}",
                f"DP: {dpsize}",
            ]
        )
        logger.info(pstr)
        # pstr = f'TP: {tensor_parallel_size}, PP: {pipeline_parallel_size}, CP: {context_parallel_size}, DP: {dpsize}'
        # logger.info(
        #     '> initializing tensor parallel with size {}'.format(
        #         tensor_parallel_size
        #     )
        # )
        # logger.info(
        #     '> initializing context parallel with size {}'.format(
        #         context_parallel_size
        #     )
        # )
        # logger.info(
        #     '> initializing pipeline with size {}'.format(
        #         pipeline_parallel_size
        #     )
        # )

    groups = torch.LongTensor(range(world_size)).reshape(
        dpsize,
        pipeline_parallel_size,
        context_parallel_size,
        tensor_parallel_size,
    )

    found = torch.where(groups == rank)
    assert all(len(x) == 1 for x in found)
    found = [x[0] for x in found]

    # Build the data parallel groups.
    global _DATA_PARALLEL_GROUP
    global _DATA_PARALLEL_RANKS
    assert _DATA_PARALLEL_GROUP is None, "data parallel group is already initialized"
    assert _DATA_PARALLEL_RANKS is None, "data parallel ranks are already initialized"
    for i in range(pipeline_parallel_size):
        for j in range(context_parallel_size):
            for k in range(tensor_parallel_size):
                ranks = groups[:, i, j, k].tolist()
                group = tdist.new_group(
                    groups[:, i, j, k].tolist(),
                    backend=data_parallel_backend,
                    timeout=timeout,
                )
                if i == found[1] and j == found[2] and k == found[3]:
                    _DATA_PARALLEL_GROUP = group
                    _DATA_PARALLEL_RANKS = ranks

    # Build the tensor parallel groups.
    global _TENSOR_PARALLEL_GROUP
    global _TENSOR_PARALLEL_RANKS
    assert (
        _TENSOR_PARALLEL_GROUP is None
    ), "tensor parallel group is already initialized"
    assert (
        _TENSOR_PARALLEL_RANKS is None
    ), "tensor parallel ranks are already initialized"
    for i in range(dpsize):
        for j in range(pipeline_parallel_size):
            for k in range(context_parallel_size):
                ranks = groups[i, j, k, :].tolist()
                group = tdist.new_group(
                    groups[i, j, k, :].tolist(),
                    backend=tensor_parallel_backend,
                    timeout=timeout,
                )
                if i == found[0] and j == found[1] and k == found[2]:
                    _TENSOR_PARALLEL_GROUP = group
                    _TENSOR_PARALLEL_RANKS = ranks

    # Build the pipeline parallel groups.
    global _PIPELINE_PARALLEL_GROUP
    global _PIPELINE_PARALLEL_RANKS
    assert (
        _PIPELINE_PARALLEL_GROUP is None
    ), "Pipeline parallel group is already initialized"
    for i in range(dpsize):
        for j in range(context_parallel_size):
            for k in range(tensor_parallel_size):
                ranks = groups[i, :, j, k].tolist()
                group = tdist.new_group(
                    ranks, backend=pipeline_parallel_backend, timeout=timeout
                )
                if i == found[0] and j == found[2] and k == found[3]:
                    _PIPELINE_PARALLEL_GROUP = group
                    _PIPELINE_PARALLEL_RANKS = ranks

    # Build the context parallel groups.
    global _CONTEXT_PARALLEL_GROUP
    global _CONTEXT_PARALLEL_GROUP_RANKS

    assert (
        _CONTEXT_PARALLEL_GROUP is None
    ), "Context parallelism is already initialized."
    for i in range(dpsize):
        for j in range(pipeline_parallel_size):
            for k in range(tensor_parallel_size):
                ranks = groups[i, j, :, k].tolist()
                group = tdist.new_group(
                    ranks, backend=context_parallel_backend, timeout=timeout
                )
                if i == found[0] and j == found[1] and k == found[3]:
                    _CONTEXT_PARALLEL_GROUP = group
                    _CONTEXT_PARALLEL_GROUP_RANKS = ranks

print_config_tree(cfg, resolve=True, save_to_file=True, verbose=True, style='tree', print_order=None, highlight=True, outfile=None)

Prints the contents of a DictConfig as a tree structure using the Rich library.

• cfg: A DictConfig composed by Hydra.
• print_order: Determines in what order config components are printed.
• resolve: Whether to resolve reference fields of DictConfig.
• save_to_file: Whether to export config to the hydra output folder.

Source code in src/ezpz/configs.py

def print_config_tree(
    cfg: DictConfig,
    resolve: bool = True,
    save_to_file: bool = True,
    verbose: bool = True,
    style: str = "tree",
    print_order: Optional[Sequence[str]] = None,
    highlight: bool = True,
    outfile: Optional[Union[str, os.PathLike, Path]] = None,
) -> Tree:
    """Prints the contents of a DictConfig as a tree structure using the Rich
    library.

    - cfg: A DictConfig composed by Hydra.
    - print_order: Determines in what order config components are printed.
    - resolve: Whether to resolve reference fields of DictConfig.
    - save_to_file: Whether to export config to the hydra output folder.
    """
    from rich.console import Console
    from ezpz.log.config import STYLES
    from rich.theme import Theme

    name = cfg.get("_target_", "cfg")
    console = Console(record=True, theme=Theme(STYLES))
    tree = Tree(label=name, highlight=highlight)
    queue = []
    # add fields from `print_order` to queue
    if print_order is not None:
        for field in print_order:
            (
                queue.append(field)
                if field in cfg
                else log.warning(
                    f"Field '{field}' not found in config. "
                    f"Skipping '{field}' config printing..."
                )
            )
    # add all the other fields to queue (not specified in `print_order`)
    for field in cfg:
        if field not in queue:
            queue.append(field)
    # generate config tree from queue
    for field in queue:
        branch = tree.add(field, highlight=highlight)  # , guide_style=style)
        config_group = cfg[field]
        if isinstance(config_group, DictConfig):
            branch_content = str(
                OmegaConf.to_yaml(config_group, resolve=resolve)
            )
            branch.add(Text(branch_content, style="red"))
        else:
            branch_content = str(config_group)
            branch.add(Text(branch_content, style="blue"))
    if verbose or save_to_file:
        console.print(tree)
        if save_to_file:
            outfpath = (
                Path(os.getcwd()).joinpath("config_tree.log")
                if outfile is None
                else Path(outfile)
            )
            console.save_text(outfpath.as_posix())
    return tree

print_dist_setup(framework=None, hostfile=None)

Print distributed setup.

