import copy import json import warnings import os from dataclasses import dataclass, field, fields from logging import getLogger from os.path import join, isfile, isdir 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 safetensors.torch import load_file as safe_load from transformers import AutoConfig, AutoModelForCausalLM, PreTrainedModel from transformers.utils.hub import PushToHubMixin, cached_file, create_repo, create_commit, CommitOperationAdd from transformers.utils.generic import ContextManagers from transformers.modeling_utils import no_init_weights 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, EXLLAMA_KERNELS_AVAILABLE, QIGEN_AVAILABLE, EXLLAMAV2_KERNELS_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) static_groups: bool = field(default=False) sym: bool = field(default=True) true_sequential: bool = field(default=True) model_name_or_path: Optional[str] = field(default=None) model_file_base_name: Optional[str] = field(default=None) 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, **kwargs): # Parameters related to loading from Hugging Face Hub cache_dir = kwargs.pop("cache_dir", None) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", False) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) subfolder = kwargs.pop("subfolder", None) commit_hash = kwargs.pop("_commit_hash", None) quantize_config_filename = "quantize_config.json" if os.path.isdir(save_dir): # Local resolved_config_file = join(save_dir, quantize_config_filename) else: # Remote resolved_config_file = cached_file( save_dir, quantize_config_filename, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, use_auth_token=use_auth_token, revision=revision, local_files_only=local_files_only, subfolder=subfolder, _raise_exceptions_for_missing_entries=False, _raise_exceptions_for_connection_errors=False, _commit_hash=commit_hash, ) field_names = [field.name for field in fields(cls)] with open(resolved_config_file, "r", encoding="utf-8") as f: args_from_json = json.load(f) filtered_args = {} for key, val in args_from_json.items(): if key in field_names: filtered_args[key] = val else: logger.warning(f"ignoring unknown parameter in {quantize_config_filename}: {key}.") return cls(**filtered_args) def to_dict(self): return { "bits": self.bits, "group_size": self.group_size, "damp_percent": self.damp_percent, "desc_act": self.desc_act, "static_groups": self.static_groups, "sym": self.sym, "true_sequential": self.true_sequential, "model_name_or_path": self.model_name_or_path, "model_file_base_name": self.model_file_base_name, } 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)) pos_ids = kwargs.get("position_ids", None) if pos_ids 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 } layer_position_ids = None if not position_ids else move_to_device(position_ids[j], cur_layer_device) if layer_position_ids 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, static_groups=self.quantize_config.static_groups ) 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 } layer_position_ids = None if not position_ids else move_to_device(position_ids[j], cur_layer_device) if layer_position_ids 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]): self.model.to(device) return self 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 push_to_hub( self, repo_id: str, save_dir: Optional[str] = None, use_safetensors: Optional[bool] = True, safetensors_metadata: Optional[Dict[str, str]] = None, commit_message: Optional[str] = "Upload of AutoGPTQ quantized model", use_auth_token: Optional[Union[bool, str]] = None, private: Optional[bool] = None, token: Optional[Union[bool, str]] = None, create_pr: Optional[bool] = False, ) -> str: """ Upload the model to the Hugging Face Hub. Parameters: repo_id (`str`): The name of the repository you want to push your tool to. It should contain your organization name when pushing to a given organization. save_dir (`str`, *optional*): The name of the local folder to save the model to. If the model has already been saved, this parameter can be omitted. use_safetensors (`bool`, *optional*): Save the model using `safetensors`. If the model has already been saved, this parameter can be omitted. safetensors_metadata: (`dict`, *optional*, defaults to `None`): Pass optional metadata dictionary to be saved