trl/models/modeling_base.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 json
import logging
import os
from copy import deepcopy

import torch
import torch.nn as nn
from accelerate import PartialState
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import (
    EntryNotFoundError,
    HFValidationError,
    LocalEntryNotFoundError,
    RepositoryNotFoundError,
)
from safetensors.torch import load_file as safe_load_file
from transformers import PreTrainedModel

from ..import_utils import is_npu_available, is_peft_available, is_transformers_greater_than, is_xpu_available


if is_peft_available():
    from peft import (
        PeftConfig,
        PeftModel,
        PeftModelForCausalLM,
        PeftModelForSeq2SeqLM,
        PromptLearningConfig,
        get_peft_model,
        prepare_model_for_kbit_training,
    )

if is_transformers_greater_than("4.33.0"):
    from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
else:
    from transformers.deepspeed import is_deepspeed_zero3_enabled

LAYER_PATTERNS = [
    "transformer.h.{layer}",
    "model.decoder.layers.{layer}",
    "gpt_neox.layers.{layer}",
    "model.layers.{layer}",
]


class PreTrainedModelWrapper(nn.Module):
    r"""

    A wrapper class around a (`transformers.PreTrainedModel`) to be compatible with the

    (`~transformers.PreTrained`) class in order to keep some attributes and methods of the

    (`~transformers.PreTrainedModel`) class.



    Attributes:

        pretrained_model: (`transformers.PreTrainedModel`)

            The model to be wrapped.

        parent_class: (`transformers.PreTrainedModel`)

            The parent class of the model to be wrapped.

        supported_args: (`list`)

            The list of arguments that are supported by the wrapper class.

    """

    transformers_parent_class = None
    supported_args = None
    supported_modules = ("v_head",)
    supported_rm_modules = ("score",)
    supported_pretrained_model_architectures = (PreTrainedModel) if not is_peft_available() else (PreTrainedModel, PeftModelForCausalLM, PeftModelForSeq2SeqLM)

    def __init__(self, pretrained_model=None, score_module=None, supports_rm_adapter=False, rm_adapter_name=None, **kwargs):
        super().__init__()
        self.pretrained_model = pretrained_model

        self.config = pretrained_model.config
        self.prepare_inputs_for_generation = pretrained_model.prepare_inputs_for_generation
        self.is_loaded_in_8bit = getattr(pretrained_model, "is_loaded_in_8bit", False)
        self.is_loaded_in_4bit = getattr(pretrained_model, "is_loaded_in_4bit", False)
        self.is_sequential_parallel = False

        if hasattr(pretrained_model, "gradient_checkpointing_disable"):
            self.gradient_checkpointing_disable = pretrained_model.gradient_checkpointing_disable

        if hasattr(pretrained_model, "gradient_checkpointing_enable"):
            self.gradient_checkpointing_enable = pretrained_model.gradient_checkpointing_enable

        self.supports_rm_adapter = supports_rm_adapter
        self.rm_adapter_name = rm_adapter_name
        self.policy_adapter_name = "default"
        if score_module is not None:
            self.score = score_module

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""

        Instantiates a new model from a pretrained model from `transformers`. The

        pretrained model is loaded using the `from_pretrained` method of the

        `transformers.PreTrainedModel` class. The arguments that are specific to the

        `transformers.PreTrainedModel` class are passed along this method and filtered

        out from the `kwargs` argument.





        Args:

            pretrained_model_name_or_path (`str` or `transformers.PreTrainedModel`):

                The path to the pretrained model or its name.

            *model_args (`list`, *optional*)):

                Additional positional arguments passed along to the underlying model's

                `from_pretrained` method.

            **kwargs (`dict`, *optional*):

                Additional keyword arguments passed along to the underlying model's

                `from_pretrained` method. We also pre-process the kwargs to extract

                the arguments that are specific to the `transformers.PreTrainedModel`

                class and the arguments that are specific to trl models. The kwargs

                also support `prepare_model_for_kbit_training` arguments from

                `peft` library.

