AutoAndroidController/py/venv/Lib/site-packages/accelerate/utils/deepspeed.py

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import base64
import json
import os
from copy import deepcopy

from torch import optim

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
from .dataclasses import DistributedType
from .imports import is_bnb_available
from .versions import compare_versions


def map_pytorch_optim_to_deepspeed(optimizer):
    """
    Args:
        optimizer: torch.optim.Optimizer

    Returns the DeepSeedCPUOptimizer (deepspeed.ops) version of the optimizer.
    """

    defaults = {k: v for k, v in optimizer.defaults.items() if k in ["lr", "weight_decay"]}

    # Select the DeepSpeedCPUOptimizer based on the original optimizer class.
    # DeepSpeedCPUAdam is the default
    from deepspeed.ops.adam import DeepSpeedCPUAdam

    optimizer_class = DeepSpeedCPUAdam

    # For DeepSpeedCPUAdam (adamw_mode)
    if compare_versions("deepspeed", ">=", "0.3.1"):
        defaults["adamw_mode"] = False
        is_adaw = isinstance(optimizer, optim.AdamW)

        if is_bnb_available() and not is_adaw:
            import bitsandbytes.optim as bnb_opt

            if isinstance(optimizer, (bnb_opt.AdamW, bnb_opt.AdamW32bit)):
                try:
                    is_adaw = optimizer.optim_bits == 32
                except AttributeError:
                    is_adaw = optimizer.args.optim_bits == 32
            else:
                is_adaw = False

        if is_adaw:
            defaults["adamw_mode"] = True

    # For DeepSpeedCPUAdagrad
    if compare_versions("deepspeed", ">=", "0.5.5"):
        # Check if the optimizer is PyTorch's Adagrad.
        is_ada = isinstance(optimizer, optim.Adagrad)
        # If not, and bitsandbytes is available,
        # # check if the optimizer is the 32-bit bitsandbytes Adagrad.
        if is_bnb_available() and not is_ada:
            import bitsandbytes.optim as bnb_opt

            if isinstance(optimizer, (bnb_opt.Adagrad, bnb_opt.Adagrad32bit)):
                try:
                    is_ada = optimizer.optim_bits == 32
                except AttributeError:
                    is_ada = optimizer.args.optim_bits == 32
        if is_ada:
            from deepspeed.ops.adagrad import DeepSpeedCPUAdagrad

            optimizer_class = DeepSpeedCPUAdagrad

    # For DeepSpeedCPULion
    if is_bnb_available(min_version="0.38.0") and compare_versions("deepspeed", ">=", "0.11.0"):
        from bitsandbytes.optim import Lion, Lion32bit

        if isinstance(optimizer, (Lion, Lion32bit)):
            try:
                is_bnb_32bits = optimizer.optim_bits == 32
            except AttributeError:
                is_bnb_32bits = optimizer.args.optim_bits == 32
            if is_bnb_32bits:
                from deepspeed.ops.lion import DeepSpeedCPULion

                optimizer_class = DeepSpeedCPULion

    return optimizer_class(optimizer.param_groups, **defaults)


def get_active_deepspeed_plugin(state):
    """
    Returns the currently active DeepSpeedPlugin.

    Raises:
        ValueError: If DeepSpeed was not enabled and this function is called.
    """
    if state.distributed_type != DistributedType.DEEPSPEED:
        raise ValueError(
            "Couldn't retrieve the active `DeepSpeedPlugin` as none were enabled. "
            "Please make sure that either `Accelerator` is configured for `deepspeed` "
            "or make sure that the desired `DeepSpeedPlugin` has been enabled (`AcceleratorState().select_deepspeed_plugin(name)`) "
            "before calling this function."
        )
    if not isinstance(state.deepspeed_plugins, dict):
        return state.deepspeed_plugins
    return next(plugin for plugin in state.deepspeed_plugins.values() if plugin.selected)


class HfDeepSpeedConfig:
    """
    This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage.

