Introduce configured_state arg for accelerator_config (#29781)
* Introduce configured_state * Include note on tuning * Allow for users to have defined a state already * Include tests * Add note on hpam tune * Guard a bit better * Update src/transformers/training_args.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/training_args.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Finish rebase * Finish rebase * Guard carefully * Fixup test * Refactor * Fin refactor * Comment * Update wrt feedback --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
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@ -1250,6 +1250,10 @@ class AcceleratorConfig:
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Whether to use non-blocking CUDA calls to help minimize synchronization during
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distributed training with prepared `DataLoader` inputs being moved to device.
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Best if used with `pin_memory=True` in the `TrainingArguments`.
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use_configured_state (`bool*, *optional*, defaults to `False`):
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Whether or not to use a pre-configured `AcceleratorState` or `PartialState` defined
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before calling `TrainingArguments`. If `True`, an `Accelerator` or `PartialState`
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must be initialized. May lead to issues using sweeps or hyperparameter tuning.
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"""
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@ -1312,6 +1316,13 @@ class AcceleratorConfig:
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" The [`accelerate.utils.GradientAccumulationPlugin`] default is `False`."
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},
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)
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use_configured_state: bool = field(
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default=False,
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metadata={
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"help": "Whether or not to use a pre-configured `AcceleratorState` or `PartialState` defined before calling `TrainingArguments`."
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"If `True`, an `Accelerator` or `PartialState` must be initialized. May lead to issues using sweeps or hyperparameter tuning."
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},
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)
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@classmethod
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def from_json_file(cls, json_file):
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@ -1331,6 +1342,9 @@ class AcceleratorConfig:
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def to_dict(self):
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return copy.deepcopy(self.__dict__)
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def pop(self, key, default=None):
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return self.__dict__.pop(key, default)
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class LayerWiseDummyOptimizer(torch.optim.Optimizer):
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"""
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@ -572,6 +572,10 @@ class TrainingArguments:
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training results are fully reproducable using a different sampling technique. While seed-to-seed results
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may differ, on average the differences are neglible when using multiple different seeds to compare. Should
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also be ran with [`~utils.set_seed`] for the best results.
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- use_configured_state (`bool`, *optional*, defaults to `False`):
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Whether or not to use a pre-configured `AcceleratorState` or `PartialState` defined before calling `TrainingArguments`.
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If `True`, an `Accelerator` or `PartialState` must be initialized. Note that by doing so, this could lead to issues
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with hyperparameter tuning.
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label_smoothing_factor (`float`, *optional*, defaults to 0.0):
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The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
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@ -1635,6 +1639,39 @@ class TrainingArguments:
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if version.parse(version.parse(torch.__version__).base_version) == version.parse("2.0.0") and self.fp16:
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raise ValueError("--optim adamw_torch_fused with --fp16 requires PyTorch>2.0")
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# We need to setup the accelerator config here *before* the first call to `self.device`
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if is_accelerate_available():
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if not isinstance(self.accelerator_config, (AcceleratorConfig)):
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if self.accelerator_config is None:
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self.accelerator_config = AcceleratorConfig()
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elif isinstance(self.accelerator_config, dict):
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self.accelerator_config = AcceleratorConfig(**self.accelerator_config)
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# Check that a user didn't pass in the class instantiator
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# such as `accelerator_config = AcceleratorConfig`
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elif isinstance(self.accelerator_config, type):
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raise NotImplementedError(
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"Tried passing in a callable to `accelerator_config`, but this is not supported. "
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"Please pass in a fully constructed `AcceleratorConfig` object instead."
