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class HfDeepSpeedConfig: |
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""" |
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This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage. |
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A `weakref` of this object is stored in the module's globals to be able to access the config from areas where |
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things like the Trainer object is not available (e.g. `from_pretrained` and `_get_resized_embeddings`). Therefore |
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it's important that this object remains alive while the program is still running. |
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[`Trainer`] uses the `HfTrainerDeepSpeedConfig` subclass instead. That subclass has logic to sync the configuration |
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with values of [`TrainingArguments`] by replacing special placeholder values: `"auto"`. Without this special logic |
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the DeepSpeed configuration is not modified in any way. |
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Args: |
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config_file_or_dict (`Union[str, Dict]`): path to DeepSpeed config file or dict. |
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""" |
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def __init__(self, config_file_or_dict): |
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if isinstance(config_file_or_dict, dict): |
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config = deepcopy(config_file_or_dict) |
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elif os.path.exists(config_file_or_dict): |
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with io.open(config_file_or_dict, "r", encoding="utf-8") as f: |
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config = json.load(f) |
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else: |
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try: |
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config_decoded = base64.urlsafe_b64decode(config_file_or_dict).decode("utf-8") |
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config = json.loads(config_decoded) |
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except (UnicodeDecodeError, AttributeError, ValueError): |
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raise ValueError( |
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f"Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}" |
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) |
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self.config = config |
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self.set_stage_and_offload() |
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def set_stage_and_offload(self): |
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self._stage = self.get_value("zero_optimization.stage", -1) |
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self._offload = False |
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if self.is_zero2() or self.is_zero3(): |
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offload_devices_valid = set(["cpu", "nvme"]) |
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offload_devices = set( |
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[ |
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self.get_value("zero_optimization.offload_optimizer.device"), |
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self.get_value("zero_optimization.offload_param.device"), |
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] |
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) |
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if len(offload_devices & offload_devices_valid) > 0: |
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self._offload = True |
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def find_config_node(self, ds_key_long): |
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config = self.config |
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nodes = ds_key_long.split(".") |
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ds_key = nodes.pop() |
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for node in nodes: |
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config = config.get(node) |
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if config is None: |
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return None, ds_key |
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return config, ds_key |
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def get_value(self, ds_key_long, default=None): |
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""" |
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Returns the set value or `default` if no value is set |
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""" |
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config, ds_key = self.find_config_node(ds_key_long) |
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if config is None: |
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return default |
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return config.get(ds_key, default) |
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def del_config_sub_tree(self, ds_key_long, must_exist=False): |
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""" |
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Deletes a sub-section of the config file if it's found. |
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Unless `must_exist` is `True` the section doesn't have to exist. |
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""" |
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config = self.config |
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nodes = ds_key_long.split(".") |
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for node in nodes: |
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parent_config = config |
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config = config.get(node) |
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if config is None: |
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if must_exist: |
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raise ValueError(f"Can't find {ds_key_long} entry in the config: {self.config}") |
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else: |
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return |
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if parent_config is not None: |
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parent_config.pop(node) |
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def is_true(self, ds_key_long): |
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""" |
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Returns `True`/``False` only if the value is set, always `False` otherwise. So use this method to ask the very |
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specific question of whether the value is set to `True` (and it's not set to `False`` or isn't set). |
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""" |
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value = self.get_value(ds_key_long) |
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return False if value is None else bool(value) |
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def is_false(self, ds_key_long): |
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""" |
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Returns `True`/``False` only if the value is set, always `False` otherwise. So use this method to ask the very |
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specific question of whether the value is set to `False` (and it's not set to `True`` or isn't set). |
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""" |
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value = self.get_value(ds_key_long) |
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return False if value is None else not bool(value) |
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def is_zero2(self): |
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return self._stage == 2 |
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def is_zero3(self): |
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return self._stage == 3 |
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def is_offload(self): |
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return self._offload |
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class DeepSpeedEngineWrapper: |
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""" |
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Internal wrapper for deepspeed.runtime.engine.DeepSpeedEngine. This is used to follow conventional training loop. |
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Args: |
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engine (deepspeed.runtime.engine.DeepSpeedEngine): deepspeed engine to wrap |
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""" |
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def __init__(self, engine): |
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self.engine = engine |
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def backward(self, loss, **kwargs): |
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self.engine.backward(loss, **kwargs) |
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self.engine.step() |
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class DeepSpeedOptimizerWrapper(AcceleratedOptimizer): |
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""" |
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Internal wrapper around a deepspeed optimizer. |
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Args: |
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optimizer (`torch.optim.optimizer.Optimizer`): |
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The optimizer to wrap. |
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""" |
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def __init__(self, optimizer): |
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super().__init__(optimizer, device_placement=False, scaler=None) |
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self.__has_overflow__ = hasattr(self.optimizer, "overflow") |
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def zero_grad(self, set_to_none=None): |
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pass |
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def step(self): |
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pass |
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@property |
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def step_was_skipped(self): |
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"""Whether or not the optimizer step was done, or skipped because of gradient overflow.""" |
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if self.__has_overflow__: |
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return self.optimizer.overflow |
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return False |
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class DeepSpeedSchedulerWrapper(AcceleratedScheduler): |
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""" |
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Internal wrapper around a deepspeed scheduler. |
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Args: |
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scheduler (`torch.optim.lr_scheduler.LambdaLR`): |
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The scheduler to wrap. |
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optimizers (one or a list of `torch.optim.Optimizer`): |
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""" |
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def __init__(self, scheduler, optimizers): |
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super().__init__(scheduler, optimizers) |
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def step(self): |
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pass |
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class DummyOptim: |
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""" |
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Dummy optimizer presents model parameters or param groups, this is primarily used to follow conventional training |
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loop when optimizer config is specified in the deepspeed config file. |
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Args: |
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lr (float): |
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Learning rate. |
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params (iterable): iterable of parameters to optimize or dicts defining |
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parameter groups |
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weight_decay (float): |
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Weight decay. |
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**kwargs: |
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Other arguments. |
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""" |
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def __init__(self, params, lr=0.001, weight_decay=0, **kwargs): |
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self.params = params |
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self.lr = lr |
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self.weight_decay = weight_decay |
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self.kwargs = kwargs |
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class DummyScheduler: |
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""" |
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Dummy scheduler presents model parameters or param groups, this is primarily used to follow conventional training |
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loop when scheduler config is specified in the deepspeed config file. |
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Args: |
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optimizer (`torch.optim.optimizer.Optimizer`): |
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The optimizer to wrap. |
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total_num_steps (int, *optional*): |
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Total number of steps. |
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warmup_num_steps (int, *optional*): |
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Number of steps for warmup. |
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lr_scheduler_callable (callable, *optional*): |
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A callable function that creates an LR Scheduler. It accepts only one argument `optimizer`. |
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**kwargs: |
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Other arguments. |
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""" |
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def __init__(self, optimizer, total_num_steps=None, warmup_num_steps=0, lr_scheduler_callable=None, **kwargs): |
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self.optimizer = optimizer |
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self.total_num_steps = total_num_steps |
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self.warmup_num_steps = warmup_num_steps |
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self.lr_scheduler_callable = lr_scheduler_callable |
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self.kwargs = kwargs |
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