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| 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(): | |
| from llama_cpp_cuda 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} | |
| 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.eval_logits[-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) | |
| 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, | |
| 'logits_all': True, | |
| } | |
| model = Llama(**params) | |
| return LlamacppHF(model) | |