Parameters:

Name	Type	Description	Default
framework	str	Framework to use. Defaults to None.	None
hostfile	PathLike	Path to the hostfile. Defaults to None.	None

Returns:

Name	Type	Description
str	str	String containing the distributed setup.

Source code in src/ezpz/dist.py

def print_dist_setup(
    framework: Optional[str] = None,
    hostfile: Optional[PathLike] = None,
) -> str:
    """Print distributed setup.

    Args:
        framework (str, optional): Framework to use. Defaults to None.
        hostfile (PathLike, optional): Path to the hostfile. Defaults to None.

    Returns:
        str: String containing the distributed setup.
    """
    rank = get_rank()
    wst = get_world_size(total=True)
    wsa = get_world_size(in_use=True)
    # world_size = get_world_size()
    local_rank = get_local_rank()
    gpus_per_node = get_gpus_per_node()
    hostfile = get_hostfile_with_fallback(hostfile)
    # NOTE:
    # We ensure that num_nodes is AT LEAST 1
    # since if gpus_per_node > wsa, wsa // gpus_per_node = 0
    # if gpus_per_node > wsa, wsa // gpus_per_node = 0
    num_nodes = max((wsa // gpus_per_node, 1))
    num_nodes_from_hostfile = get_num_nodes()
    # assert num_nodes_from_hostfile == num_nodes
    # if num_nodes != num_nodes_from_hostfile:
    #     logger.critical(f'{num_nodes=} vs. {num_nodes_from_hostfile=} ??')
    node = get_node_index()
    device = None
    # if framework.lower() in {'pt', 'torch', 'pytorch'}:
    device = get_torch_device_type()
    rank_len = len(str(rank))
    ws_len = len(str(wsa))
    lr_len = len(str(local_rank))
    gpn_len = len(str(gpus_per_node))
    node_len = len(str(node))
    num_nodes_len = len(str(num_nodes))
    dist_list = [
        f"[{device=}]",
        f"[{rank=:>{rank_len}}/{(wsa - 1):<{ws_len}}]",
        f"[{local_rank=:>{lr_len}}/{gpus_per_node - 1:<{gpn_len}}]",
        f"[{node=:>{node_len}}/{(num_nodes - 1):<{num_nodes_len}}]",
    ]
    if framework is not None:
        dist_list.append(f"[{framework=}]")
    dist_str = "".join(dist_list)
    logger.info(f"{dist_str}")
    if rank == 0:
        if wsa > 1000:
            logger.warning(
                f"WORLD_SIZE={wsa} > 1000, only printing on RANK={rank}"
            )
        logger.warning(
            f'Using [{wsa} / {wst}] available "{device}" devices !!'
        )
        if num_nodes_from_hostfile != num_nodes:
            logger.critical(
                f"num_nodes_from_hostfile = [{num_nodes_from_hostfile=}]"
                f"vs."
                f"[{wsa=} // {gpus_per_node=}] = {num_nodes}"
                r"¯\_(ツ)_/¯ ??"
            )
    return dist_str

query_environment()

Query environment variables for info about distributed setup

Returns:

Type	Description
dict[str, int]	dict[str, int]: A dictionary containing the distributed setup information. Includes keys like 'world_size', 'rank', and 'local_rank'. If the environment variables are not set, it falls back to using get_world_size(), get_rank(), and get_local_rank() functions.

Example

env_info = query_environment() print(env_info)

Source code in src/ezpz/dist.py

def query_environment() -> dict[str, int]:
    """Query environment variables for info about distributed setup

    Returns:
        dict[str, int]: A dictionary containing the distributed setup information.
            Includes keys like 'world_size', 'rank', and 'local_rank'.
            If the environment variables are not set, it falls back to using
            `get_world_size()`, `get_rank()`, and `get_local_rank()` functions.

    Example:
        >>> env_info = query_environment()
        >>> print(env_info)
        {'world_size': 4, 'rank': 0, 'local_rank': 0}
    """
    ws = os.environ.get("WORLD_SIZE", None)
    r = os.environ.get("RANK", None)
    lr = os.environ.get("LOCAL_RANK", None)
    if ws is not None and r is not None and lr is not None:
        return {
            "world_size": int(ws),
            "rank": int(r),
            "local_rank": int(lr),
            # 'machine': machine,
        }
    return {
        "world_size": int(get_world_size()),
        "rank": int(get_rank()),
        "local_rank": int(get_local_rank()),
    }

run_bash_command(cmd)

Run a bash command and return the output. Args: cmd (str): The command to run.

Returns:

Name	Type	Description
Any	Any	The output of the command.

Source code in src/ezpz/dist.py

def run_bash_command(cmd: str) -> Any:
    """
    Run a bash command and return the output.
    Args:
        cmd (str): The command to run.

    Returns:
        Any: The output of the command.
    """
    import subprocess
    import shlex

    process = subprocess.Popen(
        shlex.split(cmd, posix=True),
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE,
    )
    output, error = process.communicate()
    if process.returncode != 0:
        raise Exception(
            f"Command failed with return code {process.returncode}.\n"
            f"stdout: {output.decode().strip()}\n"
            f"stderr: {error.decode().strip()}"
        )
    if error:
        raise Exception(error.decode())
    else:
        return output

seed_everything(seed)

Set random seed for reproducibility.

