AutoGPTQ/auto_gptq/modeling/_base.py

import copy
import json
import os
from dataclasses import dataclass, field, fields
from logging import getLogger
from os.path import join, isfile
from typing import Dict, List, Optional, Union

import accelerate
import torch
import torch.nn as nn
import transformers
from accelerate.hooks import remove_hook_from_module
from safetensors.torch import save_file as safe_save
from transformers import AutoConfig, AutoModelForCausalLM, PreTrainedModel
from transformers.utils.generic import ContextManagers
from transformers.modeling_utils import no_init_weights
from transformers.utils.hub import PushToHubMixin

from ._const import *
from ._utils import *
from ..nn_modules.qlinear import GeneralQuantLinear
from ..nn_modules._fused_base import FusedBaseAttentionModule, FusedBaseMLPModule
from ..quantization import GPTQ
from ..utils.data_utils import collate_data
from ..utils.import_utils import dynamically_import_QuantLinear, TRITON_AVAILABLE, AUTOGPTQ_CUDA_AVAILABLE

logger = getLogger(__name__)


@dataclass
class BaseQuantizeConfig(PushToHubMixin):
    bits: int = field(default=4, metadata={"choices": [2, 3, 4, 8]})
    group_size: int = field(default=-1)
    damp_percent: float = field(default=0.01)
    desc_act: bool = field(default=True)
    sym: bool = field(default=True)
    true_sequential: bool = field(default=True)

    def __post_init__(self):
        fields_info = fields(self)

        if self.bits not in fields_info[0].metadata["choices"]:
            raise ValueError(f"only support quantize to {fields_info[0].metadata['choices']} bits.")
        if self.group_size != -1 and self.group_size <= 0:
            raise ValueError("unless equal to -1, group_size must greater then 0.")
        if not (0 < self.damp_percent < 1):
            raise ValueError("damp_percent must between 0 and 1.")

    def save_pretrained(self, save_dir: str, **kwargs):
        with open(join(save_dir, "quantize_config.json"), "w", encoding="utf-8") as f:
            json.dump(self.to_dict(), f, indent=2)

    @classmethod
    def from_pretrained(cls, save_dir: str):
        with open(join(save_dir, "quantize_config.json"), "r", encoding="utf-8") as f:
            return cls(**json.load(f))

    def to_dict(self):
        return {
            "bits": self.bits,
            "group_size": self.group_size,
            "damp_percent": self.damp_percent,
            "desc_act": self.desc_act,
            "sym": self.sym,
            "true_sequential": self.true_sequential,
        }


class BaseGPTQForCausalLM(nn.Module, PushToHubMixin):
    layer_type: str = None
    layers_block_name: str = None
    outside_layer_modules: List[str] = None
    inside_layer_modules: List[List[str]] = None
    lm_head_name: str = "lm_head"

    fused_attn_module_type: Optional[FusedBaseAttentionModule] = None
    fused_mlp_module_type: Optional[FusedBaseMLPModule] = None

    def __init__(
        self,
        model: PreTrainedModel,
        quantized: bool,
        quantize_config: BaseQuantizeConfig,
        is_triton_backend: bool = False,
        injected_fused_attention: bool = False,
        injected_fused_mlp: bool = False,
        trainable: bool = False
    ):
        super().__init__()

        self.model = model
        self.model_type = self.model.config.model_type
        self._quantized = quantized
        self.quantize_config = quantize_config
        self.config = self.model.config

        self.is_triton_backend = is_triton_backend
        self.injected_fused_attention = injected_fused_attention
        self.injected_fused_mlp = injected_fused_mlp
        self.trainable = trainable

    @property
    def quantized(self):
        return self._quantized

    @property
    def hf_device_map(self):
        return getattr(self.model, "hf_device_map", None)

    @staticmethod
    def _resize_attention_mask(attention_mask: List[torch.LongTensor]):
        return attention_mask

