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| import os | |
| import traceback | |
| from pathlib import Path | |
| from typing import Any, Dict, Optional, Union | |
| import torch | |
| from exllamav2 import ( | |
| ExLlamaV2, | |
| ExLlamaV2Cache, | |
| ExLlamaV2Cache_8bit, | |
| ExLlamaV2Config | |
| ) | |
| from torch.nn import CrossEntropyLoss | |
| from transformers import GenerationConfig, PretrainedConfig, PreTrainedModel | |
| from transformers.modeling_outputs import CausalLMOutputWithPast | |
| from modules import shared | |
| from modules.logging_colors import logger | |
| try: | |
| import flash_attn | |
| except ModuleNotFoundError: | |
| logger.warning( | |
| 'You are running ExLlamaV2 without flash-attention. This will cause the VRAM usage ' | |
| 'to be a lot higher than it could be.\n' | |
| 'Try installing flash-attention following the instructions here: ' | |
| 'https://github.com/Dao-AILab/flash-attention#installation-and-features' | |
| ) | |
| pass | |
| except Exception: | |
| logger.warning('Failed to load flash-attention due to the following error:\n') | |
| traceback.print_exc() | |
| class Exllamav2HF(PreTrainedModel): | |
| def __init__(self, config: ExLlamaV2Config): | |
| super().__init__(PretrainedConfig()) | |
| self.ex_config = config | |
| self.ex_model = ExLlamaV2(config) | |
| split = None | |
| if shared.args.gpu_split: | |
| split = [float(alloc) for alloc in shared.args.gpu_split.split(",")] | |
| self.ex_model.load(split) | |
| self.generation_config = GenerationConfig() | |
| self.loras = None | |
| if shared.args.cache_8bit: | |
| self.ex_cache = ExLlamaV2Cache_8bit(self.ex_model) | |
| else: | |
| self.ex_cache = ExLlamaV2Cache(self.ex_model) | |
| self.past_seq = None | |
| if shared.args.cfg_cache: | |
| if shared.args.cache_8bit: | |
| self.ex_cache_negative = ExLlamaV2Cache_8bit(self.ex_model) | |
| else: | |
| self.ex_cache_negative = ExLlamaV2Cache(self.ex_model) | |
| self.past_seq_negative = None | |
| def _validate_model_class(self): | |
| pass | |
| def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]): | |
| pass | |
| def prepare_inputs_for_generation(self, input_ids, **kwargs): | |
| return {'input_ids': input_ids, **kwargs} | |
| def device(self) -> torch.device: | |
| return torch.device(0) | |
| def __call__(self, *args, **kwargs): | |
| use_cache = kwargs.get('use_cache', True) | |
| labels = kwargs.get('labels', None) | |
| past_key_values = kwargs.get('past_key_values', None) | |
| if len(args) > 0: | |
| if not shared.args.cfg_cache: | |
| logger.error("Please enable the cfg-cache option to use CFG with ExLlamav2_HF.") | |
| return | |
| input_ids = args[0] | |
| is_negative = True | |
| past_seq = self.past_seq_negative | |
| ex_cache = self.ex_cache_negative | |
| else: | |
| input_ids = kwargs['input_ids'] | |
| is_negative = False | |
| past_seq = self.past_seq | |
| ex_cache = self.ex_cache | |
| seq = input_ids[0].tolist() | |
| if is_negative and past_key_values is not None: | |
| seq = past_key_values + seq | |
| seq_tensor = torch.tensor(seq) | |
| reset = True | |
| # Make the forward call | |
| if labels is None: | |
| if past_seq is not None: | |
| min_length = min(past_seq.shape[0], seq_tensor.shape[0]) | |
| indices = torch.nonzero(~torch.eq(past_seq[:min_length], seq_tensor[:min_length])) | |
| if len(indices) > 0: | |
| longest_prefix = indices[0].item() | |
| else: | |
| longest_prefix = min_length | |
| if longest_prefix > 0: | |
| reset = False | |
| ex_cache.current_seq_len = longest_prefix | |
| if len(seq_tensor) - longest_prefix > 1: | |
| self.ex_model.forward(seq_tensor[longest_prefix:-1].view(1, -1), ex_cache, preprocess_only=True, loras=self.loras) | |
| elif len(seq_tensor) == longest_prefix: | |
| # Very tricky: if the prefix we are reusing *is* the input_ids, then we have to back up the cache pointer by one, | |
| # because we feed input_ids[-1] to forward() below, but that last token is already in the cache! | |
| ex_cache.current_seq_len -= 1 | |
| if reset: | |
| ex_cache.current_seq_len = 0 | |
| if len(seq_tensor) > 1: | |
| self.ex_model.forward(seq_tensor[:-1].view(1, -1), ex_cache, preprocess_only=True, loras=self.loras) | |
| logits = self.ex_model.forward(seq_tensor[-1:].view(1, -1), ex_cache, loras=self.loras).to(input_ids.device).float() | |
| else: | |
| ex_cache.current_seq_len = 0 | |
| logits = self.ex_model.forward(seq_tensor.view(1, -1), ex_cache, last_id_only=False, loras=self.loras).float() | |
| if is_negative: | |
| self.past_seq_negative = seq_tensor | |
| else: | |
| self.past_seq = seq_tensor | |
| loss = None | |
| if labels is not None: | |
| # Shift so that tokens < n predict n | |
| shift_logits = logits[..., :-1, :].contiguous() | |
| shift_labels = labels[..., 1:].contiguous() | |
| # Flatten the tokens | |
| loss_fct = CrossEntropyLoss() | |
| shift_logits = shift_logits.view(-1, logits.shape[-1]) | |
| shift_labels = shift_labels.view(-1) | |
| # Enable model parallelism | |
| shift_labels = shift_labels.to(shift_logits.device) | |
| loss = loss_fct(shift_logits, shift_labels) | |
| return CausalLMOutputWithPast(logits=logits, past_key_values=seq if use_cache else None, loss=loss) | |
| def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs): | |
| assert len(model_args) == 0 and len(kwargs) == 0, "extra args is currently not supported" | |
| if isinstance(pretrained_model_name_or_path, str): | |
| pretrained_model_name_or_path = Path(pretrained_model_name_or_path) | |
| pretrained_model_name_or_path = Path(f'{shared.args.model_dir}') / Path(pretrained_model_name_or_path) | |
| config = ExLlamaV2Config() | |
| config.model_dir = str(pretrained_model_name_or_path) | |
| config.prepare() | |
| config.max_seq_len = shared.args.max_seq_len | |
| config.scale_pos_emb = shared.args.compress_pos_emb | |
| config.scale_alpha_value = shared.args.alpha_value | |
| config.no_flash_attn = shared.args.no_flash_attn | |
| config.num_experts_per_token = int(shared.args.num_experts_per_token) | |
| return Exllamav2HF(config) | |