Parameters:

Name	Type	Description	Default
seed	int	Random seed to set.	required

Source code in src/ezpz/dist.py

def seed_everything(seed: int) -> None:
    """Set random seed for reproducibility.

    Args:
        seed (int): Random seed to set.
    """
    import torch
    import numpy as np
    import random

    random.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    np.random.seed(seed)
    _ = torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(seed)
    if torch.xpu.is_available():
        torch.xpu.manual_seed(seed)

setup(framework='pytorch', backend='DDP', port='5432', seed=None, precision=None, ngpus=None)

Setup distributed environment for the specified framework.

Parameters:

Name	Type	Description	Default
framework	str	The framework to use for distributed training. Defaults to "pytorch".	'pytorch'
backend	str	The backend to use for distributed training. Defaults to "DDP".	'DDP'
port	str	The port to use for distributed communication. Defaults to "5432".	'5432'
seed	int	Random seed for reproducibility. Defaults to None.	None
precision	str	Precision to use for training. Defaults to None.	None
ngpus	int	Number of GPUs to use. Defaults to None.	None

Returns:

Type	Description
	None

Source code in src/ezpz/dist.py

def setup(
    framework: str = "pytorch",
    backend: str = "DDP",
    port: str = "5432",
    seed: Optional[int] = None,
    precision: Optional[str] = None,
    ngpus: Optional[int] = None,
):
    """
    Setup distributed environment for the specified framework.

    Args:
        framework (str): The framework to use for distributed training.
            Defaults to "pytorch".
        backend (str): The backend to use for distributed training.
            Defaults to "DDP".
        port (str): The port to use for distributed communication.
            Defaults to "5432".
        seed (int, optional): Random seed for reproducibility. Defaults to None.
        precision (str, optional): Precision to use for training. Defaults to None.
        ngpus (int, optional): Number of GPUs to use. Defaults to None.

    Returns:
        None
    """
    return (
        setup_tensorflow(precision=precision, ngpus=ngpus)
        if framework in {"tensorflow", "tf", "t"}
        else setup_torch(backend=backend, port=port, seed=seed)
    )

setup_torch(framework=None, backend=None, port=None, seed=None, timeout=None, verbose=False, tensor_parallel_size=1, pipeline_parallel_size=1, context_parallel_size=1, tensor_parallel_backend=None, pipeline_parallel_backend=None, context_parallel_backend=None, data_parallel_backend=None)

Setup torch.

Parameters:

Name	Type	Description	Default
backend	str	Backend to use. Defaults to None.	None
port	str | int	Port to use. Defaults to None.	None
seed	int	Seed to use. Defaults to None.	None
timeout	str | int	Timeout to use. Defaults to None.	None
verbose	bool	Whether to print the info. Defaults to False.	False
tensor_parallel_size	int	Tensor parallel size. Defaults to 1.	1
pipeline_parallel_size	int	Pipeline parallel size. Defaults to 1.	1
context_parallel_size	int	Context parallel size. Defaults to 1.	1
tensor_parallel_backend	str	Tensor parallel backend. Defaults to None.	None
pipeline_parallel_backend	str	Pipeline parallel backend. Defaults to None.	None
context_parallel_backend	str	Context parallel backend. Defaults to None.	None
data_parallel_backend	str	Data parallel backend. Defaults to None.	None

Returns:

Name	Type	Description
int	int	Rank of the process.

Source code in src/ezpz/dist.py

def setup_torch(
    framework: Optional[str] = None,
    backend: Optional[str] = None,
    port: Optional[str | int] = None,
    seed: Optional[int] = None,
    timeout: Optional[str | int] = None,
    verbose: Optional[bool] = False,
    tensor_parallel_size: int = 1,
    pipeline_parallel_size: int = 1,
    context_parallel_size: int = 1,
    tensor_parallel_backend: Optional[str] = None,
    pipeline_parallel_backend: Optional[str] = None,
    context_parallel_backend: Optional[str] = None,
    data_parallel_backend: Optional[str] = None,
) -> int:
    """Setup torch.

    Args:
        backend (str, optional): Backend to use. Defaults to None.
        port (str | int, optional): Port to use. Defaults to None.
        seed (int, optional): Seed to use. Defaults to None.
        timeout (str | int, optional): Timeout to use. Defaults to None.
        verbose (bool, optional): Whether to print the info. Defaults to False.
        tensor_parallel_size (int, optional): Tensor parallel size. Defaults to 1.
        pipeline_parallel_size (int, optional): Pipeline parallel size. Defaults to 1.
        context_parallel_size (int, optional): Context parallel size. Defaults to 1.
        tensor_parallel_backend (str, optional): Tensor parallel backend. Defaults to None.
        pipeline_parallel_backend (str, optional): Pipeline parallel backend. Defaults to None.
        context_parallel_backend (str, optional): Context parallel backend. Defaults to None.
        data_parallel_backend (str, optional): Data parallel backend. Defaults to None.

