trl/trainer/utils.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 random
import warnings
from collections import deque
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import torch
from accelerate import PartialState
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import IterableDataset
from transformers import BitsAndBytesConfig, DataCollatorForLanguageModeling, PreTrainedTokenizerBase

from ..import_utils import is_peft_available, is_unsloth_available, is_xpu_available
from ..trainer.model_config import ModelConfig


if is_peft_available():
    from peft import LoraConfig, PeftConfig


class AdaptiveKLController:
    """

    Adaptive KL controller described in the paper:

    https://arxiv.org/pdf/1909.08593.pdf

    """

    def __init__(self, init_kl_coef, target, horizon):
        self.value = init_kl_coef
        self.target = target
        self.horizon = horizon

    def update(self, current, n_steps):
        target = self.target
        proportional_error = np.clip(current / target - 1, -0.2, 0.2)
        mult = 1 + proportional_error * n_steps / self.horizon
        self.value *= mult


class FixedKLController:
    """Fixed KL controller."""

    def __init__(self, kl_coef):
        self.value = kl_coef

    def update(self, current, n_steps):
        pass


class DataCollatorForCompletionOnlyLM(DataCollatorForLanguageModeling):
    """

    Data collator used for completion tasks. It ensures that all the tokens of the labels are set to an 'ignore_index'

    when they do not come from the assistant. This ensure that the loss is only

    calculated on the completion made by the assistant.



    Args:

        response_template (`Union[str, List[int]]`): the template form that indicates the start of the response, typically something like

            '### Response:\n'. It can also be passed as tokenized ids, which can be useful when using a tokenizer that encodes the response

            differently if it does not have proper context.

        instruction_template (`Union[str, List[int]]`): the template form that indicates the start of the human instruction, typically something like

            '### Human:\n'. Useful for assistant-style conversation datasets. It can also be passed as tokenized ids.

        mlm (`bool`, *optional*, defaults to `False`): Whether or not to use masked language modeling in the underlying

            `DataCollatorForLanguageModeling` class. Note that this option currently has no effect but is present

             for flexibility and backwards-compatibility.

        ignore_index (`int`, *optional*, defaults to `-100`):

            The index to use to ignore the initial tokens with

    """

    def __init__(

        self,

        response_template: Union[str, List[int]],

        instruction_template: Union[str, List[int]] = None,

        *args,

        mlm: bool = False,

        ignore_index: int = -100,

        **kwargs,

    ):
        super().__init__(*args, mlm=mlm, **kwargs)

        self.instruction_template = instruction_template
        if isinstance(instruction_template, str):
            # The user provides a string, must tokenize
            self.instruction_token_ids = self.tokenizer.encode(self.instruction_template, add_special_tokens=False)
        else:
            # The user already provides the token ids
            self.instruction_token_ids = instruction_template

        self.response_template = response_template
        if isinstance(response_template, str):
            # The user provides a string, must tokenize
            self.response_token_ids = self.tokenizer.encode(self.response_template, add_special_tokens=False)
        else:
            # The user already provides the token ids
            self.response_token_ids = response_template

        if not self.mlm and self.instruction_template and self.tokenizer.pad_token_id == self.tokenizer.eos_token_id:
            warnings.warn(
                "The pad_token_id and eos_token_id values of this tokenizer are identical. "
                "If you are planning for multi-turn training, "
                "it can result in the model continuously generating questions and answers without eos token. "
                "To avoid this, set the pad_token_id to a different value."
            )

        self.ignore_index = ignore_index

    def torch_call(self, examples: List[Union[List[int], Any, Dict[str, Any]]]) -> Dict[str, Any]:
        batch = super().torch_call(examples)

        if self.instruction_template is None:
            for i in range(len(examples)):
                response_token_ids_start_idx = None

                for idx in np.where(batch["labels"][i] == self.response_token_ids[0])[0]:
                    # `response_token_ids` is `'### Response:\n'`, here we are just making sure that the token IDs match
                    if self.response_token_ids == batch["labels"][i][idx : idx + len(self.response_token_ids)].tolist():
                        response_token_ids_start_idx = idx

                if response_token_ids_start_idx is None:
                    warnings.warn(
                        f"Could not find response key `{self.response_template}` in the "
                        f'following instance: {self.tokenizer.decode(batch["input_ids"][i])} '
                        f"This instance will be ignored in loss calculation. "
                        f"Note, if this happens often, consider increasing the `max_seq_length`."
                    )
                    batch["labels"][i, :] = self.ignore_index
                else:
                    response_token_ids_end_idx = response_token_ids_start_idx + len(self.response_token_ids)

