training_args.py

# Copyright 2022 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 io
import json
import os
import warnings
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional

from packaging import version
from transformers import TrainingArguments
from transformers.debug_utils import DebugOption
from transformers.trainer_utils import (
    EvaluationStrategy,
    FSDPOption,
    HubStrategy,
    IntervalStrategy,
    SchedulerType,
)
from transformers.training_args import OptimizerNames, default_logdir, logger
from transformers.utils import (
    ExplicitEnum,
    get_full_repo_name,
    is_accelerate_available,
    is_safetensors_available,
    is_torch_available,
    is_torch_bf16_cpu_available,
    is_torch_bf16_gpu_available,
    is_torch_tf32_available,
    logging,
)
from transformers.utils.generic import strtobool

from ..utils.import_utils import check_if_transformers_greater


if is_torch_available():
    import torch

if is_accelerate_available() and check_if_transformers_greater("4.38.0"):
    from transformers.trainer_pt_utils import AcceleratorConfig


class ORTOptimizerNames(ExplicitEnum):
    """
    Stores the acceptable string identifiers for optimizers in `onnxruntime.training.optim`.
    """

    ADAMW_ORT_FUSED = "adamw_ort_fused"


@dataclass
class ORTTrainingArguments(TrainingArguments):
    """
    Parameters:
        optim (`str` or [`training_args.ORTOptimizerNames`] or [`transformers.training_args.OptimizerNames`], *optional*, defaults to `"adamw_hf"`):
            The optimizer to use, including optimizers in Transformers: adamw_hf, adamw_torch, adamw_apex_fused, or adafactor. And optimizers implemented by ONNX Runtime: adamw_ort_fused.
    """

    optim: Optional[str] = field(default="adamw_hf", metadata={"help": "The optimizer to use."})

    use_module_with_loss: Optional[bool] = field(
        default=False,
        metadata={
            "help": "Use ModuleWithLoss Wrapper to compute loss inside the training loop, having this will help save memory for ORTModule Runs."
        },
    )

    save_onnx: Optional[bool] = field(
        default=False,
        metadata={
            "help": "Configure ORTModule to save onnx models. Defaults to False. \
            The output directory of the onnx models by default is set to args.output_dir. \
            To change the output directory, the environment variable ORTMODULE_SAVE_ONNX_PATH can be \
            set to the destination directory path."
        },
    )

    onnx_prefix: Optional[str] = field(
        default=None,
        metadata={"help": "Prefix for the saved ORTModule file names. Must be provided if save_onnx is True."},
    )

    onnx_log_level: Optional[str] = field(
        default="WARNING",
        metadata={
            "help": "Configure ORTModule log level. Defaults to WARNING. \
            onnx_log_level can also be set to one of VERBOSE, INFO, WARNING, ERROR, FATAL."
        },
    )

    # This method will not need to be overriden after the deprecation of `--adafactor` in version 5 of 🤗 Transformers.
    def __post_init__(self):
        # expand paths, if not os.makedirs("~/bar") will make directory
        # in the current directory instead of the actual home
        # see https://github.com/huggingface/transformers/issues/10628
        if self.output_dir is not None:
            self.output_dir = os.path.expanduser(self.output_dir)
        if self.logging_dir is None and self.output_dir is not None:
            self.logging_dir = os.path.join(self.output_dir, default_logdir())
        if self.logging_dir is not None:
            self.logging_dir = os.path.expanduser(self.logging_dir)

        if self.disable_tqdm is None:
            self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN

        if isinstance(self.evaluation_strategy, EvaluationStrategy):
            warnings.warn(
                "using `EvaluationStrategy` for `evaluation_strategy` is deprecated and will be removed in version 5"
                " of 🤗 Transformers. Use `IntervalStrategy` instead",
                FutureWarning,
            )
            # Go back to the underlying string or we won't be able to instantiate `IntervalStrategy` on it.
            self.evaluation_strategy = self.evaluation_strategy.value

        self.evaluation_strategy = IntervalStrategy(self.evaluation_strategy)
        self.logging_strategy = IntervalStrategy(self.logging_strategy)
        self.save_strategy = IntervalStrategy(self.save_strategy)
        self.hub_strategy = HubStrategy(self.hub_strategy)

        self.lr_scheduler_type = SchedulerType(self.lr_scheduler_type)
        if self.do_eval is False and self.evaluation_strategy != IntervalStrategy.NO:
            self.do_eval = True