    Returns:
        int: Rank of the process.
    """
    device = get_torch_device()
    # if ACCELERATOR_TYPE == 'NvidiaGPU' and device == 'cuda':
    #     os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
    #     torch.backends.cudnn.deterministic = True     # type:ignore
    #     torch.backends.cudnn.benchmark = True         # type:ignore
    #     torch.backends.cudnn.allow_tf32 = True        # type:ignore
    #     torch.backends.cuda.matmul.allow_tf32 = True  # type:ignore
    # torch.use_deterministic_algorithms(True)
    ws_from_env = os.environ.get("WORLD_SIZE", None)
    framework = "DDP" if framework is None else framework
    framework = framework.lower()
    backend = str(get_torch_backend()).lower()
    if ws_from_env is not None and ws_from_env == "1":
        logger.info(
            f"Running on a single {device}, not initializing torch.distributed!"
        )
        rank = 0
        world_size = 1
        local_rank = 0
        local_size = 1
        num_nodes = 1
    else:
        dsetup = setup_torch_distributed(
            framework=framework,
            backend=backend,
            port=port,
            timeout=timeout,
            tensor_parallel_size=int(tensor_parallel_size),
            pipeline_parallel_size=int(pipeline_parallel_size),
            context_parallel_size=int(context_parallel_size),
            tensor_parallel_backend=tensor_parallel_backend,
            pipeline_parallel_backend=pipeline_parallel_backend,
            context_parallel_backend=context_parallel_backend,
            data_parallel_backend=data_parallel_backend,
        )
        rank = dsetup["rank"]
        world_size = dsetup["world_size"]
        local_rank = dsetup["local_rank"]
        try:
            local_size = get_gpus_per_node()
        except Exception:
            local_size = 1

        try:
            num_nodes = get_num_nodes()
        except Exception:
            num_nodes = 1
    os.environ["RANK"] = str(rank)
    os.environ["LOCAL_RANK"] = str(local_rank)
    os.environ["NUM_NODES"] = str(num_nodes)
    os.environ["LOCAL_SIZE"] = str(local_size)
    os.environ["WORLD_SIZE"] = str(world_size)
    # nthreads = os.environ.get('OMP_NUM_THREADS', None)
    # if ACCELERATOR_TYPE == "IntelGPU" and device == "xpu":
    if torch.xpu.is_available():
        torch.xpu.set_device(local_rank)
        # try:
        #     import intel_extension_for_pytorch as ipex  # type:ignore[missingTypeStubs]
        # except Exception:
        #     ipex = None
        # if ipex is not None:
        #     logger.debug(f"Using ipex from: {ipex.__file__}")
        #
        # try:
        #     import oneccl_bindings_for_pytorch as oneccl_bpt  # type:ignore[missingTypeStubs]
        # except Exception:
        #     oneccl_bpt = None
        # if oneccl_bpt is not None:
        #     logger.debug(f"Using oneccl_bindings from: {oneccl_bpt.__file__}")
        #
        #     # logger.warning(f'Using {get_torch_device()}:{get_local_rank()}')
        #     # os.environ['CCL_LOCAL_RANK'] = str(local_rank)
        #     # os.environ['CCL_LOCAL_SIZE'] = str(local_size)
    if seed is not None:
        seed_everything(seed * (rank + 1) * (local_rank + 1))
    if rank == 0:
        if backend in {"ds", "deepspeed", "dspeed"}:
            from ezpz.configs import git_ds_info

            git_ds_info()
        _ = get_dist_info(verbose=verbose)
        if verbose:
            _ = print_dist_setup()
    if world_size > 1:
        barrier()

    if rank == 0:
        logger.info(
            f"Using {device=} with {backend=} "
            f"+ '{get_torch_backend()}' "
            "for distributed training."
        )
    lrank = len(str(world_size - 1))
    # nz = lrank - len(str(rank))
    hn = socket.gethostname()
    psizes = [f"['{hn}']" + f"[{rank:>{lrank}}/{world_size - 1:<{lrank}}] "]
    if (
        tensor_parallel_size > 1
        or context_parallel_size > 1
        or pipeline_parallel_size > 1
    ):
        import ezpz.tp

        tpsize = ezpz.tp.get_tensor_parallel_world_size()
        cpsize = ezpz.tp.get_context_parallel_world_size()
        ppsize = ezpz.tp.get_pipeline_parallel_world_size()
        dpsize = ezpz.tp.get_data_parallel_world_size()
        if cpsize > 1 or ppsize > 1 or tpsize > 1:
            if cpsize > 1:
                lcp = len(str(cpsize - 1))
                cprank = ezpz.tp.get_context_parallel_rank()
                # cpranks = ezpz.tp.get_context_parallel_ranks()
                psizes.append(f"[cp:{cprank:>{lcp}}/{cpsize - 1:<{lcp}}]")
                barrier(group=ezpz.tp.get_context_parallel_group())
            if ppsize > 1:
                pprank = ezpz.tp.get_pipeline_parallel_rank()
                # ppranks = ezpz.tp.get_pipeline_parallel_ranks()
                lpp = len(str(ppsize - 1))
                psizes.append(f"[pp:{pprank:>{lpp}}/{ppsize - 1:<{lpp}}]")
                barrier(group=ezpz.tp.get_pipeline_parallel_group())
            if tpsize > 1:
                ltp = len(str(tpsize - 1))
                tprank = ezpz.tp.get_tensor_parallel_rank()
                # tpranks = ezpz.tp.get_tensor_parallel_ranks()
                psizes.append(f"[tp:{tprank:>{ltp}}/{tpsize - 1:<{ltp}}]")
                barrier(group=ezpz.tp.get_tensor_parallel_group())
            if dpsize > 1:
                ldp = len(str(dpsize - 1))
                dprank = ezpz.tp.get_data_parallel_rank()
                # dpranks = ezpz.tp.get_data_parallel_ranks()
                psizes.append(f"[dp:{dprank:>{ldp}}/{dpsize - 1:<{ldp}}]")
                barrier(group=ezpz.tp.get_data_parallel_group())
    logger.info("".join(psizes))
    barrier()
    return rank

setup_torch_DDP(port='2345', timeout=3600, backend=None)

Setup PyTorch Distributed Data Parallel (DDP) environment. Args: port (str, optional): The port to use for distributed communication. Defaults to "2345". timeout (int | str | timedelta, optional): Timeout for the process group initialization. Defaults to 3600 seconds. backend (str, optional): The backend to use for distributed training. Defaults to None, which will use the default backend based on the device.