    @staticmethod
    def _resize_position_ids(position_ids: List[torch.LongTensor]):
        return position_ids

    def _prepare_examples_for_quantization(
        self,
        examples: List[Dict[str, Union[List[int], torch.LongTensor]]],
        batch_size: int = 1,
    ):
        def _convert_tensor_to_list(tensor):
            if isinstance(tensor, torch.Tensor):
                if len(tensor.shape) == 1:
                    tensor = tensor.unsqueeze(0)
                tensor = tensor.long()
                return tensor.cpu().numpy().tolist()
            return [tensor]

        new_examples = []
        for example in examples:
            input_ids = _convert_tensor_to_list(example["input_ids"])
            attention_mask = _convert_tensor_to_list(example["attention_mask"])
            if "labels" in example:
                labels = _convert_tensor_to_list(example["labels"])
            elif "label" in example:
                labels = _convert_tensor_to_list(example["label"])
            elif "label_ids" in example:
                labels = _convert_tensor_to_list(example["label_ids"])
            else:
                labels = copy.deepcopy(input_ids)
            new_examples.append(
                {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels}
            )
        pad_token_id = self.config.pad_token_id
        if not pad_token_id:
            pad_token_id = self.config.eos_token_id

        new_examples = [
            collate_data(new_examples[start: start + batch_size], pad_token_id)
            for start in range(0, len(new_examples), batch_size)
        ]
        for new_example in new_examples:
            del new_example["labels"]

        return new_examples

    @torch.inference_mode()
    def quantize(
        self,
        examples: List[Dict[str, Union[List[int], torch.LongTensor]]],
        batch_size: int = 1,
        use_triton: bool = False,
        use_cuda_fp16: bool = True,
        autotune_warmup_after_quantized: bool = False,
        cache_examples_on_gpu: bool = True
    ):
        if self.quantized:
            raise EnvironmentError("can't execute quantize because the model is quantized.")
        if use_triton and not TRITON_AVAILABLE:
            logger.warning("triton is not installed, reset use_triton to False")
            use_triton = False

        device_map = self.hf_device_map
        if device_map:
            for name, device in device_map.items():
                if device == "cpu":
                    logger.info(f"truly offloading {name} to cpu with hook.")
                    module = get_module_by_name_suffix(self.model, name)
                    remove_hook_from_module(module, recurse=True)
                    accelerate.cpu_offload_with_hook(module, CUDA_0)

        layer_inputs = []
        attention_masks = []
        position_ids = []
        layer_input_kwargs = []
        layer_outputs = []

        examples = self._prepare_examples_for_quantization(examples, batch_size)

        class LayerHijacker(nn.Module):
            """hijack layer's forward pass to cache data"""

            def __init__(self, m, device):
                super().__init__()
                self.module = m
                self.data_device = device if cache_examples_on_gpu else CPU

            def forward(self, inp=None, **kwargs):
                if inp is None:  # some models use all key-value arguments in forward pass call
                    for kwarg_name in ["hidden_states"]:
                        if kwarg_name in kwargs:
                            inp = kwargs[kwarg_name]
                            break
                layer_inputs.append(move_to_device(inp, self.data_device))
                attention_masks.append(kwargs["attention_mask"].to(self.data_device))
                if (pos_ids := kwargs.get("position_ids", None)) is not None:
                    position_ids.append(move_to_device(pos_ids, self.data_device))
                one_kwargs = dict()
                for k, v in kwargs.items():  # make sure other arguments also be captured
                    if k not in ["hidden_states", "attention_mask", "position_ids"]:
                        if isinstance(v, torch.Tensor):
                            one_kwargs[k] = move_to_device(v, self.data_device)
                        else:
                            one_kwargs[k] = v
                layer_input_kwargs.append(one_kwargs)
                raise ValueError

        forward_pass_use_cache = self.model.config.use_cache
        self.model.config.use_cache = False

        num_batches = len(examples)
        layers = get_module_by_name_prefix(self.model, self.layers_block_name)

        force_layer_back_to_cpu = False
        if get_device(layers[0]) == CPU:
            layers[0] = layers[0].to(CUDA_0)
            force_layer_back_to_cpu = True

        cur_layer_device = get_device(layers[0])
        ori_outside_layer_module_devices = {}
        for module_name in self.outside_layer_modules:
            module = get_module_by_name_prefix(self.model, module_name)

            if module is None:
                continue

            ori_outside_layer_module_devices[module_name] = get_device(module)
            if module is not None:
                move_to_device(module, cur_layer_device)

        # get inputs for first layer
        layers[0] = LayerHijacker(layers[0], cur_layer_device)
        for example in examples:
            for k, v in example.items():
                if len(v.shape) == 1:
                    v = v.unsqueeze(0)
                example[k] = move_to_device(v, cur_layer_device)
            try:
                self.model(**example)
            except ValueError:
                pass
        layers[0] = layers[0].module

        move_to_device(layers[0], CPU if force_layer_back_to_cpu else cur_layer_device)
        for module_name in self.outside_layer_modules:
            module = get_module_by_name_prefix(self.model, module_name)
            if module is not None:
                move_to_device(module, ori_outside_layer_module_devices[module_name])

        torch.cuda.empty_cache()