        # eval_steps has to be defined and non-zero, fallbacks to logging_steps if the latter is non-zero
        if self.evaluation_strategy == IntervalStrategy.STEPS and (self.eval_steps is None or self.eval_steps == 0):
            if self.logging_steps > 0:
                logger.info(f"using `logging_steps` to initialize `eval_steps` to {self.logging_steps}")
                self.eval_steps = self.logging_steps
            else:
                raise ValueError(
                    f"evaluation strategy {self.evaluation_strategy} requires either non-zero --eval_steps or"
                    " --logging_steps"
                )

        # logging_steps must be non-zero for logging_strategy that is other than 'no'
        if self.logging_strategy == IntervalStrategy.STEPS and self.logging_steps == 0:
            raise ValueError(f"logging strategy {self.logging_strategy} requires non-zero --logging_steps")

        if self.logging_strategy == IntervalStrategy.STEPS and self.logging_steps > 1:
            if self.logging_steps != int(self.logging_steps):
                raise ValueError(f"--logging_steps must be an integer if bigger than 1: {self.logging_steps}")
            self.logging_steps = int(self.logging_steps)
        if self.evaluation_strategy == IntervalStrategy.STEPS and self.eval_steps > 1:
            if self.eval_steps != int(self.eval_steps):
                raise ValueError(f"--eval_steps must be an integer if bigger than 1: {self.eval_steps}")
            self.eval_steps = int(self.eval_steps)
        if self.save_strategy == IntervalStrategy.STEPS and self.save_steps > 1:
            if self.save_steps != int(self.save_steps):
                raise ValueError(f"--save_steps must be an integer if bigger than 1: {self.save_steps}")
            self.save_steps = int(self.save_steps)

        # Sanity checks for load_best_model_at_end: we require save and eval strategies to be compatible.
        if self.load_best_model_at_end:
            if self.evaluation_strategy != self.save_strategy:
                raise ValueError(
                    "--load_best_model_at_end requires the saving steps to be a multiple of the evaluation "
                    "steps, which cannot get guaranteed when mixing ratio and absolute steps for save_steps "
                    f"{self.save_steps} and eval_steps {self.eval_steps}."
                )
            if self.evaluation_strategy == IntervalStrategy.STEPS and self.save_steps % self.eval_steps != 0:
                if self.eval_steps < 1 or self.save_steps < 1:
                    if not (self.eval_steps < 1 and self.save_steps < 1):
                        raise ValueError(
                            "--load_best_model_at_end requires the saving steps to be a multiple of the evaluation "
                            "steps, which cannot get guaranteed when mixing ratio and absolute steps for save_steps"
                            f"{self.save_steps} and eval_steps {self.eval_steps}."
                        )
                    # Work around floating point precision issues
                    LARGE_MULTIPLIER = 1_000_000
                    if (self.save_steps * LARGE_MULTIPLIER) % (self.eval_steps * LARGE_MULTIPLIER) != 0:
                        raise ValueError(
                            "--load_best_model_at_end requires the saving steps to be a multiple of the evaluation "
                            f"steps, but found {self.save_steps}, which is not a multiple of {self.eval_steps}."
                        )
                raise ValueError(
                    "--load_best_model_at_end requires the saving steps to be a round multiple of the evaluation "
                    f"steps, but found {self.save_steps}, which is not a round multiple of {self.eval_steps}."
                )

