icedit/peft/tuners/vblora/model.py

# Copyright 2024-present the HuggingFace Inc. team.
#
# 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.
from __future__ import annotations

import warnings
from dataclasses import asdict
from enum import Enum
from typing import Optional

import torch
import torch.nn as nn
from tqdm import tqdm
from transformers.pytorch_utils import Conv1D

from peft.tuners.tuners_utils import BaseTuner, BaseTunerLayer, check_target_module_exists
from peft.utils import TRANSFORMERS_MODELS_TO_VBLORA_TARGET_MODULES_MAPPING, ModulesToSaveWrapper, _get_submodules

from .config import VBLoRAConfig
from .layer import Linear, VBLoRALayer


class VBLoRAModel(BaseTuner):
    """
    Creates VBLoRA model from a pretrained transformers model.

    The method is described in detail in https://arxiv.org/abs/2405.15179.

    Args:
        model ([`~transformers.PreTrainedModel`]): The model to be adapted.
        config ([`VBLoRAConfig`]): The configuration of the VBLoRA model.
        adapter_name (`str`): The name of the adapter, defaults to `"default"`.
        low_cpu_mem_usage (`bool`, `optional`, defaults to `False`):
            Create empty adapter weights on meta device. Useful to speed up the loading process.

    Returns:
        `torch.nn.Module`: The VBLoRA model.

    Example:

        ```py
        >>> from transformers import AutoModelForCausalLM
        >>> from peft import VBLoRAConfig, get_peft_model

        >>> base_model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m")
        >>> config = VBLoRAConfig(
        ...     task_type="SEQ_CLS",
        ...     r=4,
        ...     target_modules=["fc1", "fc2", "k_proj", "out_proj", "q_proj", "v_proj"],
        ...     num_vectors=60,
        ...     vector_length=256,
        ...     save_only_topk_weights=True,
        ... )
        >>> model = get_peft_model(base_model, config)
        ```

    **Attributes**:
        - **model** ([`~transformers.PreTrainedModel`]) -- The model to be adapted.
        - **peft_config** ([`VBLoRAConfig`]): The configuration of the VBLoRAConfig model.
    """

    prefix: str = "vblora_"

    def __init__(self, model, config, adapter_name, low_cpu_mem_usage: bool = False) -> None:
        super().__init__(model, config, adapter_name, low_cpu_mem_usage=low_cpu_mem_usage)

    def _init_vblora_vector_bank(self, config: VBLoRAConfig, adapter_name: str) -> None:
        vblora_vector_bank = torch.zeros(config.num_vectors, config.vector_length)
        torch.nn.init.uniform_(vblora_vector_bank, -config.init_vector_bank_bound, config.init_vector_bank_bound)
        self.vblora_vector_bank[adapter_name] = vblora_vector_bank

    def _pre_injection_hook(self, model: nn.Module, config: VBLoRAConfig, adapter_name: str) -> None:
        self.vblora_vector_bank = nn.ParameterDict({})

    def _check_new_adapter_config(self, config: VBLoRAConfig) -> None:
        """
        A helper method to check the config when a new adapter is being added.

        Raise a ValueError if there is something wrong with the config or if it conflicts with existing adapters.

        """
        # the below todo is copied from LoRA
        # TODO: there should be a check if any of the existing adapters actually has bias != "none", or else the check
        # does not fully correspond to the error message.
        if (len(self.peft_config) > 1) and (config.bias != "none"):
            raise ValueError(
                f"{self.__class__.__name__} supports only 1 adapter with bias. When using multiple adapters, "
                "set bias to 'none' for all adapters."
            )