                    # Make pytorch loss function ignore all tokens up through the end of the response key
                    batch["labels"][i, :response_token_ids_end_idx] = self.ignore_index

        else:
            for i in range(len(examples)):
                response_token_ids_idxs = []
                human_token_ids_idxs = []

                for assistant_idx in np.where(batch["labels"][i] == self.response_token_ids[0])[0]:
                    # find the indexes of the start of a response.
                    if self.response_token_ids == batch["labels"][i][assistant_idx : assistant_idx + len(self.response_token_ids)].tolist():
                        response_token_ids_idxs.append(assistant_idx + len(self.response_token_ids))

                if len(response_token_ids_idxs) == 0:
                    warnings.warn(
                        f"Could not find response key `{self.response_template}` in the "
                        f'following instance: {self.tokenizer.decode(batch["input_ids"][i])} '
                        f"This instance will be ignored in loss calculation. "
                        f"Note, if this happens often, consider increasing the `max_seq_length`."
                    )
                    batch["labels"][i, :] = self.ignore_index

                human_token_ids = self.instruction_token_ids
                for human_idx in np.where(batch["labels"][i] == human_token_ids[0])[0]:
                    # find the indexes of the start of a human answer.
                    if human_token_ids == batch["labels"][i][human_idx : human_idx + len(human_token_ids)].tolist():
                        human_token_ids_idxs.append(human_idx)

                if len(human_token_ids_idxs) == 0:
                    warnings.warn(
                        f"Could not find instruction key `{self.instruction_template}` in the "
                        f'following instance: {self.tokenizer.decode(batch["input_ids"][i])} '
                        f"This instance will be ignored in loss calculation. "
                        f"Note, if this happens often, consider increasing the `max_seq_length`."
                    )
                    batch["labels"][i, :] = self.ignore_index

                if len(human_token_ids_idxs) > 0 and len(response_token_ids_idxs) > 0 and human_token_ids_idxs[0] > response_token_ids_idxs[0]:
                    human_token_ids_idxs = [0] + human_token_ids_idxs

                for idx, (start, end) in enumerate(zip(human_token_ids_idxs, response_token_ids_idxs)):
                    # Make pytorch loss function ignore all non response tokens
                    if idx != 0:
                        batch["labels"][i, start:end] = self.ignore_index
                    else:
                        batch["labels"][i, :end] = self.ignore_index

                if len(response_token_ids_idxs) < len(human_token_ids_idxs):
                    batch["labels"][i, human_token_ids_idxs[-1] :] = self.ignore_index

        return batch


@dataclass
class RewardDataCollatorWithPadding:
    r"""

    Reward DataCollator class that pads the inputs to the maximum length of the batch.

    Args:

        tokenizer (`PreTrainedTokenizerBase`):

            The tokenizer used for encoding the data.

        padding (`Union[bool, str, `PaddingStrategy`]`, `optional`, defaults to `True`):

            padding_strategy to pass to the tokenizer.

        max_length (`Optional[int]`, `optional`, defaults to `None`):

            The maximum length of the sequence to be processed.

        pad_to_multiple_of (`Optional[int]`, `optional`, defaults to `None`):

            If set will pad the sequence to a multiple of the provided value.

        return_tensors (`str`, `optional`, defaults to `"pt"`):

            The tensor type to use.

    """

    tokenizer: PreTrainedTokenizerBase
    padding: Union[bool, str] = True
    max_length: Optional[int] = None
    pad_to_multiple_of: Optional[int] = None
    return_tensors: str = "pt"

    def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
        features_chosen = []
        features_rejected = []
        margin = []
        # check if we have a margin. If we do, we need to batch it as well
        has_margin = "margin" in features[0]
        for feature in features:
            # check if the keys are named as expected
            if "input_ids_chosen" not in feature or "input_ids_rejected" not in feature or "attention_mask_chosen" not in feature or "attention_mask_rejected" not in feature:
                raise ValueError("The features should include `input_ids_chosen`, `attention_mask_chosen`, `input_ids_rejected` and `attention_mask_rejected`")