        safetensors_available = is_safetensors_available()
        if self.save_safetensors and not safetensors_available:
            raise ValueError(f"--save_safetensors={self.save_safetensors} requires safetensors to be installed!")
        if not self.save_safetensors and safetensors_available:
            logger.info(
                f"Found safetensors installation, but --save_safetensors={self.save_safetensors}. "
                f"Safetensors should be a preferred weights saving format due to security and performance reasons. "
                f"If your model cannot be saved by safetensors please feel free to open an issue at "
                f"https://github.com/huggingface/safetensors!"
            )

        if (
            self.load_best_model_at_end or self.lr_scheduler_type == SchedulerType.REDUCE_ON_PLATEAU
        ) and self.metric_for_best_model is None:
            self.metric_for_best_model = "loss"
        if self.greater_is_better is None and self.metric_for_best_model is not None:
            self.greater_is_better = self.metric_for_best_model not in ["loss", "eval_loss"]
        if self.run_name is None:
            self.run_name = self.output_dir
        if self.framework == "pt" and is_torch_available():
            if self.fp16_backend and self.fp16_backend != "auto":
                warnings.warn(
                    "`fp16_backend` is deprecated and will be removed in version 5 of 🤗 Transformers. Use"
                    " `half_precision_backend` instead",
                    FutureWarning,
                )
                self.half_precision_backend = self.fp16_backend

            if self.bf16 or self.bf16_full_eval:
                if self.use_cpu and not is_torch_bf16_cpu_available():
                    # cpu
                    raise ValueError("Your setup doesn't support bf16/(cpu, tpu, neuroncore). You need torch>=1.10")
                elif not self.use_cpu:
                    if torch.cuda.is_available() and not is_torch_bf16_gpu_available():
                        # gpu
                        raise ValueError(
                            "Your setup doesn't support bf16/gpu. You need torch>=1.10, using Ampere GPU with cuda>=11.0"
                        )

        if self.fp16 and self.bf16:
            raise ValueError("At most one of fp16 and bf16 can be True, but not both")

        if self.fp16_full_eval and self.bf16_full_eval:
            raise ValueError("At most one of fp16 and bf16 can be True for full eval, but not both")

        if self.bf16:
            if self.half_precision_backend == "apex":
                raise ValueError(
                    " `--half_precision_backend apex`: GPU bf16 is not supported by apex. Use"
                    " `--half_precision_backend cuda_amp` instead"
                )

        if self.lr_scheduler_type == SchedulerType.REDUCE_ON_PLATEAU:
            if self.evaluation_strategy == IntervalStrategy.NO:
                raise ValueError("lr_scheduler_type reduce_lr_on_plateau requires an eval strategy")
            if not is_torch_available():
                raise ValueError("lr_scheduler_type reduce_lr_on_plateau requires torch>=0.2.0")

        try:
            self.optim = ORTOptimizerNames(self.optim)
        except ValueError:
            self.optim = OptimizerNames(self.optim)
        if self.adafactor:
            warnings.warn(
                "`--adafactor` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `--optim"
                " adafactor` instead",
                FutureWarning,
            )
            self.optim = OptimizerNames.ADAFACTOR
        if self.optim == OptimizerNames.ADAMW_TORCH_FUSED and is_torch_available():
            if version.parse(version.parse(torch.__version__).base_version) < version.parse("2.0.0"):
                raise ValueError("--optim adamw_torch_fused requires PyTorch 2.0 or higher")
            # there is a bug in fp16/AMP in pt-2.0.0
            if version.parse(version.parse(torch.__version__).base_version) == version.parse("2.0.0") and self.fp16:
                raise ValueError("--optim adamw_torch_fused with --fp16 requires PyTorch>2.0")

        if self.save_onnx:
            if not self.onnx_prefix:
                raise ValueError("onnx_prefix must be provided if save_onnx is True")
            if not os.getenv("ORTMODULE_SAVE_ONNX_PATH", None):
                os.environ["ORTMODULE_SAVE_ONNX_PATH"] = self.output_dir
            os.environ["ORTMODULE_LOG_LEVEL"] = self.onnx_log_level