        """
        if kwargs is not None:
            peft_config = kwargs.pop("peft_config", None)
            reward_adapter = kwargs.pop("reward_adapter", None)
            reward_adapter_name = kwargs.pop("reward_adapter_name", "reward_adapter")
            is_trainable = kwargs.pop("is_trainable", False)
            trl_model_args, pretrained_kwargs, peft_quantization_kwargs = cls._split_kwargs(kwargs)
            token = pretrained_kwargs.get("token", None)
        else:
            peft_config = None
            is_trainable = False
            trl_model_args = {}
            pretrained_kwargs = {}
            peft_quantization_kwargs = {}
            token = None

        if reward_adapter is not None and not isinstance(reward_adapter, str):
            raise ValueError("The `reward_adapter` argument should be a string representing the name of local path or the Hub id to the Reward Modeling adapter.")

        is_peft_model = False

        current_device = cls._get_current_device()
        if isinstance(pretrained_model_name_or_path, str):
            is_loaded_in_8bit = pretrained_kwargs["load_in_8bit"] if "load_in_8bit" in pretrained_kwargs else False
            is_loaded_in_4bit = pretrained_kwargs["load_in_4bit"] if "load_in_4bit" in pretrained_kwargs else False
        else:
            is_loaded_in_8bit = getattr(pretrained_model_name_or_path, "is_loaded_in_8bit", False)
            is_loaded_in_4bit = getattr(pretrained_model_name_or_path, "is_loaded_in_4bit", False)

        if (is_loaded_in_8bit or is_loaded_in_4bit) and "device_map" not in pretrained_kwargs:
            # warn users
            logging.warning(
                "The `device_map` argument is not provided. We will override the device_map argument."
                " to set the entire"
                " model on the current device. If you want to set the model on multiple devices, please provide"
                " a custom `device_map` argument."
            )
            pretrained_kwargs["device_map"] = {"": current_device}

        if is_peft_available() and peft_config is not None and not isinstance(peft_config, PeftConfig):
            raise ValueError("The `peft_config` argument should be an instance of `peft.PeftConfig` class.")

        # First, load the pre-trained model using the parent-class
        # either `AutoModelForCausalLM` or `AutoModelForSeq2SeqLM`
        if isinstance(pretrained_model_name_or_path, str):
            if is_peft_available():
                try:
                    # If there is a trained peft adapter in the hub, load its config.
                    remote_adapter_config = hf_hub_download(
                        pretrained_model_name_or_path,
                        "adapter_config.json",
                        token=token,
                    )
                except (EntryNotFoundError, LocalEntryNotFoundError, HFValidationError, RepositoryNotFoundError):
                    remote_adapter_config = None
            else:
                remote_adapter_config = None

            local_adapter_present = os.path.exists(os.path.join(pretrained_model_name_or_path, "adapter_config.json"))

            if (local_adapter_present or remote_adapter_config is not None) and is_peft_available():
                if peft_config is not None:
                    logging.warning("`peft_config` argument ignored since a peft config file was found in " f"{pretrained_model_name_or_path}")

                # Load the trained peft adapter config
                if local_adapter_present:
                    trained_adapter_config = PeftConfig.from_pretrained(pretrained_model_name_or_path)
                else:
                    remote_adapter_dir = os.path.dirname(remote_adapter_config)
                    trained_adapter_config = PeftConfig.from_pretrained(remote_adapter_dir)

                # Load the pretrained base model
                pretrained_model = cls.transformers_parent_class.from_pretrained(trained_adapter_config.base_model_name_or_path, *model_args, **pretrained_kwargs)