            features_chosen.append(
                {
                    "input_ids": feature["input_ids_chosen"],
                    "attention_mask": feature["attention_mask_chosen"],
                }
            )
            features_rejected.append(
                {
                    "input_ids": feature["input_ids_rejected"],
                    "attention_mask": feature["attention_mask_rejected"],
                }
            )
            if has_margin:
                margin.append(feature["margin"])
        batch_chosen = self.tokenizer.pad(
            features_chosen,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors=self.return_tensors,
        )
        batch_rejected = self.tokenizer.pad(
            features_rejected,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors=self.return_tensors,
        )
        batch = {
            "input_ids_chosen": batch_chosen["input_ids"],
            "attention_mask_chosen": batch_chosen["attention_mask"],
            "input_ids_rejected": batch_rejected["input_ids"],
            "attention_mask_rejected": batch_rejected["attention_mask"],
            "return_loss": True,
        }
        if has_margin:
            margin = torch.tensor(margin, dtype=torch.float)
            batch["margin"] = margin
        return batch


@dataclass
class DPODataCollatorWithPadding:
    r"""

    DPO DataCollator class that pads the tokenized inputs to the maximum length of the batch.

    Args:

        pad_token_id (`int` defaults to 0):

            The tokenizer's pad_token_id.

        label_pad_token_id (`int`, defaults to -100):

            The label used for masking.

        is_encoder_decoder (`Optional[bool]`, `optional`, defaults to `None`):

            Whether or not you model has an encoder_decoder architecture.

    """

    tokenizer: PreTrainedTokenizerBase
    pad_token_id: int = 0
    label_pad_token_id: int = -100
    is_encoder_decoder: Optional[bool] = False

    def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
        # first, pad everything to the same length
        padded_batch = {}
        for k in features[0].keys():
            if k.endswith("_input_ids") or k.endswith("_attention_mask") or k.endswith("_labels"):
                if self.is_encoder_decoder:
                    to_pad = [torch.LongTensor(ex[k]) for ex in features]

                    if (k.startswith("prompt")) and (k.endswith("input_ids")):
                        if self.pad_token_id is None:
                            raise ValueError(
                                "Padding is enabled, but the tokenizer is not configured with a padding token." " Explicitly set `tokenizer.pad_token` (e.g. `tokenizer.pad_token = tokenizer.eos_token`)" " before calling the trainer."
                            )
                        padding_value = self.pad_token_id
                    elif k.endswith("_attention_mask"):
                        padding_value = 0
                    elif (k.startswith("chosen")) or (k.startswith("rejected")) or ("decoder" in k):
                        padding_value = self.label_pad_token_id
                    else:
                        raise ValueError(f"Unexpected key in batch '{k}'")
                    padded_batch[k] = pad_sequence(to_pad, batch_first=True, padding_value=padding_value)
                else:
                    # adapted from https://stackoverflow.com/questions/73256206
                    if "prompt" in k:
                        to_pad = [torch.LongTensor(ex[k][::-1]) for ex in features]
                    else:
                        to_pad = [torch.LongTensor(ex[k]) for ex in features]
                    if k.endswith("_input_ids"):
                        if self.pad_token_id is None:
                            raise ValueError(
                                "Padding is enabled, but the tokenizer is not configured with a padding token." " Explicitly set `tokenizer.pad_token` (e.g. `tokenizer.pad_token = tokenizer.eos_token`)" " before calling the trainer."
                            )
                        padding_value = self.pad_token_id
                    elif k.endswith("_labels"):
                        padding_value = self.label_pad_token_id
                    elif k.endswith("_attention_mask"):
                        padding_value = 0
                    else:
                        raise ValueError(f"Unexpected key in batch '{k}'")

                    padded_batch[k] = pad_sequence(to_pad, batch_first=True, padding_value=padding_value)
                    # for the prompt, flip back so padding is on left side
                    if "prompt" in k:
                        padded_batch[k] = padded_batch[k].flip(dims=[1])
            elif k.endswith("_logps"):
                # the cached reference model logprobs
                padded_batch[k] = torch.tensor([ex[k] for ex in features])
            else:
                padded_batch[k] = [ex[k] for ex in features]

        return padded_batch


class ConstantLengthDataset(IterableDataset):
    """

    Iterable dataset that returns constant length chunks of tokens from stream of text files.

    The dataset also formats the text before tokenization with a specific format that is provided

    by the user.