        if (
            is_torch_available()
            and (self.device.type != "cuda")
            and not (self.device.type == "xla" and "GPU_NUM_DEVICES" in os.environ)
            and (self.fp16 or self.fp16_full_eval)
        ):
            raise ValueError(
                "FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation"
                " (`--fp16_full_eval`) can only be used on CUDA devices."
            )

        if (
            is_torch_available()
            and (self.device.type != "cuda")
            and not (self.device.type == "xla" and "GPU_NUM_DEVICES" in os.environ)
            and (self.device.type != "cpu")
            and (self.bf16 or self.bf16_full_eval)
        ):
            raise ValueError(
                "BF16 Mixed precision training with AMP (`--bf16`) and BF16 half precision evaluation"
                " (`--bf16_full_eval`) can only be used on CUDA or CPU/TPU/NeuronCore devices."
            )

        if self.torchdynamo is not None:
            warnings.warn(
                "`torchdynamo` is deprecated and will be removed in version 5 of 🤗 Transformers. Use"
                " `torch_compile_backend` instead",
                FutureWarning,
            )
            self.torch_compile_backend = self.torchdynamo
        if (self.torch_compile_mode is not None or self.torch_compile_backend is not None) and not self.torch_compile:
            self.torch_compile = True
        if self.torch_compile and self.torch_compile_backend is None:
            self.torch_compile_backend = "inductor"

        # accelerate integration for torch compile
        if self.torch_compile:
            # set env vars for accelerate
            prefix = "ACCELERATE_DYNAMO_"
            os.environ[prefix + "BACKEND"] = self.torch_compile_backend
            if self.torch_compile_mode is not None:
                os.environ[prefix + "MODE"] = self.torch_compile_mode

        if self.framework == "pt" and is_torch_available() and self.torch_compile:
            if is_torch_tf32_available():
                if self.tf32 is None and not self.fp16 or self.bf16:
                    logger.info(
                        "Setting TF32 in CUDA backends to speedup torch compile, you won't see any improvement"
                        " otherwise."
                    )
                    torch.backends.cuda.matmul.allow_tf32 = True
                    torch.backends.cudnn.allow_tf32 = True
            else:
                logger.warning(
                    "The speedups for torchdynamo mostly come wih GPU Ampere or higher and which is not detected here."
                )
        if is_torch_available() and self.tf32 is not None:
            if self.tf32:
                if is_torch_tf32_available():
                    torch.backends.cuda.matmul.allow_tf32 = True
                    torch.backends.cudnn.allow_tf32 = True
                else:
                    raise ValueError("--tf32 requires Ampere or a newer GPU arch, cuda>=11 and torch>=1.7")
            else:
                if is_torch_tf32_available():
                    torch.backends.cuda.matmul.allow_tf32 = False
                    torch.backends.cudnn.allow_tf32 = False
                # no need to assert on else

        # if training args is specified, it will override the one specified in the accelerate config
        if self.half_precision_backend != "apex":
            mixed_precision_dtype = os.environ.get("ACCELERATE_MIXED_PRECISION", "no")
            if self.fp16:
                mixed_precision_dtype = "fp16"
            elif self.bf16:
                mixed_precision_dtype = "bf16"
            os.environ["ACCELERATE_MIXED_PRECISION"] = mixed_precision_dtype

        if self.report_to is None:
            logger.info(
                "The default value for the training argument `--report_to` will change in v5 (from all installed "
                "integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as "
                "now. You should start updating your code and make this info disappear :-)."
            )
            self.report_to = "all"
        if self.report_to == "all" or self.report_to == ["all"]:
            # Import at runtime to avoid a circular import.
            from transformers.integrations import get_available_reporting_integrations

            self.report_to = get_available_reporting_integrations()
        elif self.report_to == "none" or self.report_to == ["none"]:
            self.report_to = []
        elif not isinstance(self.report_to, list):
            self.report_to = [self.report_to]

        if self.warmup_ratio < 0 or self.warmup_ratio > 1:
            raise ValueError("warmup_ratio must lie in range [0,1]")
        elif self.warmup_ratio > 0 and self.warmup_steps > 0:
            logger.info(
                "Both warmup_ratio and warmup_steps given, warmup_steps will override any effect of warmup_ratio"
                " during training"
            )