Returns:

Type	Description
dict[str, int]	dict[str, int]: A dictionary containing the distributed setup information. Includes keys like 'world_size', 'rank', and 'local_rank'.

Source code in src/ezpz/dist.py

def setup_torch_DDP(
    port: str = "2345",
    timeout: int | str | timedelta = 3600,
    backend: Optional[str] = None,
) -> dict[str, int]:
    """
    Setup PyTorch Distributed Data Parallel (DDP) environment.
    Args:
        port (str, optional): The port to use for distributed communication.
            Defaults to "2345".
        timeout (int | str | timedelta, optional): Timeout for the process group initialization.
            Defaults to 3600 seconds.
        backend (str, optional): The backend to use for distributed training.
            Defaults to None, which will use the default backend based on the device.

    Returns:
        dict[str, int]: A dictionary containing the distributed setup information.
            Includes keys like 'world_size', 'rank', and 'local_rank'.
    """
    if not isinstance(timeout, timedelta):
        timeout = timedelta(seconds=int(timeout))
    os_rank = os.environ.get("RANK", None)
    os_world_size = os.environ.get("WORLD_SIZE", None)
    os_local_rank = os.environ.get("LOCAL_RANK", None)
    world_size = int(get_world_size())
    rank = int(get_rank())
    local_rank = int(get_local_rank())
    # ensure there is no funny business going on
    if os_rank and int(os_rank) != int(rank):
        logger.warning(f"Mismatch between {os_rank=} and {rank=}")
    if os_world_size and int(os_world_size) != int(world_size):
        logger.warning(f"Mismatch between {os_world_size=} and {world_size=}")
    if os_local_rank and int(os_local_rank) != int(local_rank):
        logger.warning(f"Mismatch between {os_local_rank=} and {local_rank=}")
    # now, set these variables explicitly in the process' environment
    os.environ["LOCAL_RANK"] = str(local_rank)
    os.environ["RANK"] = str(rank)
    os.environ["WORLD_SIZE"] = str(world_size)
    # get `hostname` ONLY from rank 0
    master_addr = socket.gethostname() if rank == 0 else None
    if (mn := ezpz.get_machine().lower()) in {
        "aurora",
        "polaris",
        "sirius",
    }:
        master_addr = f"{master_addr}.hsn.cm.{mn}.alcf.anl.gov"
    elif mn == "sophia":
        master_addr = f"{master_addr}.lab.alcf.anl.gov"
    # check if we have specified a 'MASTER_PORT' explicitly, if so, use this
    free_port = str(get_free_port()) if rank == 0 else None
    eport = os.environ.get("MASTER_PORT", free_port)
    if eport is not None:
        _ = (
            logger.info(f"Caught MASTER_PORT={eport} from environment!")
            if rank == 0
            else None
        )
    else:
        eport = port
    # grab it from rank 0
    master_port = eport if rank == 0 else None
    # broadcast it to make sure everyones tapped in
    master_port = MPI.COMM_WORLD.bcast(master_port, root=0)
    master_addr = MPI.COMM_WORLD.bcast(master_addr, root=0)
    # set it explicitly in each process' environment
    os.environ["MASTER_ADDR"] = master_addr
    os.environ["MASTER_PORT"] = master_port
    # now, torch is ready for us
    if rank == 0:
        logger.info(
            "\n".join(
                [
                    "Using torch.distributed.init_process_group with",
                    f"- {master_addr=}",
                    f"- {master_port=}",
                    f"- {world_size=}",
                    f"- {rank=}",
                    f"- {local_rank=}",
                    f"- {timeout=}",
                    f"- {backend=}",
                ]
            )
        )
    init_process_group(
        rank=rank,
        world_size=world_size,
        timeout=timeout,
        backend=backend,
    )
    return {"world_size": world_size, "rank": rank, "local_rank": local_rank}

setup_torch_distributed(framework=None, backend=None, tensor_parallel_size=1, pipeline_parallel_size=1, context_parallel_size=1, tensor_parallel_backend=None, pipeline_parallel_backend=None, context_parallel_backend=None, data_parallel_backend=None, port=None, timeout=None)

Setup distributed environment for PyTorch.

Parameters:

Name	Type	Description	Default
framework	str	The framework to use for distributed training. Defaults to None, which will use "ddp".	None
backend	str	The backend to use for distributed training. Defaults to None, which will use the default backend based on the device.	None
tensor_parallel_size	int	Size of tensor parallelism. Defaults to 1.	1
pipeline_parallel_size	int	Size of pipeline parallelism. Defaults to 1.	1
context_parallel_size	int	Size of context parallelism. Defaults to 1.	1
tensor_parallel_backend	str	Backend for tensor parallelism. Defaults to None.	None
pipeline_parallel_backend	str	Backend for pipeline parallelism. Defaults to None.	None
context_parallel_backend	str	Backend for context parallelism. Defaults to None.	None
data_parallel_backend	str	Backend for data parallelism. Defaults to None.	None
port	str | int	Port for distributed communication. Defaults to "1234".	None
timeout	str | int	Timeout for distributed initialization. Defaults to 3600 seconds.	None

Returns:

Type	Description
dict[str, int]	dict[str, int]: A dictionary containing the distributed setup information. Includes keys like 'world_size', 'rank', and 'local_rank'.

Raises:

Type	Description
AssertionError	If the framework is not one of the supported frameworks. Supported frameworks are "ddp", "ds", "deepspeed", "horovod", and "hvd".
ValueError	If the backend is not one of the supported backends. Supported backends are "ddp", "ds", "deepspeed", "horovod", and "hvd".