                # Wrap the pretrained model with the trained peft adapter
                pretrained_model = PeftModel.from_pretrained(pretrained_model, pretrained_model_name_or_path, is_trainable=is_trainable)
                logging.info("Trained peft adapter loaded")
            else:
                pretrained_model = cls.transformers_parent_class.from_pretrained(pretrained_model_name_or_path, *model_args, **pretrained_kwargs)

                if peft_config is not None:
                    # Initialize a new peft adapter with the given config
                    if is_loaded_in_8bit or is_loaded_in_4bit:
                        pretrained_model = prepare_model_for_kbit_training(
                            pretrained_model,
                            **peft_quantization_kwargs,
                        )
                    pretrained_model = get_peft_model(pretrained_model, peft_config)
                    logging.info("peft adapter initialised")

        elif isinstance(pretrained_model_name_or_path, cls.supported_pretrained_model_architectures):
            pretrained_model = pretrained_model_name_or_path

            if peft_config is not None and isinstance(pretrained_model, PreTrainedModel):
                # Initialize a new peft adapter with the given config
                if is_loaded_in_8bit or is_loaded_in_4bit:
                    pretrained_model = prepare_model_for_kbit_training(
                        pretrained_model,
                        **peft_quantization_kwargs,
                    )
                pretrained_model = get_peft_model(pretrained_model, peft_config)
                logging.info("peft adapter initialised")
        else:
            raise ValueError("pretrained_model_name_or_path should be a string or a PreTrainedModel, " f"but is {type(pretrained_model_name_or_path)}")

        if is_peft_available():
            if isinstance(pretrained_model, PeftModel):
                is_peft_model = True
                # for backward compatibility
                if hasattr(pretrained_model, "active_peft_config") and isinstance(pretrained_model.active_peft_config, PromptLearningConfig):
                    raise ValueError("PromptLearningConfig is not supported for PPO training.")

        # Add reward modeling adapter if specified
        if not is_peft_model and reward_adapter is not None:
            raise ValueError("reward_adapter can only be used with a PeftModel. ")
        elif is_peft_model and reward_adapter is not None:
            score_module = cls.add_and_load_reward_modeling_adapter(pretrained_model, reward_adapter, reward_adapter_name, token=token)
            multi_adapter_args = {
                "score_module": score_module,
                "supports_rm_adapter": True,
                "rm_adapter_name": reward_adapter_name,
            }
        else:
            multi_adapter_args = {"supports_rm_adapter": False}

        # Then, create the full model by instantiating the wrapper class
        model = cls(pretrained_model, **multi_adapter_args, **trl_model_args)

        # if resume_training, load the state_dict again - this is ok since the
        # state_dict is removed from the model after loading it.
        is_resuming_training = True
        if isinstance(pretrained_model_name_or_path, str):
            safe_filename = os.path.join(pretrained_model_name_or_path, "model.safetensors")
            filename = os.path.join(pretrained_model_name_or_path, "pytorch_model.bin")

            sharded_index_filename = os.path.join(pretrained_model_name_or_path, "pytorch_model.bin.index.json")
            safe_sharded_index_filename = os.path.join(pretrained_model_name_or_path, "model.safetensors.index.json")
            is_sharded = False
            use_safe = os.path.exists(safe_filename)

            if not (os.path.exists(filename) or os.path.exists(safe_filename)):
                # Try with `pytorch_model.bin`
                filename, files_to_download, is_sharded, is_resuming_training = cls._get_checkpoint_from_hub(
                    pretrained_model,
                    pretrained_model_name_or_path,
                    sharded_index_filename,
                    token=token,
                )
                # Try with safetensors
                if filename is None and files_to_download is None:
                    safe_filename, files_to_download, is_sharded, is_resuming_training = cls._get_checkpoint_from_hub(
                        pretrained_model,
                        pretrained_model_name_or_path,
                        safe_sharded_index_filename,
                        token=token,
                        model_name="model.safetensors",
                        model_index_name="model.safetensors.index.json",
                    )
                    use_safe = True
                else:
                    use_safe = False