        if isinstance(self.fsdp, bool):
            self.fsdp = "full_shard" if self.fsdp else ""
        if isinstance(self.fsdp, str):
            self.fsdp = [FSDPOption(s) for s in self.fsdp.split()]
        if self.fsdp == [FSDPOption.OFFLOAD]:
            raise ValueError(
                "`--fsdp offload` can't work on its own. It needs to be added to `--fsdp full_shard` or "
                '`--fsdp shard_grad_op`. For example, `--fsdp "full_shard offload"`.'
            )
        elif FSDPOption.FULL_SHARD in self.fsdp and FSDPOption.SHARD_GRAD_OP in self.fsdp:
            raise ValueError("`--fsdp full_shard` is not compatible with `--fsdp shard_grad_op`.")

        if self.fsdp_config is None:
            self.fsdp_config = {}

        if isinstance(self.fsdp_config, str):
            if len(self.fsdp) == 0:
                warnings.warn("`--fsdp_config` is useful only when `--fsdp` is specified.")
            with io.open(self.fsdp_config, "r", encoding="utf-8") as f:
                self.fsdp_config = json.load(f)
                for k in list(self.fsdp_config.keys()):
                    if k.startswith("fsdp_"):
                        v = self.fsdp_config.pop(k)
                        self.fsdp_config[k[5:]] = v

        if self.fsdp_min_num_params > 0:
            warnings.warn("using `--fsdp_min_num_params` is deprecated. Use fsdp_config instead ", FutureWarning)

        self.fsdp_config["min_num_params"] = max(self.fsdp_config.get("min_num_params", 0), self.fsdp_min_num_params)

        # if fsdp_config["transformer_layer_cls_to_wrap"] is specified as a string, convert it to a list with a single object
        if isinstance(self.fsdp_config.get("transformer_layer_cls_to_wrap", None), str):
            self.fsdp_config["transformer_layer_cls_to_wrap"] = [self.fsdp_config["transformer_layer_cls_to_wrap"]]

        if self.fsdp_transformer_layer_cls_to_wrap is not None:
            warnings.warn(
                "using `--fsdp_transformer_layer_cls_to_wrap` is deprecated. Use fsdp_config instead ", FutureWarning
            )
            self.fsdp_config["transformer_layer_cls_to_wrap"] = self.fsdp_config.get(
                "transformer_layer_cls_to_wrap", []
            ) + [self.fsdp_transformer_layer_cls_to_wrap]

        if len(self.fsdp) == 0 and self.fsdp_config["min_num_params"] > 0:
            warnings.warn("`min_num_params` is useful only when `--fsdp` is specified.")

        if len(self.fsdp) == 0 and self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None:
            warnings.warn("`transformer_layer_cls_to_wrap` is useful only when `--fsdp` is specified.")

        if (
            len(self.fsdp) > 0
            and self.fsdp_config["min_num_params"] > 0
            and self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None
        ):
            raise ValueError("`min_num_params` and `transformer_layer_cls_to_wrap` are mutually exclusive.")
        self.fsdp_config["xla"] = self.fsdp_config.get("xla", False)
        self.fsdp_config["xla_fsdp_v2"] = self.fsdp_config.get("xla_fsdp_v2", False)
        self.fsdp_config["xla_fsdp_grad_ckpt"] = self.fsdp_config.get("xla_fsdp_grad_ckpt", False)
        if self.fsdp_config["xla"]:
            if len(self.fsdp) > 0:
                # store XLA fsdp configuration parameters into a dictionary
                self.xla_fsdp_config = self.fsdp_config.get("xla_fsdp_settings", {})
                # apply appropriate string to torch.dtype conversions for parameters
                if "compute_dtype" in self.xla_fsdp_config:
                    self.xla_fsdp_config["compute_dtype"] = getattr(torch, self.xla_fsdp_config["compute_dtype"])
                if "buffer_dtype" in self.xla_fsdp_config:
                    self.xla_fsdp_config["buffer_dtype"] = getattr(torch, self.xla_fsdp_config["buffer_dtype"])
            else:
                warnings.warn("XLA FSDP can be used only when `--fsdp` is specified.")
        else:
            if self.fsdp_config["xla_fsdp_grad_ckpt"]:
                warnings.warn("`--xla_fsdp_grad_ckpt` is useful only when `--xla` is set to true.")

