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 if torch.cuda.is_available() and not torch.version.hip: try: from llama_cpp_cuda import Llama except: from llama_cpp import Llama else: from llama_cpp import Llama class LlamacppHF(PreTrainedModel): def __init__(self, model): super().__init__(PretrainedConfig()) self.model = model self.generation_config = GenerationConfig() self.cache = 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): # TODO: Some decoding methods (such as Contrastive Search) may not work at this time assert len(args) == 0, 'no *args should be passed to forward' use_cache = kwargs.get('use_cache', True) labels = kwargs.get('labels', None) seq = kwargs['input_ids'][0].tolist() cache = kwargs['past_key_values'] if 'past_key_values' in kwargs else None # Make the forward call seq_tensor = torch.tensor(seq) if labels is None: if self.cache is None or not torch.equal(self.cache, seq_tensor[:-1]): self.model.reset() self.model.eval(seq) else: self.model.eval([seq[-1]]) logits = torch.tensor(self.model.scores[self.model.n_tokens-1, :]).view(1, 1, -1).to(kwargs['input_ids'].device) else: self.model.reset() self.model.eval(seq) logits = torch.tensor(self.model.eval_logits) logits = logits.view(1, logits.shape[0], logits.shape[1]).to(kwargs['input_ids'].device) self.cache = seq_tensor # Based on transformers/models/llama/modeling_llama.py 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=cache 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) path = Path(f'{shared.args.model_dir}') / Path(pretrained_model_name_or_path) if path.is_file(): model_file = path else: model_file = list(path.glob('*ggml*.bin'))[0] logger.info(f"llama.cpp weights detected: {model_file}\n") params = { 'model_path': str(model_file), 'n_ctx': shared.args.n_ctx, 'seed': int(shared.args.llama_cpp_seed), 'n_threads': shared.args.threads or None, 'n_batch': shared.args.n_batch, 'use_mmap': not shared.args.no_mmap, 'use_mlock': shared.args.mlock, 'low_vram': shared.args.low_vram, 'n_gpu_layers': shared.args.n_gpu_layers, 'rope_freq_base': 10000 * shared.args.alpha_value ** (64/63.), 'rope_freq_scale': 1.0 / shared.args.compress_pos_emb, 'n_gqa': shared.args.n_gqa or None, 'rms_norm_eps': shared.args.rms_norm_eps or None, 'logits_all': True, } model = Llama(**params) return LlamacppHF(model)