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