        # accelerate integration for FSDP
        if len(self.fsdp) > 0 and not self.fsdp_config["xla"]:
            os.environ["ACCELERATE_USE_FSDP"] = "true"
            from accelerate.utils.constants import (
                FSDP_AUTO_WRAP_POLICY,
                FSDP_SHARDING_STRATEGY,
            )

            prefix = "FSDP_"
            for fsdp_option in self.fsdp:
                if fsdp_option.upper() in FSDP_SHARDING_STRATEGY:
                    # set environment variable for FSDP sharding strategy
                    os.environ[f"{prefix}SHARDING_STRATEGY"] = str(
                        FSDP_SHARDING_STRATEGY.index(fsdp_option.upper()) + 1
                    )
                elif fsdp_option == FSDPOption.OFFLOAD:
                    os.environ[f"{prefix}OFFLOAD_PARAMS"] = "true"
                elif fsdp_option == FSDPOption.AUTO_WRAP:
                    os.environ[f"{prefix}AUTO_WRAP_POLICY"] = FSDP_AUTO_WRAP_POLICY[0]
                    if self.fsdp_config["min_num_params"] > 0:
                        os.environ[f"{prefix}MIN_NUM_PARAMS"] = str(self.fsdp_config["min_num_params"])
                        os.environ[f"{prefix}AUTO_WRAP_POLICY"] = FSDP_AUTO_WRAP_POLICY[1]
                    elif self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None:
                        os.environ[f"{prefix}TRANSFORMER_CLS_TO_WRAP"] = ",".join(
                            self.fsdp_config["transformer_layer_cls_to_wrap"]
                        )
            prefetch_policy = self.fsdp_config.get("fsdp_backward_prefetch", "NO_PREFETCH")
            os.environ[f"{prefix}BACKWARD_PREFETCH"] = prefetch_policy.upper()
            os.environ[f"{prefix}FORWARD_PREFETCH"] = self.fsdp_config.get("forward_prefect", "false")
            os.environ[f"{prefix}SYNC_MODULE_STATES"] = self.fsdp_config.get("sync_module_states", "true")
            os.environ[f"{prefix}USE_ORIG_PARAMS"] = self.fsdp_config.get("use_orig_params", "false")

        if is_accelerate_available() and check_if_transformers_greater("4.38.0"):
            if not isinstance(self.accelerator_config, (AcceleratorConfig)):
                if self.accelerator_config is None:
                    self.accelerator_config = AcceleratorConfig()
                elif isinstance(self.accelerator_config, dict):
                    self.accelerator_config = AcceleratorConfig(**self.accelerator_config)
                else:
                    self.accelerator_config = AcceleratorConfig.from_json_file(self.accelerator_config)
            if self.dispatch_batches is not None:
                warnings.warn(
                    "Using `--dispatch_batches` is deprecated and will be removed in version 4.41 of 🤗 Transformers. Use"
                    " `--accelerator_config {'dispatch_batches':VALUE} instead",
                    FutureWarning,
                )
                self.accelerator_config.dispatch_batches = self.dispatch_batches

            if self.split_batches is not None:
                warnings.warn(
                    "Using `--split_batches` is deprecated and will be removed in version 4.41 of 🤗 Transformers. Use"
                    " `--accelerator_config {'split_batches':VALUE} instead",
                    FutureWarning,
                )
                self.accelerator_config.split_batches = self.split_batches