        # resize attention mask and position ids for some special models
        attention_masks = self._resize_attention_mask(attention_masks)
        position_ids = self._resize_position_ids(position_ids)

        inside_layer_modules = self.inside_layer_modules
        if not self.quantize_config.true_sequential:
            inside_layer_modules = [sum(inside_layer_modules, [])]
        quantizers = {}
        for i in range(len(layers)):
            logger.info(f"Start quantizing layer {i + 1}/{len(layers)}")
            layer = layers[i]
            force_layer_back_to_cpu = False
            if get_device(layer) == CPU:
                move_to_device(layer, CUDA_0)
                force_layer_back_to_cpu = True
            cur_layer_device = get_device(layer)

            full = find_layers(layer)
            for names in inside_layer_modules:
                subset = {n: full[n] for n in names}
                gptq = {}
                for name in subset:
                    gptq[name] = GPTQ(subset[name])
                    gptq[name].quantizer.configure(
                        self.quantize_config.bits,
                        perchannel=True,
                        sym=self.quantize_config.sym,
                        mse=False,
                    )

                def add_batch(name):
                    def tmp(_, inp, out):
                        gptq[name].add_batch(inp[0].data, out.data)

                    return tmp

                handles = []
                for name in subset:
                    handles.append(subset[name].register_forward_hook(add_batch(name)))
                for j in range(num_batches):
                    layer_input = move_to_device(layer_inputs[j], cur_layer_device)
                    layer_attention_mask = move_to_device(attention_masks[j], cur_layer_device)
                    additional_layer_inputs = {
                        "attention_mask": layer_attention_mask
                    }
                    if (
                        layer_position_ids := None if not position_ids
                        else move_to_device(position_ids[j], cur_layer_device)
                    ) is not None:
                        additional_layer_inputs["position_ids"] = layer_position_ids
                    for k, v in layer_input_kwargs[j].items():
                        if isinstance(v, torch.Tensor):
                            additional_layer_inputs[k] = move_to_device(v, cur_layer_device)
                        else:
                            additional_layer_inputs[k] = v
                    layer(layer_input, **additional_layer_inputs)
                for h in handles:
                    h.remove()

                for name in subset:
                    logger.info(f'Quantizing {name} in layer {i + 1}/{len(layers)}...')
                    scale, zero, g_idx = gptq[name].fasterquant(
                        percdamp=self.quantize_config.damp_percent,
                        group_size=self.quantize_config.group_size,
                        actorder=self.quantize_config.desc_act
                    )
                    quantizers[f'{self.layers_block_name}.{i}.{name}'] = (
                        gptq[name].quantizer.to(CPU if force_layer_back_to_cpu else cur_layer_device),
                        move_to_device(scale, CPU if force_layer_back_to_cpu else cur_layer_device),
                        move_to_device(zero, CPU if force_layer_back_to_cpu else cur_layer_device),
                        move_to_device(g_idx, CPU if force_layer_back_to_cpu else cur_layer_device)
                    )
                    gptq[name].free()

            for j in range(num_batches):
                layer_input = move_to_device(layer_inputs[j], cur_layer_device)
                layer_attention_mask = move_to_device(attention_masks[j], cur_layer_device)
                additional_layer_inputs = {
                    "attention_mask": layer_attention_mask
                }
                if (
                    layer_position_ids := None if not position_ids
                    else move_to_device(position_ids[j], cur_layer_device)
                ) is not None:
                    additional_layer_inputs["position_ids"] = layer_position_ids
                for k, v in layer_input_kwargs[j].items():
                    if isinstance(v, torch.Tensor):
                        additional_layer_inputs[k] = move_to_device(v, cur_layer_device)
                    else:
                        additional_layer_inputs[k] = v
                layer_output = move_to_device(
                    layer(layer_input, **additional_layer_inputs)[0],
                    cur_layer_device if cache_examples_on_gpu else CPU
                )
                layer_outputs.append(layer_output)