    A `weakref` of this object is stored in the module's globals to be able to access the config from areas where
    things like the Trainer object is not available (e.g. `from_pretrained` and `_get_resized_embeddings`). Therefore
    it's important that this object remains alive while the program is still running.

    [`Trainer`] uses the `HfTrainerDeepSpeedConfig` subclass instead. That subclass has logic to sync the configuration
    with values of [`TrainingArguments`] by replacing special placeholder values: `"auto"`. Without this special logic
    the DeepSpeed configuration is not modified in any way.

    Args:
        config_file_or_dict (`Union[str, Dict]`): path to DeepSpeed config file or dict.

    """

    def __init__(self, config_file_or_dict):
        if isinstance(config_file_or_dict, dict):
            # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
            # modified it, it will not be accepted here again, since `auto` values would have been overridden
            config = deepcopy(config_file_or_dict)
        elif os.path.exists(config_file_or_dict):
            with open(config_file_or_dict, encoding="utf-8") as f:
                config = json.load(f)
        else:
            try:
                try:
                    # First try parsing as JSON directly
                    config = json.loads(config_file_or_dict)
                except json.JSONDecodeError:
                    # If that fails, try base64 decoding
                    config_decoded = base64.urlsafe_b64decode(config_file_or_dict).decode("utf-8")
                    config = json.loads(config_decoded)
            except (UnicodeDecodeError, AttributeError, ValueError):
                raise ValueError(
                    f"Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"
                )

        self.config = config

        self.set_stage_and_offload()

    def set_stage_and_offload(self):
        # zero stage - this is done as early as possible, before model is created, to allow
        # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
        # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
        self._stage = self.get_value("zero_optimization.stage", -1)

        # offload
        self._offload = False
        if self.is_zero2() or self.is_zero3():
            offload_devices_valid = set(["cpu", "nvme"])
            offload_devices = set(
                [
                    self.get_value("zero_optimization.offload_optimizer.device"),
                    self.get_value("zero_optimization.offload_param.device"),
                ]
            )
            if len(offload_devices & offload_devices_valid) > 0:
                self._offload = True

    def find_config_node(self, ds_key_long):
        config = self.config

        # find the config node of interest if it exists
        nodes = ds_key_long.split(".")
        ds_key = nodes.pop()
        for node in nodes:
            config = config.get(node)
            if config is None:
                return None, ds_key

        return config, ds_key

    def get_value(self, ds_key_long, default=None):
        """
        Returns the set value or `default` if no value is set
        """
        config, ds_key = self.find_config_node(ds_key_long)
        if config is None:
            return default
        return config.get(ds_key, default)

    def del_config_sub_tree(self, ds_key_long, must_exist=False):
        """
        Deletes a sub-section of the config file if it's found.

        Unless `must_exist` is `True` the section doesn't have to exist.
        """
        config = self.config

        # find the config node of interest if it exists
        nodes = ds_key_long.split(".")
        for node in nodes:
            parent_config = config
            config = config.get(node)
            if config is None:
                if must_exist:
                    raise ValueError(f"Can't find {ds_key_long} entry in the config: {self.config}")
                else:
                    return

        # if found remove it
        if parent_config is not None:
            parent_config.pop(node)

    def is_true(self, ds_key_long):
        """
        Returns `True`/``False` only if the value is set, always `False` otherwise. So use this method to ask the very
        specific question of whether the value is set to `True` (and it's not set to `False`` or isn't set).

        """
        value = self.get_value(ds_key_long)
        return False if value is None else bool(value)

    def is_false(self, ds_key_long):
        """
        Returns `True`/``False` only if the value is set, always `False` otherwise. So use this method to ask the very
        specific question of whether the value is set to `False` (and it's not set to `True`` or isn't set).
        """
        value = self.get_value(ds_key_long)
        return False if value is None else not bool(value)

    def is_zero2(self):
        return self._stage == 2

    def is_zero3(self):
        return self._stage == 3

    def is_offload(self):
        return self._offload


class DeepSpeedEngineWrapper:
    """
    Internal wrapper for deepspeed.runtime.engine.DeepSpeedEngine. This is used to follow conventional training loop.