            loading_func = safe_load_file if use_safe else torch.load
            load_kwargs = {} if use_safe else {"map_location": "cpu"}

            if is_resuming_training:
                if is_sharded:
                    # download each file and add it to the state_dict
                    state_dict = {}

                    for shard_file in files_to_download:
                        filename = hf_hub_download(
                            pretrained_model_name_or_path,
                            shard_file,
                            token=token,
                        )
                        state_dict.update(loading_func(filename, **load_kwargs))
                else:
                    state_dict = loading_func(filename if not use_safe else safe_filename, **load_kwargs)

        else:
            state_dict = pretrained_model_name_or_path.state_dict()

        model.is_peft_model = is_peft_model
        model.current_device = current_device

        if is_resuming_training:
            model.post_init(state_dict=state_dict)

        return model

    @classmethod
    def _get_checkpoint_from_hub(

        cls,

        pretrained_model,

        pretrained_model_name_or_path,

        index_filename,

        token=None,

        model_name="pytorch_model.bin",

        model_index_name="pytorch_model.bin.index.json",

    ):
        files_to_download = None
        filename = None
        is_resuming_training = True
        is_sharded = False

        try:
            filename = hf_hub_download(
                pretrained_model_name_or_path,
                model_name,
                token=token,
            )
        # sharded
        except (EntryNotFoundError, LocalEntryNotFoundError, HFValidationError, RepositoryNotFoundError):
            if os.path.exists(index_filename):
                index_file_name = index_filename
            else:
                try:
                    index_file_name = hf_hub_download(
                        pretrained_model_name_or_path,
                        model_index_name,
                        token=token,
                    )
                except (EntryNotFoundError, LocalEntryNotFoundError, HFValidationError, RepositoryNotFoundError):
                    # not continue training, do not have v_head weight
                    is_resuming_training = False
                    logging.warning(f"A {type(pretrained_model)} model is loaded from '{pretrained_model_name_or_path}', " f"and no v_head weight is found. This IS expected if you are not resuming PPO training.")
            # load json
            if is_resuming_training:
                with open(index_file_name, "r") as f:
                    index = json.load(f)
                # check filename with `v_head` or any known extra module:
                files_to_download = set()
                for k, v in index["weight_map"].items():
                    if any([module in k for module in cls.supported_modules]):
                        files_to_download.add(v)
                is_sharded = True

        return filename, files_to_download, is_sharded, is_resuming_training

    @classmethod
    def _get_current_device(cls):
        r"""

        Get the current device. For GPU, we return the local process index using the `accelerate.PartialState`

        object to handle corner cases when running scripts in distributed environments.



        Returns:

            current_device (`Union[int, str]`):

                The current device.

        """
        state = PartialState()
        if is_xpu_available():
            return f"xpu:{state.local_process_index}"
        elif is_npu_available():
            return f"npu:{state.local_process_index}"
        else:
            return state.local_process_index if torch.cuda.is_available() else "cpu"

    @classmethod
    def _split_kwargs(cls, kwargs):
        """

        Separate the kwargs from the arguments that we support inside

        `supported_args` and the ones that we don't.

        """
        check_peft_kwargs = False

        if is_peft_available():
            from peft import prepare_model_for_kbit_training

            check_peft_kwargs = True

        supported_kwargs = {}
        unsupported_kwargs = {}
        peft_kwargs = {}

        for key, value in kwargs.items():
            if key in cls.supported_args:
                supported_kwargs[key] = value
            else:
                unsupported_kwargs[key] = value

            if check_peft_kwargs:
                if key in prepare_model_for_kbit_training.__code__.co_varnames:
                    peft_kwargs[key] = value
                    if key in unsupported_kwargs:
                        unsupported_kwargs.pop(key)

        return supported_kwargs, unsupported_kwargs, peft_kwargs

    @classmethod
    def add_and_load_reward_modeling_adapter(cls, pretrained_model, adapter_model_id, adapter_name="reward_model_adapter", token=None):
        r"""

        Add and load a reward modeling adapter. This method can only be used if the

        model is a `PeftModel` and if you have initialized the model with the `reward_modeling_adapter_id`

        argument, pointing to the id of the reward modeling adapter. The latest needs also to contain the

        score head in order to produce the reward.