in the `safetensors` model file(s). Metadata is optional and is purely for informational purposes. It does not affect inference. If `None`, no metadata will be saved. commit_message (`str`, *optional*, defaults to `"Upload tool"`): Message to commit while pushing. use_auth_token (`bool` or `str`, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). Will default to `True` if `repo_url` is not specified. private (`bool`, *optional*): Whether or not the repository created should be private. token (`bool` or `str`, *optional*): The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). create_pr (`bool`, *optional*, defaults to `False`): Whether or not to create a PR with the uploaded files or directly commit. """ if (self.quantize_config.model_name_or_path is None or not isdir(self.quantize_config.model_name_or_path)) and save_dir is None: raise ValueError("Quantized model should be saved first, or you can provide save_dir to make sure model is saved to local disk before uploading.") if save_dir is not None: logger.info(f"Saving model to {save_dir}") self.save_quantized(save_dir, use_safetensors, safetensors_metadata) repo_url = create_repo( repo_id=repo_id, token=token, private=private, exist_ok=True, repo_type="model" ) repo_id = repo_url.repo_id if self.quantize_config.model_name_or_path is not None: work_dir = self.quantize_config.model_name_or_path operations = [ CommitOperationAdd(path_or_fileobj=join(work_dir, f), path_in_repo=f) for f in os.listdir(work_dir) ] logger.info(f"Uploading the following files to {repo_id}: {','.join(os.listdir(work_dir))}") return create_commit( repo_id=repo_id, operations=operations, commit_message=commit_message, token=use_auth_token, create_pr=create_pr, repo_type="model", ) def save_quantized(self, save_dir: str, use_safetensors: bool = False, safetensors_metadata: Optional[Dict[str, str]] = None): """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_base_name = self.quantize_config.model_file_base_name or f"gptq_model-{self.quantize_config.bits}bit-{self.quantize_config.group_size}g" if use_safetensors: model_save_name = model_base_name + ".safetensors" state_dict = self.model.state_dict() state_dict = {k: v.clone().contiguous() for k, v in state_dict.items()} if safetensors_metadata is None: safetensors_metadata = {} elif not isinstance(safetensors_metadata, dict): raise TypeError("safetensors_metadata must be a dictionary.") else: logger.debug(f"Received safetensors_metadata: {safetensors_metadata}") new_safetensors_metadata = {} converted_keys = False for key, value in safetensors_metadata.items(): if not isinstance(key, str) or not isinstance(value, str): converted_keys = True try: new_key = str(key) new_value = str(value) except Exception as e: raise TypeError(f"safetensors_metadata: both keys and values must be strings and an error occured when trying to convert them: {e}") if new_key in new_safetensors_metadata: logger.warning(f"After converting safetensors_metadata keys to strings, the key '{new_key}' is duplicated. Ensure that all your metadata keys are strings to avoid overwriting.") new_safetensors_metadata[new_key] = new_value safetensors_metadata = new_safetensors_metadata if converted_keys: logger.debug(f"One or more safetensors_metadata keys or values had to be converted to str(). Final safetensors_metadata: {safetensors_metadata}") # Format is required to enable Accelerate to load the metadata # otherwise it raises an OSError safetensors_metadata['format'] = "pt" # Store the quantization configuration as safetensors metadata from auto_gptq import __version__ safetensors_metadata['auto_gptq_version'] = str(__version__) safetensors_metadata['gptq_bits'] = str(self.quantize_config.bits) safetensors_metadata['gptq_group_size'] = str(self.quantize_config.group_size) safetensors_metadata['gptq_desc_act'] = str(self.quantize_config.desc_act) safetensors_metadata['gptq_damp_percent'] = str(self.quantize_config.damp_percent) safe_save(state_dict, join(save_dir, model_save_name), safetensors_metadata) else: model_save_name = model_base_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) self.quantize_config.model_name_or_path = save_dir self.quantize_config.model_file_base_name = model_base_name def save_pretrained(self, save_dir: str, use_safetensors: bool = False, safetensors_metadata: Optional[Dict[str, str]] = None, **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, safetensors_metadata) @classmethod def from_pretrained( cls, pretrained_model_name_or_path: str, quantize_config: BaseQuantizeConfig, max_memory: Optional[dict] = None, trust_remote_code: bool = False, torch_dtype: torch.dtype = torch.float16, **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 # Parameters related to loading from Hugging Face Hub cache_dir = model_init_kwargs.pop("cache_dir", None) force_download = model_init_kwargs.pop("force_download", False) resume_download = model_init_kwargs.pop("resume_download", False) proxies = model_init_kwargs.pop("proxies", None) local_files_only = model_init_kwargs.pop("local_files_only", False) use_auth_token = model_init_kwargs.pop("use_auth_token", None) revision = model_init_kwargs.pop("revision", None) subfolder = model_init_kwargs.pop("subfolder", "") commit_hash = model_init_kwargs.pop("_commit_hash", None) cached_file_kwargs = { "cache_dir": cache_dir, "force_download": force_download, "proxies": proxies, "resume_download": resume_download, "local_files_only": local_files_only, "use_auth_token": use_auth_token, "revision": revision, "subfolder": subfolder, } config = AutoConfig.from_pretrained(pretrained_model_name_or_path, trust_remote_code=True, **cached_file_kwargs) 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_dtype 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() merged_kwargs = {**model_init_kwargs, **cached_file_kwargs} model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, **merged_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, model_name_or_path: Optional[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, use_qigen: bool = False, torch_dtype: Optional[torch.dtype] = None, 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, disable_exllama: bool = True, disable_exllamav2: bool = False, **kwargs ): """load quantized model from local disk""" # Parameters related to loading from Hugging Face Hub cache_dir = kwargs.pop("cache_dir", None) force_download = kwargs.pop("force_download", False) resume_download = kwargs.pop("resume_download", False) proxies = kwargs.pop("proxies", None) local_files_only = kwargs.pop("local_files_only", False) use_auth_token = kwargs.pop("use_auth_token", None) revision = kwargs.pop("revision", None) subfolder = kwargs.pop("subfolder", "") commit_hash = kwargs.pop("_commit_hash", None) cached_file_kwargs = { "cache_dir": cache_dir, "force_download": force_download, "proxies": proxies, "resume_download": resume_download, "local_files_only": local_files_only, "use_auth_token": use_auth_token, "revision": revision, "subfolder": subfolder, "_raise_exceptions_for_missing_entries": False, "_commit_hash": commit_hash, } if use_qigen and not QIGEN_AVAILABLE: logger.warning("Qigen is not installed, reset use_qigen to False.") use_qigen = False if use_triton and not TRITON_AVAILABLE: logger.warning("Triton is not installed, reset use_triton to False.") use_triton = False if not disable_exllama and not EXLLAMA_KERNELS_AVAILABLE: logger.warning( "Exllama kernel is not installed, reset disable_exllama to True. " "This may because you installed auto_gptq using a pre-build wheel " "on Windows, in which exllama_kernels are not compiled. To use " "exllama_kernels to further speedup inference, you can re-install " "auto_gptq from source." ) disable_exllama = True if not disable_exllamav2 and not EXLLAMAV2_KERNELS_AVAILABLE: logger.warning( "Exllamav2 kernel is not installed, reset disable_exllamav2 to True. " "This may because you installed auto_gptq using a pre-build wheel " "on Windows, in which exllama_kernels are not compiled. To use " "exllama_kernels to further speedup inference, you can re-install " "auto_gptq from source." ) disable_exllamav2 = True if not AUTOGPTQ_CUDA_AVAILABLE: logger.warning( "CUDA kernels for auto_gptq are not installed, this will result in " "very slow inference speed. This may because:\n" "1. You disabled CUDA extensions compilation by setting BUILD_CUDA_EXT=0 when install auto_gptq from source.\n" "2. You are using pytorch without CUDA support.