    Args:
        engine (deepspeed.runtime.engine.DeepSpeedEngine): deepspeed engine to wrap
    """

    def __init__(self, engine):
        self.engine = engine

    def backward(self, loss, sync_gradients=True, **kwargs):
        # Set gradient accumulation boundary based on Accelerate's sync_gradients state
        # This tells DeepSpeed whether this is the final micro-batch before gradient sync
        self.engine.set_gradient_accumulation_boundary(is_boundary=sync_gradients)

        # runs backpropagation and handles mixed precision
        self.engine.backward(loss, **kwargs)

        # Only perform step and related operations at gradient accumulation boundaries
        if sync_gradients:
            # Deepspeed's `engine.step` performs the following operations:
            # - gradient accumulation check
            # - gradient clipping
            # - optimizer step
            # - zero grad
            # - checking overflow
            # - lr_scheduler step (only if engine.lr_scheduler is not None)
            self.engine.step()
        # and this plugin overrides the above calls with no-ops when Accelerate runs under
        # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
        # training loop that works transparently under many training regimes.

    def get_global_grad_norm(self):
        """Get the global gradient norm from DeepSpeed engine."""
        grad_norm = self.engine.get_global_grad_norm()
        # Convert to scalar if it's a tensor
        if hasattr(grad_norm, "item"):
            return grad_norm.item()
        return grad_norm


class DeepSpeedOptimizerWrapper(AcceleratedOptimizer):
    """
    Internal wrapper around a deepspeed optimizer.

    Args:
        optimizer (`torch.optim.optimizer.Optimizer`):
            The optimizer to wrap.
    """

    def __init__(self, optimizer):
        super().__init__(optimizer, device_placement=False, scaler=None)
        self.__has_overflow__ = hasattr(self.optimizer, "overflow")

    def zero_grad(self, set_to_none=None):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

    def step(self):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

    @property
    def step_was_skipped(self):
        """Whether or not the optimizer step was done, or skipped because of gradient overflow."""
        if self.__has_overflow__:
            return self.optimizer.overflow
        return False


class DeepSpeedSchedulerWrapper(AcceleratedScheduler):
    """
    Internal wrapper around a deepspeed scheduler.

    Args:
        scheduler (`torch.optim.lr_scheduler.LambdaLR`):
            The scheduler to wrap.
        optimizers (one or a list of `torch.optim.Optimizer`):
    """

    def __init__(self, scheduler, optimizers):
        super().__init__(scheduler, optimizers)

    def step(self):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


class DummyOptim:
    """
    Dummy optimizer presents model parameters or param groups, this is primarily used to follow conventional training
    loop when optimizer config is specified in the deepspeed config file.

    Args:
        lr (float):
            Learning rate.
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        weight_decay (float):
            Weight decay.
        **kwargs (additional keyword arguments, *optional*):
            Other arguments.
    """

    def __init__(self, params, lr=0.001, weight_decay=0, **kwargs):
        self.params = params
        self.lr = lr
        self.weight_decay = weight_decay
        self.kwargs = kwargs


class DummyScheduler:
    """
    Dummy scheduler presents model parameters or param groups, this is primarily used to follow conventional training
    loop when scheduler config is specified in the deepspeed config file.

    Args:
        optimizer (`torch.optim.optimizer.Optimizer`):
            The optimizer to wrap.
        total_num_steps (int, *optional*):
            Total number of steps.
        warmup_num_steps (int, *optional*):
            Number of steps for warmup.
        lr_scheduler_callable (callable, *optional*):
            A callable function that creates an LR Scheduler. It accepts only one argument `optimizer`.
        **kwargs (additional keyword arguments, *optional*):
            Other arguments.
    """

    def __init__(self, optimizer, total_num_steps=None, warmup_num_steps=0, lr_scheduler_callable=None, **kwargs):
        self.optimizer = optimizer
        self.total_num_steps = total_num_steps
        self.warmup_num_steps = warmup_num_steps
        self.lr_scheduler_callable = lr_scheduler_callable
        self.kwargs = kwargs