            layers[i] = move_to_device(layer, CPU if force_layer_back_to_cpu else cur_layer_device)
            del layer
            del gptq
            del layer_inputs
            layer_inputs, layer_outputs = layer_outputs, []
            torch.cuda.empty_cache()

        pack_model(
            model=self.model,
            quantizers=quantizers,
            bits=self.quantize_config.bits,
            group_size=self.quantize_config.group_size,
            use_triton=use_triton,
            use_cuda_fp16=use_cuda_fp16,
            desc_act=self.quantize_config.desc_act,
            warmup_triton=autotune_warmup_after_quantized,
            force_layer_back_to_cpu=force_layer_back_to_cpu
        )
        if device_map:
            self.model = remove_hook_from_module(self.model, recurse=True)
            self.model = simple_dispatch_model(self.model, device_map)
        self.model.config.use_cache = forward_pass_use_cache

        self._quantized = True

        torch.cuda.empty_cache()

    @property
    def device(self):
        if not self.hf_device_map:
            return self.model.device
        else:
            device = [d for d in self.hf_device_map.values() if d not in {'cpu', 'disk'}][0]
            return torch.device(device)

    def to(self, device: Union[str, torch.device]):
        return self.model.to(device)

    def forward(self, *args, **kwargs):
        return self.model(*args, **kwargs)

    def generate(self, **kwargs):
        """shortcut for model.generate"""
        with torch.inference_mode(), torch.amp.autocast(device_type=self.device.type):
            return self.model.generate(**kwargs)

    def prepare_inputs_for_generation(self, *args, **kwargs):
        """shortcut for model.prepare_inputs_for_generation"""
        return self.model.prepare_inputs_for_generation(*args, **kwargs)

    def save_quantized(self, save_dir: str, use_safetensors: bool = False):
        """save quantized model and configs to local disk"""
        os.makedirs(save_dir, exist_ok=True)

        if not self.quantized:
            raise EnvironmentError("can only save quantized model, please execute .quantize first.")

        self.model.to(CPU)

        model_save_name = f"gptq_model-{self.quantize_config.bits}bit-{self.quantize_config.group_size}g"
        if use_safetensors:
            model_save_name += ".safetensors"
            state_dict = self.model.state_dict()
            state_dict = {k: v.clone().contiguous() for k, v in state_dict.items()}
            safe_save(state_dict, join(save_dir, model_save_name))
        else:
            model_save_name += ".bin"
            torch.save(self.model.state_dict(), join(save_dir, model_save_name))

        self.model.config.save_pretrained(save_dir)
        self.quantize_config.save_pretrained(save_dir)

    def save_pretrained(self, save_dir: str, use_safetensors: bool = False, **kwargs):
        """alias of save_quantized"""
        logger.warning("you are using save_pretrained, which will re-direct to save_quantized.")
        self.save_quantized(save_dir, use_safetensors)

    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: str,
        quantize_config: BaseQuantizeConfig,
        max_memory: Optional[dict] = None,
        trust_remote_code: bool = False,
        **model_init_kwargs
    ):
        """load un-quantized pretrained model to cpu"""

        if not torch.cuda.is_available():
            raise EnvironmentError("Load pretrained model to do quantization requires CUDA available.")

        def skip(*args, **kwargs):
            pass

        torch.nn.init.kaiming_uniform_ = skip
        torch.nn.init.uniform_ = skip
        torch.nn.init.normal_ = skip

        config = AutoConfig.from_pretrained(pretrained_model_name_or_path, trust_remote_code=True)
        if config.model_type not in SUPPORTED_MODELS:
            raise TypeError(f"{config.model_type} isn't supported yet.")

        # enforce some values despite user specified
        model_init_kwargs["torch_dtype"] = torch.float16
        model_init_kwargs["trust_remote_code"] = trust_remote_code
        if max_memory:
            if "disk" in max_memory:
                raise NotImplementedError("disk offload not support yet.")
            with accelerate.init_empty_weights():
                model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
            model.tie_weights()

            max_memory = accelerate.utils.get_balanced_memory(
                model,
                max_memory=max_memory,
                no_split_module_classes=[cls.layer_type],
                dtype=model_init_kwargs["torch_dtype"],
                low_zero=False
            )
            model_init_kwargs["device_map"] = accelerate.infer_auto_device_map(
                model,
                max_memory=max_memory,
                no_split_module_classes=[cls.layer_type],
                dtype=model_init_kwargs["torch_dtype"]
            )
            model_init_kwargs["low_cpu_mem_usage"] = True