        Args:

            tokenizer (`transformers.PreTrainedTokenizer`):

                The processor used for processing the data.

            dataset (`dataset.Dataset`):

                Dataset with text files.

            dataset_text_field (`str`, **optional**):

                Name of the field in the dataset that contains the text. Used only if `formatting_func` is `None`.

            formatting_func (`Callable`, **optional**):

                Function that formats the text before tokenization. Usually it is recommended to have follows a certain

                pattern such as `"### Question: {question} ### Answer: {answer}"`

            infinite (`bool`, *optional*, defaults to `False`):

                If True the iterator is reset after dataset reaches end else stops.

            seq_length (`int`, *optional*, defaults to `1024`):

                Length of token sequences to return.

            num_of_sequences (`int`, *optional*, defaults to `1024`):

                Number of token sequences to keep in buffer.

            chars_per_token (`int`, *optional*, defaults to `3.6`):

                Number of characters per token used to estimate number of tokens in text buffer.

            eos_token_id (`int`, *optional*, defaults to `0`):

                Id of the end of sequence token if the passed tokenizer does not have an EOS token.

            shuffle ('bool', *optional*, defaults to True)

                Shuffle the examples before they are returned

            append_concat_token ('bool', *optional*, defaults to True)

                If true, appends `eos_token_id` at the end of each sample being packed.

            add_special_tokens ('bool', *optional*, defaults to True)

                If true, tokenizers adds special tokens to each sample being packed.

    """

    def __init__(

        self,

        tokenizer,

        dataset,

        dataset_text_field=None,

        formatting_func=None,

        infinite=False,

        seq_length=1024,

        num_of_sequences=1024,

        chars_per_token=3.6,

        eos_token_id=0,

        shuffle=True,

        append_concat_token=True,

        add_special_tokens=True,

    ):
        self.tokenizer = tokenizer

        if tokenizer.eos_token_id is None:
            warnings.warn(
                "The passed tokenizer does not have an EOS token. We will use the passed eos_token_id instead which corresponds" f" to {eos_token_id}. If this is not the correct EOS token, make sure to pass the correct eos_token_id."
            )

        self.concat_token_id = tokenizer.eos_token_id if tokenizer.eos_token_id else eos_token_id
        self.dataset = dataset
        self.seq_length = seq_length
        self.infinite = infinite
        self.current_size = 0
        self.max_buffer_size = seq_length * chars_per_token * num_of_sequences
        self.shuffle = shuffle
        self.append_concat_token = append_concat_token
        self.add_special_tokens = add_special_tokens
        if formatting_func is None:
            self.formatting_func = lambda x: x[dataset_text_field]
        else:
            self.formatting_func = formatting_func

        if formatting_func is not None:
            if formatting_func.__code__.co_argcount > 1:
                warnings.warn(
                    "The passed formatting_func has more than one argument. Usually that function should have a single argument `example`"
                    " which corresponds to the dictionary returned by each element of the dataset. Make sure you know what you are doing."
                )

    def __len__(self):
        return len(self.dataset)

    def __iter__(self):
        iterator = iter(self.dataset)
        more_examples = True
        while more_examples:
            buffer, buffer_len = [], 0
            while True:
                if buffer_len >= self.max_buffer_size:
                    break
                try:
                    buffer.append(self.formatting_func(next(iterator)))
                    buffer_len += len(buffer[-1])
                except StopIteration:
                    if self.infinite:
                        iterator = iter(self.dataset)
                        warnings.warn("The dataset reached end and the iterator is reset to the start.")
                    else:
                        more_examples = False
                        break
            tokenized_inputs = self.tokenizer(buffer, add_special_tokens=self.add_special_tokens, truncation=False)["input_ids"]
            all_token_ids = []
            for tokenized_input in tokenized_inputs:
                if self.append_concat_token:
                    tokenized_input = tokenized_input + [self.concat_token_id]
                all_token_ids.extend(tokenized_input)
            examples = []
            for i in range(0, len(all_token_ids), self.seq_length):
                input_ids = all_token_ids[i : i + self.seq_length]
                if len(input_ids) == self.seq_length:
                    examples.append(input_ids)
            if self.shuffle:
                random.shuffle(examples)
            for example in examples:
                self.current_size += 1
                yield {
                    "input_ids": torch.LongTensor(example),
                    "labels": torch.LongTensor(example),
                }


class RunningMoments:
    def __init__(self, accelerator):
        """

        Calculates the running mean and standard deviation of a data stream. Reference:

        https://github.com/OpenLMLab/MOSS-RLHF/blob/40b91eb2f2b71b16919addede0341d2bef70825d/utils.py#L75

        """
        self.mean = 0
        self.std = 1
        self.var = 1
        self.count = 1e-24
        self.accelerator = accelerator

