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| import sys | |
| from pathlib import Path | |
| from modules import shared | |
| from modules.logging_colors import logger | |
| from modules.relative_imports import RelativeImport | |
| with RelativeImport("repositories/exllama"): | |
| from generator import ExLlamaGenerator | |
| from model import ExLlama, ExLlamaCache, ExLlamaConfig | |
| from tokenizer import ExLlamaTokenizer | |
| class ExllamaModel: | |
| def __init__(self): | |
| pass | |
| def from_pretrained(self, path_to_model): | |
| path_to_model = Path(f'{shared.args.model_dir}') / Path(path_to_model) | |
| tokenizer_model_path = path_to_model / "tokenizer.model" | |
| model_config_path = path_to_model / "config.json" | |
| # Find the model checkpoint | |
| model_path = None | |
| for ext in ['.safetensors', '.pt', '.bin']: | |
| found = list(path_to_model.glob(f"*{ext}")) | |
| if len(found) > 0: | |
| if len(found) > 1: | |
| logger.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.') | |
| model_path = found[-1] | |
| break | |
| config = ExLlamaConfig(str(model_config_path)) | |
| config.model_path = str(model_path) | |
| if shared.args.gpu_split: | |
| config.set_auto_map(shared.args.gpu_split) | |
| config.gpu_peer_fix = True | |
| model = ExLlama(config) | |
| tokenizer = ExLlamaTokenizer(str(tokenizer_model_path)) | |
| cache = ExLlamaCache(model) | |
| generator = ExLlamaGenerator(model, tokenizer, cache) | |
| result = self() | |
| result.config = config | |
| result.model = model | |
| result.cache = cache | |
| result.tokenizer = tokenizer | |
| result.generator = generator | |
| return result, result | |
| def generate_with_streaming(self, prompt, state): | |
| self.generator.settings.temperature = state['temperature'] | |
| self.generator.settings.top_p = state['top_p'] | |
| self.generator.settings.top_k = state['top_k'] | |
| self.generator.settings.typical = state['typical_p'] | |
| self.generator.settings.token_repetition_penalty_max = state['repetition_penalty'] | |
| if state['ban_eos_token']: | |
| self.generator.disallow_tokens([self.tokenizer.eos_token_id]) | |
| else: | |
| self.generator.disallow_tokens(None) | |
| self.generator.end_beam_search() | |
| ids = self.generator.tokenizer.encode(prompt) | |
| self.generator.gen_begin_reuse(ids) | |
| initial_len = self.generator.sequence[0].shape[0] | |
| has_leading_space = False | |
| for i in range(state['max_new_tokens']): | |
| token = self.generator.gen_single_token() | |
| if i == 0 and self.generator.tokenizer.tokenizer.IdToPiece(int(token)).startswith('▁'): | |
| has_leading_space = True | |
| decoded_text = self.generator.tokenizer.decode(self.generator.sequence[0][initial_len:]) | |
| if has_leading_space: | |
| decoded_text = ' ' + decoded_text | |
| yield decoded_text | |
| if token.item() == self.generator.tokenizer.eos_token_id or shared.stop_everything: | |
| break | |
| def generate(self, prompt, state): | |
| output = '' | |
| for output in self.generate_with_streaming(prompt, state): | |
| pass | |
| return output | |
| def encode(self, string, **kwargs): | |
| return self.tokenizer.encode(string) | |