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)
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else:
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self.accelerator_config = AcceleratorConfig.from_json_file(self.accelerator_config)
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if self.dispatch_batches is not None:
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warnings.warn(
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"Using `--dispatch_batches` is deprecated and will be removed in version 4.41 of 🤗 Transformers. Use"
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" `--accelerator_config {'dispatch_batches':VALUE} instead",
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FutureWarning,
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)
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self.accelerator_config.dispatch_batches = self.dispatch_batches
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if self.split_batches is not None:
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warnings.warn(
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"Using `--split_batches` is deprecated and will be removed in version 4.41 of 🤗 Transformers. Use"
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" `--accelerator_config {'split_batches':VALUE} instead",
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FutureWarning,
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)
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self.accelerator_config.split_batches = self.split_batches
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if (
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self.framework == "pt"
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and is_torch_available()
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@ -1873,37 +1910,6 @@ class TrainingArguments:
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os.environ[f"{prefix}USE_ORIG_PARAMS"] = str(self.fsdp_config.get("use_orig_params", "true")).lower()
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if is_accelerate_available():
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if not isinstance(self.accelerator_config, (AcceleratorConfig)):
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if self.accelerator_config is None:
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self.accelerator_config = AcceleratorConfig()
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elif isinstance(self.accelerator_config, dict):
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self.accelerator_config = AcceleratorConfig(**self.accelerator_config)
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# Check that a user didn't pass in the class instantiator
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# such as `accelerator_config = AcceleratorConfig`
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elif isinstance(self.accelerator_config, type):
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raise NotImplementedError(
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"Tried passing in a callable to `accelerator_config`, but this is not supported. "
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"Please pass in a fully constructed `AcceleratorConfig` object instead."
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)
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else:
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self.accelerator_config = AcceleratorConfig.from_json_file(self.accelerator_config)
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if self.dispatch_batches is not None:
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warnings.warn(
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"Using `--dispatch_batches` is deprecated and will be removed in version 4.41 of 🤗 Transformers. Use"
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" `--accelerator_config {'dispatch_batches':VALUE} instead",
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FutureWarning,
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)
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self.accelerator_config.dispatch_batches = self.dispatch_batches
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if self.split_batches is not None:
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warnings.warn(
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"Using `--split_batches` is deprecated and will be removed in version 4.41 of 🤗 Transformers. Use"
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" `--accelerator_config {'split_batches':VALUE} instead",
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FutureWarning,
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)
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self.accelerator_config.split_batches = self.split_batches
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if self.tpu_metrics_debug:
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warnings.warn(
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"using `--tpu_metrics_debug` is deprecated and will be removed in version 5 of 🤗 Transformers. Use"
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@ -2056,32 +2062,62 @@ class TrainingArguments:
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f"Using the `Trainer` with `PyTorch` requires `accelerate>={ACCELERATE_MIN_VERSION}`: "
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"Please run `pip install transformers[torch]` or `pip install accelerate -U`"
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)
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# We delay the init of `PartialState` to the end for clarity
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accelerator_state_kwargs = {"enabled": True, "use_configured_state": False}
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if isinstance(self.accelerator_config, AcceleratorConfig):
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accelerator_state_kwargs["use_configured_state"] = self.accelerator_config.pop(
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"use_configured_state", False
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)
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if accelerator_state_kwargs["use_configured_state"]:
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if PartialState._shared_state == {}:
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raise ValueError(
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"Passing `'use_configured_state':True` to the AcceleratorConfig requires a pre-configured "
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"`AcceleratorState` or `PartialState` to be defined before calling `TrainingArguments`. "
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)
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# We rely on `PartialState` to yell if there's issues here (which it will)
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self.distributed_state = PartialState(cpu=self.use_cpu)
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if self.deepspeed and self.distributed_state.distributed_type != DistributedType.DEEPSPEED:
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raise RuntimeError(
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"Tried to use an already configured `Accelerator` or `PartialState` that was not initialized for DeepSpeed, "
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"but also passed in a `deepspeed` configuration to the `TrainingArguments`. Please set "
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"`use_configured_state:False` instead or setup your `Accelerator` or `PartialState` properly."