Example

setup_torch_distributed( ... framework="ddp", ... backend="nccl", ... tensor_parallel_size=2, ... pipeline_parallel_size=1, ... context_parallel_size=1, ... port=1234, ... timeout=3600, ... )

Source code in src/ezpz/dist.py

def setup_torch_distributed(
    framework: Optional[str] = None,
    backend: Optional[str] = None,
    tensor_parallel_size: int = 1,
    pipeline_parallel_size: int = 1,
    context_parallel_size: int = 1,
    tensor_parallel_backend: Optional[str] = None,
    pipeline_parallel_backend: Optional[str] = None,
    context_parallel_backend: Optional[str] = None,
    data_parallel_backend: Optional[str] = None,
    port: Optional[str | int] = None,
    timeout: Optional[str | int] = None,
) -> dict[str, int]:
    """
    Setup distributed environment for PyTorch.

    Args:
        framework (str, optional): The framework to use for distributed training.
            Defaults to None, which will use "ddp".
        backend (str, optional): The backend to use for distributed training.
            Defaults to None, which will use the default backend based on the device.
        tensor_parallel_size (int, optional): Size of tensor parallelism. Defaults to 1.
        pipeline_parallel_size (int, optional): Size of pipeline parallelism. Defaults to 1.
        context_parallel_size (int, optional): Size of context parallelism. Defaults to 1.
        tensor_parallel_backend (str, optional): Backend for tensor parallelism. Defaults to None.
        pipeline_parallel_backend (str, optional): Backend for pipeline parallelism. Defaults to None.
        context_parallel_backend (str, optional): Backend for context parallelism. Defaults to None.
        data_parallel_backend (str, optional): Backend for data parallelism. Defaults to None.
        port (str | int, optional): Port for distributed communication. Defaults to "1234".
        timeout (str | int, optional): Timeout for distributed initialization. Defaults to 3600 seconds.

    Returns:
        dict[str, int]: A dictionary containing the distributed setup information.
            Includes keys like 'world_size', 'rank', and 'local_rank'.

    Raises:
        AssertionError: If the framework is not one of the supported frameworks.
            Supported frameworks are "ddp", "ds", "deepspeed", "horovod", and "hvd".
        ValueError: If the backend is not one of the supported backends.
            Supported backends are "ddp", "ds", "deepspeed", "horovod", and "hvd".

    Example:
        >>> setup_torch_distributed(
        ...     framework="ddp",
        ...     backend="nccl",
        ...     tensor_parallel_size=2,
        ...     pipeline_parallel_size=1,
        ...     context_parallel_size=1,
        ...     port=1234,
        ...     timeout=3600,
        ... )
    """
    framework = "ddp" if framework is None else framework
    # if str(framework).lower() not in {"ddp", "ds", "deepspeed", "horovod", "hvd"}:
    assert str(framework).lower() in {
        "ddp",
        "ds",
        "deepspeed",
        "horovod",
        "hvd",
    }, (
        f"Invalid framework: {framework=}, expected one of "
        f"{'ddp', 'ds', 'deepspeed', 'horovod', 'hvd'}"
    )

    DEFAULT_TIMEOUT = os.environ.get("TORCH_DDP_TIMEOUT", 3600)
    timeout = (
        DEFAULT_TIMEOUT if timeout is None else (
            int(timeout) if isinstance(timeout, str) else timeout
        )
    )
    port = (
        "1234"
        if port is None
        else str(port)
        if isinstance(port, int)
        else port
    )
    rank = get_rank()
    world_size = get_world_size()
    local_rank = get_local_rank()
    fw = str(framework).lower()
    be = (
        str(get_torch_backend()).lower()
        if backend is None
        else str(backend).lower()
    )
    # be = str(framework).lower()
    # assert fw in {"ds", "deepspeed", "ddp", "horovod", "hvd"}, (
    #     f"Invalid backend: {framework=}, expected one of "
    #     f"{'ds', 'deepspeed', 'ddp', 'horovod', 'hvd'}"
    # )
    # assert be in BACKENDS['pytorch']
    if rank == 0:
        logger.info(
            " ".join(
                [
                    f"Using {fw=} with",
                    "torch_{device,backend}=",
                    "{" + f"{get_torch_device_type()}, {be}" + "}",
                ]
            )
        )
    if fw == "ddp":
        dsetup = setup_torch_DDP(port, timeout, backend=be)
        world_size = dsetup["world_size"]
        rank = dsetup["rank"]
        local_rank = dsetup["local_rank"]
        if torch.cuda.is_available():
            torch.cuda.set_device(local_rank)
    elif fw in {"deepspeed", "ds"}:
        init_deepspeed(timeout=timeout)
        world_size = get_world_size()
        rank = get_rank()
        local_rank = get_local_rank()
    elif fw in {"horovod", "hvd"}:
        import horovod.torch as hvd  # type:ignore noqa

        _ = None if hvd.is_initialized() else hvd.init()  # type:ignore
        # hvd.init() if not hvd.is_initialized() else None
        rank = hvd.rank()  # type:ignore
        world_size = hvd.size()  # type:ignore
        local_rank = hvd.local_rank()  # type:ignore
        if torch.cuda.is_available():
            torch.cuda.set_device(hvd.local_rank())  # type:ignore
    else:
        raise ValueError(f"Unable to parse backend: {be=}")

    if (
        tensor_parallel_size > 1
        or context_parallel_size > 1
        or pipeline_parallel_size > 1
    ):
        ezpz.tp.initialize_tensor_parallel(
            tensor_parallel_size=tensor_parallel_size,
            pipeline_parallel_size=pipeline_parallel_size,
            context_parallel_size=context_parallel_size,
            tensor_parallel_backend=tensor_parallel_backend,
            pipeline_parallel_backend=pipeline_parallel_backend,
            context_parallel_backend=context_parallel_backend,
            data_parallel_backend=data_parallel_backend,
            timeout=timedelta(seconds=timeout),
        )

    os.environ["world_size"] = str(world_size)
    os.environ["RANK"] = str(rank)
    os.environ["LOCAL_RANK"] = str(local_rank)

    return {"world_size": world_size, "rank": rank, "local_rank": local_rank}

setup_wandb(project_name=None, entity=None, config=None, start_method='thread', outdir=None, init_timeout=300)

Setup wandb for logging.