        """
        pretrained_model.load_adapter(adapter_model_id, adapter_name, is_trainable=False)
        pretrained_model.train()

        filename = os.path.join(adapter_model_id, "adapter_model.bin")
        safe_loading = False
        if not os.path.exists(filename):
            try:
                local_filename = hf_hub_download(
                    adapter_model_id,
                    "adapter_model.bin",
                    token=token,
                )
            except:  # noqa
                filename = os.path.join(adapter_model_id, "adapter_model.safetensors")
                safe_loading = True
                if not os.path.exists(filename):
                    try:
                        local_filename = hf_hub_download(
                            adapter_model_id,
                            "adapter_model.safetensors",
                            token=token,
                        )
                    except:  # noqa
                        raise ValueError("Could not find adapter model in the Hub, make sure you have the correct adapter model id.")
                else:
                    local_filename = filename
        else:
            local_filename = filename

        loading_func = safe_load_file if safe_loading else torch.load
        load_kwargs = {} if safe_loading else {"map_location": "cpu"}

        adapter_state_dict = loading_func(local_filename, **load_kwargs)

        for score_name_candidate in cls.supported_rm_modules:
            if any([score_name_candidate in name for name in adapter_state_dict.keys()]):
                score_name = score_name_candidate
                # we have found the correct head name and can break
                break

        score_dict = {}

        for name, param in adapter_state_dict.items():
            if score_name in name:
                key_name = ".".join(name.split(".")[-1:])
                score_dict[key_name] = param.to(cls._get_current_device())

        num_labels, hidden_dim = score_dict["weight"].shape
        has_bias = any(["bias" in name for name in adapter_state_dict.keys()])

        score = nn.Linear(hidden_dim, num_labels, bias=has_bias).to(
            device=cls._get_current_device(),
            dtype=pretrained_model.dtype,
        )
        score.load_state_dict(score_dict)
        for param in score.parameters():
            param.requires_grad = False

        return score

    def push_to_hub(self, *args, **kwargs):
        r"""

        Push the pretrained model to the hub. This method is a wrapper around

        `transformers.PreTrainedModel.push_to_hub`. Please refer to the documentation

        of `transformers.PreTrainedModel.push_to_hub` for more information.



        Args:

            *args (`list`, *optional*):

                Positional arguments passed along to the underlying model's

                `push_to_hub` method.

            **kwargs (`dict`, *optional*):

                Keyword arguments passed along to the underlying model's

                `push_to_hub` method.

        """
        raise NotImplementedError

    def save_pretrained(self, *args, **kwargs):
        r"""

        Save the pretrained model to a directory. This method is a wrapper around

        `transformers.PreTrainedModel.save_pretrained`. Please refer to the documentation

        of `transformers.PreTrainedModel.save_pretrained` for more information.



        Args:

            *args (`list`, *optional*):

                Positional arguments passed along to the underlying model's

                `save_pretrained` method.

            **kwargs (`dict`, *optional*):

                Keyword arguments passed along to the underlying model's

                `save_pretrained` method.

        """
        state_dict = kwargs.get("state_dict")
        if state_dict is None:
            state_dict = self.state_dict()
            kwargs["state_dict"] = state_dict

        # if it is a peft model only save the `v_head` state_dict and
        # pop the `state_dict` from the kwargs to avoid slient bugs with `peft`
        if self.is_peft_model:
            save_path = args[0]
            save_path = os.path.join(save_path, "pytorch_model.bin")
            torch.save(state_dict, save_path)
            _ = kwargs.pop("state_dict", None)

        return self.pretrained_model.save_pretrained(*args, **kwargs)

    def state_dict(self, *args, **kwargs):
        r"""

        Return the state_dict of the pretrained model.