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)
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else:
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AcceleratorState._reset_state(reset_partial_state=True)
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self.distributed_state = None
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self.distributed_state = None
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if not self.use_ipex and "ACCELERATE_USE_IPEX" not in os.environ:
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os.environ["ACCELERATE_USE_IPEX"] = "false"
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self._n_gpu = 1
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if self.use_cpu or strtobool(os.environ.get("ACCELERATE_USE_CPU", "False")):
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self.distributed_state = PartialState(cpu=True, backend=self.ddp_backend)
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accelerator_state_kwargs["cpu"] = True
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accelerator_state_kwargs["backend"] = self.ddp_backend
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self._n_gpu = 0
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elif is_sagemaker_mp_enabled():
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accelerator_state_kwargs["enabled"] = False
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local_rank = smp.local_rank()
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device = torch.device("cuda", local_rank)
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self._n_gpu = 1
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torch.cuda.set_device(device)
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elif is_sagemaker_dp_enabled():
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self.distributed_state = PartialState(_use_sagemaker_dp=True)
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self._n_gpu = 1
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accelerator_state_kwargs["_use_sagemaker_dp"] = True
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elif self.deepspeed:
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# Need to do similar for Accelerator init
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os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
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self.distributed_state = PartialState(timeout=timedelta(seconds=self.ddp_timeout))
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del os.environ["ACCELERATE_USE_DEEPSPEED"]
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self._n_gpu = 1
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accelerator_state_kwargs["use_deepspeed"] = True
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accelerator_state_kwargs["timeout"] = timedelta(seconds=self.ddp_timeout)
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else:
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self.distributed_state = PartialState(
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backend=self.ddp_backend, timeout=timedelta(seconds=self.ddp_timeout)
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)
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self._n_gpu = 1
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accelerator_state_kwargs["backend"] = self.ddp_backend
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accelerator_state_kwargs["timeout"] = timedelta(seconds=self.ddp_timeout)
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# Now we pop everything
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if accelerator_state_kwargs.pop("enabled", False) and not accelerator_state_kwargs.pop(
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"use_configured_state", False
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):
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# We need to patch this env var when enabling to detect deepspeed
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use_deepspeed = accelerator_state_kwargs.pop("use_deepspeed", False)
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if use_deepspeed:
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os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
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self.distributed_state = PartialState(**accelerator_state_kwargs)
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if use_deepspeed:
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del os.environ["ACCELERATE_USE_DEEPSPEED"]
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if not is_sagemaker_mp_enabled():
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device = self.distributed_state.device
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self.local_rank = self.distributed_state.local_process_index
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@ -2108,23 +2144,17 @@ class TrainingArguments:
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"Either you do not have an MPS-enabled device on this machine or MacOS version is not 12.3+ "
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"or current PyTorch install was not built with MPS enabled."
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)
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if device.type == "mps":
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self._n_gpu = 1
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elif self.use_cpu:
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if self.use_cpu:
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device = torch.device("cpu")
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self._n_gpu = 0
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elif is_torch_xpu_available():
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device = torch.device("xpu:0")
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torch.xpu.set_device(device)
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self._n_gpu = 1
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elif is_torch_mlu_available():
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device = torch.device("mlu:0")
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torch.mlu.set_device(device)
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self._n_gpu = 1
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elif is_torch_npu_available():
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device = torch.device("npu:0")
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torch.npu.set_device(device)
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self._n_gpu = 1
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else:
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# if n_gpu is > 1 we'll use nn.DataParallel.
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# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
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@ -131,6 +131,10 @@ if is_torch_available():
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# for version specific tests in TrainerIntegrationTest
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require_accelerate_version_min_0_28 = partial(require_accelerate, min_version="0.28")
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GRAD_ACCUM_KWARGS_VERSION_AVAILABLE = is_accelerate_available("0.28")
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if is_accelerate_available():
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from accelerate import Accelerator
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from accelerate.state import AcceleratorState
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PATH_SAMPLE_TEXT = f"{get_tests_dir()}/fixtures/sample_text.txt"
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@ -3266,6 +3270,16 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
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trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset)
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self.assertEqual(trainer.accelerator.split_batches, True)
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def test_accelerator_custom_state(self):
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AcceleratorState._reset_state(reset_partial_state=True)
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with tempfile.TemporaryDirectory() as tmp_dir:
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with self.assertRaises(ValueError) as cm:
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_ = RegressionTrainingArguments(output_dir=tmp_dir, accelerator_config={"use_configured_state": True})
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self.assertIn("Please define this beforehand", str(cm.warnings[0].message))
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_ = Accelerator()
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_ = RegressionTrainingArguments(output_dir=tmp_dir, accelerator_config={"use_configured_state": True})
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AcceleratorState._reset_state(reset_partial_state=True)
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@require_accelerate_version_min_0_28
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def test_accelerator_config_from_dict_grad_accum_num_steps(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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