Parameters:

Name	Type	Description	Default
project_name	str	The name of the project. Defaults to None.	None
entity	str	The entity name. Defaults to None.	None
config	dict | DictConfig	The configuration dictionary. Defaults to None.	None
start_method	str	The start method for wandb. Defaults to "thread".	'thread'
outdir	str | Path | PathLike	The output directory. Defaults to None.	None
init_timeout	int	The timeout for wandb initialization. Defaults to 300.	300

Example

setup_wandb(project_name="my_project", entity="my_entity")

Source code in src/ezpz/dist.py

def setup_wandb(
    project_name: Optional[str] = None,
    entity: Optional[str] = None,
    config: Optional[dict | DictConfig] = None,
    start_method: str = "thread",
    outdir: Optional[str | Path | os.PathLike] = None,
    init_timeout: int = 300,
):
    """Setup wandb for logging.

    Args:
        project_name (str, optional): The name of the project. Defaults to None.
        entity (str, optional): The entity name. Defaults to None.
        config (dict | DictConfig, optional): The configuration dictionary. Defaults to None.
        start_method (str, optional): The start method for wandb. Defaults to "thread".
        outdir (str | Path | os.PathLike, optional): The output directory. Defaults to None.
        init_timeout (int, optional): The timeout for wandb initialization. Defaults to 300.

    Example:
        >>> setup_wandb(project_name="my_project", entity="my_entity")
    """
    WANDB_DISABLED = os.environ.get("WANDB_DISABLED", False)
    WANDB_MODE = os.environ.get("WANDB_MODE", "").lower()
    if WANDB_DISABLED or WANDB_MODE == "disabled":
        logger.warning(
            f"Logging with W&B is disabled!, caught: {WANDB_DISABLED=}"
        )
        return None

    try:
        import wandb
    except (ImportError, ModuleNotFoundError) as e:
        logger.warning(
            "Unable to import `wandb`. Install with `pip install wandb`"
        )
        raise e

    outdir = (
        Path(os.getcwd()).as_posix()
        if outdir is None
        else Path(outdir).as_posix()
    )
    rank = get_rank()
    project_name = (
        project_name
        if project_name is not None
        else os.environ.get(
            "WB_PROJECT",
            os.environ.get(
                "WANDB_PROJECT",
                os.environ.get("WB_PROJECT_NAME", None),
            ),
        )
    )
    if project_name is None:
        import sys

        frame = sys._getframe().f_back
        assert frame is not None
        calling_module = frame.f_code.co_filename
        fp = Path(calling_module)
        project_name = f"{fp.parent.stem}.{fp.stem}"

    logger.info(f"Setting up wandb from {rank=}")
    logger.info(f"Using WB_PROJECT={project_name}")
    tensorboard_dir = (
        os.environ.get("TENSORBOARD_DIR", None)
        if config is None
        else config.get("tensorboard_dir", None)
    )
    if tensorboard_dir is not None:
        logger.info(f"Patching tensorboard from {tensorboard_dir}")
        try:
            wandb.tensorboard.patch(root_logdir=tensorboard_dir)  # type:ignore
        except Exception as exc:
            logger.exception(exc)
    # wbrun_id = wandb.util.generate_id()
    now = datetime.datetime.now()
    dstr = now.strftime("%Y-%m-%d-%H%M%S")
    run = wandb.init(
        entity=entity,
        # resume='allow',
        dir=outdir,
        sync_tensorboard=(tensorboard_dir is not None),  # True,
        project=(project_name if project_name is not None else None),
        # dir=(tensorboard_dir if tensorboard_dir is not None else None),
        settings=wandb.Settings(
            start_method=start_method, init_timeout=init_timeout
        ),
    )
    assert run is not None and run is wandb.run
    # run.log_code(HERE.as_posix(), include_fn=include_file)
    logger.info(f"wandb.run=[{run.name}]({run.url})")
    if (
        wandb is not None
        and wandb.run is not None
        and "DIST_INFO" not in wandb.run.config
    ):
        wandb.run.config.update({"DIST_INFO": get_dist_info()})
    torch_version = torch.__version__
    torch_file = torch.__file__
    run.config.update(
        {
            "created_at": dstr,
            "day": ezpz.get_timestamp("%d"),
            "ezpz_file": ezpz.__file__,
            "ezpz_version": ezpz.__version__,
            "hostname": get_hostname(),
            "month": ezpz.get_timestamp("%m"),
            "outdir": os.getcwd(),
            "pytorch_backend": str(get_torch_backend()).lower(),
            "torch_version": torch_version,
            "torch_version_as_float": get_torch_version_as_float(),
            "torch_file": torch_file,
            "world_size": get_world_size(),
            "year": ezpz.get_timestamp("%Y"),
            "working_directory": os.getcwd(),
        }
    )
    if config is not None:
        if isinstance(config, DictConfig):
            cfg = OmegaConf.to_container(
                config, resolve=True, throw_on_missing=True
            )
            run.config.update({"config": cfg})
        else:
            run.config.update({"config": config})
    env = {
        k: v
        for k, v in dict(os.environ).items()
        if not k.startswith("_ModuleTable")
    }
    _ = env.pop("LS_COLORS", None)
    _ = env.pop("PS1", None)
    run.config.update({"env": env})
    machine = get_machine()
    logger.info(f"Running on {machine=}")
    run.config.update({"machine": machine})
    model_size = os.environ.get("MODEL_SIZE", None)
    if model_size is not None:
        run.config.update({"MODEL_SIZE": model_size})
    return wandb.run

summarize_dict(d, precision=6)

Summarize a dictionary into a string with formatted key-value pairs.