        """
        raise NotImplementedError

    def post_init(self, *args, **kwargs):
        r"""

        Post initialization method. This method is called after the model is

        instantiated and loaded from a checkpoint. It can be used to perform

        additional operations such as loading the state_dict.

        """
        raise NotImplementedError

    def compute_reward_score(self, input_ids, attention_mask=None, **kwargs):
        r"""

        Computes the reward score for a given input. The method has first to enable the adapter

        and then compute the reward score. After that the model disables the reward modeling

        adapter and enables the default ppo adapter again.

        """
        if not self.supports_rm_adapter:
            raise ValueError("This model does not support reward modeling adapter.")

        # enable rm adapter
        self.pretrained_model.set_adapter(self.rm_adapter_name)
        self.pretrained_model.eval()

        with torch.no_grad():
            base_model_output = self.pretrained_model(
                input_ids=input_ids,
                attention_mask=attention_mask,
                output_hidden_states=True,
                return_dict=True,
                **kwargs,
            )

            last_hidden_states = base_model_output.hidden_states[-1]
            scores = self.score(last_hidden_states)

        self.pretrained_model.set_adapter(self.policy_adapter_name)
        self.pretrained_model.eval()

        return scores


def create_reference_model(model: PreTrainedModelWrapper, num_shared_layers: int = None, pattern: str = None) -> PreTrainedModelWrapper:
    """

    Creates a static reference copy of a model. Note that model will be in `.eval()` mode.



    Args:

        model (`PreTrainedModelWrapper`): The model to be copied.

        num_shared_layers (`int`, *optional*): The number of initial layers that are shared between both models and kept frozen.

        pattern (`str`, *optional*): The shared layers are selected with a string pattern

            (e.g. "transformer.h.{layer}" for GPT2) and if a custom pattern is necessary it can be passed here.



    Returns

        `PreTrainedModelWrapper`

    """
    if is_deepspeed_zero3_enabled():
        raise ValueError("DeepSpeed ZeRO-3 is enabled and is not compatible with `create_reference_model()`. Please instantiate your reference model directly with `AutoCausalLM.from_pretrained()`.")

    parameter_names = [n for n, _ in model.named_parameters()]
    ref_model = deepcopy(model)

    # if no layers are shared, return copy of model
    if num_shared_layers is None:
        for param_name in parameter_names:
            param = ref_model.get_parameter(param_name)
            param.requires_grad = False
        return ref_model.eval()

    # identify layer name pattern
    if pattern is not None:
        pattern = pattern.format(layer=num_shared_layers)
    else:
        for pattern_candidate in LAYER_PATTERNS:
            pattern_candidate = pattern_candidate.format(layer=num_shared_layers)
            if any([pattern_candidate in name for name in parameter_names]):
                pattern = pattern_candidate
                break

    if pattern is None:
        raise ValueError("Layer pattern could not be matched.")

    # divide parameters in shared and unshared parameter lists
    shared_param_list = []
    unshared_param_list = []

    shared_parameter = True
    for name, param in model.named_parameters():
        if pattern in name:
            shared_parameter = False
        if shared_parameter:
            shared_param_list.append(name)
        else:
            unshared_param_list.append(name)

    # create reference of the original parameter if they are shared
    for param_name in shared_param_list:
        param = model.get_parameter(param_name)
        param.requires_grad = False

        ref_param = ref_model.get_parameter(param_name)  # noqa
        ref_param = param  # noqa

    # for all other parameters just make sure they don't use gradients
    for param_name in unshared_param_list:
        param = ref_model.get_parameter(param_name)
        param.requires_grad = False

    if pattern is not None and len(unshared_param_list) == 0:
        logging.warning("Pattern passed or found, but no layers matched in the model. Check for a typo.")

    return ref_model.eval()