    @staticmethod
    def _check_target_module_exists(vblora_config, key):
        return check_target_module_exists(vblora_config, key)

    def _create_and_replace(
        self,
        vblora_config,
        adapter_name,
        target,
        target_name,
        parent,
        current_key,
    ):
        if current_key is None:
            raise ValueError("Current Key shouldn't be `None`")

        bias = hasattr(target, "bias") and target.bias is not None
        kwargs = {
            "fan_in_fan_out": vblora_config.fan_in_fan_out,
            "bias": bias,
        }
        self._init_vblora_vector_bank(vblora_config, adapter_name)
        # TODO: add quantization support

        if isinstance(target, Linear):
            target.update_layer(
                adapter_name=adapter_name,
                vblora_vector_bank=self.vblora_vector_bank,
                r=vblora_config.r,
                topk=vblora_config.topk,
                num_vectors=vblora_config.num_vectors,
                vector_length=vblora_config.vector_length,
                vblora_dropout=vblora_config.vblora_dropout,
                init_logits_std=vblora_config.init_logits_std,
            )
        else:
            new_module = self._create_new_module(
                vblora_config=vblora_config,
                vblora_vector_bank=self.vblora_vector_bank,
                adapter_name=adapter_name,
                target=target,
                **kwargs,
            )
            if adapter_name not in self.active_adapter:
                # adding an additional adapter: it is not automatically trainable
                new_module.requires_grad_(False)
            self._replace_module(parent, target_name, new_module, target)

    @staticmethod
    def _replace_module(parent, child_name, new_module, child):
        setattr(parent, child_name, new_module)
        # It's not necessary to set requires_grad here, as that is handled by
        # _mark_only_adapters_as_trainable

        # child layer wraps the original module, unpack it
        if hasattr(child, "base_layer"):
            child = child.base_layer

        if not hasattr(new_module, "base_layer"):
            new_module.weight = child.weight
            if hasattr(child, "bias"):
                new_module.bias = child.bias

        if getattr(child, "state", None) is not None:
            if hasattr(new_module, "base_layer"):
                new_module.base_layer.state = child.state
            else:
                new_module.state = child.state
            new_module.to(child.weight.device)

        meta = torch.device("meta")
        # dispatch to correct device
        for name, module in new_module.named_modules():
            if "vblora_" in name:
                if not any(p.device == meta for p in module.parameters()):
                    module.to(child.weight.device)

    def _mark_only_adapters_as_trainable(self, model: nn.Module) -> None:
        for n, p in model.named_parameters():
            if self.prefix not in n:
                p.requires_grad = False

        for active_adapter in self.active_adapters:
            bias = self.peft_config[active_adapter].bias
            if bias == "none":
                continue
            if bias == "all":
                for n, p in model.named_parameters():
                    if "bias" in n:
                        p.requires_grad = True
            elif bias == "vblora_only":
                for m in model.modules():
                    if isinstance(m, VBLoRALayer) and hasattr(m, "bias") and m.bias is not None:
                        m.bias.requires_grad = True
            else:
                raise NotImplementedError(f"Requested bias: {bias}, is not implemented.")

    @staticmethod
    def _create_new_module(vblora_config, vblora_vector_bank, adapter_name, target, **kwargs):
        if isinstance(target, BaseTunerLayer):
            target_base_layer = target.get_base_layer()
        else:
            target_base_layer = target

        if isinstance(target_base_layer, torch.nn.Linear):
            if kwargs["fan_in_fan_out"]:
                warnings.warn(
                    "fan_in_fan_out is set to True but the target module is `torch.nn.Linear`. "
                    "Setting fan_in_fan_out to False."
                )
                kwargs["fan_in_fan_out"] = vblora_config.fan_in_fan_out = False
        elif isinstance(target_base_layer, Conv1D):
            kwargs["is_target_conv_1d_layer"] = True
            if not kwargs["fan_in_fan_out"]:
                warnings.warn(
                    "fan_in_fan_out is set to False but the target module is `Conv1D`. "
                    "Setting fan_in_fan_out to True."
                )
                kwargs["fan_in_fan_out"] = vblora_config.fan_in_fan_out = True
        else:
            raise ValueError(
                f"Target module {target} is not supported. Currently, only the following modules are supported: "
                "`torch.nn.Linear`, `transformers.pytorch_utils.Conv1D`."
            )
        new_module = Linear(
            base_layer=target,
            vblora_vector_bank=vblora_vector_bank,
            adapter_name=adapter_name,
            r=vblora_config.r,
            num_vectors=vblora_config.num_vectors,
            vector_length=vblora_config.vector_length,
            topk=vblora_config.topk,
            vblora_dropout=vblora_config.vblora_dropout,
            init_logits_std=vblora_config.init_logits_std,
            **kwargs,
        )