Parameters:

Name	Type	Description	Default
d	dict	The dictionary to summarize.	required
precision	int	The precision for floating point values. Default: 6.	6

Returns:

Name	Type	Description
str	str	A string representation of the dictionary with formatted key-value pairs.

Source code in src/ezpz/utils.py

def summarize_dict(d: dict, precision: int = 6) -> str:
    """
    Summarize a dictionary into a string with formatted key-value pairs.

    Args:
        d (dict): The dictionary to summarize.
        precision (int): The precision for floating point values. Default: ``6``.

    Returns:
        str: A string representation of the dictionary with formatted key-value pairs.
    """
    return " ".join(
        [format_pair(k, v, precision=precision) for k, v in d.items()]
    )

synchronize(device=None)

Synchronize the given device.

Parameters:

Name	Type	Description	Default
device	device | int | str	The device to synchronize. If None, the default device will be used. Defaults to None.	None

Returns:

Type	Description
	None

Source code in src/ezpz/dist.py

def synchronize(device: Optional[torch.device | int | str] = None):
    """
    Synchronize the given device.

    Args:
        device (torch.device | int | str, optional): The device to synchronize.
            If None, the default device will be used. Defaults to None.

    Returns:
        None
    """
    return (
        torch.cuda.synchronize(device)
        if torch.cuda.is_available()
        else (
            torch.xpu.synchronize(device)
            if torch.xpu.is_available()
            else (
                torch.mps.synchronize()
                if torch.backends.mps.is_available()
                else torch.cpu.synchronize(device)
            )
        )
    )

tensor_parallel_is_initialized()

Check if tensor and data parallel groups are initialized.

Source code in src/ezpz/tp/__init__.py

def tensor_parallel_is_initialized() -> bool:
    """Check if tensor and data parallel groups are initialized."""
    if (
        _TENSOR_PARALLEL_GROUP is None
        or _DATA_PARALLEL_GROUP is None
        or _PIPELINE_PARALLEL_GROUP is None
        or _CONTEXT_PARALLEL_GROUP is None
    ):
        return False
    return True

timeit(func)

Decorator to time a function and log the time taken.

Parameters:

Name	Type	Description	Default
func	Callable	Function to be timed.	required

Example

@timeit def my_function(arg1, arg2): # Function implementation pass

Source code in src/ezpz/dist.py

def timeit(func: Callable):
    """
    Decorator to time a function and log the time taken.

    Args:
        func (Callable): Function to be timed.

    Example:
        @timeit
        def my_function(arg1, arg2):
            # Function implementation
            pass
    """
    try:
        import wandb
    except Exception:
        wandb = None  # type:ignore

    @wraps(func)
    def wrapper(*args, **kwargs):
        t0 = time.perf_counter()
        result = func(*args, **kwargs)
        dt = time.perf_counter() - t0
        fname = getattr(
            func, "__qualname__", getattr(func, "__name__", "unknown")
        )
        logger.info(f"{fname}({args}, {kwargs}) took: {dt=:.4f}s")
        if wandb is not None and wandb.run is not None:
            wandb.log({f"timeit/{fname}": dt})
        return result

    return wrapper

timeitlogit(rank=None, record=True, verbose=False, prefix=None)

Decorator to time a function and log the time taken.

Parameters:

Name	Type	Description	Default
rank	int	Rank of the process. Defaults to None.	None
verbose	bool	Whether to log the time taken. Defaults to True.	False

Example

@timeitlogit(rank=0, verbose=True) def my_function(arg1, arg2): # Function implementation pass

Source code in src/ezpz/dist.py

def timeitlogit(
    rank: Optional[int] = None,
    record: bool = True,
    verbose: bool = False,
    prefix: str | None = None,
):
    """Decorator to time a function and log the time taken.

    Args:
        rank (int, optional): Rank of the process. Defaults to None.
        verbose (bool, optional): Whether to log the time taken. Defaults to True.

    Example:
        @timeitlogit(rank=0, verbose=True)
        def my_function(arg1, arg2):
            # Function implementation
            pass
    """
    rank = get_rank() if rank is None else rank
    prefix = "timeit" if prefix is None else prefix
    try:
        import wandb
    except Exception:
        wandb = None  # type:ignore

    def decorator(func: Callable):
        """Decorator to time a function and log the time taken.

        Args:
            func (Callable): Function to be timed.
        """

        @wraps(func)
        def wrapper(*args, **kwargs):
            t0 = time.perf_counter()
            assert isinstance(rank, int)
            result = func(*args, **kwargs)
            dt = time.perf_counter() - t0
            fname = getattr(
                func, "__qualname__", getattr(func, "__name__", "unknown")
            )
            if record and wandb is not None and wandb.run is not None:
                wandb.log({f"{prefix}/{fname}": dt}, commit=False)
            if verbose and rank == 0:
                arg_str = ", ".join(map(str, args))
                kw_str = ", ".join(f"{k}={v}" for k, v in kwargs.items())
                inner = ", ".join(filter(None, [arg_str, kw_str]))
                logger.info(f"{fname}({inner}) took {dt:.4f} s")
                # if wandb is not None and wandb.run is not None:
                #     wandb.log({f"timeit/{fname}": dt}, commit=False)
            # if verbose:
            #     if rank == 0:
            #         astr = []
            #         if len(args) > 0:
            #             astr.append(f"({args}")
            #         _ = (
            #             astr.append(f", {kwargs})")
            #             if len(kwargs) > 0
            #             else (astr.append(")") if len(args) > 0 else "")
            #         )
            #         zstr = [f"Called: '{fname}' with arguments:"]
            #         if len(astr) > 0:
            #             zstr.append(f"{''.join(astr)}")
            #         zstr.append(f"'{fname}' took: {dt=:.4f} s")
            #         logger.info("\n".join(zstr))
            return result

        return wrapper

    return decorator