            del model
        else:
            model_init_kwargs["device_map"] = None
            model_init_kwargs["low_cpu_mem_usage"] = False

        torch.cuda.empty_cache()

        model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, **model_init_kwargs)
        model_config = model.config.to_dict()
        seq_len_keys = ["max_position_embeddings", "seq_length", "n_positions"]
        if any([k in model_config for k in seq_len_keys]):
            for key in seq_len_keys:
                if key in model_config:
                    model.seqlen = model_config[key]
                    break
        else:
            logger.warning("can't get model's sequence length from model config, will set to 4096.")
            model.seqlen = 4096
        model.eval()

        return cls(model, False, quantize_config)

    @classmethod
    def from_quantized(
        cls,
        save_dir: str,
        device_map: Optional[Union[str, Dict[str, Union[int, str]]]] = None,
        max_memory: Optional[dict] = None,
        device: Optional[Union[str, int]] = None,
        low_cpu_mem_usage: bool = False,
        use_triton: bool = False,
        inject_fused_attention: bool = True,
        inject_fused_mlp: bool = True,
        use_cuda_fp16: bool = True,
        quantize_config: Optional[BaseQuantizeConfig] = None,
        model_basename: Optional[str] = None,
        use_safetensors: bool = False,
        trust_remote_code: bool = False,
        warmup_triton: bool = False,
        trainable: bool = False,
        **kwargs
    ):
        """load quantized model from local disk"""
        if use_triton and not TRITON_AVAILABLE:
            logger.warning("triton is not installed, reset use_triton to False")
            use_triton = False

        # == step1: prepare configs and file names, and check values of arguments passed in == #
        config = AutoConfig.from_pretrained(save_dir, trust_remote_code=trust_remote_code)
        if config.model_type not in SUPPORTED_MODELS:
            raise TypeError(f"{config.model_type} isn't supported yet.")

        if quantize_config is None:
            quantize_config = BaseQuantizeConfig.from_pretrained(save_dir)

        if model_basename is None:
            model_basename = f"gptq_model-{quantize_config.bits}bit-{quantize_config.group_size}g"

        model_save_name = join(save_dir, model_basename)

        extensions = []
        if use_safetensors:
            extensions.append(".safetensors")
        else:
            extensions += [".bin", ".pt"]

        for ext in extensions:
            if isfile(model_save_name + ext):
                model_save_name += ext
                break
        else:
            raise FileNotFoundError(f"Could not find model at {model_save_name}")

        if not use_triton and trainable:
            raise NotImplementedError("For now, trainable mode only supports triton backend.")

        # == step2: convert model to gptq-model (replace Linear with QuantLinear) == #
        def skip(*args, **kwargs):
            pass

        torch.nn.init.kaiming_uniform_ = skip
        torch.nn.init.uniform_ = skip
        torch.nn.init.normal_ = skip

        transformers.modeling_utils._init_weights = False

        init_contexts = [no_init_weights()]
        if low_cpu_mem_usage:
            init_contexts.append(accelerate.init_empty_weights(include_buffers=False))

        with ContextManagers(init_contexts):
            model = AutoModelForCausalLM.from_config(
                config,
                trust_remote_code=trust_remote_code,
                torch_dtype=torch.float16
            )

            layers = find_layers(model)
            ignore_layers = [cls.lm_head_name] + cls.outside_layer_modules
            for name in list(layers.keys()):
                if any([name.startswith(ignore_layer) for ignore_layer in ignore_layers]):
                    logger.info(f"{name} not been quantized, will be ignored when make_quant.")
                    del layers[name]

            make_quant(
                model,
                layers,
                quantize_config.bits,
                quantize_config.group_size,
                use_triton=use_triton,
                use_cuda_fp16=use_cuda_fp16,
                desc_act=quantize_config.desc_act,
                trainable=trainable
            )
            model.tie_weights()

