import os from pathlib import Path from typing import Any, Dict, Optional, Union import torch 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: from exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig except: logger.warning('Exllama module failed to load. Will attempt to load from repositories.') try: from modules.relative_imports import RelativeImport with RelativeImport("repositories/exllama"): from model import ExLlama, ExLlamaCache, ExLlamaConfig except: logger.error("Could not find repositories/exllama/. Make sure that exllama is cloned inside repositories/ and is up to date.") raise class ExllamaHF(PreTrainedModel): def __init__(self, config: ExLlamaConfig): super().__init__(PretrainedConfig()) self.ex_config = config self.ex_model = ExLlama(self.ex_config) self.generation_config = GenerationConfig() self.lora = None self.ex_cache = ExLlamaCache(self.ex_model) self.past_seq = None if shared.args.cfg_cache: self.ex_cache_negative = ExLlamaCache(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} @property 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 ExLlama_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, lora=self.lora) 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, lora=self.lora) logits = self.ex_model.forward(seq_tensor[-1:].view(1, -1), ex_cache, lora=self.lora).to(input_ids.device) else: ex_cache.current_seq_len = 0 logits = self.ex_model.forward(seq_tensor.view(1, -1), ex_cache, last_id_only=False, lora=self.lora) 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) @classmethod 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 = ExLlamaConfig(pretrained_model_name_or_path / 'config.json') # from 'oobabooga/text-generation-webui/modules/exllama.py' weight_path = None for ext in ['.safetensors', '.pt', '.bin']: found = list(pretrained_model_name_or_path.glob(f"*{ext}")) if len(found) > 0: weight_path = found[-1] break assert weight_path is not None, f'could not find weight in "{pretrained_model_name_or_path}"' config.model_path = str(weight_path) config.max_seq_len = shared.args.max_seq_len config.compress_pos_emb = shared.args.compress_pos_emb if shared.args.gpu_split: config.set_auto_map(shared.args.gpu_split) config.gpu_peer_fix = True if shared.args.alpha_value > 1 and shared.args.rope_freq_base == 0: config.alpha_value = shared.args.alpha_value config.calculate_rotary_embedding_base() elif shared.args.rope_freq_base > 0: config.rotary_embedding_base = shared.args.rope_freq_base if torch.version.hip: config.rmsnorm_no_half2 = True config.rope_no_half2 = True config.matmul_no_half2 = True config.silu_no_half2 = True # This slowes down a bit but align better with autogptq generation. # TODO: Should give user choice to tune the exllama config # config.fused_attn = False # config.fused_mlp_thd = 0 return ExllamaHF(config)