        return new_module

    def __getattr__(self, name: str):
        """Forward missing attributes to the wrapped module."""
        try:
            return super().__getattr__(name)  # defer to nn.Module's logic
        except AttributeError:
            if name == "model":  # see #1892: prevent infinite recursion if class is not initialized
                raise
            return getattr(self.model, name)

    def get_peft_config_as_dict(self, inference: bool = False):
        config_dict = {}
        for key, value in self.peft_config.items():
            config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(value).items()}
            if inference:
                config["inference_mode"] = True
        config_dict[key] = config
        return config

    def _set_adapter_layers(self, enabled: bool = True) -> None:
        for module in self.model.modules():
            if isinstance(module, (BaseTunerLayer, ModulesToSaveWrapper)):
                module.enable_adapters(enabled)

    def enable_adapter_layers(self) -> None:
        """Enable all adapters.

        Call this if you have previously disabled all adapters and want to re-enable them.
        """
        self._set_adapter_layers(enabled=True)

    def disable_adapter_layers(self) -> None:
        """Disable all adapters.

        When disabling all adapters, the model output corresponds to the output of the base model.
        """
        for active_adapter in self.active_adapters:
            val = self.peft_config[active_adapter].bias
            if val != "none":
                msg = (
                    f"Careful, disabling adapter layers with bias configured to be '{val}' does not produce the same "
                    "output as the the base model would without adaption."
                )
                warnings.warn(msg)
        self._set_adapter_layers(enabled=False)

    def set_adapter(self, adapter_name: str | list[str]) -> None:
        """Set the active adapter(s).

        Additionally, this function will set the specified adapters to trainable (i.e., requires_grad=True). If this is
        not desired, use the following code.

        ```py
        >>> for name, param in model_peft.named_parameters():
        ...     if ...:  # some check on name (ex. if 'lora' in name)
        ...         param.requires_grad = False
        ```

        Args:
            adapter_name (`str` or `list[str]`): Name of the adapter(s) to be activated.
        """
        for module in self.model.modules():
            if isinstance(module, VBLoRALayer):
                if module.merged:
                    warnings.warn("Adapter cannot be set when the model is merged. Unmerging the model first.")
                    module.unmerge()
                module.set_adapter(adapter_name)
        self.active_adapter = adapter_name

    @staticmethod
    def _prepare_adapter_config(peft_config, model_config):
        if peft_config.target_modules is None:
            if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_VBLORA_TARGET_MODULES_MAPPING:
                raise ValueError("Please specify `target_modules` in `peft_config`")
            peft_config.target_modules = set(
                TRANSFORMERS_MODELS_TO_VBLORA_TARGET_MODULES_MAPPING[model_config["model_type"]]
            )
        return peft_config

    def _unload_and_optionally_merge(
        self,
        merge=True,
        progressbar: bool = False,
        safe_merge: bool = False,
        adapter_names: Optional[list[str]] = None,
    ):
        key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key]
        desc = "Unloading " + ("and merging " if merge else "") + "model"
        for key in tqdm(key_list, disable=not progressbar, desc=desc):
            try:
                parent, target, target_name = _get_submodules(self.model, key)
            except AttributeError:
                continue

            if hasattr(target, "base_layer"):
                if merge:
                    target.merge(safe_merge=safe_merge, adapter_names=adapter_names)

                self._replace_module(parent, target_name, target.get_base_layer(), target)
            elif isinstance(target, ModulesToSaveWrapper):
                # save any additional trainable modules part of `modules_to_save`
                setattr(parent, target_name, target.modules_to_save[target.active_adapter])

        return self.model

    def delete_adapter(self, adapter_name: str) -> None:
        """
        Deletes an existing adapter.

