ezpz.__init__

This module is part of the ezpz package

ezpz/init.py

History

Source code in src/ezpz/history.py

class History:
    def __init__(self, keys: Optional[list[str]] = None) -> None:
        self.keys = [] if keys is None else keys
        self.history = {}

    def _update(
        self,
        key: str,
        val: Union[Any, ScalarLike, list, torch.Tensor, np.ndarray],
    ):
        try:
            self.history[key].append(val)
        except KeyError:
            self.history[key] = [val]
        return val

    def update(
        self,
        metrics: dict,
        precision: int = 6,
        use_wandb: Optional[bool] = True,
        commit: Optional[bool] = True,
        summarize: Optional[bool] = True,
    ) -> str:
        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,
    ):
        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",
            )

    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.
        """
        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

    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,
    ):
        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)

    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")

    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"
                )

    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)

    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,
        )

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

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.

Source code in src/ezpz/history.py

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.
    """
    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

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}")

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_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_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:
        return ipex.xpu.device_count()  # type:ignore
    return get_cpus_per_node()

get_local_rank()

Return get_rank() % get_gpus_per_node()

Source code in src/ezpz/dist.py

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

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_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_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

Source code in src/ezpz/dist.py

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

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

Source code in src/ezpz/dist.py

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

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_torch(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(
    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)
    backend = "DDP" if backend is None else backend
    backend = 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(
            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"]
        local_size = get_gpus_per_node()
        num_nodes = get_num_nodes()
    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:
    #     tdist.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())
                # tdist.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())
    # tdist.all_gather(psizes)
    logger.info("".join(psizes))
    barrier()
    return rank

setup_torch_distributed(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)

Returns {'world_size': int, 'rank': int, 'local_rank': int}

Source code in src/ezpz/dist.py

def setup_torch_distributed(
    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]:
    """Returns {'world_size': int, 'rank': int, 'local_rank': int}"""
    backend = "DDP" if backend is None else backend
    assert backend.lower() in {
        "ddp",
        "ds",
        "deepspeed",
        "horovod",
        "hvd",
    }
    timeout = (
        3600
        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()
    be = backend.lower()
    # assert be in BACKENDS['pytorch']
    if be == "ddp":
        dsetup = setup_torch_DDP(port, timeout)
        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 be in {"deepspeed", "ds"}:
        init_deepspeed(timeout=timeout)
        world_size = get_world_size()
        rank = get_rank()
        local_rank = get_local_rank()
    elif be in {"horovod", "hvd"}:
        import horovod.torch as hvd  # type:ignore noqa

        _ = None if hvd.is_initialized() else hvd.init()
        # hvd.init() if not hvd.is_initialized() else None
        rank = hvd.rank()
        world_size = hvd.size()
        local_rank = hvd.local_rank()
        if torch.cuda.is_available():
            torch.cuda.set_device(hvd.local_rank())
    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")
    """
    # try:
    #     import wandb
    # except Exception:
    #     wandb = None
    # try:
    #     import wandb
    #
    WANDB_DISABLED = os.environ.get("WANDB_DISABLED", False)
    WANDB_MODE = os.environ.get("WANDB_MODE", "").lower()
    # except Exception:
    #     wandb = None
    #     WANDB_DISABLED = True

    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"),
            "month": ezpz.get_timestamp("%m"),
            "outdir": os.getcwd(),
            "torch_version": torch_version,
            "torch_file": torch_file,
            "world_size": get_world_size(),
            "year": ezpz.get_timestamp("%Y"),
            "ezpz_version": ezpz.__version__,
            "ezpz_file": ezpz.__file__,
        }
    )
    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

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

timeitlogit(rank=None, verbose=True)

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.	True

Source code in src/ezpz/dist.py

def timeitlogit(rank: Optional[int] = None, verbose: bool = True):
    """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.
    """
    rank = get_rank() if rank is None else rank
    try:
        import wandb
    except Exception:
        wandb = None

    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
            if verbose:
                if rank == 0:
                    tstr = [f"`{func.__name__}`"]
                    if len(args) > 0:
                        tstr.append(f"({args}")
                    # _ = tstr.append(f"({args}") if len(args) > 0 else None
                    _ = (
                        tstr.append(f", {kwargs})")
                        if len(kwargs) > 0
                        else (tstr.append(")") if len(args) > 0 else "")
                    )
                    _ = tstr.append(f" took: {dt=:.4f}s")
                    logger.info("".join(tstr))
                if wandb is not None and wandb.run is not None:
                    # logger.info(
                    #     f'Logging timeit/{func.__name__}/{dt=:.4f} to W&B'
                    # )
                    wandb.run.log({f"timeit/{func.__name__}": dt}, commit=False)
            return result

        return wrapper

    return decorator