        if self.tpu_metrics_debug:
            warnings.warn(
                "using `--tpu_metrics_debug` is deprecated and will be removed in version 5 of 🤗 Transformers. Use"
                " `--debug tpu_metrics_debug` instead",
                FutureWarning,
            )
            if self.debug is None:
                self.debug = " tpu_metrics_debug"
            else:
                self.debug += " tpu_metrics_debug"
            self.tpu_metrics_debug = False

        if isinstance(self.debug, str):
            self.debug = [DebugOption(s) for s in self.debug.split()]
        elif self.debug is None:
            self.debug = []

        self.deepspeed_plugin = None
        if self.deepspeed:
            # - must be run very last in arg parsing, since it will use a lot of these settings.
            # - must be run before the model is created.
            if not is_accelerate_available():
                raise ValueError("--deepspeed requires Accelerate to be installed: `pip install accelerate`.")
            from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig

            # will be used later by the Trainer
            # note: leave self.deepspeed unmodified in case a user relies on it not to be modified)
            self.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.deepspeed)
            self.hf_deepspeed_config.trainer_config_process(self)

            # Accelerate DeepSpeed Plugin
            from accelerate.utils import DeepSpeedPlugin

            os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
            self.deepspeed_plugin = DeepSpeedPlugin(hf_ds_config=self.hf_deepspeed_config)
        elif strtobool(os.environ.get("ACCELERATE_USE_DEEPSPEED", "false")):
            # Accelerate DeepSpeed Plugin
            from accelerate.utils import DeepSpeedPlugin

            self.deepspeed_plugin = DeepSpeedPlugin()
            mixed_precision = os.environ.get("ACCELERATE_MIXED_PRECISION", "no")
            self.deepspeed_plugin.set_mixed_precision(mixed_precision)
            self.deepspeed_plugin.set_deepspeed_weakref()

        if self.push_to_hub_token is not None:
            warnings.warn(
                "`--push_to_hub_token` is deprecated and will be removed in version 5 of 🤗 Transformers. Use "
                "`--hub_token` instead.",
                FutureWarning,
            )
            self.hub_token = self.push_to_hub_token

        if self.push_to_hub_model_id is not None:
            self.hub_model_id = get_full_repo_name(
                self.push_to_hub_model_id, organization=self.push_to_hub_organization, token=self.hub_token
            )
            if self.push_to_hub_organization is not None:
                warnings.warn(
                    "`--push_to_hub_model_id` and `--push_to_hub_organization` are deprecated and will be removed in "
                    "version 5 of 🤗 Transformers. Use `--hub_model_id` instead and pass the full repo name to this "
                    f"argument (in this case {self.hub_model_id}).",
                    FutureWarning,
                )
            else:
                warnings.warn(
                    "`--push_to_hub_model_id` is deprecated and will be removed in version 5 of 🤗 Transformers. Use "
                    "`--hub_model_id` instead and pass the full repo name to this argument (in this case "
                    f"{self.hub_model_id}).",
                    FutureWarning,
                )
        elif self.push_to_hub_organization is not None:
            self.hub_model_id = f"{self.push_to_hub_organization}/{Path(self.output_dir).name}"
            warnings.warn(
                "`--push_to_hub_organization` is deprecated and will be removed in version 5 of 🤗 Transformers. Use "
                "`--hub_model_id` instead and pass the full repo name to this argument (in this case "
                f"{self.hub_model_id}).",
                FutureWarning,
            )

        # if training args is specified, it will override the one specified in the accelerate config
        if self.half_precision_backend != "apex":
            mixed_precision_dtype = os.environ.get("ACCELERATE_MIXED_PRECISION", "no")
            if self.fp16:
                mixed_precision_dtype = "fp16"
            elif self.bf16:
                mixed_precision_dtype = "bf16"
            os.environ["ACCELERATE_MIXED_PRECISION"] = mixed_precision_dtype

        if self.use_module_with_loss is True:
            logger.info(
                "Using ModuleWithLoss Wrapper."
                "loss will be computed during training loop and it will save memory peak "
            )
        else:
            logger.info("Not Using ModuleWithLoss Wrapper.")