    @torch.no_grad()
    def update(self, xs: torch.Tensor) -> Tuple[float, float]:
        """

        Updates running moments from batch's moments computed across ranks

        """
        if self.accelerator.use_distributed:
            xs_mean, xs_var, xs_count = get_global_statistics(self.accelerator, xs)
        else:
            xs_count = xs.numel()
            xs_var, xs_mean = torch.var_mean(xs, unbiased=False)
        xs_mean, xs_var = xs_mean.float(), xs_var.float()

        delta = xs_mean - self.mean
        tot_count = self.count + xs_count

        new_sum = xs_var * xs_count
        # correct old_sum deviation accounting for the new mean
        old_sum = self.var * self.count + delta**2 * self.count * xs_count / tot_count
        tot_sum = old_sum + new_sum

        self.mean += delta * xs_count / tot_count
        self.var = tot_sum / tot_count
        self.std = (self.var * tot_count / (tot_count - 1)).float().sqrt()
        self.count = tot_count

        return xs_mean.item(), (xs_var * xs_count / (xs_count - 1)).float().sqrt().item()


@torch.no_grad()
def get_global_statistics(accelerator, xs: torch.Tensor, mask=None, device="cpu") -> Tuple[float, float, int]:
    """

    Computes element-wise mean and variance of the tensor across processes. Reference:

    https://github.com/OpenLMLab/MOSS-RLHF/blob/40b91eb2f2b71b16919addede0341d2bef70825d/utils.py#L57C1-L73C75

    """
    xs = xs.to(accelerator.device)
    sum_and_count = torch.tensor([xs.sum(), (xs.numel() if mask is None else mask.sum())], device=xs.device)
    sum_and_count = accelerator.reduce(sum_and_count)
    global_sum, count = sum_and_count
    global_mean = global_sum / count

    sum_var = torch.sum(((xs - global_mean) ** 2).mul(1 if mask is None else mask))
    sum_var = accelerator.reduce(sum_var)
    global_var = sum_var / count

    return global_mean.to(device), global_var.to(device), count.to(device)


def compute_accuracy(eval_pred) -> Dict[str, float]:
    predictions, labels = eval_pred
    # Here, predictions is rewards_chosen and rewards_rejected.
    # We want to see how much of the time rewards_chosen > rewards_rejected.
    if np.array(predictions[:, 0] == predictions[:, 1], dtype=float).sum() > 0:
        warnings.warn(f"There are {np.array(predictions[:, 0] == predictions[:, 1]).sum()} out of {len(predictions[:, 0])} instances where the predictions for both options are equal. As a consequence the accuracy can be misleading.")
    predictions = np.argmax(predictions, axis=1)

    accuracy = np.array(predictions == labels, dtype=float).mean().item()
    return {"accuracy": accuracy}


def pad_to_length(tensor: torch.Tensor, length: int, pad_value: Union[int, float], dim: int = -1) -> torch.Tensor:
    if tensor.size(dim) >= length:
        return tensor
    else:
        pad_size = list(tensor.shape)
        pad_size[dim] = length - tensor.size(dim)
        return torch.cat(
            [
                tensor,
                pad_value * torch.ones(*pad_size, dtype=tensor.dtype, device=tensor.device),
            ],
            dim=dim,
        )


def disable_dropout_in_model(model: torch.nn.Module) -> None:
    for module in model.modules():
        if isinstance(module, torch.nn.Dropout):
            module.p = 0


def exact_div(a, b, a_str, b_str, custom_error_message=""):
    q = a // b
    if a != q * b:
        raise ValueError(f"{custom_error_message}, {a_str}={a}, {b_str}={b}, inexact division: {a} / {b} = {a / b}")
    return q


# copied from https://github.com/kvablack/ddpo-pytorch/blob/main/ddpo_pytorch/stat_tracking.py#L5
class PerPromptStatTracker:
    r"""

    Class for tracking statistics per prompt. Mainly used to calculate advantage for the DPPO algorithm



    Args:

        buffer_size (`int`):

            Size of the buffer to keep for each prompt.

        min_count (`int`):

            Minimum number of samples to keep in the buffer before calculating the mean and std.