\n" "3. CUDA and nvcc are not installed in your device." ) if use_qigen and QIGEN_AVAILABLE: logger.warning("QIgen is active. Ignores all settings related to cuda.") inject_fused_attention = False inject_fused_mlp = False use_triton = False disable_exllama = True disable_exllamav2 = True if not disable_exllamav2 and not disable_exllama: logger.warning( "You have activated both exllama and exllamav2 kernel. Setting disable_exllama to True and keeping disable_exllamav2 to False" ) disable_exllama = True # == step1: prepare configs and file names == # config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=trust_remote_code, **cached_file_kwargs) 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(model_name_or_path, **cached_file_kwargs, **kwargs) if model_basename is None: if quantize_config.model_file_base_name: model_basename = quantize_config.model_file_base_name else: model_basename = f"gptq_model-{quantize_config.bits}bit-{quantize_config.group_size}g" quantize_config.model_name_or_path = model_name_or_path quantize_config.model_file_base_name = model_basename extensions = [] if use_safetensors: extensions.append(".safetensors") else: extensions += [".bin", ".pt"] model_name_or_path = str(model_name_or_path) is_local = isdir(model_name_or_path) resolved_archive_file = None if is_local: model_save_name = join(model_name_or_path, model_basename) for ext in extensions: if isfile(model_save_name + ext): resolved_archive_file = model_save_name + ext break else: # remote for ext in extensions: resolved_archive_file = cached_file(model_name_or_path, model_basename + ext, **cached_file_kwargs) if resolved_archive_file is not None: break if resolved_archive_file is None: # Could not find a model file to use raise FileNotFoundError(f"Could not find model in {model_name_or_path}") model_save_name = resolved_archive_file if (not disable_exllama or not disable_exllamav2) and trainable: logger.warning("QuantLinear with exllama backend not support trainable mode yet, Switch to the pytorch backend.") disable_exllama = True disable_exllamav2 = True elif not use_triton and trainable: logger.warning("QuantLinear with cuda backend not support trainable mode yet, Switch to the pytorch backend.") # == step2: convert model to gptq-model (replace Linear with QuantLinear) == # def skip(*args, **kwargs): pass if torch_dtype is None: if not use_qigen: torch_dtype = torch.float16 else: torch_dtype = torch.float32 if not use_qigen: 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_dtype ) 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, disable_exllama=disable_exllama, disable_exllamav2=disable_exllamav2, 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, bits=quantize_config.bits) 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) else: if quantize_config.desc_act: NotImplementedError('desc_act=True is not yet supported.') model = AutoModelForCausalLM.from_config( config, trust_remote_code=trust_remote_code, torch_dtype=torch_dtype ) 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] if model_save_name.endswith('.safetensors'): checkpoint = safe_load(model_save_name) else: checkpoint = torch.load(model_save_name) make_quant( model, layers, quantize_config.bits, quantize_config.group_size, use_triton=use_triton, disable_exllama=disable_exllama, disable_exllamav2=disable_exllamav2, use_cuda_fp16=use_cuda_fp16, desc_act=quantize_config.desc_act, trainable=trainable, use_qigen=True ) preprocess_checkpoint_qigen( model, layers, quantize_config.bits, quantize_config.group_size, checkpoint ) model.load_state_dict(checkpoint) # == 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, bits=quantize_config.bits, disable_exllama=disable_exllama, disable_exllamav2=disable_exllamav2 ) 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 ) # Any post-initialization that require device information, for example buffers initialization on device. model = autogptq_post_init(model, use_act_order=quantize_config.desc_act) 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, bits=quantize_config.bits ) 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, bits: int): GeneralQuantLinear.inject_to_model( model, dynamically_import_QuantLinear(use_triton, desc_act, group_size, bits=bits) ) def __getattr__(self, item): try: return super().__getattr__(item) except: return getattr(self.model, item) __all__ = ["BaseGPTQForCausalLM", "BaseQuantizeConfig"]