        Args:
            adapter_name (str): Name of the adapter to be deleted.
        """
        if adapter_name not in list(self.peft_config.keys()):
            raise ValueError(f"Adapter {adapter_name} does not exist")
        del self.peft_config[adapter_name]

        key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key]
        new_adapter = None
        for key in key_list:
            _, target, _ = _get_submodules(self.model, key)
            if isinstance(target, VBLoRALayer):
                target.delete_adapter(adapter_name)
                if new_adapter is None:
                    new_adapter = target.active_adapter[:]

        self.active_adapter = new_adapter or []

    def merge_and_unload(
        self, progressbar: bool = False, safe_merge: bool = False, adapter_names: Optional[list[str]] = None
    ) -> torch.nn.Module:
        r"""
        This method merges the VBLoRA layers into the base model. This is needed if someone wants to use the base model
        as a standalone model.

        Args:
            progressbar (`bool`):
                whether to show a progressbar indicating the unload and merge process
            safe_merge (`bool`):
                whether to activate the safe merging check to check if there is any potential Nan in the adapter
                weights
            adapter_names (`list[str]`, *optional*):
                The list of adapter names that should be merged. If None, all active adapters will be merged. Defaults
                to `None`.

        Example:

        ```py
        >>> from transformers import AutoModelForCausalLM
        >>> from peft import PeftModel

        >>> base_model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-40b")
        >>> peft_model_id = "smangrul/falcon-40B-int4-peft-lora-sfttrainer-sample"
        >>> model = PeftModel.from_pretrained(base_model, peft_model_id)
        >>> merged_model = model.merge_and_unload()
        ```
        """
        return self._unload_and_optionally_merge(
            progressbar=progressbar, safe_merge=safe_merge, adapter_names=adapter_names
        )

    def unload(self):
        """
        Gets back the base model by removing all the VBLoRA modules without merging. This gives back the original base
        model.
        """
        return self._unload_and_optionally_merge(merge=False)

    def get_nb_savable_parameters(self, adapter="default") -> tuple[int, int]:
        r"""
        Returns the number of savable VB-LoRA parameters and other savable parameters.
        """
        logits_params = 0
        vector_bank_params = 0
        other_params = 0
        for name, param in self.named_parameters():
            if "vblora_logits" in name:
                logits_params += param.numel()
            elif "vblora_vector_bank" in name:
                vector_bank_params += param.numel()
            elif param.requires_grad:
                other_params += param.numel()
        if self.peft_config[adapter].save_only_topk_weights:
            num_vectors = self.peft_config[adapter].num_vectors
            factor = 1  # factor to count float32-equivalent parameters
            if num_vectors < 2**8:
                factor = 0.25
            elif num_vectors < 2**15:
                factor = 0.5
            elif num_vectors < 2**31:
                factor = 1
            else:
                factor = 2
            topk_weight_params = (
                logits_params / self.peft_config[adapter].num_vectors * (self.peft_config[adapter].topk - 1)
            )
            topk_indices_params = (
                logits_params / self.peft_config[adapter].num_vectors * self.peft_config[adapter].topk * factor
            )
            vblora_params = int(vector_bank_params + topk_weight_params + topk_indices_params)
        else:
            vblora_params = vector_bank_params + logits_params
        return vblora_params, other_params

    def print_savable_parameters(self) -> None:
        r"""
        Prints the number of savable VB-LoRA parameters and total savable parameters.
        """
        vblora_params, other_params = self.get_nb_savable_parameters()
        print(
            f"VB-LoRA params to-be-saved (float32-equivalent): {vblora_params:,d} "
            f"|| total params to-be-saved: {(vblora_params + other_params):,d}"
        )