    """

    def __init__(self, buffer_size, min_count):
        self.buffer_size = buffer_size
        self.min_count = min_count
        self.stats = {}

    def update(self, prompts, rewards):
        prompts = np.array(prompts)
        rewards = np.array(rewards)
        unique = np.unique(prompts)
        advantages = np.empty_like(rewards)
        for prompt in unique:
            prompt_rewards = rewards[prompts == prompt]
            if prompt not in self.stats:
                self.stats[prompt] = deque(maxlen=self.buffer_size)
            self.stats[prompt].extend(prompt_rewards)

            if len(self.stats[prompt]) < self.min_count:
                mean = np.mean(rewards)
                std = np.std(rewards) + 1e-6
            else:
                mean = np.mean(self.stats[prompt])
                std = np.std(self.stats[prompt]) + 1e-6
            advantages[prompts == prompt] = (prompt_rewards - mean) / std

        return advantages

    def get_stats(self):
        return {k: {"mean": np.mean(v), "std": np.std(v), "count": len(v)} for k, v in self.stats.items()}


def neftune_post_forward_hook(module, input, output):
    """

    Implements the NEFTune forward pass for the model using forward hooks. Note this works only for

    torch.nn.Embedding layers. This method is slightly adapted from the original source code

    that can be found here: https://github.com/neelsjain/NEFTune



    Simply add it to your model as follows:

    ```python

    model = ...

    model.embed_tokens.neftune_noise_alpha = 0.1

    model.embed_tokens.register_forward_hook(neftune_post_forward_hook)

    ```



    Args:

        module (`torch.nn.Module`):

            The embedding module where the hook is attached. Note that you need to set

            `module.neftune_noise_alpha` to the desired noise alpha value.

        input (`torch.Tensor`):

            The input tensor to the model.

        output (`torch.Tensor`):

            The output tensor of the model (i.e. the embeddings).

    """
    if module.training:
        dims = torch.tensor(output.size(1) * output.size(2))
        mag_norm = module.neftune_noise_alpha / torch.sqrt(dims)
        output = output + torch.zeros_like(output).uniform_(-mag_norm, mag_norm)
    return output


def peft_module_casting_to_bf16(model):
    from peft.tuners.tuners_utils import BaseTunerLayer

    for name, module in model.named_modules():
        if isinstance(module, BaseTunerLayer):
            module = module.to(torch.bfloat16)
        elif isinstance(module, torch.nn.LayerNorm) or "norm" in name:
            module = module.to(torch.float32)
        elif any(x in name for x in ["lm_head", "embed_tokens", "wte", "wpe"]):
            if hasattr(module, "weight"):
                if module.weight.dtype == torch.float32:
                    module = module.to(torch.bfloat16)


def trl_sanitze_kwargs_for_tagging(model, tag_names, kwargs=None):
    if is_unsloth_available():
        # Unsloth adds a new attribute in the model config `unsloth_version`
        # to keep track of models that have been patched with unsloth.
        if hasattr(model, "config") and getattr(model.config, "unsloth_version", None) is not None:
            tag_names.append("unsloth")

    if kwargs is not None:
        if "tags" not in kwargs:
            kwargs["tags"] = tag_names
        elif "tags" in kwargs and isinstance(kwargs["tags"], list):
            kwargs["tags"].extend(tag_names)
        elif "tags" in kwargs and isinstance(kwargs["tags"], str):
            tag_names.append(kwargs["tags"])
            kwargs["tags"] = tag_names
    return kwargs


def get_quantization_config(model_config: ModelConfig) -> Optional[BitsAndBytesConfig]:
    if model_config.load_in_4bit:
        quantization_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=model_config.torch_dtype,  # For consistency with model weights, we use the same value as `torch_dtype`
            bnb_4bit_quant_type=model_config.bnb_4bit_quant_type,
            bnb_4bit_use_double_quant=model_config.use_bnb_nested_quant,
        )
    elif model_config.load_in_8bit:
        quantization_config = BitsAndBytesConfig(
            load_in_8bit=True,
        )
    else:
        quantization_config = None

    return quantization_config


def get_kbit_device_map() -> Optional[Dict[str, int]]:
    if is_xpu_available():
        return {"": f"xpu:{PartialState().local_process_index}"}
    elif torch.cuda.is_available():
        return {"": PartialState().local_process_index}
    else:
        return None


def get_peft_config(model_config: ModelConfig) -> "Optional[PeftConfig]":
    if model_config.use_peft is False:
        return None

    peft_config = LoraConfig(
        r=model_config.lora_r,
        lora_alpha=model_config.lora_alpha,
        lora_dropout=model_config.lora_dropout,
        bias="none",
        task_type="CAUSAL_LM",
        target_modules=model_config.lora_target_modules,
        modules_to_save=model_config.lora_modules_to_save,
    )

    return peft_config