        # == step3: load checkpoint and dispatch == #
        if isinstance(device_map, str) and device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
            raise ValueError(
                "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or "
                "'sequential'."
            )
        if isinstance(device_map, dict):
            max_memory = None
        else:
            if device is None and not device_map and not max_memory:
                device_map = "auto"
            if device is not None:
                device = torch.device(device)
                if not max_memory and not device_map:
                    device_map = {"": device.index if device.type == "cuda" else device.type}
            if not isinstance(device_map, dict) and device_map != "sequential":
                max_memory = accelerate.utils.get_balanced_memory(
                    model=model,
                    max_memory=max_memory,
                    no_split_module_classes=[cls.layer_type],
                    low_zero=(device_map == "balanced_low_0")
                )
        if not isinstance(device_map, dict):
            device_map = accelerate.infer_auto_device_map(
                model,
                max_memory=max_memory,
                no_split_module_classes=[cls.layer_type]
            )

        if low_cpu_mem_usage:
            make_sure_no_tensor_in_meta_device(model, use_triton, quantize_config.desc_act, quantize_config.group_size)

        accelerate.utils.modeling.load_checkpoint_in_model(
            model,
            checkpoint=model_save_name,
            device_map=device_map,
            offload_state_dict=True,
            offload_buffers=True
        )
        model = simple_dispatch_model(model, device_map)

        # == step4: set seqlen == #
        model_config = model.config.to_dict()
        seq_len_keys = ["max_position_embeddings", "seq_length", "n_positions"]
        if any([k in model_config for k in seq_len_keys]):
            for key in seq_len_keys:
                if key in model_config:
                    model.seqlen = model_config[key]
                    break
        else:
            logger.warning("can't get model's sequence length from model config, will set to 4096.")
            model.seqlen = 4096

        # == step5: (optional) inject optimized module == #
        if inject_fused_attention:
            if cls.fused_attn_module_type is None:
                inject_fused_attention = False
                logger.warning(f"{cls.__name__} hasn't fused attention module yet, will skip inject fused attention.")
            else:
                cls.fused_attn_module_type.inject_to_model(
                    model,
                    use_triton=use_triton,
                    group_size=quantize_config.group_size,
                    use_cuda_fp16=use_cuda_fp16,
                    desc_act=quantize_config.desc_act,
                    trainable=trainable
                )
        if inject_fused_mlp:
            if cls.fused_mlp_module_type is None:
                inject_fused_mlp = False
                logger.warning(f"{cls.__name__} hasn't fused mlp module yet, will skip inject fused mlp.")
            else:
                cls.fused_mlp_module_type.inject_to_model(
                    model,
                    use_triton=use_triton
                )

        model.eval()
        # == step6: (optional) warmup triton == #
        if use_triton and warmup_triton:
            from ..nn_modules.qlinear.qlinear_triton import QuantLinear
            QuantLinear.warmup(model, seqlen=model.seqlen)

            if inject_fused_mlp and cls.fused_mlp_module_type is not None:
                cls.fused_mlp_module_type.warmup(model, seqlen=model.seqlen)

        # == step7: make model compatible with peft
        cls.make_sure_compatible_with_peft(
            model, use_triton, quantize_config.desc_act, quantize_config.group_size
        )

        return cls(
            model,
            True,
            quantize_config,
            is_triton_backend=use_triton,
            injected_fused_attention=inject_fused_attention,
            injected_fused_mlp=inject_fused_mlp and use_triton,
            trainable=trainable
        )

    def warmup_triton(self, enabled: bool = True):
        if not enabled:
            return
        if not TRITON_AVAILABLE:
            logger.warning(f"triton is not available, skip warmup stage directly.")
            return

        from ..nn_modules.qlinear.qlinear_triton import QuantLinear
        QuantLinear.warmup(self.model, seqlen=self.model.seqlen)

        if self.fused_mlp_module_type is not None:
            self.fused_mlp_module_type.warmup(self.model, seqlen=self.model.seqlen)

    def enable_trainable_mode(self, enabled: bool = True):
        if not self.is_triton_backend and enabled:
            raise NotImplementedError("For now, trainable mode only supports triton backend.")
        for n, m in self.model.named_modules():
            if hasattr(m, "trainable"):
                setattr(m, "trainable", enabled)

    def disable_trainable_mode(self):
        self.enable_trainable_mode(enabled=False)

    @staticmethod
    def make_sure_compatible_with_peft(model: PreTrainedModel, use_triton: bool, desc_act: bool, group_size: int):
        GeneralQuantLinear.inject_to_model(
            model,
            dynamically_import_QuantLinear(use_triton, desc_act, group_size)
        )

    def __getattr__(self, item):
        try:
            return super().__getattr__(item)
        except:
            return getattr(self.model, item)


__all__ = ["BaseGPTQForCausalLM", "BaseQuantizeConfig"]